<%BANNER%>

Carbon Sequestration Potential of Agroforestry Systems in the West African Sahel

Permanent Link: http://ufdc.ufl.edu/UFE0021453/00001

Material Information

Title: Carbon Sequestration Potential of Agroforestry Systems in the West African Sahel An Assessment of Biological and Socioeconomic Feasibility
Physical Description: 1 online resource (184 p.)
Language: english
Creator: Takimoto, Asako
Publisher: University of Florida
Place of Publication: Gainesville, Fla.
Publication Date: 2007

Subjects

Subjects / Keywords: bank, clean, development, fence, fodder, kyoto, live, mechanism, parkland, protocol
Forest Resources and Conservation -- Dissertations, Academic -- UF
Genre: Forest Resources and Conservation thesis, Ph.D.
bibliography   ( marcgt )
theses   ( marcgt )
government publication (state, provincial, terriorial, dependent)   ( marcgt )
born-digital   ( sobekcm )
Electronic Thesis or Dissertation

Notes

Abstract: In recent years, carbon (C) sequestration potential of agroforestry systems has attracted attention, especially following Kyoto Protocol's recognition of agroforestry as an option for mitigating green house gasses. Although the possible benefits of agroforestry in carbon (C) sequestration have been conceptually discussed, field measurements to validate these concepts have not been undertaken to any significant extent. In addition to the traditional agroforestry systems, improved practices and technologies are now being expanded into the dry regions such as the West African Sahel for perceived benefits such as arresting desertification, reducing water and wind erosion hazards, and improving biodiversity. Thus, it is imperative to investigate C sequestration potential of agroforestry practices in these regions. My research hypothesizes that the tree-based systems will retain more C in the systems both above- and below-ground than tree-less land-use systems. By joining the C credit market, the landowners could sell the C sequestered in their agroforestry systems. My research consisted of three components. The first examined C (biomass + soil) stored in five target land-use systems: two traditional parkland systems involving Faidherbia albida and Vitellaria paradoxa trees as the dominant species, two improved agroforestry systems (live fence and fodder bank), and land that is out of cultivation (abandoned or degraded) in the S?gou Region, Mali. The second component involved a study of soil C dynamics of these systems: the extent of soil C storage/accumulation by trees and stability of the C accumulated were investigated. In the third component, socioeconomic feasibility of the agroforestry systems was examined in the context of C sequestration and C credit sale. Research results show that the selected agroforestry systems have the potential for sequestering more C both above- and belowground than in tree-less land-use systems, and that the trees tend to contribute to storing more stable C in the soil. Among the selected land-use systems, live fence and fodder bank are more suitable to start as agroforestry C sequestration projects than the traditional parkland systems for smallholder farmers in the studied region. Between the two improved systems, live fence has higher C sequestering potential per unit area and is economically less risky than fodder banks. Adopting these systems on cultivated land rather than on abandoned land is likely to sequester more C and be more profitable. Since parklands are traditionally practiced, they are not likely to qualify as a new C sequestration project soon. Nevertheless, F. albida trees are more attractive than V. paradoxa trees in terms of C sequestration potential. These results can be used for development of recommendations and guidelines on selection of land use-systems and species and their management, for planning successful C sequestration projects in the West African Sahel.
General Note: In the series University of Florida Digital Collections.
General Note: Includes vita.
Bibliography: Includes bibliographical references.
Source of Description: Description based on online resource; title from PDF title page.
Source of Description: This bibliographic record is available under the Creative Commons CC0 public domain dedication. The University of Florida Libraries, as creator of this bibliographic record, has waived all rights to it worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law.
Statement of Responsibility: by Asako Takimoto.
Thesis: Thesis (Ph.D.)--University of Florida, 2007.
Local: Adviser: Nair, Ramachandr P.

Record Information

Source Institution: UFRGP
Rights Management: Applicable rights reserved.
Classification: lcc - LD1780 2007
System ID: UFE0021453:00001

Permanent Link: http://ufdc.ufl.edu/UFE0021453/00001

Material Information

Title: Carbon Sequestration Potential of Agroforestry Systems in the West African Sahel An Assessment of Biological and Socioeconomic Feasibility
Physical Description: 1 online resource (184 p.)
Language: english
Creator: Takimoto, Asako
Publisher: University of Florida
Place of Publication: Gainesville, Fla.
Publication Date: 2007

Subjects

Subjects / Keywords: bank, clean, development, fence, fodder, kyoto, live, mechanism, parkland, protocol
Forest Resources and Conservation -- Dissertations, Academic -- UF
Genre: Forest Resources and Conservation thesis, Ph.D.
bibliography   ( marcgt )
theses   ( marcgt )
government publication (state, provincial, terriorial, dependent)   ( marcgt )
born-digital   ( sobekcm )
Electronic Thesis or Dissertation

Notes

Abstract: In recent years, carbon (C) sequestration potential of agroforestry systems has attracted attention, especially following Kyoto Protocol's recognition of agroforestry as an option for mitigating green house gasses. Although the possible benefits of agroforestry in carbon (C) sequestration have been conceptually discussed, field measurements to validate these concepts have not been undertaken to any significant extent. In addition to the traditional agroforestry systems, improved practices and technologies are now being expanded into the dry regions such as the West African Sahel for perceived benefits such as arresting desertification, reducing water and wind erosion hazards, and improving biodiversity. Thus, it is imperative to investigate C sequestration potential of agroforestry practices in these regions. My research hypothesizes that the tree-based systems will retain more C in the systems both above- and below-ground than tree-less land-use systems. By joining the C credit market, the landowners could sell the C sequestered in their agroforestry systems. My research consisted of three components. The first examined C (biomass + soil) stored in five target land-use systems: two traditional parkland systems involving Faidherbia albida and Vitellaria paradoxa trees as the dominant species, two improved agroforestry systems (live fence and fodder bank), and land that is out of cultivation (abandoned or degraded) in the S?gou Region, Mali. The second component involved a study of soil C dynamics of these systems: the extent of soil C storage/accumulation by trees and stability of the C accumulated were investigated. In the third component, socioeconomic feasibility of the agroforestry systems was examined in the context of C sequestration and C credit sale. Research results show that the selected agroforestry systems have the potential for sequestering more C both above- and belowground than in tree-less land-use systems, and that the trees tend to contribute to storing more stable C in the soil. Among the selected land-use systems, live fence and fodder bank are more suitable to start as agroforestry C sequestration projects than the traditional parkland systems for smallholder farmers in the studied region. Between the two improved systems, live fence has higher C sequestering potential per unit area and is economically less risky than fodder banks. Adopting these systems on cultivated land rather than on abandoned land is likely to sequester more C and be more profitable. Since parklands are traditionally practiced, they are not likely to qualify as a new C sequestration project soon. Nevertheless, F. albida trees are more attractive than V. paradoxa trees in terms of C sequestration potential. These results can be used for development of recommendations and guidelines on selection of land use-systems and species and their management, for planning successful C sequestration projects in the West African Sahel.
General Note: In the series University of Florida Digital Collections.
General Note: Includes vita.
Bibliography: Includes bibliographical references.
Source of Description: Description based on online resource; title from PDF title page.
Source of Description: This bibliographic record is available under the Creative Commons CC0 public domain dedication. The University of Florida Libraries, as creator of this bibliographic record, has waived all rights to it worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law.
Statement of Responsibility: by Asako Takimoto.
Thesis: Thesis (Ph.D.)--University of Florida, 2007.
Local: Adviser: Nair, Ramachandr P.

Record Information

Source Institution: UFRGP
Rights Management: Applicable rights reserved.
Classification: lcc - LD1780 2007
System ID: UFE0021453:00001


This item has the following downloads:


Full Text
xml version 1.0 encoding UTF-8
REPORT xmlns http:www.fcla.edudlsmddaitss xmlns:xsi http:www.w3.org2001XMLSchema-instance xsi:schemaLocation http:www.fcla.edudlsmddaitssdaitssReport.xsd
INGEST IEID E20101118_AAAAAO INGEST_TIME 2010-11-18T10:00:58Z PACKAGE UFE0021453_00001
AGREEMENT_INFO ACCOUNT UF PROJECT UFDC
FILES
FILE SIZE 1784 DFID F20101118_AAAKJK ORIGIN DEPOSITOR PATH takimoto_a_Page_166.txt GLOBAL false PRESERVATION BIT MESSAGE_DIGEST ALGORITHM MD5
431d10077dd79f42cd7cc965df4a4cbb
SHA-1
68c5e44347c98e540637114701577ec62333dec4
1051970 F20101118_AAALMM takimoto_a_Page_131.jp2
05c785f7a0842d4cacd27f3d1a815852
1584b83a71625a822d46a6a525f797f4f506853c
623398 F20101118_AAALLY takimoto_a_Page_105.jp2
567e90d52f5b74935d0832089cd60d0b
047bdbf144d53265aff217074a18d1050d2bfcd9
54140 F20101118_AAALNB takimoto_a_Page_159.jp2
c2074d847d813808759e1b8429657b35
530e0d49198a6723b518487fb192fb8877021b08
3349 F20101118_AAAKJL takimoto_a_Page_143thm.jpg
a7c4d637435b759070c61ecfda76e8d7
c58f4ae7ec43abbd64344cea87523b4ae7d05e37
1051985 F20101118_AAALMN takimoto_a_Page_134.jp2
5036bd86714e0a1a4eb7d304f8418534
e4e1a95d0929e0ac1370e89923031adb93eb89a6
664806 F20101118_AAALLZ takimoto_a_Page_106.jp2
ad88ad0c8e7ac6da4eb90683d33d2fa1
9891387be4fb1b7334e5473a8fbda6d8c48766a3
6925 F20101118_AAAKKA takimoto_a_Page_046thm.jpg
9f982bfae426ad7fc76862901c498344
158e845ac7c3b8ba192896f453f43e4601794756
76356 F20101118_AAALNC takimoto_a_Page_160.jp2
3e989c3db7635764e2a4f1d008a2fdcd
c797e583faf266b3263c61c0e1abc6aeea8d8de5
1053954 F20101118_AAAKJM takimoto_a_Page_167.tif
dba89795772a314cacdf206afb4ae133
0305ce304389d0be517e046c168bee8cf9fc6f4a
117812 F20101118_AAALMO takimoto_a_Page_135.jp2
3089c06c6260f9a025d37632854ab741
01235a9b0309adf0ea8ee71e6a7b6b33bc97135f
960817 F20101118_AAAKKB takimoto_a_Page_090.jp2
513d97632de73d35f243f5c2c4664537
4fac35fa5b551022042799c2cbce5640ac9bc2ad
32787 F20101118_AAALND takimoto_a_Page_162.jp2
f4cbcec006b639037b337fdfe315502c
1e6c88a1661b6c4ebf167e690e36d4bebfdb972e
27490 F20101118_AAAKJN takimoto_a_Page_098.QC.jpg
6d31deca4dc63c4c135a483c2cc7b818
81111c25f96774f3802574ef47364f9feac01b06
83709 F20101118_AAALMP takimoto_a_Page_138.jp2
51b00d0a0acda932522adce314e3c2c5
708040035c7c5ef55a46941f95dcbcaac9116d24
25271604 F20101118_AAAKIZ takimoto_a_Page_104.tif
08532478b81bfed07ccc373907ba64d7
e869cc0c0f5d301d6573cb485107696c2e001e3f
61900 F20101118_AAAKKC takimoto_a_Page_161.jp2
86f20e93cd0bdcb6b1fabd82b2c2292c
4758fa72157ede76df53c511968842370e7bb4e9
83019 F20101118_AAALNE takimoto_a_Page_164.jp2
540ff2576ead4b9029f5051f96d2af8d
50b9a6d4ac2e8659b35c3d2f2b42c53cadc89140
48995 F20101118_AAALNF takimoto_a_Page_167.jp2
cf72006c3ea077588e430d143b9c07f2
6d4c9718f432d665d7ded66f8f2b4d26084dff46
7005 F20101118_AAAKJO takimoto_a_Page_135thm.jpg
28c66c72894482b12477f7e16beae3ae
7a4fc507fde3220d162a123cbfba696b631ede1c
362224 F20101118_AAALMQ takimoto_a_Page_141.jp2
50e8564cc0b392efe27a6f85c02d2c69
eada927de420a7f7c4865b5e923bef9c9bc3c039
F20101118_AAAKKD takimoto_a_Page_070.tif
808834e6a1d12a59ea308595739f0632
0e63eb2df0cef06da02dabd6e7069e3ec16f9f86
94210 F20101118_AAALNG takimoto_a_Page_168.jp2
327b10100a9bbce52fff2c680c725228
982e4c642917a7c40d07763720769684597fdbe5
53253 F20101118_AAAKJP takimoto_a_Page_042.pro
c6859f4edc4cdd5e40153e6a4531f0a3
e3366a9979c4fe04da4564170c198744e7828426
355319 F20101118_AAALMR takimoto_a_Page_143.jp2
f7b5927157d6091f1ba4f459f5c98c92
487086d326664442b15d4b873f81217d566b1a19
1051980 F20101118_AAAKKE takimoto_a_Page_017.jp2
bd02630320477660dd0017fe3950fabb
61d257e30041e6762b0d2df32a64c89b872ad060
93112 F20101118_AAALNH takimoto_a_Page_169.jp2
497089195ea3dd58ef90be4d114d9939
7b603064064ad563dbacbe86000b2b58ab1b586d
114867 F20101118_AAAKJQ takimoto_a_Page_042.jp2
9e99495fe15c6033ec068e3ce73baf67
f1757a92c53577422360fe6a78f1b99252cc5a7a
385672 F20101118_AAALMS takimoto_a_Page_145.jp2
e26ff1cba3f12554b2d3b119077aec4c
e81a5cca4624b47b185e5e384fbe2cb5f5e03e47
F20101118_AAAKKF takimoto_a_Page_129.tif
fa63d36394e05fe6eab5c57ea34fb4cb
60f7e867918ba112454440ed99651f672575e669
134068 F20101118_AAALNI takimoto_a_Page_171.jp2
14f6d873ab0211c8df064ee4c55b6beb
59824b372551a69a562c31d709e5a7c16fd25e14
F20101118_AAAKJR takimoto_a_Page_130.tif
777e4000bc77f952a63fea6102ba68c0
c47866cb36a233d1836adfa5b5c0cbabc491d030
360490 F20101118_AAALMT takimoto_a_Page_147.jp2
ade4949f8e48ae45e242ab42ea65f4b3
dd15c60d1772144f5527bcca5b5d790152b2e59c
F20101118_AAAKKG takimoto_a_Page_087.tif
40ad681d12e45b42c5e87e1deafbac99
88de68ec152e752f9cb46618af8fb8b7032f40a5
F20101118_AAALNJ takimoto_a_Page_172.jp2
683a07449d96bff7282fbaf3fe25bf31
698db6728478bd8ca1a4bd7d5546850b4ef4a5ca
F20101118_AAAKJS takimoto_a_Page_183.tif
7d3a83fddd90ee96e4debad5a320ffea
56145b8b313ee76f4d1cf4b5b6a59802ff8bfe0a
113069 F20101118_AAALMU takimoto_a_Page_149.jp2
0ae5834538190f96350577531b181cd4
ecdd1b5832720f4d84b9db18ceb05ebf09175ac2
27681 F20101118_AAAKKH takimoto_a_Page_022.QC.jpg
dbc1dfbda222d0fc6d2e87d004c1a2d4
809664752473f704b12a94bab38c2333c6b6dcfe
F20101118_AAALNK takimoto_a_Page_173.jp2
b3e66610bed4a8bc07dfab7742808815
2fe0b80ca7b96f6ee69d1799295b0579e5c2e706
4711 F20101118_AAAKJT takimoto_a_Page_161thm.jpg
8cf356ef22d8fb73c5aac21a8a70bc16
36aa319c5769a384bf8cb732d0c7fd77254e6d2a
117870 F20101118_AAALMV takimoto_a_Page_150.jp2
13efbe932a2226ad9a155d48e88517b4
2e646e753f0348a2347ff88e0fdb7eccf2a9d2cd
87050 F20101118_AAAKKI takimoto_a_Page_134.jpg
e0249f6049f5c2f6f6d521e51dad682b
56c3aefa7e3867286c0a109670d30e283ae991bc
F20101118_AAALNL takimoto_a_Page_175.jp2
5e3fd8f49fc5ca07fe3856e96e20b9be
e12017e07a9ffb5821e7845349fa824dbaa39e7c
13581 F20101118_AAAKJU takimoto_a_Page_109.pro
e69f1b3b5fbe0ed489caa20305b5830e
69749da52b58d4ca1d7c2aff7495c0c2ec9e3ca7
110076 F20101118_AAALMW takimoto_a_Page_152.jp2
9260cd20dfa34e652cb841585dd890fc
324e2fde23e1ee0750fc1144b942205b441bf100
24515 F20101118_AAAKKJ takimoto_a_Page_057.QC.jpg
52884f701d93033cb971ad58ed6b7272
296c7abb8e61b1bf9f934ee57b7bc8d39c44a4de
F20101118_AAALOA takimoto_a_Page_023.tif
9479ac750b90a4f3dc337485f4099e25
f682a022b29612ed8ffac65fcd0dc18fff085cb5
1051979 F20101118_AAALNM takimoto_a_Page_179.jp2
8a37fe1a9ab181105654c81a035ad6b4
83f7e8f1a28a7b8737d42f24a3edbf2a5761aed8
13542 F20101118_AAAKJV takimoto_a_Page_114.pro
1d225ef596e53cb65848c605b435c3a5
6d79a9ba1499bdcf2f10c885e7b76f732b485099
118638 F20101118_AAALMX takimoto_a_Page_155.jp2
7fc76bc62620ee81e242ef109a899a14
d23576da5377557a729a6eab199edc405ebfb7b5
3659 F20101118_AAAKKK takimoto_a_Page_008.txt
c7a295d0475766fa45e6cdcc8ca2cbb1
6e91d48d4f1f1c0493470a2e104d059208fb9b1e
F20101118_AAALOB takimoto_a_Page_025.tif
3c0834edd04a7217aaf8c40595340796
fa72fdb6efdc4b94758f68bd59bedf6c5f8d0ef9
19319 F20101118_AAAKJW takimoto_a_Page_156.QC.jpg
d557cb6e5756a8bd7640bbddd558ac8b
158c247595f6ce9ee69c20b97b5307ddefb8e1b2
77882 F20101118_AAALMY takimoto_a_Page_156.jp2
34ec1b722d0884f54a4a607187518542
de8e1f951a7c7da2e4f5190c086c591e98a8b3f9
F20101118_AAALOC takimoto_a_Page_027.tif
efa18b36bd7b7947faf6137bdb5cd290
dd9ce21bd059f1bbce6e6b4365e4ba9904fc6e82
147031 F20101118_AAALNN takimoto_a_Page_181.jp2
0174d1ce975f4cdc3ef487c095e14fa1
dceeb82f36882785e055acfeedfbcabf63bb2685
25962 F20101118_AAAKJX takimoto_a_Page_155.QC.jpg
0906b14253547cdc616369425ed47658
96bc12d94f8e151c3406798d1680c0de90146b5c
76436 F20101118_AAALMZ takimoto_a_Page_157.jp2
f7abc4bdff6a4e87492a93828e0a2890
0fddec8f45a35369c280500086fda3ba3d44a8fd
F20101118_AAAKLA takimoto_a_Page_138.tif
99f81c0ea0647291e5545246a2e43373
75d8f28bf34fa179de53263b69aae1cb3fb55bf4
137378 F20101118_AAAKKL takimoto_a_Page_029.jp2
0ecdf26940362f8abc2680abcdf4696a
d910f2768f1aee47536651dcd49ad69de8a53c8b
F20101118_AAALOD takimoto_a_Page_028.tif
c98667281873699e548a15f68e840609
69d06af9899acc51298b5aea109dab8f02a4092f
40290 F20101118_AAALNO takimoto_a_Page_184.jp2
8a8cc1ff2eca30fbdefb571b2ff30c82
b129277595ca617db6c9b12c1d159a62a7e56c9f
F20101118_AAAKJY takimoto_a_Page_107.tif
3959dc14ec367bd15aba6f668c552a9b
2a2774706b2e1e370c69cdd3594ce37929f6074f
46476 F20101118_AAAKLB takimoto_a_Page_078.jpg
d49dbb7f8f294547c6dc6a43afd67583
dc0391da60468f036b88687a601958e5f407b1f1
739 F20101118_AAAKKM takimoto_a_Page_137.txt
2da3be4e2edfdae4a156ac8d912b63d6
04257180a1eb84dedfb50f1d3d48b58d0cf8441b
F20101118_AAALOE takimoto_a_Page_029.tif
ea1a97d084b5c8ca01de0e7ec908191b
869409056aff0304dfd8cb995bb58ff80983b485
F20101118_AAALNP takimoto_a_Page_005.tif
cd9227fcadbb36dd0ca960e6f64fffb4
649aeeec533b38793cc680c2f0681b405135cb88
63187 F20101118_AAAKJZ takimoto_a_Page_089.jpg
c8c66b396085a1c2b5cfbb8850983da9
9116a2ffa2b70441c2f16384925e8d9ce8aae9ec
110829 F20101118_AAAKLC takimoto_a_Page_015.jp2
0b6329f6fcd76deacf7f2fdea1b2b7f0
e4f007d781ddcf37f71164223ebcfee3963ff4f6
F20101118_AAAKKN takimoto_a_Page_166.tif
0817f3589268a01a5ef0f35923890b82
cad4373967a483f40f66cf65a4a1e1acccd42ac3
F20101118_AAALOF takimoto_a_Page_030.tif
0c66155928284c7201e95b3597ad1ec6
10059d0df71c1a3d1be3659c2c67a2b7b0728935
F20101118_AAALNQ takimoto_a_Page_006.tif
e120e2793e22b53f15d8079affefe8eb
0265261b0ee1ce07b264ef3fc280085625c3f877
89279 F20101118_AAAKLD takimoto_a_Page_170.jp2
ca5fd8a198fd376a404bfd3714304d6e
9d6350933f61240cfba996484ccb6d83041c8d6d
44471 F20101118_AAAKKO takimoto_a_Page_124.pro
cfac9dc69c61435b5c494ca1ac614068
65335cfd216e3775e620c4a6cd497c8348a81c72
F20101118_AAALOG takimoto_a_Page_031.tif
c5b2770b689f10943f97fee0463daf75
16137b2891da6e949b23fe8acf7173ad2c5bc803
F20101118_AAALNR takimoto_a_Page_007.tif
aed59ebbbb8aeb74fafb9e6ef4b9cf41
5a8ec95b06eadb39d5a252a272d84d751fbd5998
7415 F20101118_AAAKLE takimoto_a_Page_133thm.jpg
f2090e26298e1b7d86d1b5ac0747827d
19f98d46023fb03d493ad21e21476cb6ab5b435f
20903 F20101118_AAAKKP takimoto_a_Page_058.jp2
64c17482180951b9976174945cff7c2e
8a54c83000a23ac1bcdf62e378002f031e52d4dd
F20101118_AAALOH takimoto_a_Page_034.tif
7313fd8b2a7abfeed293db0935cc5bcb
82dd1cb6213ac44c0886697426d4a00be8337d99
F20101118_AAALNS takimoto_a_Page_010.tif
9d2287f6084d9f1ef0722bf59e17d7a6
f63d6e86752f3e842de85a0b328ee2191ae571aa
876 F20101118_AAAKLF takimoto_a_Page_113.txt
26fcbf26933a4ce926b583ff9a8ff8b4
ab050da42f37043a059aae690b49758106fa2437
1051964 F20101118_AAAKKQ takimoto_a_Page_021.jp2
bf0397db8a92b5ecef8cb2c1b3df28b7
91392dfc6679b9e63f569c63b4313c20c966ba61
F20101118_AAALOI takimoto_a_Page_035.tif
14e08cd525c6483c1b014289e1f30c06
4cda1b324e34ad8f3f8e1ce060d501faada275fc
F20101118_AAALNT takimoto_a_Page_011.tif
5c1b0e1bd057ddef1c019f05b94656ea
41a28edebcfbd18144274872febddb016f45b85f
14687 F20101118_AAAKLG takimoto_a_Page_082.pro
4c96f496de8fa08103c68017d8cb2dde
775676f5be0e82c497b89fadd48a546591cafba8
90430 F20101118_AAAKKR takimoto_a_Page_074.jpg
e3a4b21b405bf03b93f668d04f4ad172
4b554a7d18fbc7bede9d626a50c19497477bf7cc
F20101118_AAALOJ takimoto_a_Page_036.tif
398a317bd9e55f99d7237ad01a9b0560
c506117310b19c749311f103e6cce22eae4d9871
F20101118_AAALNU takimoto_a_Page_012.tif
5b5e1499b1a34454da9b4923e789b299
d18647c4c6a4b537b8ae3638b673b16e27522690
64338 F20101118_AAAKLH takimoto_a_Page_030.jpg
19cd143a853f6cbaadecad66ea85059e
d056f2debfa02a4996b92f4ee1b9ddcc8b3b0481
F20101118_AAAKKS takimoto_a_Page_182.tif
91ee911973273a775cb272514f488cfc
39c26991452c8001ddea19581c140c06233b5e25
F20101118_AAALOK takimoto_a_Page_038.tif
a1425579646e0b88a3d20cffcab196f6
396816740a53a3907dd2a7d21bd4e4a7629b054e
F20101118_AAALNV takimoto_a_Page_013.tif
6af2ac6dd7e3ee83d29b1b409232a771
1e2198d489c6bce842b02239cd903726f5c3ef34
85513 F20101118_AAAKLI takimoto_a_Page_051.jpg
cb5f7337c418c5fad895a96762245c1d
5bd642ab7dfe913328c68289f1ff674600ee3c84
51897 F20101118_AAAKKT takimoto_a_Page_054.pro
301190808f7ed79e302d4c2713ae77d5
83e52afbfb372bc26bbdde07ef8c79a211d890f0
F20101118_AAALOL takimoto_a_Page_039.tif
331aab2d4118a781fc45c5bec8b88f78
5d7b3a4d188f44db4536c2c25616b484aaee3320
F20101118_AAALNW takimoto_a_Page_014.tif
0e97f9a954b76839664f8ace4bbc838f
95640f4298d4322971e9b09dfc49324685132d63
69340 F20101118_AAAKLJ takimoto_a_Page_024.jpg
43b0a688b902d1d7920e751bef9408b5
04d37c1ea86fd4d9ae1c82132445edc81fb4eab4
F20101118_AAAKKU takimoto_a_Page_086.tif
67d901e9703a3726a07291489556e998
4aafde1fdd79b7a3df664ff5d0e54ceea48975a3
F20101118_AAALPA takimoto_a_Page_061.tif
0a5c738a10eec6d37451fc99d889e47c
feaa8b802e55f834099d29572ff1caaf1f6307b5
F20101118_AAALOM takimoto_a_Page_043.tif
89c3735e0afa2ae8bdd76c0cb89449b5
19de5f40c73c8c61cba227fca085cc0340a9975a
F20101118_AAALNX takimoto_a_Page_017.tif
ae6f176b8b6da7d790c21421d2cc693c
a183f7a6ae98ee4a9e6636b5d71365e3a8392af6
790 F20101118_AAAKLK takimoto_a_Page_167.txt
83bdb0d4ffd734c69dd4abdddcbdd0b4
6992333b3b289e145164c33af8b9b53025928b95
77126 F20101118_AAAKKV takimoto_a_Page_049.jpg
e4a24a59674b954a75985b64360c55cf
241bf267e79b16e6d6cf0e5e3e14b1f2fde75460
F20101118_AAALPB takimoto_a_Page_062.tif
3316244e0915915c76737fdb825ef25b
64570b0c6e3d81502319b5b69563c5c77116d4ab
F20101118_AAALON takimoto_a_Page_045.tif
c18cb05d779d32eac0bfa093a6931a3f
af277d3efb9e0c978782d6dfbb5451bd3fb4d962
F20101118_AAALNY takimoto_a_Page_019.tif
bc528fccce058bb3f4381ec56eb06502
45103862242ab049a286139d03a81f8e4b526174
F20101118_AAAKLL takimoto_a_Page_178.tif
c38d0d162bccf8442b975fad98b75dbe
5e64ed69d9b7551a9ca1308b869ac7acade73ca9
F20101118_AAAKKW takimoto_a_Page_011.jp2
744041a84cba83a49600dc60f95d2877
a7383908ccb2735312b3c21dbb9e04b6ce0a6fbd
F20101118_AAALPC takimoto_a_Page_064.tif
8d7c9ca6c27ccdbade283d2f305188ae
51226df9c5217fe46abf17bfe82de0b6f0cc8d9b
F20101118_AAALNZ takimoto_a_Page_021.tif
e46a9087d76f5a49015d68d3d17859cd
7d2d749cdd5addaa213a0af0149161d65ed62d16
6702 F20101118_AAAKMA takimoto_a_Page_004thm.jpg
b9d8a09c9b6328e6b3c474348c974ada
161f3489a572e01237e7eac39c0143e1a50640d1
7475 F20101118_AAAKKX takimoto_a_Page_092thm.jpg
ab6f0e4768c8b6469fce4c26c8988d8f
20086d33c225583f00cad6859f2e9a684388fe76
F20101118_AAALPD takimoto_a_Page_066.tif
09b32e1861bc444880516292d02421c8
d52579788b00b51427775f27c2a968704e397f64
F20101118_AAALOO takimoto_a_Page_046.tif
6475ebde859cd37cf8d908143a07d05d
f0182964a0ffafd398e3ed9cf33164a5698c3fb3
55621 F20101118_AAAKMB takimoto_a_Page_107.jpg
709e6412c5dff283f4ff55262f3c8794
34413d1405629da2a0666abd13be4431e1dd8e65
38055 F20101118_AAAKLM takimoto_a_Page_032.jpg
4bf145c6fed379b1a5313d4b7256ae53
4285e381d5fc821c7f9deb87505b54d13f38d294
2979 F20101118_AAAKKY takimoto_a_Page_179.txt
2f42bb039a24a378f956fcf698713ea4
b5a35c03c8d7b4c7ae6f4d64fdcca7bdedadc313
F20101118_AAALPE takimoto_a_Page_068.tif
3b1bc1d53457ed056536b554aa07fe45
3dce1a41e4783bc5022a1d4e79c008adae3c846c
F20101118_AAALOP takimoto_a_Page_047.tif
4b57f131312393b6793e538bad38bca0
19ed8820ce931d9654612b872ab73d51a086f21a
34720 F20101118_AAAKMC takimoto_a_Page_156.pro
8c43ca730b8bc98c0bb356d0dbef105e
9c2df541f8a27ddcc39ff83a35da617db84f84e3
55069 F20101118_AAAKLN takimoto_a_Page_055.pro
9c9313e5be0a2eafd75fd464ed2aa8ec
9b6cb7f1d6fec053b194f45e4f6a21487bceb99e
28171 F20101118_AAAKKZ takimoto_a_Page_134.QC.jpg
b786dc37758f6df6a3b3e70c179db62e
9d3c2747dfa7322294de894167b149e81755b8e3
F20101118_AAALPF takimoto_a_Page_071.tif
511acc2877f85b6b07384f1409a127de
36c9d7a4e3811662151fc095e7a8ef3b3fa72477
F20101118_AAALOQ takimoto_a_Page_048.tif
5ea59e468b02d729c6cfbbb9076f0722
2b11fd8763f7cebed3fadf6d965d3178c112357c
F20101118_AAAKMD takimoto_a_Page_081.tif
fd69540eae3a8fc15cbe3af02f627fe7
afcbdc54887459b09ebc9cf159cb17766a45d04e
50757 F20101118_AAAKLO takimoto_a_Page_015.pro
31c8a35afbf10a2941cf38d7322804b2
811ecd3e7a550b9f9ff6db7ed0d8745f7f1e964f
F20101118_AAALPG takimoto_a_Page_072.tif
31d5bbfca96400a65acf8571b0e44301
3a97dfed3ab29c3da2259c73ee6d3129917108fd
F20101118_AAALOR takimoto_a_Page_049.tif
5cc1dd2bf1708d23a7ea2d06e1ef298f
43dbfa1b83e05579e7f08f3a3e2b7fa4a654744a
6913 F20101118_AAAKLP takimoto_a_Page_150thm.jpg
403ad109e8edd8c93ed9cb1e397f7d15
fc37255880362d81c111d9a7da3090b618ee0502
6288 F20101118_AAAKME takimoto_a_Page_140thm.jpg
46e9035da8f9fdb1e746dcb751986097
5bf5bf7350f6e15cb05a73e7dceb56891d40583b
F20101118_AAALPH takimoto_a_Page_074.tif
a9b5e2829f76867e408c5bdd58d42179
93fa8b06f8e58421d04d37a176f574b93b167acf
F20101118_AAALOS takimoto_a_Page_051.tif
c1895c89fe1b6b28928656ee2abec66b
577312c6312bed9cd5a32a54289680a733520860
113572 F20101118_AAAKLQ takimoto_a_Page_064.jp2
ad8312e3e7b9612fbb0f1dd9094c15d3
f46d66e0ffde37badfb81a97dbfb6f1568cb36b9
F20101118_AAAKMF takimoto_a_Page_109.tif
6161c1a6394f89648b23926aee976929
bbca2a7b8f4c6010e56a638102b4b2d215fef395
F20101118_AAALPI takimoto_a_Page_075.tif
9d7e54344014dd05af416828e048ea00
c35fe1abbca0b2be7a5c6e26e3a7089fcf3c48d5
F20101118_AAALOT takimoto_a_Page_052.tif
94a29e373c34b4c0533ff272454d9e80
dbdf49d2295e1d2fa56694ade46f61729de4fb0b
1299 F20101118_AAAKLR takimoto_a_Page_106.txt
45af1b16c40ca947a169590b26d7b1d9
4e43cc3bcab8506de24697443403a16f5e45fe54
53866 F20101118_AAAKMG takimoto_a_Page_038.pro
a9a2e07f90dc21916a0746989b6d46cd
5b0dc130d4801edbc43a0acf46724534d55bed7d
F20101118_AAALPJ takimoto_a_Page_078.tif
2ac6d38adf3705d79b3070321412ea35
2a3b95c1457aae0b33605114c40da163b598c4db
F20101118_AAALOU takimoto_a_Page_053.tif
63c3526baafce6958108b046851ebdba
c157df361ede3b5dc2bb91a02cc88f8016419ad7
16015 F20101118_AAAKLS takimoto_a_Page_034.QC.jpg
5a7fd72d7b33e51ebdcaa4132e27e3a1
77c93ee95f6aa83b740e503e789672985dbb767b
80475 F20101118_AAAKMH takimoto_a_Page_016.jpg
316102e456e1bca699bfb73cc0267c67
d3c1017425e354b7789bba6f53b5280864820e86
F20101118_AAALPK takimoto_a_Page_082.tif
afac8627bcfd2730080906e384f9b9e8
930e02b557dd4190e339a97ee15c8a63a01014fb
F20101118_AAALOV takimoto_a_Page_054.tif
da5d6eafaa0335f6252ecf0b93bca7bb
faf0ba707aa90395794f3578a1421cae81199089
17933 F20101118_AAAKLT takimoto_a_Page_157.QC.jpg
8345d0a11ab0ebddb91b5b1a8ef63cfa
4a515beac2f65ac416d62610f2ae6e813f0a5386
8153 F20101118_AAAKMI takimoto_a_Page_179thm.jpg
91805cab7f65522d4f864d7dbe89e336
1f8e8b29635f4a5d9041f4f041a762d82176172f
F20101118_AAALPL takimoto_a_Page_084.tif
c9bfe1fc3aee91e1c383d817290d89fa
508b4d2a29d4214010f60ec16ee766245524cd3b
F20101118_AAALOW takimoto_a_Page_056.tif
e7f5e39c0ae97d60fc66070470ff903c
0659c6e25b8184b88da289bb725c8863cc24a54d
F20101118_AAAKLU takimoto_a_Page_144.tif
8332a8ab08aeef32626a2a682f2301b1
314f9e56194089c0fb43ab5f02e0effdc3240e41
536 F20101118_AAAKMJ takimoto_a_Page_001.txt
05987d8dacbce26e93d2681658cb15fd
da88d9a16b22841d46f3b6f8c2bcf10336fcdaa5
F20101118_AAALQA takimoto_a_Page_116.tif
c638576ebdc80a0dc41f339d311b68be
3c557c4b29396f85f1cf4989a721a0ddd557e0a9
F20101118_AAALPM takimoto_a_Page_085.tif
0801b42adf2e4e008649c43956998dab
39fde7675c9176a52b71fdd4d0d1e3da99b88974
F20101118_AAALOX takimoto_a_Page_057.tif
fe7ce6d17e6da810e56aef6864de1f09
1639f3631116716ea03fc87cfe9d8b8ec7e6266e
14638 F20101118_AAAKLV takimoto_a_Page_111.QC.jpg
098aaf60255c192376074280ba70df1f
4058970905f150962e73e4a4b89e607a8af45e01
25180 F20101118_AAAKMK takimoto_a_Page_087.QC.jpg
c2173c8ffdb0f8651c26470eb47d65d6
5fb3c6002ba2f64a0dbf9c174ee7404b31fff7d2
F20101118_AAALQB takimoto_a_Page_122.tif
bd94baa7523bedff17ba7dd574cfe400
e9abea360dafbee58ef37be33c4742a2d71d0508
F20101118_AAALPN takimoto_a_Page_088.tif
f3f63c23e3e20513778ae0816d20a0dd
bf059aab16593c1241d874b1eb044c3b4f54da29
F20101118_AAALOY takimoto_a_Page_058.tif
6e1306516aa481ee2bebf10827c2a075
18db7265090888796e5c8344cb40392497f8fbab
83934 F20101118_AAAKLW takimoto_a_Page_017.jpg
02411a0129cf9300c020a0c83e20fd53
03e84603f8a2d311cbe400177f1a53e8140031f8
22726 F20101118_AAAKML takimoto_a_Page_139.pro
2c18521ccb85f135ebcb168254b9d9af
664a850c71266986bbc956fa2abe09914fa804a1
F20101118_AAALQC takimoto_a_Page_124.tif
5afb3c4f4ee5e38b5340c856ca371f2a
0362c24b9bcf88f943c3a88f1eefdcf52c94ad94
F20101118_AAALPO takimoto_a_Page_090.tif
df5a8a2e2a108fa45bac333fc4158a6c
ccabf5f67bb8672eac4ddaa6c56cc9fb45832853
F20101118_AAALOZ takimoto_a_Page_059.tif
f39cfd477db130928e4f33a2b0842ed5
4369b97797f85aec5dde90f25a88a940491deb57
F20101118_AAAKLX takimoto_a_Page_099.tif
4d37fea5012d2dbeb8c99762fd4141ba
5d9adbd4fbb2207b467ae11b9185e4b392709aae
73506 F20101118_AAAKNA takimoto_a_Page_056.jpg
864431d864604c8481b1c4033fc1a95d
fe079593e21f034dad344f9af8983cc6b5c44443
5606 F20101118_AAAKMM takimoto_a_Page_012thm.jpg
5f98cff6f3fab97dff8a2d26f193d7a5
8a9ac60f96af6ea12cfcad475546c27d780650c5
F20101118_AAALQD takimoto_a_Page_125.tif
340f625a76b0fdebe89ba0ca7a8547e6
642163147d9f658cfee18d68f3593590c92dc642
10593 F20101118_AAAKLY takimoto_a_Page_163.QC.jpg
1b4c5abaf7f27895c456c3ca97e68f10
f986f278d5dd6eeb5dc77b17e18bf100db2f23c2
1413 F20101118_AAAKNB takimoto_a_Page_157.txt
00330d5a2760762b574e6a867ea57e3c
489c957a222a4dbb8362e75baacad622cacf4986
F20101118_AAALQE takimoto_a_Page_128.tif
7ea4082f0db00713f40cef5b4d33469b
74652d65fefe27ca40aeadfdaaf66b03685ed20f
F20101118_AAALPP takimoto_a_Page_092.tif
c113dc6e7f283c71aaf572fe2f7d31b5
36afdeebcf5fb86df8359c87c774bfbd8c3c65f0
586 F20101118_AAAKLZ takimoto_a_Page_114.txt
0af42d0a386fab24e7f5f246c9a55749
853e989ea07284066d9bd5cb0a4505d9a7d5e1f8
16879 F20101118_AAAKNC takimoto_a_Page_183.QC.jpg
24ce47b677a69999c9303b11e442e49f
71fa84abad06c2b10b5b4e9b9463eaf0d6dcd7a4
57105 F20101118_AAAKMN takimoto_a_Page_168.jpg
0e1b73d5f0838172b1116c5733abe572
c76dd85a01758e23bb557a608a09e8d0f6009ce9
F20101118_AAALQF takimoto_a_Page_131.tif
0045a965f323d3a042b77041218f2072
20268c79d763f353e0f1d2883f2425b048778f8b
F20101118_AAALPQ takimoto_a_Page_095.tif
cc36288081bcbf7123bde3f16fbec746
8bfbbdfe15b5ade4c99eec5196135366d5d839f6
F20101118_AAAKND takimoto_a_Page_126.tif
444ee888843a62364a64a38e32338405
d0c4fcdf8d606330e674022759c76bc672b56c25
54211 F20101118_AAAKMO takimoto_a_Page_097.pro
483f5da371065c6e304db0c69d906a8f
81d80b284b5a714a66aeb005913f115436b7dc43
F20101118_AAALQG takimoto_a_Page_132.tif
ad72cc5a98ad4dff1fb3c6b91ee5153f
e68027f62593dc16700ecd157d6f0e878238b09c
F20101118_AAALPR takimoto_a_Page_096.tif
c4e0d654529395695c9311bcc4ddc3fc
e69157068e23f6da3e69dbd3b309687ccc2d8293
F20101118_AAAKNE takimoto_a_Page_076.tif
4426589c5771dc491ae28d1e818b0e61
7bbc49a03f3df9ba6372c7cf59750dc84ec9a7f8
5338 F20101118_AAAKMP takimoto_a_Page_028.QC.jpg
7e758eaae2bf36cdb1911410ce5a8de4
66bbf0e9e7bb3178d4aeed6c13a599e535ad6432
F20101118_AAALQH takimoto_a_Page_133.tif
c6ce5f4421e1d7c72425c188eebc085f
68dd5c5126aa26c99d948fe56845ab17f90a2c9a
F20101118_AAALPS takimoto_a_Page_097.tif
aabf72b0c03e1b67ebf6ec31f7c8c2c6
b55aaec6ee983fe10f89e533aff84f76199a1bc1
27056 F20101118_AAAKNF takimoto_a_Page_095.QC.jpg
7cccaff7441eeb481d6da9aa7e3ca619
1241d3f9e9d4193982a93610abde4b97fe58f102
F20101118_AAAKMQ takimoto_a_Page_089.tif
4f1f15ff797ce6257b702313714f0d89
a6afc756610caa8659213aeb3cd7c55beac17b67
F20101118_AAALQI takimoto_a_Page_134.tif
85c89f93d8de5e1bc1eff9add7a5a3b2
7e83e5d1f69721bfb8a5e798aef24d95ae95e05e
F20101118_AAALPT takimoto_a_Page_101.tif
63ea94df8b254a121a36ba32821ec5c7
0bb92a49e9196d872768bd3ccfd07b07abcb99cc
110470 F20101118_AAAKNG takimoto_a_Page_177.jpg
619bf4ee78a6b42006f50ffccfba4ec8
4d4a5b859ff220972ecb18cadaa7b11392db9f87
37296 F20101118_AAAKMR takimoto_a_Page_115.jpg
98dabe93cd05bbe752abce3401ee2c02
10ee377b7195608f497afb34892390ad96044af7
F20101118_AAALQJ takimoto_a_Page_135.tif
c51de2399ca201bf3ef7424a3cd391d6
c72a537d839a4cd6b9deaa33c6fb02af9d2682d3
F20101118_AAALPU takimoto_a_Page_102.tif
9d4251490686e1f0a961549ee9711200
de73a91f7e050bf3d8036213f6b5507fa33c2205
7866 F20101118_AAAKNH takimoto_a_Page_182thm.jpg
611f9edb4d8fbd58e6efcd8cd353c393
044125cf8ba0425311eb873c51fb9475879c19ef
3062 F20101118_AAAKMS takimoto_a_Page_006.txt
68cfe1a43c3194922e1ec49071589cd3
a83eaa3619186f5bddfef68fc189281fd8a99c2c
F20101118_AAALQK takimoto_a_Page_140.tif
28cfc06972bf8b9827acd47da4ccbfdf
b5bafdbdca021382e5200cddbd999401b52d667c
F20101118_AAALPV takimoto_a_Page_103.tif
822504f9e4de58e87e213250b4f0a4dc
6c22c516ad87aab62ac24f03a874b9d81207a7c1
2032 F20101118_AAAKNI takimoto_a_Page_077.txt
2ae72aaed764ddeefa251090044f8696
d858595bc9642caa575cacee6082e6f97072fcc4
2144 F20101118_AAAKMT takimoto_a_Page_127.txt
efe4b5f023e169c9c8f391aec69e7a90
30d994da95d51bf77609a8949c05222d9672932a
F20101118_AAALQL takimoto_a_Page_141.tif
7aa0bd7c700ef4f426b8f9ffc9176666
ae33eb118aaac7a3fa8aca9d316ffa5c963e35e4
F20101118_AAALPW takimoto_a_Page_105.tif
1e0c0ad3c6e6c993f6b0bad9ade4f7ca
72b026f4947bfa82552e83bbdd874da80fa4cadd
51855 F20101118_AAAKNJ takimoto_a_Page_063.pro
f616c662fc26a8b0c602f954856d821c
637c6a2f61a5ba69fd9a197db3a3045a641daaca
F20101118_AAAKMU takimoto_a_Page_004.tif
e4202515d398865889d8e7229244d136
2476789c8edaa7b73dc0b6060a39b59979686c2f
F20101118_AAALRA takimoto_a_Page_169.tif
72fce52354d24d703d7c560679ba89dd
442d900154255d0bbdf273e20cd922162e9f87e9
F20101118_AAALQM takimoto_a_Page_142.tif
585b92e2f81be1b59ca09ca5db324d01
475a9b3c88fb6dd7911d38775a025311767a58c5
F20101118_AAALPX takimoto_a_Page_106.tif
572b216ad877ebbe16961ac7165cb831
e39eca1aafdceaea26e36c09276d268d794abaee
86315 F20101118_AAAKNK takimoto_a_Page_022.jpg
3c5cb32568a81ab89ea55cc6ea5a005e
572519fa77d5af35024c45f59fc4acf85efb345a
7396 F20101118_AAAKMV takimoto_a_Page_051thm.jpg
5bffd1c366b4d40f8657a7583273784a
19b25ebcb335f68130f3e5874f354d00cf7e99fe
F20101118_AAALRB takimoto_a_Page_171.tif
2d92dd88781bc2b4c18a2209c5751b4e
440be899b1c4e63a5b92d135b4f361ce28e9667a
F20101118_AAALQN takimoto_a_Page_145.tif
88aa18effb2be77982f02f8151933502
d00d0dfd2ecf72f3bc3ad7f5874250bac00fca82
F20101118_AAALPY takimoto_a_Page_108.tif
8598480cb46c0d00064d5af1cc34d527
35fc28d9ae71eda6d24277c70c49fdfdb8b0cfe5
34289 F20101118_AAAKNL takimoto_a_Page_183.pro
e19a96e5c90c7164443d88d9dd6047c1
6e84f268290328040596b926ec0d9728ff4d48ea
30605 F20101118_AAAKMW takimoto_a_Page_177.QC.jpg
4f37ec56e1417549c231b41d9a9a47d7
52c6eba5e04d9ee0338e51c83f0e2a63f2d6fd9f
F20101118_AAALRC takimoto_a_Page_172.tif
9f95ec833db7882b13b20aeb301904e2
6ae4080091ca27327dd76a272e317748b83cf992
F20101118_AAALQO takimoto_a_Page_146.tif
e1b7c10ccaaea28a88a6ba1e81cfc086
8ff42be1e9ed28eddfdd8da5ca2433b1627257be
F20101118_AAALPZ takimoto_a_Page_114.tif
3bc834c2241335d6bc6e6d259993c434
d0217590562323c75fd7ab43bd7a087f2fabc414
55129 F20101118_AAAKOA takimoto_a_Page_132.pro
5876c0d21bfa6d10dee4881c951a35f5
e6b83ce5ff2ce4353f0cde234b33e66112c55106
2220 F20101118_AAAKNM takimoto_a_Page_026.txt
f54597b9b94bf3e4739cb1032bf1cb99
55470089c902abf39a7b5f058910c1d6fee5ae13
108791 F20101118_AAAKMX takimoto_a_Page_024.jp2
4fc02b960669df613e27a7f5f4991fc1
4faa9382300e3bce0928dbac3a2e269ea977d2c0
F20101118_AAALRD takimoto_a_Page_173.tif
86e78f78348d255fc2a46c4156af6401
ae9db9a08c48f4da45176c083e2591f76641b834
F20101118_AAALQP takimoto_a_Page_147.tif
fd30feb90cc841fe80fa17a3b5af9a93
5d63bf96ec3b7e10012fe797e021f2f9058644c0
55733 F20101118_AAAKOB takimoto_a_Page_117.pro
f9598b44ec1f7819272157a74f538d92
254c7bf58b7fdd53380de0496252c1c21026c3e7
6942 F20101118_AAAKNN takimoto_a_Page_171thm.jpg
af04b99d6c2d07575b828e49cfe013b8
8ad0cbb125726a746b627b8b3f72b5d381ba33ce
114292 F20101118_AAAKMY takimoto_a_Page_153.jp2
9ce1d18f08ecaecfb810ac0432a021a2
d79c2649d6b730a27c1c23acf7e502c801e8e513
F20101118_AAALRE takimoto_a_Page_174.tif
ba312ccc33c4e83fa895dbcdf846f2e1
a5f2d89d794597a4bdc1914f62436b2bf67fb9d6
4532 F20101118_AAAKOC takimoto_a_Page_159thm.jpg
33e90f26d36fde9b8f28b63ebf5f446c
d45588bddd03a847f52aa0df458111b008d388aa
7612 F20101118_AAAKMZ takimoto_a_Page_052thm.jpg
5f6bf8570d6c9a2d5f46ca4d088a52b7
ae7646380f6f142120351ba1c04cca2517b69d4f
F20101118_AAALRF takimoto_a_Page_175.tif
8af20d128e4655a5410daeff125c036f
b941002f1853f7cadccd000c9ab3296d9c0bbfd0
F20101118_AAALQQ takimoto_a_Page_148.tif
db34bbbf28eee46492809122da58181b
2802052b9bd9436f700d0476a8376ae6ca5b28cc
2203 F20101118_AAAKOD takimoto_a_Page_058thm.jpg
f6600fcc49b310dd513c69d327c3af41
b6c33278fa94f09b53b802480513e0a643d02e0f
F20101118_AAAKNO takimoto_a_Page_073.tif
9ddffd5c388e2e3f37655e4287103b9e
f0bab958eb1ff619792c5010b4673d27fc395e68
F20101118_AAALRG takimoto_a_Page_177.tif
90e81b0a29ea2c4bd22b525b8be231b8
575966c53478852d8c5ca35e5b3e0100977593e7
F20101118_AAALQR takimoto_a_Page_150.tif
3197e3bf5a84ef4d37bef425436bbdcd
1c6b263a94042dace7d54b07a524413f073b3b4a
F20101118_AAAKOE takimoto_a_Page_041.tif
6938a2e30d506472330fa110724c6c72
6697124b930fd157bb086c20f5dd3c758d7c172e
105260 F20101118_AAAKNP takimoto_a_Page_148.jp2
6c803fa2b3f17464829f4c139ab08371
2c1cb02a64bbf78ea686698cc003c0224d8ad0a7
F20101118_AAALRH takimoto_a_Page_180.tif
a32d1a2ed3094cd9aae7371352648236
3d98c13fa827705d2da44b0576ad4145ac95f433
F20101118_AAALQS takimoto_a_Page_154.tif
be72eaa616ad7128a53c337c01fbfe18
bb501a9b684d5df0181865931bc24432db01b405
26126 F20101118_AAAKOF takimoto_a_Page_161.pro
c0f447aca02887ebb658f77aca8f44a4
0cebdce3d8e0f0365638feaefc99359f74b43dc7
782 F20101118_AAAKNQ takimoto_a_Page_080.txt
4b452e0965f36dc13dda7d90f52907ad
a699b2432b9ffb1d04ae5efe19f2c9c7cfa65fe2
F20101118_AAALRI takimoto_a_Page_184.tif
f8f8a3a803dd9b60dd662469e2403cd5
abbac370d1a45cd1ae86bcca5f23e60461dd96aa
F20101118_AAALQT takimoto_a_Page_156.tif
5712ec59ae9b794e149a91055ea82965
bd1ff465c006eb3fb8a05134145f3306a6399530
77669 F20101118_AAAKOG takimoto_a_Page_068.jpg
3a6dead9d9db8df7844134cedbc6278c
a4977f30d549611bb88bcb1f26db76c3f87e4c83
1051982 F20101118_AAAKNR takimoto_a_Page_092.jp2
26ce5e62e9b49cceed5d7a81ba17de65
637d046e29052805160579afc16c92e44287d058
806 F20101118_AAALRJ takimoto_a_Page_002.pro
0b60a94dc66fcff21805833779e9637e
79dee0d733e5f24e9acd118aac09c169a35ecdb0
F20101118_AAALQU takimoto_a_Page_160.tif
f35cb434b2f347c4e8758a50d2556248
fbcb377cd28c5ed7fad89319d3bab67efaf6cf70
2128 F20101118_AAAKOH takimoto_a_Page_049.txt
fbcb2789d397b2859b09fa7c1255171e
6229eecaef35f808939f8c43d25826c094d7f36c
16876 F20101118_AAAKNS takimoto_a_Page_111.pro
acec7bca165af8482b57eff6c726c7c3
44e47c5a0122756a3ba6b0d9afc0e2b097f6c6e6
1040 F20101118_AAALRK takimoto_a_Page_003.pro
04925ffef11a6dc18c0f555309d59234
3d738bc8a9fb479273f5d0f72d81f9ef10f0d36b
F20101118_AAALQV takimoto_a_Page_161.tif
d3ab07c1de07155f588def94d6271b6d
f74fecd3108b0df31e8fd15f17a0e05efe52f3a4
10493 F20101118_AAAKOI takimoto_a_Page_142.QC.jpg
2695e4dc47583d33c7c15a19caa07518
bd4c906e243473bdacbc8943af69f0ed119445e9
24390 F20101118_AAAKNT takimoto_a_Page_036.QC.jpg
0c3f03e605ca726978cb9f3af47c6a09
6cdf62c3cfb980233aa63797f6b09506d0c8c20d
49547 F20101118_AAALRL takimoto_a_Page_004.pro
e25b7002ace8e8ed55d18a82b420f0d0
67d82f6d5ca754c86feae6bcb08322bd6d834bfe
F20101118_AAALQW takimoto_a_Page_162.tif
55f82d1f8b2380ddd7b398653f03b40b
0b84f112b8647f1e861816f70362891901875013
118581 F20101118_AAAKOJ takimoto_a_Page_065.jp2
2880c4a98890e6201a06e55188505a13
5b3257f5797434c4f7e4bff9b2136646b68101c8
44868 F20101118_AAAKNU takimoto_a_Page_069.pro
5a559dd4360224e3323d8627f0b8e9db
b31dde350593c808a6108c9a5aa933c6ba76238d
8648 F20101118_AAALSA takimoto_a_Page_034.pro
8fbe8d698f059eb1d67406a5992e4b3b
04d87ca68732acdb09950310395489f2d9524737
7376 F20101118_AAALRM takimoto_a_Page_005.pro
0c72c3c25f4d2973bdc304eaad70a471
3905c83afa8a56bbb86d927ada5a196fd0dc38cf
F20101118_AAALQX takimoto_a_Page_163.tif
233c3874ebcd00d9a61ba2bf06e325fb
dca1863a4b97c93844f2d3cb3f0c80f076d9c005
F20101118_AAAKOK takimoto_a_Page_153.tif
d7b928bd08fcba8c5c242e2d5769ae5c
2d192d21bf948f5e3c40619ec6389fee8d5f34cc
F20101118_AAAKNV takimoto_a_Page_120.tif
24af327819605be1d27e2591c6e7d2d3
80288afe2a70e408f398c6dd4da7e7be555039ce
50668 F20101118_AAALSB takimoto_a_Page_040.pro
8e23e9584447e9cae367b04acb4f45b8
f7b69aff45c37822fe2d91b76a5cf03371073b50
68599 F20101118_AAALRN takimoto_a_Page_006.pro
63d215fdb58655d398c503203e88e6e6
3f4ca4679b71aaf5d94ed13ff6d48c2b34a29311
F20101118_AAALQY takimoto_a_Page_164.tif
e562d835207e22eaeef4b410e66fe1ef
1e026b708db422875c197cbb3734e121f1f83f26
F20101118_AAAKOL takimoto_a_Page_083.tif
f7f5a4431594545fcc531e93360fa3a8
7264f5f68a9034c09dde9bf5aca00db5740eaa9b
2271 F20101118_AAAKNW takimoto_a_Page_117.txt
8d519106c5b3a1fd5942ec7c15deb3b9
19f539ca10f685ab1c02fd0d0f4d2fc4edadd0ee
52264 F20101118_AAALSC takimoto_a_Page_043.pro
39a2327874d06358f6f30cb51e79277c
b41a937ef977637930abea4921496a2d64c1515c
84215 F20101118_AAALRO takimoto_a_Page_008.pro
2624ae36d26dec6193a1f2752ccdcd79
58b223790824bcb53ed795af6e21b03c950ad420
F20101118_AAALQZ takimoto_a_Page_165.tif
9701d09ad2f6811b188baa57b4bf7640
bf4c1053feb11d8548cc2682f69259f2f6c3ae2a
6228 F20101118_AAAKOM takimoto_a_Page_090thm.jpg
cc38cda86b82b5d76f2012072cbed732
2e48eb763d53cc3ed178c868a4d4b3b57e1f13a0
21585 F20101118_AAAKNX takimoto_a_Page_124.QC.jpg
50845f33de941dc58755c891e33f722e
a31925a88c732e94a6963b8303f83f23294d4f31
25728 F20101118_AAAKPA takimoto_a_Page_083.QC.jpg
c7bcbd5db03a2249666b0d3a188e2f91
825e552e2d3c5e1bfff91a5aa09c62c8c2fd6fb8
54396 F20101118_AAALSD takimoto_a_Page_044.pro
fe19a41b64446ed282dcf1d4bd8ba0bc
8f8b3eff45be46499fccfb8594f6a6ff55fd861f
23015 F20101118_AAALRP takimoto_a_Page_009.pro
84399fa09234fee33b615865ece1ede9
8ff2640c203a4634304b115789846baa8841e3db
24894 F20101118_AAAKON takimoto_a_Page_055.QC.jpg
adba5e72ffe2eb90a99268fb75033866
e96649e7236190ff85353e72a7e87930d0809e48
15817 F20101118_AAAKNY takimoto_a_Page_075.QC.jpg
136b7881879f81d077e78ffccc60279c
e9022ae601ffd38c0376f98b3cf1f2e153c8bbc2
14869 F20101118_AAAKPB takimoto_a_Page_078.QC.jpg
1881fc5fbe290061d5c15d57ddb9b113
69e3b1933b47a535bb46cc42f26d79a6a0a92352
51094 F20101118_AAALSE takimoto_a_Page_045.pro
7f298491218a66ca360ab617379498c0
3d6a16227b11d59fb6c2002c64a9d0f50ecd6924
57477 F20101118_AAALRQ takimoto_a_Page_010.pro
e9a067327736fe30c3d7706aa8b7c72b
7c91a0d1e8cb141f644095fc9ce084dce5ce7433
F20101118_AAAKOO takimoto_a_Page_033.tif
a696271e8eab04a2d930ffdfa7ecb2a1
a328c135a4e88d99edfb46867657f4fce81c8233
6792 F20101118_AAAKNZ takimoto_a_Page_112.QC.jpg
8132f5682e03f24f77d0283d9edfc2ba
648537860a111a9bfac72085ea7f6b017f932cea
73206 F20101118_AAAKPC takimoto_a_Page_181.pro
e441183667ffe7b373815810201ec588
75bf89ffe05ab2467a17a1e45443d171a54d203f
53510 F20101118_AAALSF takimoto_a_Page_046.pro
c3b606f58ef48c1036387e554abf1869
f18554429af1d1035cc45a6d48d030f76e4a0a87
2051 F20101118_AAAKPD takimoto_a_Page_056.txt
ce9d559d2bd7bdb589bd7888f4c7a720
2431ccb69b284353cff112541370ae0dc6f4d249
46735 F20101118_AAALSG takimoto_a_Page_047.pro
e9d269e879de24bef162ff107feadcdf
1329630e1d55c4215ad7948e48801ab1726f9e96
62793 F20101118_AAALRR takimoto_a_Page_011.pro
7b2ce2cc0a70a5f1244c4110370cf743
e32cffd958df54ff73542720d74781597d663d27
837637 F20101118_AAAKOP takimoto_a_Page_079.jp2
f2a0e65020e91ca718c557caf6792734
928bd643bf08cb32ec79b4538822452a1e5628e5
23681 F20101118_AAAKPE takimoto_a_Page_119.QC.jpg
4e9f2cde6d734c1660fd9ff2bc6f3d2a
62e28db011abc4ddba14ee07452832cedd58ea7f
55286 F20101118_AAALSH takimoto_a_Page_050.pro
f4d35d9241a3ff1c3bf2e64a2f8f6e95
23a97f18400152a2149935b5291d108439649e5e
42543 F20101118_AAALRS takimoto_a_Page_014.pro
b0afbd97e0b6b681d17d9b80fb682690
57ef2fd4c944d95f7df1d20b6e18781a21df54ea
83734 F20101118_AAAKOQ takimoto_a_Page_020.jpg
516a3d39374b9d837ea533bfdc318154
a817775928bfc60093d48b3d47720f4e829efdcc
975 F20101118_AAAKPF takimoto_a_Page_033.txt
9cd2e048d7fea303e3edca397cb20334
9fee1acca8a760abfc9f1cd1ec4640d9ab256494
54001 F20101118_AAALSI takimoto_a_Page_051.pro
d62b79b1c20e23938639f4a047707b61
f3703f2c9a89f202cda5dfa8420f983bdba65e0f
57243 F20101118_AAALRT takimoto_a_Page_018.pro
fd83e45321ac5627a486067e5ae3e432
856907021c2f3c30ff25842888c6c0cff5fb8fc6
76596 F20101118_AAAKOR takimoto_a_Page_127.jpg
71665056058cfc5df50274595e89dff2
53e526f17012cd95d9fbf764f5d76ec03825b837
5878 F20101118_AAAKPG takimoto_a_Page_124thm.jpg
9cf2bcefe61bc55718c1fc47fa74add9
df5878490716137a8c66cb15fc79d85d52b13b24
54294 F20101118_AAALSJ takimoto_a_Page_052.pro
a0677a9ab5bf4cc53ce2948f76027967
bad0f893b5e6ac6e7db498fae70808451d73709f
53951 F20101118_AAALRU takimoto_a_Page_022.pro
9408ae024b4a579477655118277c8a61
2faa70cbe5a479fed29d2decd613e09cfcd94bdf
53728 F20101118_AAAKOS takimoto_a_Page_041.pro
e9d04f533681df82b57e6f034de89e41
80f7728829a6dbcf22caa9edf4cc6cf679e62e11
F20101118_AAAKPH takimoto_a_Page_002.tif
0a877f847dedd3cb94e596bfae3d8cec
f2fac519f18ae54de8735e3245c3ffcf9d0cf792
51787 F20101118_AAALSK takimoto_a_Page_056.pro
0decbe79f29db8ad58d3e3d79192a58d
a623f6ad6c9aa62a11bc36bb2cf04ee252fba797
52626 F20101118_AAALRV takimoto_a_Page_023.pro
f629037b34bc8ce7b088c8bf9dc637c5
b66710ed28fa230962e4fe86ce3efa23c6fae899
29350 F20101118_AAAKOT takimoto_a_Page_181.QC.jpg
d56124b649889d69c7a5622ac9feece0
ff2e0544eb4b937e5a7118434ce74652c2c23805
109117 F20101118_AAAKPI takimoto_a_Page_119.jp2
4be11b5225f538ebe3ef203d4d1d25a9
667b93af261878200031e68c209c41ad54a9b84c
52632 F20101118_AAALSL takimoto_a_Page_057.pro
1562f84134d1537c4df5a795314a9b9c
4e4be92c36d756d2e5dad140ea2cf9350c93fe27
47761 F20101118_AAALRW takimoto_a_Page_024.pro
28d30a483f3492217794d51629727884
58b2f9f867d13b27642bcd4bf13570d003d01957
32231 F20101118_AAAKOU takimoto_a_Page_031.pro
a60d6e3429f699e9bf24e2f037898341
74f1aca45cf51752088c3a04b6996f5c0894bd26
4265 F20101118_AAAKPJ takimoto_a_Page_158thm.jpg
d43bdf26b551df2f75d555df9acf3f6e
d25dbe73d52b390223b0eb17d257f46b43dfa7be
18040 F20101118_AAALTA takimoto_a_Page_080.pro
10a53043d8b17d29d7c8dbd75b40b528
9efbf03e19d9951f3298cd35395cd5573c35b02e
7637 F20101118_AAALSM takimoto_a_Page_058.pro
55c7c3e82227a499b5fcb652b3ba1650
a74a68545a88f91755154d0c8f33fd3da7356392
56656 F20101118_AAALRX takimoto_a_Page_026.pro
9f62a4ef2620dfa50964948e3900c25a
0fbcc6073032fbcaa835eaee91ba0dc15ee4dbe8
55146 F20101118_AAAKOV takimoto_a_Page_125.pro
148a537d0071f257144dcfc63341ab48
7e7d9ba9be339bd57130c52bd018b9cf1c6af8f1
34646 F20101118_AAAKPK takimoto_a_Page_144.jpg
7b8a96a3e45c02039b0525ce800a6853
be8717e2d9be93f4726825b7372183313ccc5600
54778 F20101118_AAALTB takimoto_a_Page_083.pro
25901b09d7a4a592cfb7a8b45cd3017e
9452b3964049912cba4f97af8c92861e86853791
36488 F20101118_AAALSN takimoto_a_Page_060.pro
d5f0a8ff1b8d37e01e0726e23c6e0011
e68b1900efbf91decb1cf79456060f5e78570ac0
50734 F20101118_AAALRY takimoto_a_Page_027.pro
68fd5e676c07bcf302c0e70ccec6869d
c3accb89ed080d04b0c15b7b403cc1987618697a
18864 F20101118_AAAKOW takimoto_a_Page_058.jpg
8a0a3e3cd47e267b4c7ff7beb653953a
e91c259dae5c7017685de0999f486e6a65527964
2161 F20101118_AAAKPL takimoto_a_Page_095.txt
6482e3b62819ca17773dd0128f54d04d
8f79b169d1f751e444b172fcecdd2d5a98694abe
52046 F20101118_AAALTC takimoto_a_Page_085.pro
b12c29c1a877cae294cb60a8664b30fd
517ae7be00ff7f006865cff7af23c55904195d15
53783 F20101118_AAALSO takimoto_a_Page_061.pro
0e828503883eb5fd3ddbfd9f26dc5c96
c1e75bd7d0a3ab280c419266572c680e27b33a10
50581 F20101118_AAALRZ takimoto_a_Page_030.pro
9cc2d76d21235ba7f71f8b537334e470
f7863bcbf1eb9befeed6a9792b98b17ea1621290
110491 F20101118_AAAKOX takimoto_a_Page_053.jp2
f2b7affe800771dcebad6478ec653819
1e5434d9cbfb959e24f9ebdfdd6a2dce13eb07d4
2077 F20101118_AAAKQA takimoto_a_Page_023.txt
f2ebc26faf4e630e7b46ad9f2ac3ac61
60de2f5ca45ebbc69ed38dcbe8109f3b42c1c941
24327 F20101118_AAAKPM takimoto_a_Page_064.QC.jpg
719d903bebb1941f49f145490b94a38f
04598e653f1e9abd7905f4c9cef9b80bc2f86b12
52648 F20101118_AAALTD takimoto_a_Page_087.pro
5a09d0dcabf9b6190d7a5ef55d0fd510
0624114a3e1b7cd86723b3bf4a8cd11232a08c97
49187 F20101118_AAALSP takimoto_a_Page_062.pro
c0ab8f52340ec18449110d86e539b9e5
578ceecc80fc0dc397790e3681b0212af50fc7fa
2001 F20101118_AAAKOY takimoto_a_Page_148.txt
2372e10c5bb8074f0e288a86e52e4bd5
c6546ee56354b8a8352315f04c336123280e7ca7
46338 F20101118_AAAKQB takimoto_a_Page_111.jpg
fcbd4f1c7e68746782e8eab29621c0c0
df3b72d25b8646afce435fdf5ca6face677343db
6789 F20101118_AAAKPN takimoto_a_Page_023thm.jpg
478b61d5bac97b885787916d2ae77d72
5eea82f7b3cad48d6ea8b4f06d1e678c573c900f
50640 F20101118_AAALTE takimoto_a_Page_088.pro
29d53244ad58128c8412c448ebc56380
dcf3ff286736dfca6f255f1a594ec35ed464c76f
54167 F20101118_AAALSQ takimoto_a_Page_066.pro
839b900d1e9f32e10770b3788d27021d
19734602bc49588393a151a797f99bb180ef6a41
6527 F20101118_AAAKOZ takimoto_a_Page_036thm.jpg
09099581aa57fe63628c6cf462c82cbd
28180541abd96222f8219f67eee4069a5cee7413
83964 F20101118_AAAKQC takimoto_a_Page_010.jpg
941e5726f4486726298992147f9f6dcb
51e1f3e8715b7fffcc86b3c069f7901ad251d995
68471 F20101118_AAAKPO takimoto_a_Page_173.pro
0363f86d58e88ed1e8dd5641a17fdd95
a25d9e607c74de654368e545a30bceb32839f3fa
43927 F20101118_AAALTF takimoto_a_Page_089.pro
87e6a529b37df8bc522577994165669a
295f738f3aadfd849d28367a7bb62826ea8794c2
53751 F20101118_AAALSR takimoto_a_Page_068.pro
9684975f77430faf9ed655a22101143e
c9d89f08beae5d9aaea428cbd5077d57a6701515
6943 F20101118_AAAKQD takimoto_a_Page_044thm.jpg
2643791eb0036164fcad965db79e335e
f629f5a82d754d38713e8c44275fce0bf531958c
79888 F20101118_AAAKPP takimoto_a_Page_128.jpg
34690a5f84c46e3db1e85f356e372208
89d21552a602280a437efd4fe4e70bc35c7c58a8
54710 F20101118_AAALTG takimoto_a_Page_091.pro
5374b86d4d2ffe03a7565cf4faf23ace
469680d4e0e5ea00c51a0823b05a90d041711ec6
F20101118_AAAKQE takimoto_a_Page_037.tif
6debe1ed7ecf9bf88f27ab9c376f69fe
80532d1d315a91a1caa351f34514da1d8e17219b
57209 F20101118_AAALTH takimoto_a_Page_093.pro
906127b767e9c5f9355367148c8a4cac
b2e3182b0ac25c7e99c9421180d00d4a401f6e6f
45163 F20101118_AAALSS takimoto_a_Page_070.pro
ee44368ef2bead4429cb6a92a9abfeb3
4620468dc469e7c3bcb62cb156403132143e07b5
6971 F20101118_AAAKQF takimoto_a_Page_016thm.jpg
be300c5f09a08540f622d09d885435b4
03bdef716e932f9ad6ea50e4ac9a2191e3d66430
2766 F20101118_AAAKPQ takimoto_a_Page_173.txt
0246930f384757c23b95bc98f359de41
9d482860cd0c1959bb84d21e94c3e04751790c74
55676 F20101118_AAALTI takimoto_a_Page_098.pro
e7e349da63c8f63b96fbad833872385f
1f746ad37c03ac64671fff2ac1eb1ede06eddbf4
35021 F20101118_AAALST takimoto_a_Page_071.pro
d0014ac28162f9003274951450ee6e95
d1d71a7a20f6c58b4cc7f32909b8e2386e9f36fc
6724 F20101118_AAAKQG takimoto_a_Page_178thm.jpg
edfff95a61f1c6939f789ca214ab6e63
96fb025ae5547bc862cba4d0c7afdfca36b1fc97
3111 F20101118_AAAKPR takimoto_a_Page_141thm.jpg
73140f795aa9361b6892fde119504c61
371a95d473a926ca8b1b6916c3deb24093859aad
56097 F20101118_AAALTJ takimoto_a_Page_099.pro
d705f83438a11b657ec841f76fa3c192
305de9b037edb268b1f144a4283b73f89c279ed2
51279 F20101118_AAALSU takimoto_a_Page_072.pro
979d8b318b3fda724a0d875880e42f8c
b8e046566f50cda7fdabbbad56684c3af2980cc5
6937 F20101118_AAAKQH takimoto_a_Page_151thm.jpg
ea091a14cf45da28e4e6922099f1549b
8da0801df5e2247840c7e170d26cc1ce942cc6a3
18543 F20101118_AAAKPS takimoto_a_Page_035.pro
232710ef9670d21020109f4f1cf2ec45
d4aeadca8d4bd1e87056fa56197fa80b9473bef3
20619 F20101118_AAALTK takimoto_a_Page_100.pro
5e577973ed15acca0d243c2965c403b5
a3f7e3d79405ce328a35b2d4f7dfb886bc0822c8
53183 F20101118_AAALSV takimoto_a_Page_073.pro
ec00f739eb17b7f4b204280ac5fbf416
04a93bf2cc73efc2a4684d3e601eec61402a72cc
F20101118_AAAKQI takimoto_a_Page_050.tif
ec08faea21a84d275eedda6a2994a55f
ab2a9053c2b1ea46dbd19017f7532ed800a82fed
25407 F20101118_AAAKPT takimoto_a_Page_068.QC.jpg
f82e19cc1fc0677138cca8fdc249078d
ddb07c3bc62966002ef801cedc1962e0ce10ed0a
19401 F20101118_AAALTL takimoto_a_Page_101.pro
76b25a0fae31d564be784b808e46ec56
b92d034530ffd4a856e05ba39495503ee55d1109
57439 F20101118_AAALSW takimoto_a_Page_074.pro
7a589b272e5436c10f08de263d1b14ea
b3102cae726fbcd39c1b0cff9bc5632fdb41b4ec
19742 F20101118_AAAKQJ takimoto_a_Page_158.pro
c61f42466377e9e0858dfbcfeba414ea
934c4b2ec3f79efd11b8c514240082a970348500
2154 F20101118_AAAKPU takimoto_a_Page_151.txt
bc186321d56db3ff3536fb548fac93fd
d6d76b16efb2e8207a138cde274fdadb4a195ddd
18564 F20101118_AAALUA takimoto_a_Page_137.pro
32136297d0def6cd05f5af6c0be7a6ae
47b1db7b47f6b745b64535c70c7ed3d0da9fc5a8
8804 F20101118_AAALTM takimoto_a_Page_104.pro
4c5a7cf08536cb62f15d46df5a194f9f
dc4cd8e4337f1ceb8950b357832dcdd9cf8ea764
37754 F20101118_AAALSX takimoto_a_Page_076.pro
7076f6c2c60d2f3876532676106b26aa
60b7c8b83c82204550a28e0149d037d64d32ed4b
2003 F20101118_AAAKQK takimoto_a_Page_039.txt
594d6829bd510595515824ee8316e3d8
9a4e8e6ccac6f94a01846188e1b7c0e260b22b57
F20101118_AAAKPV takimoto_a_Page_119.tif
b2bfec9130c5a098abbca88310c6893a
362d831c8b19af7b33d7a4b2613e8be51996f0fc
21616 F20101118_AAALUB takimoto_a_Page_140.pro
580d0dd2b87c148902aa42e14900eab4
ad51a8384b9b22846c399769cb3de4e3941eae13
23504 F20101118_AAALTN takimoto_a_Page_106.pro
ebadca8c113862da457ebcddad694697
cce3aed4af2f881ac9ee8d81ac20c4c3300b5b84
44927 F20101118_AAALSY takimoto_a_Page_077.pro
4d2a6960f4c901817ef5b790d235dbe8
aee1214d74eae289b37977c69e59225da100dace
14863 F20101118_AAAKQL takimoto_a_Page_106.QC.jpg
16e7bb2ffef00c751bdbfb22fff3f527
2b7ff2d217d0cfd2ab5db5c7e67c61726c11772f
73882 F20101118_AAAKPW takimoto_a_Page_149.jpg
9b567d81be1dc8babe611fe0ccefb600
32887d10422aef0f5ea4f59cc22382e022f1bc49
10033 F20101118_AAALUC takimoto_a_Page_141.pro
467764f7538c4687002aaef83bd29479
4f77a8c45a9763a694017c5358339767193db013
13334 F20101118_AAALTO takimoto_a_Page_112.pro
e94ede10bdefdf2b951cedecb3d1bede
23145e62fe1fca89d322eb64f411e90aea1bc13b
5667 F20101118_AAALSZ takimoto_a_Page_078.pro
bdf47e5003df5fd6446ec71d58f27a72
410584a8001788d53aa6c80264642ac687897e34
90054 F20101118_AAAKRA takimoto_a_Page_093.jpg
61c3356ffd688aa724dd73546dee2218
4962e92d119948ab56de4525798d7369af6253af
25591 F20101118_AAAKQM takimoto_a_Page_099.QC.jpg
5e1936aa64b1c2ded52e6b88480bfd17
e92bdcd26eeeeebe80d1785797f927847424e43c
15687 F20101118_AAAKPX takimoto_a_Page_159.QC.jpg
4f560452e0b175aa6d40b19f539ebde3
1a51de5b0249e42b32a202137d24dba096e593cb
13521 F20101118_AAALUD takimoto_a_Page_142.pro
723aa1d5362f8539677fc2bab1d79a54
ef3e9d9e7af6e259a9475f1939a45780fca74cde
10707 F20101118_AAALTP takimoto_a_Page_115.pro
b4f1866902f3ed954774474f84ffd40b
db8cab8b7d4cf35a1c365fb1e7eb1fa7a037a8c7
6119 F20101118_AAAKQN takimoto_a_Page_062thm.jpg
67643d2935e631db8185e5b4df75923b
60d2ecef34e375b64bd40b61feea2d3d4463162e
7588 F20101118_AAAKPY takimoto_a_Page_032.pro
48324516dc887d38e39af039c5ef3637
2b5481da47d09739dc7911530cabe0e44dfb9504
2022 F20101118_AAAKRB takimoto_a_Page_064.txt
d04c8f24ade4038d205af117706689a6
ac031644c31dc996935aae6a37d6db3b826c6cd3
13674 F20101118_AAALUE takimoto_a_Page_143.pro
b327e3a20367a2533dbf8fddded6b53a
b7bf96f2827969c541e360d99967df21487677fb
52203 F20101118_AAALTQ takimoto_a_Page_118.pro
76b51ce7a03af6bdadc440cf171419f4
c48fa7ed9fb8f400c66ce67193e56019819faf39
51404 F20101118_AAAKQO takimoto_a_Page_152.pro
63689cd8d3802e08ab78d0b7d3eeb560
37ed6731ee5ec73af19320dbc9c733d1901e21f6
F20101118_AAAKPZ takimoto_a_Page_127.tif
c5e995fe1984997f480171a68b55c1cd
c72f04aa10cb2f5187606e93daa454e49d4e0df5
7445 F20101118_AAAKRC takimoto_a_Page_022thm.jpg
4f3a1e6ffe18bc88f3b6324dfcd1db63
14030b0e8c147a6a8fa5d00726fe412da93b541d
12788 F20101118_AAALUF takimoto_a_Page_146.pro
06e35ded501c045eb27f966b30ce0813
5538ba32dcc80cc02ad85a3c4d769a0497c423e1
49409 F20101118_AAALTR takimoto_a_Page_119.pro
b1261c7e341ded40167775c840aa07b5
9aa8788131dba7a149ad57416d03bc6e0099684a
2235 F20101118_AAAKQP takimoto_a_Page_083.txt
7fa2e54919d23718e7a60db3793483f6
3c550f004eb02355c6f03078f12fd0886c91aab9
2053 F20101118_AAAKRD takimoto_a_Page_063.txt
5dc00b3b8221d5f4cbe27f5c4d887e66
f65d272823dca92a385495ce953b534c5f5aa444
12780 F20101118_AAAMAA takimoto_a_Page_139.QC.jpg
01408240279d787e7bb3221f9e44de2e
2d33ee315b5af501482434bd92c008e6e7c686f4
47730 F20101118_AAALUG takimoto_a_Page_148.pro
674ffac068c4fef0cef9fed107eebb97
c33a906cc4344628feac0689ea8cca72eb86ec89
53712 F20101118_AAALTS takimoto_a_Page_120.pro
2a86a906e0acc7173818467b1e3cb1c6
4cc83021433c97e2a597e7187ef651357c62962d
F20101118_AAAKQQ takimoto_a_Page_063.tif
8916df899353abb153ca7c3a29b034d8
7f96e05a034e0790509c5789dac0c6bb059ff1b9
25014 F20101118_AAAKRE takimoto_a_Page_125.QC.jpg
3b76d6eed3bf65b63598a914f6c0aeab
fdbc1ff2f9b4f66f8423f0fd5948c804ea4c1a1e
31603 F20101118_AAAMAB takimoto_a_Page_179.QC.jpg
a3d606f9100135f23f77e7665024b481
d6d62644fccf1befdcc8f79b62d68b1dec2f3e91
52250 F20101118_AAALUH takimoto_a_Page_149.pro
b1ee39c271ec236957d08f2279bbbdbe
de157401331996db3523c6b683151b3106ea187c
7019 F20101118_AAAKRF takimoto_a_Page_127thm.jpg
30bf9649e0c35a60aea8c2f452d01150
680fc4dbf88a66b2fbb676980aa51b73ac01993f
5368 F20101118_AAAMAC takimoto_a_Page_168thm.jpg
d76b851ef867367ab896a5bb47574403
3c88c3fb284bf839436b39937f280ec96f08e61d
55686 F20101118_AAALUI takimoto_a_Page_150.pro
f6c3c691dacb8713686d5af204474979
c7f2fd2803e28e4a5fff50d6b5561b42d7f831cd
39555 F20101118_AAALTT takimoto_a_Page_121.pro
68f9dbccd646a6eef2f0acad4bf00d58
a4735dff8b8a5d3bb8b09fd6661699f04c243a23
6504 F20101118_AAAKQR takimoto_a_Page_126thm.jpg
51ef45739de0e1d96c4a5ed30ad390b0
b685bbab1ed90117491d9256dfa5424386ee77c4
786129 F20101118_AAAKRG takimoto_a_Page_183.jp2
e8a900cf2c0d652df58143d7c3a08735
2d0a82173a7558be58f6fb1084a23571563a3851
3544 F20101118_AAAMAD takimoto_a_Page_146thm.jpg
44c42df80a8c5ae0a8731625a5f120e6
8e5b235b2a98334d09d8a6c2a1421c479589002c
54686 F20101118_AAALUJ takimoto_a_Page_151.pro
8f59e298a5b8e2b584f5ca8e977c42db
ecf58cef88097d4104b54ba87a9597b5a1bd2790
54265 F20101118_AAALTU takimoto_a_Page_127.pro
0bd5c444bd72e5553fe9dc0f500c1fef
fcc6ab14fb9a472e8ff79e0fdc415649c9a456ad
F20101118_AAAKQS takimoto_a_Page_182.jp2
5d6f7cb5c56350e274a4924315d163a8
1bf3a33665dd615d15ed7661355492357e987398
2551 F20101118_AAAKRH takimoto_a_Page_176.txt
5ca23000d953b338b0ae9abd89045c76
fc4131d727a4db78d37a25690865cf58a24e6c80
4069 F20101118_AAAMAE takimoto_a_Page_139thm.jpg
737969c1323dba045eb31c3ba9ba8956
e7ff5ca30726d1dc4489d968de11ccb338ff79b6
53614 F20101118_AAALUK takimoto_a_Page_153.pro
41ae04b0d38791bf45f1801cb02dc39c
d4f20763843b3038ee81dc7bcdf26edca1324703
56673 F20101118_AAALTV takimoto_a_Page_128.pro
897a0849205822f9bd741f3b120ca30e
91cc7ec2eeddd0df40388693dd4dd88d183499c5
30496 F20101118_AAAKQT takimoto_a_Page_075.pro
38a99438807632a73d5b923ef904d631
520c8054908bb4e00505ae2e70b8a400aa5f74cb
1051974 F20101118_AAAKRI takimoto_a_Page_025.jp2
a4a57be0f9c02690c06fe64ca46a1691
6906d1d5e00441c6e71df653ac58fab4fbe01571
6169 F20101118_AAAMAF takimoto_a_Page_148thm.jpg
2e9c0bbed3e56725cad35a142df04c9a
81e942c8d8af6dad0591acb2e45ded5cd8bd1872
33798 F20101118_AAALUL takimoto_a_Page_157.pro
d56d9d668ebf0cea21a1a4c2ab35d3d8
75cb3ad07aadd7b32ec07ff0366f10c298fc2a89
52062 F20101118_AAALTW takimoto_a_Page_131.pro
4fd8d73f317574285e1d293fa65d5519
43622ed0c5d20010527c587b14869b62f51a8933
1790 F20101118_AAAKQU takimoto_a_Page_090.txt
3aa0a850cda6807ba8a948e29f88b207
325000b880ccfb04bf395ac1638b055b0ee7287d
33836 F20101118_AAAKRJ takimoto_a_Page_100.jpg
08d98acbf5dcb41c06b1dc476f6e1877
5b442587339ac7a08d4eef4398393ee380cefa97
20786 F20101118_AAAMAG takimoto_a_Page_140.QC.jpg
90a464a4d7f3d241cb8540ea6c193f5e
1350f86d0aab1b17f2f7e882f1e721f6c74436c6
67239 F20101118_AAALVA takimoto_a_Page_182.pro
efdac48db84773d661bdeb3441df6f49
c234080b85aeaf143a1be117fe43c4bc7b0e999f
32440 F20101118_AAALUM takimoto_a_Page_160.pro
2187f4a0fd423e588be920d86a984416
a4e13571e8f8c9a12a1f8fb53b7836d16c7aee7d
55089 F20101118_AAALTX takimoto_a_Page_133.pro
1ff746ea8356a2b68f5c332c30bd4118
a2c678c74c64360b72e81e884e8de685b787ea38
2006 F20101118_AAAKQV takimoto_a_Page_169.txt
b4a3bd5c0b3b81a5588984d07170393c
e078362ae76fe3878f06874adac5ec6805664dee
84670 F20101118_AAAKRK takimoto_a_Page_165.jp2
ff24328ecbcbc8d14e1e94b72cb0f0a7
26733aaf909bd75126636972cec0bf01f0d25791
25104 F20101118_AAAMAH takimoto_a_Page_042.QC.jpg
0def1c68e46acfdbe2c7a1d2d41ba905
582685a4e303fbbee7908e01e6a083365a57e935
16537 F20101118_AAALVB takimoto_a_Page_184.pro
6cf7821d03ea4bdfd7461ea3e85faf1b
fc319c908ff7af8ab3080907d8baaa0196947f68
12421 F20101118_AAALUN takimoto_a_Page_162.pro
482e91f86423fa9d9cb39ac032b2b022
5903bb20ac423870473a4d93058a3225886df941
55627 F20101118_AAALTY takimoto_a_Page_135.pro
93d38f45471b67fd2911388c53718210
f06aa980f09ad0f374585c6bf7b408909dc1eef2
F20101118_AAAKQW takimoto_a_Page_139.tif
4ff07097e76eba70dc28f1df0faad372
8ca440d0ef054c1f319f219c8705268c72032ef1
932 F20101118_AAAKRL takimoto_a_Page_009.txt
6c4c5d42c58ffd5e66a3e35b491d0e4f
cc74c4fd7fa96c2418b4aee24a50200b81f86a95
10777 F20101118_AAAMAI takimoto_a_Page_081.QC.jpg
80d27da6ee31c23744ce27d1ee24e0e5
faf5b60347956828301dad3a9833fe4df2efefac
91 F20101118_AAALVC takimoto_a_Page_002.txt
197042641c50fce38fbcba446f0a083f
8cdb467ba4af56999560a4fb7ddfbd9587c896b9
17404 F20101118_AAALUO takimoto_a_Page_163.pro
651a1327b2d24bbdcb84e3a6fd4b82ba
c31cd77dc102aef55573e7c31c15d8f0e11a14db
55604 F20101118_AAALTZ takimoto_a_Page_136.pro
14ddb60fb034505957be41ec585c36b2
b050ab6f689c449498f51084a3475c78ba40dc23
54105 F20101118_AAAKQX takimoto_a_Page_049.pro
ef8d8bf603f7e4d3f7af47b0f0995f63
5606d71dbeb7f125c469023b1502e52f19faf6c1
F20101118_AAAKSA takimoto_a_Page_149.tif
8fa8255d0248b70a896b23a3718536ce
90237d6c1365651aa3416acb680718345aa3c3d5
24472 F20101118_AAAKRM takimoto_a_Page_109.jpg
1f9a0783bf66d50f062f56169275c218
268a394949b496cf6f7ef8c3d15a3e05bc056d09
6955 F20101118_AAAMAJ takimoto_a_Page_067thm.jpg
4487519cb8870835d879d591f7e28cec
c6d165a91941bd8735eb88c501fb0d97f291b31b
96 F20101118_AAALVD takimoto_a_Page_003.txt
69840e6067b23e43ee009c020b5f568f
ce710dceafda6084c11de159a6462826cbaa9f06
45140 F20101118_AAALUP takimoto_a_Page_164.pro
a0f49b4dd17c55d436dd809c09f9df6b
78d0cd1c6963ffd1fbe95775a8b5e1de64b0c86f
F20101118_AAAKQY takimoto_a_Page_067.tif
33fe2d764006746f987a94ad927fef68
1d04b4a705fef472c4ee290ffa0342d99f918b74
2738 F20101118_AAAKSB takimoto_a_Page_162thm.jpg
f857c4db0964a39b95c1dc3e0b1ee296
690b91acf795c2039dff60ffc3bbd2cb2b14e6d3
130598 F20101118_AAAKRN takimoto_a_Page_176.jp2
7fa9a470908e4a86e05637ade2aa11e0
a52e83173014fba58c1055a9a1a9224905426c58
24484 F20101118_AAAMAK takimoto_a_Page_073.QC.jpg
0bc654f490d5c603c17a7926542d304f
a4712830bd1a80f21148169c7828be3017b9a8ed
2000 F20101118_AAALVE takimoto_a_Page_004.txt
7ee6cc22790772a31e6aab8a38145c8b
eee000bd6785bd1cf96ac9315e13246bf845ca27
43305 F20101118_AAALUQ takimoto_a_Page_165.pro
47b1510ec1173b51886f324974640893
5611d2c6561281b49d93b55289fc1abb151833ae
16861 F20101118_AAAKQZ takimoto_a_Page_170.QC.jpg
54ecb9228d325732d7085d76aec6a3ab
c46be3bcce1fbdcc095dc02505867006fe6b0041
80008 F20101118_AAAKSC takimoto_a_Page_166.jp2
be531e4802ab28d3c7dbcae31926b177
80895497129aa3a12df7dea4cf25eb1de85c55a5
2121 F20101118_AAAKRO takimoto_a_Page_012.txt
fc50c457dbd7e60578a7a198cb80c8da
30878847b3e6f83f57c353fd95922757e17fd942
4731 F20101118_AAAMAL takimoto_a_Page_166thm.jpg
1aeaaba48ec37cbad4dce0d021c4d667
8c7c7c6c427ad197b0681cc2ee5247510482d3cb
1698 F20101118_AAALVF takimoto_a_Page_014.txt
d9abe835a31fc669b8d6d41aed97d059
3430424456438a751839ca1c486e3ba6646513fb
40792 F20101118_AAALUR takimoto_a_Page_166.pro
7736cf26d83e22c148ac335643563dfc
b3e2a491733705ddec6df3771356e59c5a4928b8
F20101118_AAAKSD takimoto_a_Page_015.tif
1845109c77469dbb9e6dcedc11a2a8ce
f265ae45651e0a07f965d6e699ff10c7be183404
1051 F20101118_AAAKRP takimoto_a_Page_139.txt
2f4b382e9959a3a7698739dd172bd7a1
38a3dd690f24631037cf977a0f9f7067fc70e0f5
6749 F20101118_AAAMBA takimoto_a_Page_042thm.jpg
e3b6e4fc0c3c81f75cc5c5f4a26a52b6
aff48e2190b6b1dbf395de3921184232a34e57fe
7382 F20101118_AAAMAM takimoto_a_Page_021thm.jpg
53f7a28eecb19d29aadaf4b0744aa6c8
f30429381595eea72baba6b49ec70b88ec9e9ac5
2234 F20101118_AAALVG takimoto_a_Page_016.txt
86f1e920f4907190b20c2ff55314e421
32dfffeda9b0f3129e58fbdce083fc80ceb70e1f
18134 F20101118_AAALUS takimoto_a_Page_167.pro
2545529662ca157246bc3e100d3401d9
cb0d54c41b2872e9fb0c1cea8fbfac68dfe00ac9
12930 F20101118_AAAKSE takimoto_a_Page_144.pro
ce28b25fbd3447d66c8d6c8ea20543db
c12d1358280a6bc057b32f652e1ca423dac182de
F20101118_AAAKRQ takimoto_a_Page_118.tif
319bb8392bbfc97c1ce6ee3e1a284895
5993584f430c76eeaa029b61e5af292d30a0b734
6875 F20101118_AAAMBB takimoto_a_Page_039thm.jpg
fa75d98f9231f32233615da96b769b0b
518af39b45263d7fb89298f86194963662281bf2
5818 F20101118_AAAMAN takimoto_a_Page_014thm.jpg
2802d07018b681bf057a72c332dbd8e2
93ce347114e7cc581077730c95e1e17d4e9e54b2
2150 F20101118_AAALVH takimoto_a_Page_017.txt
67630b142f27248eba852f028953ee75
f5902ffad442f38bdc7144a97054ea5cc0c1db67
48436 F20101118_AAALUT takimoto_a_Page_168.pro
e149a51358affd62a8335e5678b5b76a
72ba0edcd03c360a180aecc4d45f846aa35cf7a8
2119 F20101118_AAAKSF takimoto_a_Page_022.txt
05472a168305b46b58ef01d05f3179d5
e240ad84703dbb7a0bb93f427aeb9b2b38321953
70289 F20101118_AAAKRR takimoto_a_Page_090.jpg
9a95e8d39645f878d7692ab350fc2b73
04a1b6e4cbb883edd46d6d0ca4dce1c79dd34d13
23573 F20101118_AAAMBC takimoto_a_Page_053.QC.jpg
a533ca4d9a3f77130bbccb06edf87247
1c09ac0f1c1cebbe90c3057f50d2ef22d86ba331
6568 F20101118_AAAMAO takimoto_a_Page_058.QC.jpg
87ef59127329dd45988c19fbb7d3eb24
21280176224a785c317826bd8bab9b2be2c3605d
2066 F20101118_AAALVI takimoto_a_Page_020.txt
8747d170c093a04e57984d8d9a4edc89
846fdc43bc322bdb46ffe9ad5edc3f7d550e53fd
5976 F20101118_AAAKSG takimoto_a_Page_089thm.jpg
b868a75a009de3d14fce07bbfc58b037
9894444af035ebc6769d8d7b06f7675f48c54e16
14560 F20101118_AAAMBD takimoto_a_Page_110.QC.jpg
0a060632bfff60cdb79d1b268856a5f9
1d7ddadba71e2456ab80121015cea381c50813e7
24321 F20101118_AAAMAP takimoto_a_Page_118.QC.jpg
3c99bd816180c58e6b397804a273aa90
bc162ae2e24860d51d4f81ce3c1a67bc2cf109d3
2094 F20101118_AAALVJ takimoto_a_Page_021.txt
04c6c2b23c4d3c40ef17690404145aa6
456cf6c4285e2b326231ce965afeec028f62ac60
44960 F20101118_AAALUU takimoto_a_Page_170.pro
37064a4942d9f7647251b6efc88a6e0f
150f65266601bd8ad5ac440afca4068ee80f9a5c
F20101118_AAAKSH takimoto_a_Page_069.tif
7b2c2364721c4beef7408bee350c192f
3e7279727989817e40c41a4712a23a563ed064dd
11181 F20101118_AAAKRS takimoto_a_Page_137.QC.jpg
a2fb6d8fe493c50c37ba277b840f3051
736a531f0f2f9777edfd54d3740024492f5cabce
24534 F20101118_AAAMBE takimoto_a_Page_067.QC.jpg
7a831dbbd56bd6ebe00c2ed5542a1adb
cf2d9a3f01f3c69b4d45415aba4fb4288e477306
22025 F20101118_AAAMAQ takimoto_a_Page_013.QC.jpg
7b30ad036e2c02288ae1db78356305f2
ea52c333367b2a05f8d8f8c11dc021a95b40c0c0
1915 F20101118_AAALVK takimoto_a_Page_024.txt
211e0a9def0165304b931bd7b1a3ed0b
08feda6c11ef9be788f8c85154a445f12fecacb6
64896 F20101118_AAALUV takimoto_a_Page_171.pro
844b42a0c813702ebe4d3354b08c41aa
285974a5c69eb89ed34b4647851134e3b54e9ce1
85198 F20101118_AAAKSI takimoto_a_Page_097.jpg
4bd57516321f50603ea372c5dd1f0dc3
140883d6f132740447e4246643fa46817096f604
56042 F20101118_AAAKRT takimoto_a_Page_169.jpg
42ee2d526f8fb676fbfb04dff605c843
c9904008a595bae32d69cf94dd132e8e9c9d01d2
7354 F20101118_AAAMBF takimoto_a_Page_129thm.jpg
62d76c3265dfdef4ca7f8a00c90fe0d4
3750905718abfff215294cc0d50575b8f37a47a2
25376 F20101118_AAAMAR takimoto_a_Page_049.QC.jpg
f08f0f0f4af37372ffe1a76dd8c5aa40
15c4e138815d8d2042f5c281fec59519f8695890
1999 F20101118_AAALVL takimoto_a_Page_027.txt
7a456f9014880a1eed035445bd2b2579
bfe1a46006ffd7f1c6ea22b7bcca2fb95417a280
62355 F20101118_AAALUW takimoto_a_Page_172.pro
382be96170880b7c9f289dfa1c5a690c
ed4c69907745999aae30915d3aafc12b365f8eb7
F20101118_AAAKSJ takimoto_a_Page_044.tif
e6384bbf821e51a5b497c4bbb4b34c66
94d99b0050d8ccd0c627611f99bf7aa10e41b47c
11866 F20101118_AAAKRU takimoto_a_Page_032.QC.jpg
c5470da74d07d0e3140743b926f90d9c
4cff873a079a7ddbe4040f71a86da79175bb6e23
13148 F20101118_AAAMBG takimoto_a_Page_059.QC.jpg
c1362a4a08847280f8e05e3a4db7a29c
3730a9fe901febf970b830a56305f0cc1b7ba17c
4896 F20101118_AAAMAS takimoto_a_Page_165thm.jpg
df4f087255b6c8fffc468289c803f7f8
368639912563f1b17a9a21890b363165daa4c8c9
2017 F20101118_AAALWA takimoto_a_Page_053.txt
dadc01d07f02cb7665e55388dcb24768
99e6ecab468fff69729bc77c40e07dad340bc301
205 F20101118_AAALVM takimoto_a_Page_028.txt
cb00fc339f063d46d720a3a754b22de8
4e194366c28640934658f88bde2dce8c837d1502
63123 F20101118_AAALUX takimoto_a_Page_176.pro
9bee5136699139e81270cadde99c7679
c0227580455d0338fda56a6ea45e05662756c1c1
6932 F20101118_AAAKSK takimoto_a_Page_176thm.jpg
a4f30ebc2458f2f387e928cc84d14135
89e4e3c17bd32b8e02589ea55aa392989c17c79d
45090 F20101118_AAAKRV takimoto_a_Page_110.jpg
953ce521ee2a00b46f4a7d433d7a9d45
19b404a33cdcfd29e2865c7a4ab51376e00c0639
7145 F20101118_AAAMBH takimoto_a_Page_088thm.jpg
c250cebf0ae6df41fec2a9972e5f17cd
7669ceac7eb3d25936a65e7439651827a70999b2
30329 F20101118_AAAMAT takimoto_a_Page_182.QC.jpg
26f45bd78c7662fbbd6af21fc9a925ca
04606dc57724614de6738c91586478fcbd46486b
F20101118_AAALWB takimoto_a_Page_054.txt
8b71fded985e8aac65675603e162c481
28a45193012f2326ecf5c264f54efe6f9cce54e5
3047 F20101118_AAALVN takimoto_a_Page_029.txt
a822dfb7c88cf1fbe8d0ce38fe7c9a1a
60a6f2c02a0cf4e82398cd484cdd667df95d6ef9
60804 F20101118_AAALUY takimoto_a_Page_178.pro
5b07200203f551a4772ba799b89e8c67
0140123bff2b422248bf3aa2af70019fdcdb83c4
2187 F20101118_AAAKSL takimoto_a_Page_136.txt
d5283f652f2dd77f41247e33ff11bd39
9ae72efa3f117a823894384e258ae53e063bd8e3
24457 F20101118_AAAKRW takimoto_a_Page_054.QC.jpg
619b1522bcb5b0c9a81a4a8be9edf2aa
3f5314d223a7466aff0e6eff389d44ff2444e540
18154 F20101118_AAAMBI takimoto_a_Page_076.QC.jpg
a4baa01b22e3f047df826a8c41342f19
4b890adfae92c082826d71d97fb3c279858b012a
5447 F20101118_AAAMAU takimoto_a_Page_071thm.jpg
3e39c056ff527d85477736c149f18871
247a1ffd423c175a25e7a5d8e7c448763eea6abc
2073 F20101118_AAALWC takimoto_a_Page_057.txt
4e9413e715a06296caf7eb12df5a531b
d2b80b74eb64f90c3dd28dbb56532bd534192734
2118 F20101118_AAALVO takimoto_a_Page_030.txt
76963453e303cb9517e90317900b07e3
567faf532212125ce27ba4c57e2605166a9d23c4
73918 F20101118_AAALUZ takimoto_a_Page_179.pro
dfe9e0153558d39cb252c326ba6a0401
b75ed85923a0d23cea35df8b7eb1726ba4b8f150
2247 F20101118_AAAKTA takimoto_a_Page_096.txt
bfcc1a5d05a60af286361203b7977c8f
db39d35169d537f2e2ac80e754fd87c1d410c477
4901 F20101118_AAAKSM takimoto_a_Page_138thm.jpg
3b45672e84c6053e900f44a988b6778a
52631099cd69eb1183a1e2cfab0a72ed87947c13
25620 F20101118_AAAKRX takimoto_a_Page_153.QC.jpg
c5cba3dd3cf91a414a5331ea13fa764a
80300ce423a11e3551c092d28a3d28b7c92f4fd7
26674 F20101118_AAAMBJ takimoto_a_Page_117.QC.jpg
dcd27017df753b2e169128830e48523e
5dfdc2dc846a4529685b53e7f6da97319be78e64
27104 F20101118_AAAMAV takimoto_a_Page_129.QC.jpg
b053a192730c3e21a7519df3e790661d
8f29eb1b3cc30e2231565643b9c81f1bc9c98b56
309 F20101118_AAALWD takimoto_a_Page_058.txt
309737959a2d7fd873e4e8c01d78c3ab
c21aa64b4267417492a969af067eee33e503b4af
1315 F20101118_AAALVP takimoto_a_Page_031.txt
3c1c088e64141d6d1ecb8bc57bb62934
40c94bc6c1a08686f78e66122bdd270de4ed9f1d
76490 F20101118_AAAKTB takimoto_a_Page_038.jpg
a500f5c3bfc3fb211c1c6acea7dbc8c5
129b3cc4407979bb3ee5b92bcbcf8ae7ee8139fd
6004 F20101118_AAAKSN takimoto_a_Page_010thm.jpg
32948ae415bf4964d9d3df0113e8003f
f3faecd379e633de27b819e4b3243b662c93e55d
5955 F20101118_AAAKRY takimoto_a_Page_005.QC.jpg
5a02eb94ceb9497eeaa9911ad7a119c1
4aea02e5c0127c93fbe210ead9621d0f8ad806d0
28139 F20101118_AAAMBK takimoto_a_Page_133.QC.jpg
e35dbbab9888d3ffb10fb40ad281993f
c90e4b8a4be6e984d0f9e2cb15b3532034f2e3c2
14309 F20101118_AAAMAW takimoto_a_Page_104.QC.jpg
2058e05224de3064901f1173ad261ec7
a8bdc71cb417a053b62aa4bb1646f5abe75776f4
2196 F20101118_AAALWE takimoto_a_Page_061.txt
12c98ebc37cb00f6767cfa3c6c0ab0a5
0478bf8bd8bd1998b2705806834a6e67e1271fe0
452 F20101118_AAALVQ takimoto_a_Page_034.txt
3f2ce05a878aa20d7c8c071aad79d318
bc4d34152ab0a476b78b3d2f0842c2ae0346bd09
125599 F20101118_AAAKTC takimoto_a_Page_178.jp2
c2e73395991319e5603a4a2e92b0b168
9b4b71562050194becded4c3440b542bc5f0d0d2
6416 F20101118_AAAKSO takimoto_a_Page_029thm.jpg
66b82723d6c7f180a853e50af10ed215
0248fd85835f0fb00c93c420657e39515be98897
86383 F20101118_AAAKRZ takimoto_a_Page_007.pro
efba4771da877ec4a1e722fd839f055e
9ca030b8ee28a4f50772dcb6c6ce04f4435d9653
20860 F20101118_AAAMCA takimoto_a_Page_014.QC.jpg
ad76ac5b710b05fa46d455234ad2c810
bdd4db48784d25c95fad2ffb26c7023b75044dde
3256 F20101118_AAAMBL takimoto_a_Page_081thm.jpg
888ee4f1b52994518859ea44047cd7b4
e43ae4a795ea66ac46e5f06e95d73728a5204db6
26461 F20101118_AAAMAX takimoto_a_Page_130.QC.jpg
9656cffbf0696aedb9d10167ca7a47b3
63c13d4e2e629259d23e256c841d158dc3d937f2
2174 F20101118_AAALWF takimoto_a_Page_065.txt
0ccbc932c3df9a8cbf1880c4c4156241
1833071bed3eba138519398d0c966c67de73d395
2027 F20101118_AAALVR takimoto_a_Page_036.txt
1328cec6f92106289402a2c35d146488
e07f5aa49e6eadfa65056420fd8825db6c675c34
3108 F20101118_AAAKTD takimoto_a_Page_009thm.jpg
a1eb99e5e7bca97a56cf059169addff4
68f4414a85958e39af90bbd9b1593a049e9e1baa
1843 F20101118_AAAKSP takimoto_a_Page_028thm.jpg
9c20e1ecff53fd37cf3e74aa89ed1402
7d8e1809a9cf3aa941a68dee83290b2f151044c5
9139 F20101118_AAAMBM takimoto_a_Page_162.QC.jpg
cb1e24ba08381ed717ad8d779d462c8e
8a5f059f6ed5d4928155ab5cb492b274833e3a5b
17174 F20101118_AAAMAY takimoto_a_Page_035.QC.jpg
892a4ff045d34f239c63f752e89ec70d
f995a21f41f381736314d8392599e096824664b3
2109 F20101118_AAALWG takimoto_a_Page_068.txt
95ac497d513632b7414ca3e1bd92c720
60471db900118624ee3b27804588ac3a8d105984
2205 F20101118_AAALVS takimoto_a_Page_037.txt
0a4bce2993069e161e6e3d2d48fb7772
65aa8a7c781aa81397b639deeb0624675c1d7f2f
17550 F20101118_AAAKTE takimoto_a_Page_169.QC.jpg
3f3f8fec29051560fe82ec3fdbef36eb
fe235c21c7d29792ff03d8f8498dd3b079e14f24
57001 F20101118_AAAKSQ takimoto_a_Page_084.pro
7dc6d88ec1b039e26ceedc4c4902b475
2471c7fa1bf65cd38b83d0784f3848c1fc48f034
24488 F20101118_AAAMCB takimoto_a_Page_015.QC.jpg
943562d4033c916eaa45e9769c1f6a43
992947efdef06da4bfcb20b86b2f09cda1740adf
6759 F20101118_AAAMBN takimoto_a_Page_054thm.jpg
667aa774745911648d8220512d35e5c6
e315056a8d859079ed11e18e766ecfec2f07cea5
7294 F20101118_AAAMAZ takimoto_a_Page_136thm.jpg
279c89c945d27941dfbb15d6c02d9a6e
438876340dfbb5ebd7ffea2b9a5d4828b017a698
1800 F20101118_AAALWH takimoto_a_Page_069.txt
c3fc0742787d7535b987a67f3f8e6c42
6eb76bd66163baab91df45bc83fa990a1a7fc4af
2123 F20101118_AAALVT takimoto_a_Page_038.txt
07453fa1a01271b8ff2bf999a0e87343
8114d42f4a0e8857ff77a109bcc3dc4a017f9cdc
43808 F20101118_AAAKTF takimoto_a_Page_158.jpg
f04715dd767271225d8b9d950bbf6762
17adac51718e464cf2b8e827c0e85b964bdc4449
4700 F20101118_AAAKSR takimoto_a_Page_060thm.jpg
b635024a032b55e477dc340e93a66a76
2b1f89360d48c672c17e8a2bfceaed8afe08e91b
26090 F20101118_AAAMCC takimoto_a_Page_017.QC.jpg
b8bcc7d92a47cc5c9e3fafdd27cebec2
a97d7b5f5674bd5ac51da0966423e87fe16f4a0e
276967 F20101118_AAAMBO UFE0021453_00001.xml FULL
215c5fcf408b750ac8166840e11c6b03
0d9fdc45a78b0548a0deecfe301628e8cc36c713
1828 F20101118_AAALWI takimoto_a_Page_070.txt
b95918e4055887d9e74875a803e68ef9
22f9a385ac0fe9e42df4ceb08beba4dc60301525
2002 F20101118_AAALVU takimoto_a_Page_040.txt
bda4c0739e4cf8c65571fcde17539666
03af5a1a26608356719b40d159091c71f308f215
F20101118_AAAKTG takimoto_a_Page_143.tif
3d8e4c5f7be7f7051cd28d473a6f9ccf
728edac5e42448b1823081b04bc44f909cd19c5f
47815 F20101118_AAAKSS takimoto_a_Page_075.jpg
d7d4588241acddbcc76c77de64754538
0110c8a9efab8c602b3045c6aee0540489b989c4
25803 F20101118_AAAMCD takimoto_a_Page_018.QC.jpg
20e401e1509c959ea8410f0a5128cf2e
9b37f6d7ea37c17b2649a2daf7700ba972a3db39
3078 F20101118_AAAMBP takimoto_a_Page_002.QC.jpg
6e02f1898111e5598d90b1651fc126fb
61f3f51600f0e43fd292a1de9da9c23afca13494
1432 F20101118_AAALWJ takimoto_a_Page_071.txt
04e18393b9a6dd21ab9f9495d919a7d5
b614fe28852bc6f0399d03c9fba8056764e5f738
29935 F20101118_AAAKTH takimoto_a_Page_180.QC.jpg
567aeb7bb6e22522322468159d3b8161
76a4f312c4c696069cf25d57f5719c2401208b4b
7183 F20101118_AAAMCE takimoto_a_Page_018thm.jpg
16a0a5ef8bf91c3cafd7f20d9cbae78f
7e695106cf291841beb48def5603b5982e2717f2
3132 F20101118_AAAMBQ takimoto_a_Page_003.QC.jpg
9375d2a94dcc65a00b588204ba3d454b
311e893008dc84f63392a33f60887d2a5efda9a5
2253 F20101118_AAALWK takimoto_a_Page_074.txt
a193687b332c09d76d67c0d6c76d2a77
bfb4e4e59a433c0df787a3a7b411d99bb05e4f05
2113 F20101118_AAALVV takimoto_a_Page_041.txt
8ff1dbe4e2d9ed5a8b6526f66b660a37
c6d4a459faf693455be82de6db5ed5ecd7e03637
2212 F20101118_AAAKTI takimoto_a_Page_114thm.jpg
c0e1788cbe55c9dae8f56c2c5219d404
a388d68acf4c0d63f5923618dfe702673b4ba37f
1633 F20101118_AAAKST takimoto_a_Page_060.txt
290c3c039fccc01a36b64d6c0b67702e
95648430bed0ce9dad70c566fae46f682e007812
4721 F20101118_AAAMCF takimoto_a_Page_019.QC.jpg
46d978280a5e9b1fec2771c6f781b0cb
138d4343cde2e16b33c673c26b13c20c1fadf26e
2102 F20101118_AAAMBR takimoto_a_Page_005thm.jpg
6a2daf2b50a6f15de28f0cd770c94bb4
f6b8ec06085deb308b79dd1c5594813cbf952760
1989 F20101118_AAALWL takimoto_a_Page_076.txt
a91438dd3e074c189c747e36835b0dfb
51d4b2a88768484261dc37b9d948fdeec0e152a7
1860 F20101118_AAALVW takimoto_a_Page_047.txt
c4aa91d0cb681da13434c4962f179248
b02cccf34b47e1a73bb406407c2d3b901882d921
7024 F20101118_AAAKTJ takimoto_a_Page_103.pro
ef8f81091893c806906bee92850dd76a
9d3c1afb5390b619df2ced6e21fcf9a5fecaec95
F20101118_AAAKSU takimoto_a_Page_062.txt
4cc0013685589d7c6c32f2f00b71a269
6e4a1efc528485f962eada20489bc2a85c3a8df4
1684 F20101118_AAAMCG takimoto_a_Page_019thm.jpg
34c7bb4e3115424e652076876d630890
e44a7410adc25a89d3664b9dff85fbb016c6b773
23307 F20101118_AAAMBS takimoto_a_Page_006.QC.jpg
17c8f252507a40e4155818a103d7fde1
d5d5961e3de90ba424ca4e38739556f673ca665f
1813 F20101118_AAALXA takimoto_a_Page_102.txt
a3a14ffb358d2029b07e0e647755fe3a
d7468db730f789816c0f3775528d2723eee8bf85
715 F20101118_AAALWM takimoto_a_Page_082.txt
502a166c2cb9d267116151c1bd4404da
d890e856aa0e98b15ef4a61fc7a6a46698b0887b
2033 F20101118_AAALVX takimoto_a_Page_048.txt
b17073167b9f9e8d2ede5b408cffdaed
0ae072fea46d324d5da1fe4d7a85ca4adcf85504
7787 F20101118_AAAKTK takimoto_a_Page_172thm.jpg
23bb98ec2baae313871276ef2a50da4d
91624993d431d0d04ce9de9a6d7fff45ceee6da7
4654 F20101118_AAAKSV takimoto_a_Page_078thm.jpg
d27c0966d3b807de50d1ab0de9bba44c
58947e6eb25b6e9184a5aaf65b03d1f4acb02337
26298 F20101118_AAAMCH takimoto_a_Page_020.QC.jpg
11a46ea60ecf4da1b1646851256320db
9666349be9fcf8d7e81ebe7b0b8762ab44e8041c
5608 F20101118_AAAMBT takimoto_a_Page_006thm.jpg
14266ee8f2f4650cfbb9cc369a3cc67e
5fc083979e6896b0b1509b0d89d79edbc215babb
529 F20101118_AAALXB takimoto_a_Page_105.txt
2b4d2e483a0ab0886b4cafa8ef5bf10e
f160e96a68f928269b5ea5ea8abf17fda3147d7d
2238 F20101118_AAALWN takimoto_a_Page_084.txt
6c471ec8e05e5ee1b6bd4667080db3b3
c2771b0e58ae8e1f5e2b8ec921ebf94ff178ccf3
2209 F20101118_AAALVY takimoto_a_Page_050.txt
1e1c8d5d89626dfb21298641145c91ec
1e8da23341a54104d5d7844afae369f2ae041bfa
8239 F20101118_AAAKTL takimoto_a_Page_001.QC.jpg
504b43e69c3205044df6ff7158d4500c
ce50f6e227c80342d882eba7b7ea3139a1a05521
870210 F20101118_AAAKSW takimoto_a_Page_009.jp2
720964d4984d505bb2eea00ca25caac9
ed023253d85add3ecbbdb1a8756b6a2091c20af1
7105 F20101118_AAAMCI takimoto_a_Page_020thm.jpg
c2230dd91ac603acda1a482cb3cecc95
81d7063644790e311ee4069ef8f7de55985b518e
24047 F20101118_AAAMBU takimoto_a_Page_007.QC.jpg
d29065e459d85eede11f73670141ef06
69889915b71d534d594e50828c9a06f286c7569a
588 F20101118_AAALXC takimoto_a_Page_109.txt
30727a872fbd45e63f11dc1849db8af9
92f11c216be88dcd3313d49a98c31b8ea2c820a2
2125 F20101118_AAALWO takimoto_a_Page_085.txt
b66d823c4dd2d32fd64d9d18a0c1ee83
527eb721ac368ac58b645a29b48bfe148a8b49e1
F20101118_AAALVZ takimoto_a_Page_051.txt
cd070597afc6559a84160025506729fa
d281403a1fa830262dddc20e6807386a5e182549
2183 F20101118_AAAKTM takimoto_a_Page_044.txt
af1ade6a65807a2959be56401fd1ed75
82675da9c5832e103d0b549c5c7d6c353d7e19d0
5364 F20101118_AAAKSX takimoto_a_Page_169thm.jpg
891e8bda0d0a4cec4c72015539b91bc9
14c9791e40b03f1724561ebc21a07a835e4312cb
4528 F20101118_AAAKUA takimoto_a_Page_110thm.jpg
614abdac31c2c0fe2020be581f3c392d
a98322a0e392a2a07e393d9b0813ab52b92314ed
24713 F20101118_AAAMCJ takimoto_a_Page_023.QC.jpg
028159751b0f5d19dd825807e779da19
9ff74db7ef5243d1211b12f411d9c1bd59358de1
25145 F20101118_AAAMBV takimoto_a_Page_008.QC.jpg
54457d3221e59b1c57f4a98102e112b0
372b4d2db3d1155e7c6be1f024d906b5fbc636ae
916 F20101118_AAALXD takimoto_a_Page_110.txt
06d12ac18304f4158c9e4e5f0615df6d
5c91f60e5d8dcb2301467c15d3102c17d0b7298f
1736 F20101118_AAALWP takimoto_a_Page_086.txt
df20f866d9552f31564a9de51b37051e
c75c683072daf378a4aa38f692777387823196c8
14378 F20101118_AAAKTN takimoto_a_Page_158.QC.jpg
eeee14a7f8a3e5c75d7026792ea65659
be0fc80cfb299f50caeb0e3156e0c56480ec1602
F20101118_AAAKSY takimoto_a_Page_009.tif
5af5ff57bc663d3a9b3243614ed3218e
82d080e6fdbbef8cf96839979113cacb1a3e22c7
578 F20101118_AAAKUB takimoto_a_Page_112.txt
a117798e2132eb9ec4e49cb7894b06df
ae7f4264a10c139507fee20ec1f289a8055ba4fe
26405 F20101118_AAAMCK takimoto_a_Page_025.QC.jpg
5cb59705de448129f29e84a175dc6568
54c69e8353e76901d483d40ca83e0360b0ba7bab
6281 F20101118_AAAMBW takimoto_a_Page_008thm.jpg
4eb8468b2e040d15f97cb0788247e772
d0243c0c7a852a85e960eadf433415a7dacbea69
954 F20101118_AAALXE takimoto_a_Page_111.txt
5ee90113489d8f150ccfec807a413931
97aacb49653429d8f7905f6e3dfc34cd33a47477
2137 F20101118_AAALWQ takimoto_a_Page_087.txt
f93002b50230c7bb66f49ea6bc7b188f
7d10e5c2e348f601bd2945327ecc83f100195a60
387 F20101118_AAAKTO takimoto_a_Page_104.txt
1b69fa534dd55e4e5e8a0ae9426f3ad0
072e1f62be1012b0514bd933682a27f8c8c98a72
6099 F20101118_AAAKSZ takimoto_a_Page_033thm.jpg
0c5dea5711d5558a5fbc32f330c01a3b
fd87d6c859a93dee8d996d8cfe33d254a0d4663b
23761 F20101118_AAAKUC takimoto_a_Page_045.QC.jpg
766159f5884f7c2fe4c850be0cc763f0
7ee172569da09225e28065a12987c3a134f5fce6
6706 F20101118_AAAMDA takimoto_a_Page_043thm.jpg
5f9ba259ea6165d1019ea65597db6798
b062c19b8008692e5b6c4a147ecc295789b20287
7241 F20101118_AAAMCL takimoto_a_Page_025thm.jpg
3f9f951ebeba9d0c5bb032fe7b3e9de3
dc986a0c8dd12782b670b112eb1beee45effba36
9922 F20101118_AAAMBX takimoto_a_Page_009.QC.jpg
9e590b2225f8cf1d0c141ed7eb3d36db
69ae1f6198cab7061d553230ddf3c81845134557
411 F20101118_AAALXF takimoto_a_Page_116.txt
47be4864fe5da8b17112b5c9e6538d20
169e15397d7d5aeba02abd8808335784dbba0bdc
2011 F20101118_AAALWR takimoto_a_Page_088.txt
c9527898930d6ee68d3971774cf8771e
a4d8a1c22d26063e65d1c7ee8086eeb4019e1883
1051944 F20101118_AAAKTP takimoto_a_Page_095.jp2
e9f86e17e5072151809f958dcbb4f597
82819fdce930620aa2f3a9fbf5760de4b97fb487
52961 F20101118_AAAKUD takimoto_a_Page_122.pro
17a503733d7ad6b6606128a3473e4ac3
1fb904b1ea8732d180f66d0d64ebdc5741f2a938
26004 F20101118_AAAMDB takimoto_a_Page_044.QC.jpg
534d3e59981171596a90daeba864a736
2a46bbd13566fdf08f933af7bc6a4932df2f0e56
28157 F20101118_AAAMCM takimoto_a_Page_026.QC.jpg
022b407d0f489b033a18f882abe1845a
feca3f0c2e57ff37b11a018011a47901b000b5da
25731 F20101118_AAAMBY takimoto_a_Page_011.QC.jpg
bb0056d4834115e00c7c5ad67d2b7896
71d19ef834bacbca77d52dc6ec524d1ff83b8866
2099 F20101118_AAALXG takimoto_a_Page_118.txt
379fd7e10d6bec4fd6c377727bfb7dd3
b471a97ac3761aafd93b4d101ec9966a1d4a4dd0
1815 F20101118_AAALWS takimoto_a_Page_089.txt
843d3e2af84ddc2ed5eb0d373c6c60ba
55ca0940a439ec3d9d9af541a43315d05ac5543d
63344 F20101118_AAAKTQ takimoto_a_Page_108.jpg
1d1e121a902cd727992c4e02d9b5840d
3a63a1fe9e7bc171cebe29f8a7e4bdccaba1b2ed
53198 F20101118_AAAKUE takimoto_a_Page_129.pro
21191787051b73d5f8c9a1d2ef62c59a
82ae8788e61f6b7f4627a28f24c035f78cf54e88
7409 F20101118_AAAMCN takimoto_a_Page_026thm.jpg
dac55efb1f9c5244fd3a0b3981237285
ca91775464e0bc131473491bb0e05a48834af23c
20987 F20101118_AAAMBZ takimoto_a_Page_012.QC.jpg
d155ed96dfb967f77bc7ba60425559f0
2405739e306d9b202e75d05161c6f5a10b5302de
F20101118_AAALXH takimoto_a_Page_120.txt
b0c42a15476d1e0c05e417a339915a74
e488f7674c96e466c2679f4cf483e88a0657bff7
F20101118_AAALWT takimoto_a_Page_091.txt
1ba9534d329446e42d3061de83881768
9d866c79d2338c45f2226d343fdb5766466440ff
15525 F20101118_AAAKTR takimoto_a_Page_028.jpg
d5cd0c32fb8189f477f8d63f42726f2e
40628c9b362cbf43347380ce7eecd775d7fbb913
F20101118_AAAKUF takimoto_a_Page_087.jp2
2e9a202fcc25550f6271557b8bb1b043
32b454372041c869e96a89c6844792c2dadb4be6
25015 F20101118_AAAMDC takimoto_a_Page_046.QC.jpg
2ad7ad28d2251226900a781fa665780a
7962c2f0d9083e8399b8d326a6f593b9416c8d32
23329 F20101118_AAAMCO takimoto_a_Page_027.QC.jpg
9ca898790e6cf7fc7e43f1826d6a4db1
efa6b27a33d4cf3fdf221edaaa0dbb6133e433d5
1700 F20101118_AAALXI takimoto_a_Page_121.txt
f123a751e3acab7954b59f4a76840617
d72b926b533b3222e2c6354a4c83a69aa1a7b4aa
F20101118_AAALWU takimoto_a_Page_092.txt
94ded01939893c63af8deaff22411544
f9c78c683ec9448fc66047b6be94ca9ec4be3abf
117434 F20101118_AAAKTS takimoto_a_Page_055.jp2
e4585689b0028fee4ab26779c79f105d
e427a0f1347bfe90f480fef4e1c61e938e3cb3d6
7993 F20101118_AAALAA takimoto_a_Page_173thm.jpg
e3da87ef5c41826a90b0ccc2d4e8e641
d1fe98536f895ab891ba691a3997da0a78e9b91c
99469 F20101118_AAAKUG takimoto_a_Page_175.jpg
f32b88ca4d29f71e40594a1e40ee2157
c3f2485fb0c6c389c2ac9c7822b284e7da94d5eb
22611 F20101118_AAAMDD takimoto_a_Page_047.QC.jpg
e0c0abdc820c20078377949db1994acc
c7406eb9028c48325063ac560967584431b4a514
24353 F20101118_AAAMCP takimoto_a_Page_029.QC.jpg
398fbf4ca3bfffff6b6341e9b58bd583
30789806250716eb9b95400cde78d901f579ae92
1771 F20101118_AAALXJ takimoto_a_Page_124.txt
240e966270d1be5dfffac5383ecda448
bb6a490e6cb18ed34a88389855543cd21233bfc5
2167 F20101118_AAALWV takimoto_a_Page_094.txt
22ed25741689b0307389274bbd56a3ca
0286c1df4421d890ef030e9134033ecff1ccaa00
468401 F20101118_AAAKTT takimoto_a_Page_146.jp2
8b91a12c375ad1a95214a4ceb1a2d545
36f52de9ffbb74213e0aad1f27b0d6a02ad6bcd2
2042 F20101118_AAALAB takimoto_a_Page_152.txt
6e3e83467bfe13d2aa93efd13c59f876
944cd62811d787ee69df856d3bf9965891731ad0
1435 F20101118_AAAKUH takimoto_a_Page_160.txt
9765078423db048bf3beb5917b4be0f0
3c720db5ad8eadbd0c702be1f7885b7bfa50bccd
26184 F20101118_AAAMDE takimoto_a_Page_048.QC.jpg
84655a328fc8b070e3d09b3ed6b1bbc2
09a1cd4e8ee8c398cd9f0a5e146aee0ecd17ed5b
14180 F20101118_AAAMCQ takimoto_a_Page_031.QC.jpg
0a8f4736545e3f015e7c23914208c8f5
86b27e4895fa4cd55f45a156b380f808fe654459
2194 F20101118_AAALXK takimoto_a_Page_125.txt
c0da3b34cdf3b118258d9aa85c06de45
b4bb2b36fce6c27147c1d4f514770ded77c6db95
16872 F20101118_AAALAC takimoto_a_Page_165.QC.jpg
b558a8e991965b39e8ecce1070c6cf61
477a67f881c7f4c37c232aecf224b35e687a00ca
4651 F20101118_AAAKUI takimoto_a_Page_183thm.jpg
876c9b1df2262b88a36edfc9df905e5e
4d31278949f866802e721f35ddf39b86b9da6eea
7407 F20101118_AAAMDF takimoto_a_Page_048thm.jpg
08f5b1c5dfb15bf34791b9bc48f47594
5985a18825fc8c6587e911d950d72b949c196077
4210 F20101118_AAAMCR takimoto_a_Page_031thm.jpg
4c94d18ddc89ea0beaf2ec84ad69cf10
aa1d38bcaf550d8c4617b45262332f2cf0342133
1970 F20101118_AAALXL takimoto_a_Page_126.txt
a348a25bfeff921ec6a535d8a257ead0
a030e6335be7744d5ddb4980b3fc05a25b0d8d7d
F20101118_AAALWW takimoto_a_Page_097.txt
97eca663717929dfe672908e0a8ede2e
941039a1d35c5148946965d23763783c2c2af083
3683 F20101118_AAAKTU takimoto_a_Page_007.txt
980e84c2953ae30d3fcf171813026f4f
122105d2a12cc6ecb9af9569bf771adb84c2f2b7
F20101118_AAALAD takimoto_a_Page_155.tif
fd33f1ee3a4e5f5978350c0b40321847
aff5aa7586a45d6d20b907cfb335f27fe8dbae0d
28601 F20101118_AAAKUJ takimoto_a_Page_175.QC.jpg
b60c913b5862b4f059c0c99d539da68c
3bca42424775e6a5ec5327ce24f0754af994b023
27133 F20101118_AAAMDG takimoto_a_Page_050.QC.jpg
7a2756e0d3719c86d14cd4886746bdcf
575927be76f82429ac7e5dac134b055876885186
4009 F20101118_AAAMCS takimoto_a_Page_032thm.jpg
24430b7f9afa5abc1e30593fbec63da0
fa7dbee29f55eaafa46f8762279a32d3f3a45f0f
2115 F20101118_AAALYA takimoto_a_Page_153.txt
6873bc03ec13f59f3dc2c67f1ae3259e
bc361b3decd84808637a043b94e4581095fa5dc3
2224 F20101118_AAALXM takimoto_a_Page_128.txt
8106193655ade9ccdbd854c9ea5b03cd
f466065b9257442c5a10b16702a7dc8cd39e5504
2186 F20101118_AAALWX takimoto_a_Page_098.txt
99a2cd9c24689ad0ef221a0924314b20
87fb1e384b3fcc3fd3f8d1697a358af017d47106
115633 F20101118_AAAKTV takimoto_a_Page_046.jp2
1abe1cda33bf4738ce15d3fc957d7656
7ef5c027c6600f09ab2d1e2e4146c952b8ab000c
422780 F20101118_AAALAE takimoto_a_Page_111.jp2
83a6b7696206af40fc937660aefcb001
c0d9e1ce41ea11cd624f997e9c4179312ede87f5
64370 F20101118_AAAKUK takimoto_a_Page_124.jpg
a4fcb4a5aeec2e5b8c7445bfebc16d06
a90cd7ecfbaaabeca647d70fe3d67d014a07d9f1
7402 F20101118_AAAMDH takimoto_a_Page_050thm.jpg
4df6f8377f478e013fb1a6667430d083
7ff78bfbd64c10b5c65e931329adb83dcaa1ed56
19916 F20101118_AAAMCT takimoto_a_Page_033.QC.jpg
6be865884761562df5be8bb475366020
f1ebb3743e66a7f804f854a9ba121ca5222c2aa8
2135 F20101118_AAALYB takimoto_a_Page_154.txt
f1a1997734db6b635cc4f1b6354f5f00
bac3c55dd5a7bf08b7771ba70364b1606b09476f
F20101118_AAALXN takimoto_a_Page_130.txt
01fdaf470e3f1e39c9b122de25651af0
941e8bb0dbf83c0bd425e7474cd510594a7bd35c
822 F20101118_AAALWY takimoto_a_Page_100.txt
493accba427503df3c743dfbab41f2f1
219bcc2d3ab4cbe2bb8d23577f0d3d5d6f9738d8
89971 F20101118_AAAKTW takimoto_a_Page_096.jpg
67cbf3ff2bdeb25fc2a95e8d7cc49d4d
2ef794769219cae14f1665d9f8e6eaf12cfe65cd
1327 F20101118_AAALAF takimoto_a_Page_002thm.jpg
4862cdd325d8643635f76c573f2ac717
b47fe6a4a2daa5490a618c7c25a52dffaaa879a0
88759 F20101118_AAAKUL takimoto_a_Page_132.jpg
6784f894c8caee5758ce9c5f6f74c8ea
969c596071187ca252971b0b893385313d15f0c8
6733 F20101118_AAAMDI takimoto_a_Page_053thm.jpg
b562ef9cc92321d1d17648f73d9796b3
10c0e291edf9ff16b4403c94aa1825fe2a26a1d6
7199 F20101118_AAAMCU takimoto_a_Page_037thm.jpg
f914fe67f23eb7eb3211f1df4e87d01d
a600c0bfb6e1af6f3487a44f037e181d909ee08e
2221 F20101118_AAALYC takimoto_a_Page_155.txt
9cf83d95f3ad8dc0793087f285cd6c0e
f12e19aa1abd9d5217449b7d6fc37b2efb058030
F20101118_AAALXO takimoto_a_Page_131.txt
266a1b4687a75725ccb90b31c25b0761
2f14f067db5592b7169764779a1517b396d1aa83
991 F20101118_AAALWZ takimoto_a_Page_101.txt
7f2874f67098b81d29e4c1735dd7bb00
9c4107e7cedc4698a9b47edac324e0ac9d4b1211
73829 F20101118_AAAKTX takimoto_a_Page_057.jpg
fa1131762abda036c995a39f3b32041d
1e3b1b4657ab983236e6467223d3a04aeb8b65b7
F20101118_AAALAG takimoto_a_Page_113.tif
da4a872cf9719d2654b252a97b37805e
2b96a50cc525419e71a366f3533f98f8096d704d
3291 F20101118_AAAKVA takimoto_a_Page_142thm.jpg
4cb44eac60c524438f53b0b4726b1733
be64969f87e2046ef1c5523c29735dda6a50f6f2
1560 F20101118_AAAKUM takimoto_a_Page_059.txt
7d3b8dedae9507ec7e35a546fd9a1cf0
d08574bc4916ec4f1a6e4a2e05c54a997cbf2bf0
F20101118_AAAMDJ takimoto_a_Page_055thm.jpg
0b1f956177e1dceb883427790a122e26
1999c54e54759951203ce473d841be180ddf450b
6664 F20101118_AAAMCV takimoto_a_Page_038thm.jpg
57d26f4580254bede070dcd83a3c114e
652158e29a31c54cbde0dfe88508a6c55074ab95
1538 F20101118_AAALYD takimoto_a_Page_156.txt
3533fad75ec5f73449b479d486e350d7
be6565c8ef0580f4094f8b2b3f676973677988a8
F20101118_AAALXP takimoto_a_Page_134.txt
9ab1970a28f9a8aa50d65d016e6e4bb4
057081f61ebbcb5920af2ff5a45a6767ae8db94f
101199 F20101118_AAAKTY takimoto_a_Page_181.jpg
c24bacc11bafa51d951d51f792bced62
fa6590fbb0584d90e63e0222c9feaf032a308b71
F20101118_AAALAH takimoto_a_Page_151.tif
5995acdd521c5c671421a8e8f4591b31
0c2473b45a9a2c1d4211a51e80d041148b20014c
F20101118_AAAKVB takimoto_a_Page_077.tif
3ca186812dc74b912fca5a3847f3ce16
05e4ebc48d90b5ac532affb08efff00d52d8b894
F20101118_AAAKUN takimoto_a_Page_159.tif
f64256bc660b5a29cdc6683593b5f184
d543f20cebe60c8fc6ab60e3de5bffe8c442c722
24753 F20101118_AAAMDK takimoto_a_Page_056.QC.jpg
7f5d1e6f6a4ec9e408579990833105fa
622c017eceb1b2689a102bf5a71f890685a7940a
25464 F20101118_AAAMCW takimoto_a_Page_039.QC.jpg
337c2aaaa24660a865e60ebec1b21f5f
bf4310270b6ce14afc02e9556bbeb2c54c8bddc0
912 F20101118_AAALYE takimoto_a_Page_158.txt
d433914d53fd85ac27b2236f52ed1506
29c2ed01d3d0714d45ac238098cafc8b48d11561
2178 F20101118_AAALXQ takimoto_a_Page_135.txt
43b5c73896d5076f5ddf7d9f8f88f4c8
c22a0ee6fdeb0780c7752b114625fafe19370c51
5193 F20101118_AAALAI takimoto_a_Page_035thm.jpg
731b5ad07fb76bda65012acc0e2e7594
bf4652280b11fe80bf23076bc5ccf4899dc03db1
1051943 F20101118_AAAKVC takimoto_a_Page_120.jp2
dd26c66cab267dfdc9907401a59a172b
35fc0be4eeffd5f96dbb42582c8818c90194f049
1051975 F20101118_AAAKUO takimoto_a_Page_180.jp2
48e85910476bf1323a8d73f107250253
8efe8e04ad60d564692a8d332b79c09f59cd9e8f
50834 F20101118_AAAKTZ takimoto_a_Page_012.pro
4cdc1873d6c086d9f7bb731a1a2ecff5
4d0ff147193d3ade72d31bc7173eeee9576a0800
26471 F20101118_AAAMEA takimoto_a_Page_084.QC.jpg
13dfdacf07de59e71b7fcbe310b76b5f
546372d074333c01250ab90d3de025ba4bab5be7
6802 F20101118_AAAMDL takimoto_a_Page_056thm.jpg
528731f56482cb0b6beb1d6fa52d6e80
e238d7b57c513763bd665506502cd037e3c3d95a
24191 F20101118_AAAMCX takimoto_a_Page_040.QC.jpg
98b3cc8578d17033fa0bd599c7fde80f
63ed161a42362857f0b4a0095a2a2b31415b27df
1055 F20101118_AAALYF takimoto_a_Page_161.txt
75c2c7ef0013ea89d16b44c1aa6c7ab6
f784231e8cb52df9ea326a849d30e701d77bdbbc
1745 F20101118_AAALXR takimoto_a_Page_138.txt
5f1b150c3bc2289f1df347d9ee1987ee
2330c9ee8d0b889be34f64be74288a78e0d5625a
51273 F20101118_AAALAJ takimoto_a_Page_123.pro
e760498e4c595933759a03ef557d09ab
6c830427f4bd26aaaa4347383713fa546d423a64
22183 F20101118_AAAKVD takimoto_a_Page_069.QC.jpg
0cd67d3aeee3b0ae9efb718639c16264
fa2db87ea00f287ef2ce0ebf61dfd7a8c3083f3b
F20101118_AAAKUP takimoto_a_Page_170.tif
629d90f38512013d739ecf7168318360
92dfeefd78228caaf6753b8e110f0780f7de2746
21336 F20101118_AAAMEB takimoto_a_Page_086.QC.jpg
841d92898856fc9b7b5654e434b216e4
1cf5997cb1312941a18ec4119b4b5e8f68b17dbd
6591 F20101118_AAAMDM takimoto_a_Page_057thm.jpg
50fdc112283d31af9aedab4c8b819cb8
451ac4a0e565d6f702a9199c4d6d848fa6a4a39d
6669 F20101118_AAAMCY takimoto_a_Page_040thm.jpg
98b6343eaafbfbd7bb3bde27126788de
fc372eb5bf493c9e156757f198b8be5783d017a1
524 F20101118_AAALYG takimoto_a_Page_162.txt
edda0b150133c5a2259ad35605fd9135
dfced42b204772b5096e0ed5a546fe71d710c9b6
998 F20101118_AAALXS takimoto_a_Page_140.txt
53321863826b38732419bba847814c69
2b653193aae46a946ad75a283decc40df7e36eed
55962 F20101118_AAALAK takimoto_a_Page_037.pro
ecb7a0bac76a1dfbc8e86e2e11286df6
433dcea8a2859d44435ae97205f6126c7147aa8c
16741 F20101118_AAAKVE takimoto_a_Page_079.QC.jpg
d34dfc1f94daf38f4e2b1419f784ef6e
58c903cd0c6a706251c66b4bcecb6d06f95c95f9
109153 F20101118_AAAKUQ takimoto_a_Page_036.jp2
91d4ecf56f90b4bd565622d24cb4b933
e7d83f39e037a784c83b4c893dc81cb789cf0a5e
25944 F20101118_AAAMEC takimoto_a_Page_088.QC.jpg
d41fd1f2dfcbfede67c69b13250f574b
3f6b8796602a3721f530e783d61837ae9fbe5d99
3952 F20101118_AAAMDN takimoto_a_Page_059thm.jpg
6989381b49f6c99b3a9ab0914a400737
37d53115e320780cdaa4d8ca50aa7272132a0ea2
24839 F20101118_AAAMCZ takimoto_a_Page_041.QC.jpg
3d9e24efd92e7dc222d70895b15b4455
f2800e90b8c5481a1c381ee527d33aa62fce447b
1998 F20101118_AAALYH takimoto_a_Page_164.txt
20454da097b1eb87a27d7ca3f175c140
d46d22babec03ae957f519f41c05a0d57e616635
629 F20101118_AAALXT takimoto_a_Page_141.txt
d55f761f3f142219108a37fd732b06f8
37b606e54622194e521830c0ca41086b4908d3eb
F20101118_AAALAL takimoto_a_Page_040.tif
c6c57316e254d185219d0af933eaf5e9
44c6df9f032d898e3d082781150e02c76dd1bed1
23516 F20101118_AAAKVF takimoto_a_Page_010.QC.jpg
3ebc5e9359e4e191d7e97b5e7ffd8258
5824adc16e7cfe30d7eff758694b3571b8b2e2d8
343 F20101118_AAAKUR takimoto_a_Page_078.txt
fc88e51de98b993c9f1a3995150a2bb9
0441d1dea0499210a95a352d8788536d95511a05
55687 F20101118_AAALBA takimoto_a_Page_134.pro
6261276cd4c351e9f3592fcaa907c044
02aeb6b5e4888cea705813a30e14378cb5f7a166
25354 F20101118_AAAMDO takimoto_a_Page_063.QC.jpg
8eb5b4d92a353d483f1bd8daf3e84219
ddfc73644814a3eb0933f5426dc5cff02f724945
1844 F20101118_AAALYI takimoto_a_Page_165.txt
010f1ef234796f2e3ce1403c8f546dd6
cb0f893daefc45aaccbfcd6a7ae10c239bc0afe7
648 F20101118_AAALXU takimoto_a_Page_142.txt
d0a5ba114bc30d97484fd4eb6c038bd1
9241e3ed30a9f88825ba44af5bde9fbfe971e275
7561 F20101118_AAALAM takimoto_a_Page_074thm.jpg
f1f5853b14eb1862938158394a54b5be
9a581ba830cb2582f8a5d57f0b08f1ea74bb72ec
3730 F20101118_AAAKVG takimoto_a_Page_103thm.jpg
46858934f23556aa7931685955180220
5d2a5a7e09d50179aab2806311c83bf76a048d5e
2061 F20101118_AAAKUS takimoto_a_Page_025.txt
ea868adbad99b39c0193110d359ae6d3
25d99df552213a5b59ba80728d10605c3e8b798b
22460 F20101118_AAAMED takimoto_a_Page_090.QC.jpg
bbea0a378afc8a926f493fd239189177
9a63662411410064121e880dce31e4081a678417
6623 F20101118_AAAMDP takimoto_a_Page_064thm.jpg
63d3a4b5546a31ec5f951c98cc614dcc
67ab7715ac701bf8bebf8fa42f4c43412b8f50a3
2200 F20101118_AAALYJ takimoto_a_Page_168.txt
c653ee0ee299b2e48a86fa545566feb7
4fa711361b06e70ee2d00380ff076e23b88e2c43
696 F20101118_AAALXV takimoto_a_Page_143.txt
38d3396c1d8eec3fdd12c8c59f89effa
fdc0e53f170ea759eae8d381eaa19f56ed4e7e0b
2172 F20101118_AAALAN takimoto_a_Page_055.txt
a47c2c3a49b50dd0d567e0d97420b9e7
66c74e0a38f4815f96684e233f672708d2af3918
40708 F20101118_AAAKVH takimoto_a_Page_102.pro
695ca011577a7b80024923581a59288c
aecb19da60882dc5afff2c443f77120460741ea4
4525 F20101118_AAAKUT takimoto_a_Page_111thm.jpg
0f0ad2fa68c3e8040f81192434300d4b
db679e37f4c734b3303f72e6d0f872501be0bbba
115663 F20101118_AAALBB takimoto_a_Page_043.jp2
bd2fadc3652cc771adee32dc86f85d39
dd550e2831274826355c6c5152285468fda39e1b
26980 F20101118_AAAMEE takimoto_a_Page_091.QC.jpg
5cbb354b513a8e74b83b32182485a3cf
0e2e0020e9277481e761a0ef7678c18f30d269d6
27027 F20101118_AAAMDQ takimoto_a_Page_066.QC.jpg
c77723ceed16853080628ab026939335
a17d781618c3d33955ea25e31c5ffa8a05fd40c6
1874 F20101118_AAALYK takimoto_a_Page_170.txt
2c42a4c62d76f741d514274556b192b1
cde93a996330fdde25bb43ac6de2e8e4f210abdf
F20101118_AAALXW takimoto_a_Page_146.txt
1eb705041deb999933896c6d7ab3a905
c840eccc45e087ad937e39e712bb69a2759ffc3e
318746 F20101118_AAALAO takimoto_a_Page_115.jp2
f34623d7e26e414bf56ef89358688edd
60351435136ad24d2819ce179ab7d6d223bb84bb
F20101118_AAAKVI takimoto_a_Page_049thm.jpg
1e1246ecb49e7df2b770dc6fcf15d028
27f5807c64cfa50a80e825218b2273d38b0153fa
3057 F20101118_AAAKUU takimoto_a_Page_184thm.jpg
e2703e11040e35f568d2e6ad10d18adb
6b75733750882dc0c14ce0368ac47c00e4fdea3a
F20101118_AAALBC takimoto_a_Page_155.pro
c13563a03ef299a3dd840f0f2efcee1f
5aa1b94e12ee6cba60ce8538613afd7ae811e8d3
27394 F20101118_AAAMEF takimoto_a_Page_092.QC.jpg
967e00fe00e1190b73d127d121ff5b56
a8621f7d554286b8703987da4ee5695c3b07bf94
6912 F20101118_AAAMDR takimoto_a_Page_068thm.jpg
1492833977938023da68fe951029c67d
3c585f46d8dd7ad7466b422174d82314cd10d1ca
2606 F20101118_AAALYL takimoto_a_Page_171.txt
1301590112f2aa8eb1b5beab5bd15c99
c6ad1858c8cfd326180a4638ce6972d04f7d6550
2429 F20101118_AAALAP takimoto_a_Page_010.txt
6801efe1b7d47e40b2bb9550403cc166
1657fd6f49faba78a9c0f6a5b719a967e12cce0a
6863 F20101118_AAAKVJ takimoto_a_Page_087thm.jpg
8a5b1d4a04243a5175780e0f87fbd545
aa9215001bdd355b323295905230707a2a8ae8a0
23453 F20101118_AAALBD takimoto_a_Page_085.QC.jpg
7ec80b667bb73a59aa9a09efe9e4d97c
595559f767ed32b93c93af78c032df831f594fed
7724 F20101118_AAAMEG takimoto_a_Page_093thm.jpg
108ebeb1294961252f22029b90b90fcb
e2e6c0dee5c9112c9e6cf38195ca4ed23e23a651
6041 F20101118_AAAMDS takimoto_a_Page_070thm.jpg
d041c8dcd182e34225c38e2db4005283
7f8d6cbf8e6c07d0b7867dcd75561c9b42b9f9fb
6826 F20101118_AAALZA takimoto_a_Page_045thm.jpg
365c52ad644e6af20191a6b22cbe1dcb
47af5b979f0090c593f0f14f035076c2bf94efb6
2509 F20101118_AAALYM takimoto_a_Page_172.txt
63e81b7fd48bba81a4ac2fa87c2b4187
7e4934a26151ceab2257d92dd60c843419cd4799
526 F20101118_AAALXX takimoto_a_Page_147.txt
e136bce54015377bfdd78cef78cc2b12
9bbbff35690a60303e56c6335fd8382a4436ef54
82615 F20101118_AAALAQ takimoto_a_Page_072.jpg
73043e76c30734366ac214801c2c9893
7b4b5dcb0895c0ee6b8acdbf50fc51275bc43387
21599 F20101118_AAAKVK takimoto_a_Page_033.pro
6c3f8a263f1c1be2c14270d07a9d83ee
af57e3f9c14bedeb9939cb6c7bda40806e17ae7d
2290 F20101118_AAAKUV takimoto_a_Page_018.txt
af531c2b5cf4973cf294ea02c23143d4
7a2f9f30b96bf794a10c4dfa80e004a038190a0a
7533 F20101118_AAALBE takimoto_a_Page_181thm.jpg
24407349567befc58bd1e6a43c930b2b
b1bdfe0a009b7ae175cfedcec28b2600aba9c8ff
7677 F20101118_AAAMEH takimoto_a_Page_094thm.jpg
3cba5506ad2e24b303c7c5d1c89a8d7c
3a6b41d96217591c08ee8000fc600b2e757766e6
6617 F20101118_AAAMDT takimoto_a_Page_073thm.jpg
ee4f188540bfed36df99238cee5b5de6
c067d5afaba4103ac9d2b9db6be8b62db7244184
22141 F20101118_AAALZB takimoto_a_Page_062.QC.jpg
8dba08a4d0ab730a5b901548ebfbe44d
cad0543d81d3b673cfd509e8a5d0bdf47a7980a8
2870 F20101118_AAALYN takimoto_a_Page_177.txt
c46545a46bdce0605ed61cf1bd407f99
bfcecb32af8a15b968e25c3eeb418f50705877eb
2056 F20101118_AAALXY takimoto_a_Page_149.txt
eb47cf4abc3450797eb7cb96136597ff
884031098d73302544a8e5df40133dddebc0b07f
26342 F20101118_AAALAR takimoto_a_Page_016.QC.jpg
28aae26fc0d03b98deeec4c8c1d66eef
76036f0281f843432f82792b50448ab9fd1a7cb7
61633 F20101118_AAAKVL takimoto_a_Page_175.pro
f1e84a6e2f94bb0f5066248317fcf67e
ba221b3b230998283d62b3bc098771ee3064488f
F20101118_AAAKUW takimoto_a_Page_181.tif
a4794aa2eb9cc8aa2249a9154e1720aa
033fd7ed383686ae5afc67a953c40ee195104130
945 F20101118_AAALBF takimoto_a_Page_115.txt
d57b19426dbcc859c9599a0f4c22505a
03e4e6480bd70f432b60962978278b4ca43044cd
7537 F20101118_AAAMEI takimoto_a_Page_095thm.jpg
57849c8bc6c64a10f922a7fd10a79ec6
9d3d7ddf71cab612b946250923d7be40191aa4fd
5600 F20101118_AAAMDU takimoto_a_Page_076thm.jpg
218ec46363fd19d391f539db0bc158d2
9fd91a1ceafe00cab6da984aca2174261286e60a
28705 F20101118_AAALZC takimoto_a_Page_093.QC.jpg
43485f8221cd3d76edd619fbbc9fae4d
3db3b785304a161775095c96d2fb7f1b1cbfbfd0
2727 F20101118_AAALYO takimoto_a_Page_180.txt
53d9ac55a3c7f69ce8f5041fb6df6a98
e1b9a407e83322ebc7d7cb0bb3980d9022485529
2189 F20101118_AAALXZ takimoto_a_Page_150.txt
84e42c75575dcc5168ff8504a49da6fe
4f5786c88b6389b3db3d7bb141ca9291795a1bab
1987 F20101118_AAALAS takimoto_a_Page_119.txt
2f8a995cf490cc184d026a906e928e8a
ee8edf339b4710e6da87d3130279d6ef44c11c04
29609 F20101118_AAAKVM takimoto_a_Page_112.jp2
d6a7619bde6bb19564db039b29ce466e
bb37559722fbb54f03cc03e9d309f6821e35b4e0
50126 F20101118_AAAKUX takimoto_a_Page_039.pro
00a74f2fb3f007b3754f0d28e5e13c56
814a6d66ad3ba1ce59f48dcbba1d7a45a913b958
117843 F20101118_AAALBG takimoto_a_Page_083.jp2
338fce55eb9242ca488cb13695063e04
3f109be73226673bab9444fa65d5f15acfae8fd1
5111 F20101118_AAAKWA takimoto_a_Page_028.pro
0a5c701940bb068504a3777eb9c6b668
c9d215fe9a0ca7b8cbcc7e1a31fe5f069f6b7abc
28372 F20101118_AAAMEJ takimoto_a_Page_096.QC.jpg
96aab3ef7c9e55c55860d7b227472d09
26d04117a57e0edbae6d43c1c9e442bad3c3603d
19744 F20101118_AAAMDV takimoto_a_Page_077.QC.jpg
11bb590ea69de4b62804be3d0169ee6a
ac134da5719e005e4d8b02a78eccb547ca798aa3
2821 F20101118_AAALZD takimoto_a_Page_116thm.jpg
fc718ef3148c873302216c8b54e9f39d
c0d11741296c8061c51902bdec8130c45fee499d
2946 F20101118_AAALYP takimoto_a_Page_181.txt
584c1c33b7e887c3ef6a7cf57a23cb7a
6fe1c648062d7bae795354ef0bb9b02bde4a3578
77994 F20101118_AAALAT takimoto_a_Page_065.jpg
d74fb543573519c7ed33eba900531844
04f50d07e24759517794c63f4f62a08a5e79d7de
57867 F20101118_AAAKVN takimoto_a_Page_076.jpg
f5765b3d0039b8deaad9b08ceb85277a
8584092b212bf617bc80f4cc1aeb96c50960c7a7
11235 F20101118_AAAKUY takimoto_a_Page_147.pro
671dafa965811e4f5491455d7423f595
2c1f2896c2e92a90ce37fa2e63a3075c601471c1
4626 F20101118_AAALBH takimoto_a_Page_034thm.jpg
d683ed8cc0f050c0772373f4640c1560
0658a9d5798a4ee2b2f684922b5c355816221b2b
67041 F20101118_AAAKWB takimoto_a_Page_160.jpg
f3c77cc487877cc25cc9eb6c1b27f2b4
da47a88ddc36a2e8715f9be0416bfe79a680e264
26299 F20101118_AAAMEK takimoto_a_Page_097.QC.jpg
0c63ae871afb48ae0161990b602848ef
645e99879c4498844797a954cdc23d698fbbb814
5349 F20101118_AAAMDW takimoto_a_Page_077thm.jpg
94146c3118494751849f250e841ff742
3b90106dca30a902eb7babe5b412b36663666c06
24800 F20101118_AAALZE takimoto_a_Page_122.QC.jpg
15815177e5fc7de8933ed9c5d9fa940c
8e9c70326a83fe486c3b8e06437387e02257067f
2713 F20101118_AAALYQ takimoto_a_Page_182.txt
5bef388e3522129121897362fcefaf2d
dafa5fb0a65f9efebbd06397f98af15cb96ee2fa
6194 F20101118_AAALAU takimoto_a_Page_013thm.jpg
1063dd3f5373816c83d2b0093f98d4ce
1c31ebf3bd6887a22fd50aa16c53ce429429a325
F20101118_AAAKVO takimoto_a_Page_055.tif
39abc48d9531870597a6ff1efd2f5ff4
1accf7086ac51627c35c49cb839c1cbe5d9445c6
25660 F20101118_AAAKUZ takimoto_a_Page_065.QC.jpg
5235f20204f351e3de530e8c104bc99b
9abcd9d9ee8a9d37aa13e6f6d973682391cdb7bb
377 F20101118_AAALBI takimoto_a_Page_032.txt
1969b02651e01ba9991e00144c5b25da
e360db05a087d8a2b278b78ae4c75b349ed1a20f
67530 F20101118_AAAKWC takimoto_a_Page_180.pro
b506198155fff2430d72350db52d9853
6a3509e18847a17fd7f6a5469ea32684825ea09f
9051 F20101118_AAAMFA takimoto_a_Page_116.QC.jpg
a59629f9db08579dd2454f8590f894fc
ebfe691a814186a484ec5d2fab5f3cf323c14a5e
11076 F20101118_AAAMEL takimoto_a_Page_100.QC.jpg
c703de183fc3cd0391cfc572182d0418
880b9b457d06f123dcb6076cb7cdad9362ba2ef8
5102 F20101118_AAAMDX takimoto_a_Page_079thm.jpg
fcba0db39bf5a7c2dd4973b9a4413479
39f472444307d938149432dde575dcf2e49b17b4
10588 F20101118_AAALZF takimoto_a_Page_145.QC.jpg
eab118c763315892412cae6e39afb1ba
3cef4347874260a6b00ea871379769adb4b6842f
695 F20101118_AAALYR takimoto_a_Page_184.txt
010e7222f0591ee0e62e0e3cb0bd92f9
d962487789a73f46ecd8173aac8d1a2e7271e4fa
1051966 F20101118_AAALAV takimoto_a_Page_072.jp2
65c0875c1bf0c60b04f8214d636fe992
a44a269b5e127b82da55678c3331d65d0cf2a4cb
20803 F20101118_AAAKVP takimoto_a_Page_070.QC.jpg
bc1313fc5529e2f68ff74dad80842a16
406e739ec93e72d57cbae5980f88a8275f2b27f4
2107 F20101118_AAALBJ takimoto_a_Page_015.txt
c03a2117db9fe79a897bc1f8025c1c3f
3f183c7c3a8312670b96397c329bc2bd71166fca
1051981 F20101118_AAAKWD takimoto_a_Page_136.jp2
d33633cb4d06dc955108dc18673979cf
4c371e698095c4406232fb7209650c75dcf44131
7104 F20101118_AAAMFB takimoto_a_Page_117thm.jpg
952cebe99522a5fdb6010468a9f79983
d3748701c299b65bb604b07e8d878e8a69009aef
3345 F20101118_AAAMEM takimoto_a_Page_100thm.jpg
dfa452e23245583fe027f06a7fc539ea
ad8c62653f21896a5da9d819b703c462c289e0d2
18406 F20101118_AAAMDY takimoto_a_Page_080.QC.jpg
8b7504ab533a897d2b3aae431086cc30
b81e8abc62f9530310daeccdc51b1fe16ac0430f
5021 F20101118_AAALZG takimoto_a_Page_170thm.jpg
84edda780b9d24f22474538cda11b0bd
efde28c678a09e8d8616461ab964619661f9accc
2549 F20101118_AAALYS takimoto_a_Page_001thm.jpg
841d94233aa9999869410a98ec5bdb48
8e20c4a4c0f5d625a110024c13777354dfa59f88
53441 F20101118_AAALAW takimoto_a_Page_170.jpg
b4e7a1546cf74eb2085f0b2bf166da17
8b5dd2a476f5be749122dae9a52a27588cb58911
55266 F20101118_AAAKVQ takimoto_a_Page_065.pro
1e71c4df33767ab0afb3c76b3f78f65f
819fd415aa17315147b55bd0dd8d1809ea8738df
304 F20101118_AAALBK takimoto_a_Page_005.txt
14f0f12c3eedb8b8defe33968b347fdc
8d6b0a09ba6b2e510a63fb83b09b5bcbde24b2d4
66090 F20101118_AAAKWE takimoto_a_Page_077.jpg
5a659fc6fc334dd13201e33b237aae06
83d9ce58dcc5e230b1be2445ee174d9885f3b201
27528 F20101118_AAAMFC takimoto_a_Page_120.QC.jpg
77eb5bf5053ae35a1f6d79cee21f7420
689acac1545cde695f688b62bec2e424e1406fa5
11141 F20101118_AAAMEN takimoto_a_Page_101.QC.jpg
19be4a25e0ade65fe671383e93da8376
564d1fc2e9f0ab6ed89e5f73db2599807c15b952
10647 F20101118_AAAMDZ takimoto_a_Page_082.QC.jpg
4c2e1626ba03764c35671a78eb6ef19d
82750cb097f880653abedac6d61abeceb91e8ee9
3342 F20101118_AAALZH takimoto_a_Page_137thm.jpg
e31d7ed00c237aac1ba2c9667d5db32c
f5cb2b31c933d82c9654fa956281f77c4ae683d9
2203795 F20101118_AAALYT takimoto_a.pdf
18c2a8745dbdaf046adc843ffcc32acd
36691e1f71e0ec714ac624aacfdd3b254d3fc3f0
51174 F20101118_AAAKVR takimoto_a_Page_053.pro
75abb7d78fd3b07c01c0e723f02355fb
0ffc03771299ae87d47448c2d5c725502d477fde
165 F20101118_AAALCA takimoto_a_Page_019.txt
fa87e3c954fb29e7082346c4d5666229
b3c638015bb327bf08d759ae876a764a63d10099
F20101118_AAALBL takimoto_a_Page_018.tif
1c646e94accf1316f82da99274a963ce
4f3f0999d05eb22a7c3d9374b0ed904b6ad0067f
54956 F20101118_AAAKWF takimoto_a_Page_094.pro
b3adbfd405f06876b344ebb4ed34fa6d
36f7de0865d0b890ad88f1734f1144f00ff3ce05
27024 F20101118_AAALAX takimoto_a_Page_051.QC.jpg
2ee2c026eb4ec51d26acbf01f5643dbf
c3f2c369b6e0045b3861e597a87ea30d96211736
18971 F20101118_AAAMFD takimoto_a_Page_121.QC.jpg
76fa29f56410ea2e0b074f2007f83164
0a8a8e421672852243294ebf0b5f1f57dd9fcdac
17045 F20101118_AAAMEO takimoto_a_Page_102.QC.jpg
308a4f8ac7441e5b20dad25e9231d235
424eafd16fd218500b3cfa45cf524d5316285dd5
2206 F20101118_AAALZI takimoto_a_Page_109thm.jpg
3880e6acf51fed031fb669ebc565ed6c
cc45da595d3b8d59d721521f3920a7e8419427fa
3528 F20101118_AAALYU takimoto_a_Page_101thm.jpg
379d5e745768ae9509abbf6236ba4bad
37be6009fd9d64562611c4b0174a866d5f74c684
6924 F20101118_AAAKVS takimoto_a_Page_149thm.jpg
ee3c5f7d8d24ed2b7bcd53518d5b00b5
f0f3fea899013dcc2af88446bddc9595765d8522
2482 F20101118_AAALCB takimoto_a_Page_174.txt
c84c55f866ff486c783cde282caa4d57
123a7c091cd328c997325b6ef55c8dcdb2775b70
56328 F20101118_AAALBM takimoto_a_Page_016.pro
04f402aa92222c3206692f67b36dca88
9ab13beba2664555a0f2dca2345082397cb68788
20120 F20101118_AAAKWG takimoto_a_Page_089.QC.jpg
7fe6283f589890212d1f201bcba53069
4691bb88918bd7b354cc6fb66bfe6feaab39b464
34076 F20101118_AAALAY takimoto_a_Page_147.jpg
d359148767ac13f0d8bb276ca0d6c9cd
10f9104c853fe05a074fa55d6d8bcc5ba93a3e34
11560 F20101118_AAAMEP takimoto_a_Page_103.QC.jpg
48ecf6d2a9c3c7c81d581567520f2b91
73d10b947e8cbb80b789c09d491057328f930265
27432 F20101118_AAALZJ takimoto_a_Page_052.QC.jpg
dfcc9d5ea729cfed2c358cb4ed36f2f0
a7940df5bcfb59617a5f3606df052cd3d291bd99
3573 F20101118_AAALYV takimoto_a_Page_144thm.jpg
b9cf4da9b9e75c5114f48215b875f685
e2f83ccc30b93982cc3778cbcfbc89c6d9ce6e4e
259863 F20101118_AAAKVT takimoto_a_Page_116.jp2
6ba0e94c2edba2b6ff3fd691bb2bc2f4
00fc2365f2e85e8d221c128c9eb09b9d32c08306
444295 F20101118_AAALBN takimoto_a_Page_142.jp2
2e41068253374040c9028ec48a332890
1de44382e9df817d8a7436a7d6c229b4598fab2d
49716 F20101118_AAAKWH takimoto_a_Page_126.pro
cb686c38850b2706d3b8156f86a4f0a1
d4fe2bd9194622b504bd525134675a89635b8ab0
2171 F20101118_AAALAZ takimoto_a_Page_132.txt
20611eb2808023a6e6995fc62dd6b335
09d1f47a828145ca8c6264c927c9e704b3434f0f
5565 F20101118_AAAMFE takimoto_a_Page_121thm.jpg
90e56f2cfdcfe02fe5b0235c92553220
eb489d52aa4fecaf4ca2705573a0a2956c4d1c04
4430 F20101118_AAAMEQ takimoto_a_Page_104thm.jpg
52130b75039b5004686e0c75f26b31ca
dc19154f3f6e72a27fe0600aa303e6bb9335bc35
24756 F20101118_AAALZK takimoto_a_Page_151.QC.jpg
acc8bf589e668dec14949d2be5a88699
f14dbf34f9c155045b20daace94ed68cc1f4f27e
4184 F20101118_AAALYW takimoto_a_Page_115thm.jpg
f8d1624e5c8ef1db1f0c216baa060391
cdadd44f3be598601cb7f496c6696510671fd237
116213 F20101118_AAAKVU takimoto_a_Page_151.jp2
60f6ad4e5b8b1c657e94c9d8a63c1223
b0e362dbead9046d939b5eb10424e917f43bcb98
105892 F20101118_AAALCC takimoto_a_Page_013.jp2
c5e9f9234791562f8825f200ce99ac69
e48c66be85813993e099e806a91e98a120ff9825
7386 F20101118_AAALBO takimoto_a_Page_063thm.jpg
39f8860f428d63dfc9619a5a675bd972
87995a6786f4e1a279c8193c8e6aecab9b6b7099
F20101118_AAAKWI takimoto_a_Page_110.tif
98523214a431b1bf86b932d28ee5d302
eb2a491f68394844886aab233995856c338bf8a0
24093 F20101118_AAAMFF takimoto_a_Page_123.QC.jpg
e4c8b6e0c59cabc88cb2d04ed142ce4e
bd990a1f89c65ce9b73c798c342b8c0a612c848f
13721 F20101118_AAAMER takimoto_a_Page_105.QC.jpg
5a03ebc9f4ef58535d209dc6d4a1cf34
1bdf3cab842be50af573816c74e22b197be99ed4
16998 F20101118_AAALZL takimoto_a_Page_138.QC.jpg
74f55f670987a10b0b391fc7fb8c7a1d
08c3b419afeaf32991f05350364fa9f18ea7ddbd
6835 F20101118_AAALYX takimoto_a_Page_153thm.jpg
6fef60f9b610a4c2cb43e59b35011211
e7e27ba4bcbb4639bc2b5d5adcd6c9a930f9445a
1051967 F20101118_AAAKVV takimoto_a_Page_006.jp2
503ff696d98758f8a9f2d686f14fee1b
9e08111ec27e0738af247c8adaffb58e6243ad23
421 F20101118_AAALCD takimoto_a_Page_081.txt
afa33b798b1d74e1def83529727084ce
02b86016d724c39d6268cd0ce6c247ad75e1af42
6286 F20101118_AAALBP takimoto_a_Page_024thm.jpg
4d2ac4a5a5e39afe0d393970fe2dcf69
f3f874c2d56361cc71c705bdae5d94b58b787321
60811 F20101118_AAAKWJ takimoto_a_Page_035.jpg
c024d433acc05a0d3a9c429d776a5ba6
5c69887c69d8b1cb7c1cbf370c0a3bd42eb770f6
6656 F20101118_AAAMFG takimoto_a_Page_123thm.jpg
eb83efb581e327790f1073e62efbf76c
9c99624d1924ab776ad05b3474e4d1738b369ee0
4422 F20101118_AAAMES takimoto_a_Page_106thm.jpg
2e8683132027eeb8eeb1cf537d24c607
c24bfde517b670596c750c2e02513497aba842b2
15431 F20101118_AAALZM takimoto_a_Page_060.QC.jpg
045be3e4f980f684b1378251b12d13ef
57e8ccf48ab910dab2c6aba8f823d06c3a664b85
102030 F20101118_AAALCE takimoto_a_Page_174.jpg
4b0132cf12dc377b9fd72becca2cf00f
cce554abbe1c349ee6044561024329a875519401
F20101118_AAALBQ takimoto_a_Page_179.tif
9e7c8a8183e9fdce92d441c66d665b9b
50c270d2a33fde21548b64d68a1888741ce9f620
F20101118_AAAKWK takimoto_a_Page_091.tif
59239dc76adcebef970546dfd536b730
0fae9c7f863035aed4c186b60eae6c0d5c0e093b
7016 F20101118_AAAMFH takimoto_a_Page_125thm.jpg
4d5306d04d21b991194958e37ce7d16f
fbf3467b12c483f898928331a78cf9329c188074
16935 F20101118_AAAMET takimoto_a_Page_107.QC.jpg
39e9b79718409f22a33d530b016d5798
33eb1bf83f2f113abf0884fde9f3526f721293e8
27147 F20101118_AAALZN takimoto_a_Page_094.QC.jpg
1e69a2dd97e82612fc953b85dfd62387
9ae42d9c07eca5f0f4f7126b4d4ca6baace7acb9
6217 F20101118_AAALYY takimoto_a_Page_086thm.jpg
a9b47a04eff7d32a1b22aa8461d58148
336bce49a39c4dfb333706ac8a6786908db48085
11905 F20101118_AAAKVW takimoto_a_Page_113.pro
7c160a3efd9fc7c88d4562d4672f61a4
00460319f6d8fe5046a324bb72cde26509fb2f6f
6675 F20101118_AAALCF takimoto_a_Page_152thm.jpg
e4cfa021407257b5e725022b81b3c16d
ac9351abf9120bc669837f4c1893442a21524e0b
53798 F20101118_AAALBR takimoto_a_Page_017.pro
2167b40809960eecfdcb707afe6aaff4
059057ccf66eca5d2da1321ed2d1ec7c368cac6d
2133 F20101118_AAAKWL takimoto_a_Page_073.txt
e857eb6d454fb0cddc2b37c5ed35fa7a
dad3c17bcf08e77e16757123c89a951839e4b4df
25153 F20101118_AAAMFI takimoto_a_Page_127.QC.jpg
755c689288090ab1dff8e2a2587c25e4
4620fa91475347ae8b10ac2582d40a42666a056a
4942 F20101118_AAAMEU takimoto_a_Page_107thm.jpg
c3cc4cd5789bf64c7b2279461e334d11
02eaa8fe89a26dbe2ed0d91096e1c61bf5274150
24513 F20101118_AAALZO takimoto_a_Page_004.QC.jpg
dc9c283da10d30ad4e782e846d5fc2e2
6dc4f1ee783d07f9a23956afc6cc4af08b119747
9964 F20101118_AAALYZ takimoto_a_Page_184.QC.jpg
275e478eeda558fde058c7a693b0b665
ab166aea3b03988c071dde112783445bb92c21cb
71180 F20101118_AAAKVX takimoto_a_Page_177.pro
15d714e225e3aa54cb3bd9b077309321
14d47122b6fa15ca9974f7ee7f0369483387e606
F20101118_AAALCG takimoto_a_Page_137.tif
e634429f4ae381603ca0bfd5e259b0b2
9481b820fd18e306584277bfc87a1def2208d640
15529 F20101118_AAAKXA takimoto_a_Page_110.pro
0f7e389e7ad152f174b3d181052b71d6
129e7805dcfbc1862bb6bee2b336d5b42e969b68
26263 F20101118_AAALBS takimoto_a_Page_072.QC.jpg
b086a381864b4fed6c549722d1bad249
ef4fd9128502df0f252f03afe5d3563fc76206de
74989 F20101118_AAAKWM takimoto_a_Page_029.pro
26454b253da6577e232e661c40b19ffb
814b4ef9a754cb1b44b74966b390cbf927c68376
26173 F20101118_AAAMFJ takimoto_a_Page_128.QC.jpg
492f34634588ea6dbbc0e1a2d99007f9
a468818dc89c41453354782ab84c988205560cee
6581 F20101118_AAAMEV takimoto_a_Page_108thm.jpg
49174e41818b61a5f51699f7e959f127
69650687f31564b33188b2b26efdc8b07c85e629
9536 F20101118_AAALZP takimoto_a_Page_141.QC.jpg
8ea9e486fb1cc7d7ca7542163cd9d5d5
8f586bb53d8845b114ca60ad98d6aff2c0719699
1051929 F20101118_AAAKVY takimoto_a_Page_066.jp2
599920c2953097721aa57d7db334a0dc
1af4562efa43407cd393b81633f19173dbe51098
F20101118_AAALCH takimoto_a_Page_003.tif
074f6e55268af5dac061e600cae2d8c1
17aed4a7ae60e67f3195d25d8d324d4fdd5eadd7
73083 F20101118_AAAKXB takimoto_a_Page_027.jpg
0a0ea5e7fd943c2e48c28a786800c9dc
754cd08c47aa3d805c5027d49c6f7cdcabe23000
30124 F20101118_AAALBT takimoto_a_Page_141.jpg
a5ab50b11617449e7be36f9df220177a
4c559b0fa7aa5591763861f4b43dea4b5ae8bd70
81569 F20101118_AAAKWN takimoto_a_Page_117.jpg
32a6e1211ef3da488e0865bff4341ae9
3621543801f16c389a3f2a6253bd739df102f503
7134 F20101118_AAAMFK takimoto_a_Page_130thm.jpg
2d797c6c4ace590266858adb541024aa
004a9e2c49dfc9e8cb655d0062a29c38c0f5c935
2229 F20101118_AAAMEW takimoto_a_Page_112thm.jpg
c1c755ea86d3db9f857f6fda0787e3d2
a7efb6f2fa92cd951728040f3ba4cc17cba29289
26189 F20101118_AAALZQ takimoto_a_Page_037.QC.jpg
67ab964c29d34d1c67a5b6098b795e32
bddfb6ef3aea684148dc9c3f074743842e3ea64d
28974 F20101118_AAAKVZ takimoto_a_Page_059.pro
d67f8db64ca5fd2b8cae1862747480d2
a8bd69cd9353167639c09b7bc59343f9c2cc4887
76248 F20101118_AAALCI takimoto_a_Page_046.jpg
11340784f0b9d458a8deb1962bebae05
55c8bb4da71b20a9145deae49c9323d722753ccb
78604 F20101118_AAAKXC takimoto_a_Page_099.jpg
7a2ef656b9d39182122b3edf36d089ed
15cc65ea3680e48411e6f051986e22066ee7dc06
35166 F20101118_AAALBU takimoto_a_Page_142.jpg
374eb1a484c40fe5cb9f7472a941198f
3d00bc2e8c811879d76c383a43efd5aa5f2779d8
53399 F20101118_AAAKWO takimoto_a_Page_154.pro
221cccce7a10795a9248a6dc8b35b6de
9bce6602c4538ebdfefb7daefde98c7a5c983b6a
6770 F20101118_AAAMGA takimoto_a_Page_154thm.jpg
59ad8bee503dd319d2e4b2fb6224b25e
c6046173950b620fb491e611e6290d560d7338ca
26802 F20101118_AAAMFL takimoto_a_Page_131.QC.jpg
6ebb9c0aede3f4da8d224481d622e846
b5c0bf7ff2db0b78399b3694d90b95f87aa60569
13135 F20101118_AAAMEX takimoto_a_Page_113.QC.jpg
dcc45324cd3b5a19535f818ea51be09f
268cc4331fdf71fd0c3f200356094589743334cd
3530 F20101118_AAALZR takimoto_a_Page_167thm.jpg
0a10cb43d09738280ac499156830b1a4
f0536c682a188e804e7186b9af74b345fb7a61f2
53897 F20101118_AAALCJ takimoto_a_Page_095.pro
be0845bbf49a225a1700a72bb1b7c6c4
b751c37dcc87057106be7bd8e569fadc2f3c76da
110880 F20101118_AAAKXD takimoto_a_Page_040.jp2
8034f4f0fd736ecce0cbc62f4aa7e4bc
0b36db5bade996be487cab4cd476c713613f2644
676 F20101118_AAALBV takimoto_a_Page_145.txt
02f5b7d87a0e25d7f025cfec0a354843
68c6a2d4e43f903bb0ab064b92e17f5397502dd2
6598 F20101118_AAAKWP takimoto_a_Page_015thm.jpg
4ce6ec98139dffbea1142d703e82ff75
5451199239a964916c5610927b192af4973af5e3
6814 F20101118_AAAMGB takimoto_a_Page_155thm.jpg
15e68194c1aa38f87640f5f7b122bcb8
a1c7e87d109311f1828ab4c7ee97b64586ea6503
7373 F20101118_AAAMFM takimoto_a_Page_131thm.jpg
4fa5913ac48ed2e038e352446067d474
5a1915595af635511508de787eefe973aed9cc31
4032 F20101118_AAAMEY takimoto_a_Page_113thm.jpg
8fd93b03dbe6a7a2957cd8cb9bade212
b73b8d72fd8d865f32fa5e0b25dd876acedc6a89
10260 F20101118_AAALZS takimoto_a_Page_147.QC.jpg
1e5f484a7728fc8a5965a6d782fdaee4
186f5ee7ec5a3faf2209887347a8ab355d797e64
F20101118_AAALCK takimoto_a_Page_111.tif
48023e0f177703933e5a198f5e337ab7
eb1c1c09ff45f8e3493735901d2258af28456421
89718 F20101118_AAAKXE takimoto_a_Page_029.jpg
1f649b187571186d07738014ef2332cf
49adf9c56a19986381d921d2028fe3bce9156784
597 F20101118_AAALBW takimoto_a_Page_144.txt
3c9fc49a8dbafb27b34364a2f5a17752
c594a11bd5f11a00911f25413df5cbae05faf0dc
766 F20101118_AAAKWQ takimoto_a_Page_163.txt
743682972ffefb2cd265c9e2d79cbd10
47ed78b935a1681b918d95bed69909b96f2dc9dd
5340 F20101118_AAAMGC takimoto_a_Page_156thm.jpg
3cb59ad6285075d7b30d7e7f1e430296
e0a3525b5f35efd83c490f25dd349ffe20e55a20
27737 F20101118_AAAMFN takimoto_a_Page_132.QC.jpg
4e710cae54c2cdb9ba770331b71c504e
2deb9a7cf554d37d2112032cae63d109f85a75e6
6836 F20101118_AAAMEZ takimoto_a_Page_114.QC.jpg
1e7118a2bf0e50aad0322775313526d6
5aeb2058f00f87d4f68c97a6e51ef163294c80cb
6452 F20101118_AAALZT takimoto_a_Page_119thm.jpg
7b28dfe1e83c4b743e92b52e4137e880
182ea5016132138aea0818dd41220f47e40c67f6
2165 F20101118_AAALCL takimoto_a_Page_133.txt
86aebb7de7775b5aba9035ad3b9999b5
bdb2da31091244e42778128cde128b77dd6b84ad
F20101118_AAAKXF takimoto_a_Page_020.tif
361347f62cbd68f7d1062e7862e24d82
da1d7072d2bbe5b85c936e96fdc4fb1da9449b7c
544932 F20101118_AAALBX takimoto_a_Page_139.jp2
a674f17bbf524a9506e37002643bf880
b066097e74e1b6b6482064ab4f55b1b6ead3705a
15567 F20101118_AAAKWR takimoto_a_Page_164.QC.jpg
54c3b27b21893fb2ca8e7e3df6783221
2cc213cf88bf32aa8afad7110494ce9db07bb860
69013 F20101118_AAALDA takimoto_a_Page_075.jp2
3f9d605f69b888654429929e1c4afbff
bc1ae366f9e244067dd4f88d39d822526b7f54c1
21465 F20101118_AAAMGD takimoto_a_Page_160.QC.jpg
643ccd3dfc5bc9ba049d501cd1df45af
c8f637ca4b0c6f303c3c749b77a39e0e8306b3f1
7429 F20101118_AAAMFO takimoto_a_Page_132thm.jpg
571d3cc7351f112bd6aa601401bd8c3d
ace74c53a0d69ec1bd728d3418beb0af5bf0387c
1340 F20101118_AAALZU takimoto_a_Page_003thm.jpg
2323ceca022d126247c77d70fb786000
5cbb468b0e22bec14274ea6b2e97cca51514419c
7215 F20101118_AAALCM takimoto_a_Page_072thm.jpg
12b7537b310b8907b6eda539f6fefa52
26a5ec1cbaac22f15c5087f071b3080b03cad8cb
61432 F20101118_AAAKXG takimoto_a_Page_174.pro
23180f7a7c3bd7bdedef189901375bcf
69b4148e046ad40ce48133e12f6585671f329572
2471 F20101118_AAALBY takimoto_a_Page_178.txt
279fc29a3331862beb8ed71fedb6dc03
a2ab40676d2d1258c4e11fed913460384ea75d9c
F20101118_AAAKWS takimoto_a_Page_066.txt
f9285ae6992a5e47bf0dd0107ac8fb71
3804f8f9148f4b2983826d9c751c793ed6b2142e
5855 F20101118_AAALDB takimoto_a_Page_007thm.jpg
041bace7a2957f49f8b2a45bcb679526
e46d072999455b03d659e38e12b09f50e9590063
5887 F20101118_AAAMGE takimoto_a_Page_160thm.jpg
a1bafd1a36a6b5bce9077c86833b0d06
cbca7f2caedd6773fe0cef73b04d69a790ea2b51
7709 F20101118_AAAMFP takimoto_a_Page_134thm.jpg
95adce90b322ff4ff2d09795cdb87547
4ca33daa4e8128710cf3bbf32dbf9ef5c31fc124
7135 F20101118_AAALZV takimoto_a_Page_083thm.jpg
d4be4e407582b156cb6e5ff6d6f7c4ec
e2c30af052fcdce17b8a1b0c6c5397c6db6b6bac
43198 F20101118_AAALCN takimoto_a_Page_086.pro
643c828d8c418cd17db394e7b342dd0a
e6c2045d08b205ecc02e8ee90c99cfd4f7956dd5
46052 F20101118_AAAKXH takimoto_a_Page_031.jpg
b1942b27154253a03775370122536055
267c94d55277d37f5a4a52c2f83e64f914497077
2100 F20101118_AAALBZ takimoto_a_Page_042.txt
49f74168572fc285da533959e27b4caf
504850adaa3d06d3cd0a9c12f7a676bf09b36380
F20101118_AAAKWT takimoto_a_Page_122thm.jpg
a17c6f5af3fcd5b02b75d5107ae651d2
e5e1ea400fc2d79bcb8f35e00359443c90c6aa58
79196 F20101118_AAALDC takimoto_a_Page_037.jpg
73a279cc2a7763715bcb46bffc3db7af
305711a9707f1e7575b324e0d3838fee1e2fadb4
27325 F20101118_AAAMFQ takimoto_a_Page_136.QC.jpg
b8a5081c9b2629ee05423c532471ea30
57cc19c5810e9bd698766a3d3ecf08baebe0bb48
23181 F20101118_AAALZW takimoto_a_Page_024.QC.jpg
9c6bcb1ffa03a78acfd04c2e143f38f1
96533ae732f54c4d122047ca5e264e2c91489efe
F20101118_AAALCO takimoto_a_Page_060.tif
8c965e24048e6cb296a033ac5ae617a1
49439afeeccbfd41f02ec795826dae0436f04779
56213 F20101118_AAAKXI takimoto_a_Page_130.pro
4db08ec5607f9fb92bd523d164ef8d1b
6aead69106ccbf0e5d548965755309c7a368c041
38059 F20101118_AAAKWU takimoto_a_Page_138.pro
cdf0a1415e771e1caff2b868295f03a7
3f9497f665c23cbc03d43d38841fe69601f071f4
18438 F20101118_AAAMGF takimoto_a_Page_161.QC.jpg
0aa27eca1fe55235bf6349893f654e7f
3cb72d0e2b4a8a3798e31cdc4bad0738f7a32a2b
10172 F20101118_AAAMFR takimoto_a_Page_143.QC.jpg
e3eccc95cb1e4378e4f562a17d202e7a
a3eb8a1179fd950b185c928d40ecee548a77518f
26360 F20101118_AAALZX takimoto_a_Page_021.QC.jpg
aee94c43a07e2278ecc4a9fb2b16659b
7d99982d6d06839d84a9efefb95cfe0dce2c9925
7489 F20101118_AAALCP takimoto_a_Page_097thm.jpg
3cb5a3a8f52a97f444a1eacfe9b4875a
520bdde1389d98da7ee32fb4bcaba419d5a71d61
119856 F20101118_AAAKXJ takimoto_a_Page_128.jp2
719b6ffc846c1ff05a80b8bf79470762
1bf3a0cd6835bbc938c5c862b44c6f63c21f874a
1293 F20101118_AAAKWV takimoto_a_Page_107.txt
98894236913012aba8155754050179a9
2fbff015a6ff09e05aef859169c471c8a6d1845b
4923 F20101118_AAALDD takimoto_a_Page_157thm.jpg
bc5b476344d433f4a14579d08d4fa20c
ca8ccad989fc9f074b6b5f83399c8f5a3324e2f1
3458 F20101118_AAAMGG takimoto_a_Page_163thm.jpg
4a3ad54db0ba9539cfa4cd0856387da9
b55d535fd57ad267cb11b8d7266063899a9a5365
10830 F20101118_AAAMFS takimoto_a_Page_144.QC.jpg
4314195b87d7a570a3323365c806aa37
74b43afa348d9ab306755b8e4fe54eb1da5c246c
7563 F20101118_AAALZY takimoto_a_Page_098thm.jpg
71389056af73d2b3ec7f77fc9a3a8390
7e837c8e4435e325ff6fa2952ee775b17e9030d6
22763 F20101118_AAALCQ takimoto_a_Page_159.pro
d0674255e1da4534e1d228c990765180
aa7abe59fb2a3e9d38b3529f50d778563ddf452b
F20101118_AAAKXK takimoto_a_Page_008.tif
cd5603faf3fd058f85d0ec27f991b3d9
e10cff924199f0b97e111a55c2cabdcd8edaedbd
25362 F20101118_AAAKWW takimoto_a_Page_178.QC.jpg
a88ff3d6e2df6ae4adeb1c6967226077
7226b3fb7892128804fb9b6ff9e415e777518a82
2129 F20101118_AAALDE takimoto_a_Page_052.txt
e54596799e9476189434685d737ba9c9
1472286918f417fd5bae092db872a9568d01aa62
4830 F20101118_AAAMGH takimoto_a_Page_164thm.jpg
f13590ffe7ae2774c9313843ff4581ab
20c3729c5f214545dd57b0b01667ea0f7c1e1c09
3604 F20101118_AAAMFT takimoto_a_Page_145thm.jpg
1a5e4e486d6216421c6488ae48bf34ea
63b722481b960906ff7a103cecdd61d10d66a1f6
47497 F20101118_AAALCR takimoto_a_Page_013.pro
73a9b34640d0e60ab1c7ad213d5cdc89
8988b602a211c988cd79dbd0c7e59c303b9cfa7e
1051960 F20101118_AAAKXL takimoto_a_Page_177.jp2
a304e9e541246994db5fea8f0d1fc589
c2ba6b164a6039a528543469e8d31a7b6e5553ea
24734 F20101118_AAALDF takimoto_a_Page_154.QC.jpg
53aa071e62796b5cf145eeb82f185e4c
24f0e1a89cd1fd2dfa79b37f0cb7727c99d78891
16424 F20101118_AAAMGI takimoto_a_Page_166.QC.jpg
3b147cbdb2d5cb51ae5790deff533443
85eac70e1b7436a28e48b6b86791cb0013847143
10834 F20101118_AAAMFU takimoto_a_Page_146.QC.jpg
cdb9accef4b26435a160f3be4b53678b
1c7a9dde6500fd389b63428ed91ec15a84c34813
6711 F20101118_AAALZZ takimoto_a_Page_041thm.jpg
16676a014d359b63832527c78f2da1cf
e6e8f88be508c43b64ae7dce105ea9cb3d3a2523
4776 F20101118_AAAKYA takimoto_a_Page_102thm.jpg
ff5fd6820c9fc8b687853b0a81ef2400
576cfe3ddc06690b6b6f8cbaef70bd1aec4ae89c
6897 F20101118_AAALCS takimoto_a_Page_099thm.jpg
6e2ad15fa0bf4d3a534f96fb2c050613
e8d9d75ff6592cbf663d03b649b1e3dbc59ffafe
57288 F20101118_AAAKXM takimoto_a_Page_096.pro
da8ce31d88d407f36b7db2c3a33f6852
2adb7d262926f292c6a16add389d8c3aa92588f7
1051978 F20101118_AAAKWX takimoto_a_Page_133.jp2
a27a02b98a967e34a0ccc7a3f9fe4968
e76bdca525602cd34cb6a0ca38a067b58dbe057b
10535 F20101118_AAALDG takimoto_a_Page_145.pro
f3033077ae77f0acb34c593afefc388a
5ec3ac91118bf156af3fe1107aaa48a8c83e077e
11750 F20101118_AAAMGJ takimoto_a_Page_167.QC.jpg
ed25ecf817d087d308c62661a13a3cd5
ad7e4ae2936dc3b833e34d08548f0e8da9cf4bf9
3356 F20101118_AAAMFV takimoto_a_Page_147thm.jpg
7ff16ff2bfe648ef77c6a37875d69890
b33f33fda65295a5bf2b52a2daf7705b72b46d46
1051983 F20101118_AAAKYB takimoto_a_Page_050.jp2
a6e709df470fa64d02bf583c974e0449
63a7de6df6cdde010a3dbd4c21c99e0f7bf3f237
F20101118_AAALCT takimoto_a_Page_022.tif
003651ed4e8502379a678988fe670f43
421b94f80aea25a45e5721c5e677546bf375a3d1
1216 F20101118_AAAKXN takimoto_a_Page_075.txt
9f43e1737a454b68556471511426b9de
ca68e210e7ae7f51bbb2f50ebae2f3a4a9355e39
7136 F20101118_AAAKWY takimoto_a_Page_128thm.jpg
beef436977cb076005f73dc0b51d35a5
2e9759f5f0773515300939dd41e1bfd7712e8463
1910 F20101118_AAALDH takimoto_a_Page_067.txt
c21f7844bf32fd98c8b5a7535075b522
d461e71e34097801ea98a5e514e4062f33163d50
17928 F20101118_AAAMGK takimoto_a_Page_168.QC.jpg
a8e555a0e89e84de1a6ed6a5b1912035
1ec420ea5b0374c54278673808b6bb2613b2f432
21836 F20101118_AAAMFW takimoto_a_Page_148.QC.jpg
8619c06de3c9f860e52b26746ae5407a
fbadc69706fabe1fbee0bf313ab2133cee7208b0
7656 F20101118_AAAKYC takimoto_a_Page_174thm.jpg
39a146dbac9024504c9d39eed51c6a18
a3bc108a9382bd3471318f3f22d5555e2f8838b4
F20101118_AAALCU takimoto_a_Page_046.txt
43b8b8e8ecb3cae2e6766acc665fea17
f63df013e783dc72190795c78c87227f1e2e4b89
76424 F20101118_AAAKXO takimoto_a_Page_125.jpg
23cf840ba52676503e78ede12fdb6105
a7653fac3b203094ea6f91605a35582e2f2f836f
5378 F20101118_AAAKWZ takimoto_a_Page_080thm.jpg
1d9acbbdedd378d49f3e5aa15c15d2bb
dffa39bde91f841c107c8e26f0b793a46464575f
8937 F20101118_AAALDI takimoto_a_Page_081.pro
74464bcd7e1b2ade33da3321fa777ae8
d78d6bf01638753bc70d2c6327dbe5b9c228ab0b
25476 F20101118_AAAMGL takimoto_a_Page_171.QC.jpg
ffb7b25be6848fbcb9c9762d46b6e3c7
30de35eae7228c0e7d0d8c0269e0fd1890b5e70e
24400 F20101118_AAAMFX takimoto_a_Page_149.QC.jpg
a258f8a796a090dc2e1e1d780a04d32b
4d714ff9e9978bb34c5da47af32a49d2f7b0e324
2127 F20101118_AAAKYD takimoto_a_Page_129.txt
8e29e5072fd5b4afaa0c36b07c261dbc
010daaeaed2f34723f5905b80f8511a9688546e8
86377 F20101118_AAALCV takimoto_a_Page_121.jp2
9f60136b1e9dfc8dd552ea50fa3b34ce
7f5dbaa57523c3c484d89360384ab5991b96e084
7108 F20101118_AAAKXP takimoto_a_Page_084thm.jpg
29fb5a816086f2576ae19b6e696dbd88
4f1ab8e5c146dc0454c33c79a7817172ccf749e3
6206 F20101118_AAALDJ takimoto_a_Page_069thm.jpg
b8bc52e24c691769078a4c29b0172700
95d72580adc3c33db869084f074448f72019137d
29044 F20101118_AAAMGM takimoto_a_Page_172.QC.jpg
ee1a6107d079674061e0ce09b4249877
de24e3392a6190a52c787bff975ae1fb18e23ebc
25796 F20101118_AAAMFY takimoto_a_Page_150.QC.jpg
2409b373ec3377d6997dd05ac6a56603
46b296490cd2d8a9e530c8b4c5fef06a5240c089
6565 F20101118_AAAKYE takimoto_a_Page_085thm.jpg
f198421b4f2317a32ba3f5cd0292f847
df6b0398cc98030a9b6c1e1315b6e4ba98c2c909
F20101118_AAALCW takimoto_a_Page_174.jp2
c986657728dbdfcfe7f583a238aa4819
1cdd5522406b1fb081ff410cd9f5c80f3e5171e8
2036 F20101118_AAAKXQ takimoto_a_Page_072.txt
042466311213d34e542114248bc13207
c886852fa98a1ec3e1a926ed808af87dd37dbb45
49015 F20101118_AAALDK takimoto_a_Page_036.pro
ec6937cc098a6209b3c0ebc417d17d07
9907a16f79b317d3a0bf5ce07ec6b3c806680c1f
30070 F20101118_AAAMGN takimoto_a_Page_173.QC.jpg
1373de0dc10cbb2911b103ad3c2cd391
3461af21b90925897ec62163ea26db298d2a1222
23650 F20101118_AAAMFZ takimoto_a_Page_152.QC.jpg
91dc73f14450c62b75c30b60219d0b9c
4ff4ce715c3bf1f9acc33f7225215af66c21004a
3546 F20101118_AAAKYF takimoto_a_Page_082thm.jpg
3ee9b7b55cab83a39e14c3b5f8cf2c09
4a24543632d4af39a5f6b1b09e65e9c1882f4234
15511 F20101118_AAALCX takimoto_a_Page_079.pro
9fb375a82959d7d67df9b3732ca9a349
cecf39bd582a1cafd1ad272056c582e087e0665b
109105 F20101118_AAAKXR takimoto_a_Page_045.jp2
3b5ba8159fdbdc0d7c5f396a6270d305
bab835b482f311860b946513feee6fab1edf4672
1051976 F20101118_AAALEA takimoto_a_Page_093.jp2
a5e3dce0c9b6db6bd1263434c60775fa
956cb6deb32fbe7bfcadac2855c02742887df3fc
F20101118_AAALDL takimoto_a_Page_176.tif
cbada61a59d1aeabfe9c0be245035e12
6106d801806e9858032a3a0992ca8e5a9f241f92
28481 F20101118_AAAMGO takimoto_a_Page_174.QC.jpg
b43ac9cee9be4c0718a3324d90eb3dcf
0db1b0503a12ac6a09a6db983a463122ba48f14f
25966 F20101118_AAAKYG takimoto_a_Page_135.QC.jpg
056ca1d1ac2c714a095ed132d004f7a3
3219304cabc240f6844a50addcc77c4738946bc9
14535 F20101118_AAALCY takimoto_a_Page_028.jp2
9717a4de79fe3119b2e4307820c53869
bbadcfe10e7051e11b1ab92d1c6c5711ef9f8d73
F20101118_AAAKXS takimoto_a_Page_094.tif
eb77a85c4a35fe26e5f7ebe4e412fa3e
96fde76b986fde573937986a259e85219c41e4af
2489 F20101118_AAALEB takimoto_a_Page_175.txt
d02bae2b4a48018e3ed60e6db41392a9
31be519cd4b2d2013fb9bff4ce42e07277d75e78
F20101118_AAALDM takimoto_a_Page_024.tif
6194e0430ba6cb690eaf53288e3517e0
a6515d445d66ac9db638342629eaa383ca306044
7651 F20101118_AAAMGP takimoto_a_Page_175thm.jpg
8e2c4e9d260354dcbda2d7a40d65fac4
6ffbb6fb58ece45674734b46d134ca886e333a15
2080 F20101118_AAAKYH takimoto_a_Page_013.txt
670ebf17143e8664b6d0cc0a5452ecfd
20cb87228234e809859d0f74bdeee4c46901dd24
F20101118_AAALCZ takimoto_a_Page_132.jp2
d358e310f31cd72ea45f7d723513c98e
b154ce0e244bab547be059ce09b482fbcb9fd7e5
43246 F20101118_AAAKXT takimoto_a_Page_090.pro
31883255182ec50cb1c4708c92925290
ed7b1378a552a61e16ccb82b96fedfb34dab71bf
24475 F20101118_AAALEC takimoto_a_Page_043.QC.jpg
fdbadcde9fc393bb6463c9773ec7d98b
137e1b273204014fc49bdf10777164374087c832
2197 F20101118_AAALDN takimoto_a_Page_099.txt
98dc3ba9161a565e5180d526fe471768
234ebd9cc3a975d516715de658a749f1c0f13c5a
26772 F20101118_AAAMGQ takimoto_a_Page_176.QC.jpg
0b2078d1e0e3531a37b4dbebc9403efe
7fa4c856adad80494bbb58ebf5879751c1c40f07
670 F20101118_AAAKYI takimoto_a_Page_079.txt
c65b6bd71ed9c3386f4c00c5ff4fa6ac
7fa32664d107e5108d75b98e68ca5cf443e427dd
443273 F20101118_AAAKXU takimoto_a_Page_144.jp2
142d715d9f25c402ad2a8135fd869d48
11e6dee5db351096c5f6bddd04632e3803e13726
F20101118_AAALED takimoto_a_Page_093.tif
1bd5043353b08a6d1c0d5cbc396a7400
f6cd9d7452830f33d701c5a4023dbd23af80ece5
2023 F20101118_AAALDO takimoto_a_Page_123.txt
1868c37fe72dbc179099b9c4f4edea8b
7e3bd44575262c3f3029d3148a3773844a654b4c
7765 F20101118_AAAMGR takimoto_a_Page_180thm.jpg
5160c4c177c11de6b9c2cb9d25318bb1
dfbbcc63f5665d7ce671aad71ab249385fac7874
50544 F20101118_AAAKYJ takimoto_a_Page_020.pro
ee0f4dd7008dfd88cb776570ac60d1b2
7df9eb7711cf832f78aa8fbffbea24758d556ef5
86031 F20101118_AAAKXV takimoto_a_Page_129.jpg
59bfa0efdb6a6dc9561a01e50c3610c3
1ddf00f9ef5821d7f7d1c22f0a1e0fcf6f774c0d
43873 F20101118_AAALDP takimoto_a_Page_163.jp2
e706a4fc7e8fc1b5b4762bf1f645b5df
e8d0070c6ae06a69f0355609231f2b49e6a414af
84964 F20101118_AAAKYK takimoto_a_Page_006.jpg
4abfe08c439ede07f8a2cfb93503b276
983389cfd4f239c019b0f7b3fe85af59fb46c67b
1051984 F20101118_AAAKXW takimoto_a_Page_096.jp2
b2b699a48d110238bc43a6e78c704b62
44aaa7872b31e402a7773085c95706c882f58327
24788 F20101118_AAALEE takimoto_a_Page_038.QC.jpg
1e333b447b70f191be8994473dca9bc3
3f5146c7ac0ab1820ca9065426bf0756e05d7187
83431 F20101118_AAALDQ takimoto_a_Page_025.jpg
a5244ee97fe5b407662f707f6cd6b72e
12a5866fa3fa99c3f5ab8f32e00aa6b74a50edd4
28804 F20101118_AAAKYL takimoto_a_Page_074.QC.jpg
1b49dfa2c6e469ed00205aece0ba7da0
c0ea55c32fef3d3bf8c4b692947b4352ac07a31a
F20101118_AAAKXX takimoto_a_Page_168.tif
dafca85493bcf7c936d20fc9624a9dbc
5bc740b83ae3392bfd558f6e8b83642bf9bc9784
58263 F20101118_AAALEF takimoto_a_Page_121.jpg
607f173cbf27b563ae56f1dd06406529
75659a6ce2f5b541fa80c1d2c4b7920cda482e4c
2536 F20101118_AAALDR takimoto_a_Page_011.txt
e679d39b464d96b8b6733d0a21334c88
2705f4d7fe46b3c880be5930fc298f12fd86f4df
F20101118_AAAKYM takimoto_a_Page_157.tif
31017a971926a7829590d8cbc4d94f40
5b0ad92df31d13926e38861bed3d1c6aea774415
7875 F20101118_AAALEG takimoto_a_Page_177thm.jpg
0ac81062647787ee01da5d6fec2216ce
ab91a5124e431d227d664ded3a2b2941ebe149d4
12486 F20101118_AAAKZA takimoto_a_Page_115.QC.jpg
0991db295dc3cc706a1397af699ca7d5
48a67089c2f7ae49d2f0780ea76d8737966d117b
53223 F20101118_AAALDS takimoto_a_Page_021.pro
0d3bbd9651ed941d0cbfd3ff35c24fa3
ba7428b55ef82ac07d67a1c7106ba0b402d59c56
F20101118_AAAKYN takimoto_a_Page_026.tif
c53d7b5633401c0beeebfe2a30a3f41b
821ba33b3e653fe382db36b8131934834aa1c4be
F20101118_AAAKXY takimoto_a_Page_032.tif
0f396b3411cc5ee3a351744b5c46bd97
4e2729bcb495cfd5ea45a5bf4aa3379f46daa802
46403 F20101118_AAALEH takimoto_a_Page_100.jp2
20fa9e29d32b3e30cce6cf1c979ceb1d
364d771322d8aa8eae0b10d85fcd2d7d4f96c7f9
1051919 F20101118_AAAKZB takimoto_a_Page_140.jp2
558d69d8aa69d765aa398418e5826554
43d79cb1e21217ffd76cdcba822d4166f9d53f7c
12175 F20101118_AAALDT takimoto_a_Page_019.jp2
0bdf29934ff69b32bb57713c60886a32
4efd54b6bdcc0ca2ef0065aa0f9af8fa36b17719
7581 F20101118_AAAKYO takimoto_a_Page_017thm.jpg
4867ab3a1a2c4cb0af4889616e034fe5
f53a7d4296c8176fc81baeb1966cf9837b3d8815
4640 F20101118_AAAKXZ takimoto_a_Page_030thm.jpg
f467ac197b324f2c2ec4ba870fcf52cf
a36ac66497bed57fa4e2e04f0e07faba1bb0b0ef
75690 F20101118_AAALEI takimoto_a_Page_042.jpg
9e9ce9a4565c92227d8c9d238e123c29
2ff1739ab4040fafb6b32533edc5dccf23189e7c
52281 F20101118_AAAKZC takimoto_a_Page_102.jpg
f401a5d41b3859d0846097849a2abab4
1fc42b842b7b4e45777be765486b3e870495c74f
107695 F20101118_AAALDU takimoto_a_Page_173.jpg
f5cbd7604fc11d2049ccef28fcc57089
f5bfdd2ee265724f0309e3761505aba5bef1b6a5
7204 F20101118_AAAKYP takimoto_a_Page_061thm.jpg
fdf7bc6ea07ff1e718c59ec585e45350
d57d9fb3b4ff505aa2b4d6bea94186f397b1c2ea
F20101118_AAALEJ takimoto_a_Page_100.tif
ce6f5958c9871484848801ac19e179cf
db15324725a92508b1faf0210b19a0ef0e409d1e
47790 F20101118_AAAKZD takimoto_a_Page_169.pro
6edb586410f7947adf785acfbef46444
ed58934956a35042e444e6c18a1df8c976985cdd
9715 F20101118_AAALDV takimoto_a_Page_001.pro
7ed7289db22aab262d92f83657c907f6
a9f0daf4b4ced72acbce8cd557a28b3f5d4dfea5
22704 F20101118_AAAKYQ takimoto_a_Page_126.QC.jpg
eab69f87e92593e92802a3489053818a
3ad7a4ef975ef983c72d5710472898d5156ad14d
79088 F20101118_AAALEK takimoto_a_Page_061.jpg
1adfb9e8caeef3ab01b0b799265b2e8e
2ac923ba93bcf6dc0b9b1de0c19e0388def3cff9
43163 F20101118_AAAKZE takimoto_a_Page_059.jpg
3dc20b8bcffb98ce73b397d555d1428f
4fa1e1691274f8e009ed8d5ab08c34cd06263c29
88868 F20101118_AAALDW takimoto_a_Page_133.jpg
2ecae2ee70c25fd8bfc666fa2e6d190e
e0f9a79af8bc2f8a73259dddae0fded17e94e102
2248 F20101118_AAAKYR takimoto_a_Page_093.txt
287d10e9f975cdd471de0f03306cffed
6cdd8cad33b7ae0d424dc234032664b85e6ae363
121960 F20101118_AAALFA takimoto_a_Page_016.jp2
518bce71554aa3efa900482ded4ec725
a1ec8bfc6d8987b67044189c3595c334ee5f15b1
4438 F20101118_AAALEL takimoto_a_Page_075thm.jpg
dc1bcb0828c5be71b44e96a7714a5df1
28096afa2b6c7b9d87c1e9f8ac4b0728a7c981e8
17801 F20101118_AAAKZF takimoto_a_Page_071.QC.jpg
558b559e5499373c8f4b9d57ae32b5c0
b81b94973e5d14a93b97fa0a0ffd7057ef74e3b4
52366 F20101118_AAALDX takimoto_a_Page_025.pro
0fc35a3b51501119ee1bbcb6e78ecb55
a2f63729c351c3c2b3b33d673dc3698fc4703069
53820 F20101118_AAAKYS takimoto_a_Page_092.pro
c48fa9e4005b13034c23740beb9dc7f2
a97cf9519af8238d54bcf7a9902092bc9da8a0bb
F20101118_AAALFB takimoto_a_Page_016.tif
4c711d9579ca6304a96bf08cd35da7aa
0787ce2c61d550e15be16bb7ce731c2e735676ec
F20101118_AAALEM takimoto_a_Page_008.jp2
1b910fab965af0f49aa79ce824f5b426
482f8c0627f61239edf33026f7cbe0da07f43f0a
25874 F20101118_AAAKZG takimoto_a_Page_061.QC.jpg
90893d50d94910e1154a92d1b2efd70a
22dd788bfc2b031836518a1f7fbee9f3cb01fcec
2019 F20101118_AAALDY takimoto_a_Page_045.txt
7bd652dd5b74a6533ed519a3a02d8f31
20ce0e859d98785c794c5af7f8aba1678bbc4f02
17255 F20101118_AAAKYT takimoto_a_Page_030.QC.jpg
03219e18f379084f3bff30536024cc8c
8cd838b0e04ebce7463e658dea311c4ce83e00aa
F20101118_AAALFC takimoto_a_Page_080.tif
ca8ed0e788c6d952c3bb985db2bed36f
89f70e073a1b11b916254a10a5c229147a1a6fd0
7334 F20101118_AAALEN takimoto_a_Page_066thm.jpg
27eaea4f0f707d444890d707ba54736f
f9b18a847029f90d2d3871cafcaffc3143b359ae
980713 F20101118_AAAKZH takimoto_a_Page_033.jp2
8441a908210aafa0fd46c38542dc15a5
11b0778fdf2f2785e50b877faffca814e978dfe5
424581 F20101118_AAALDZ takimoto_a_Page_110.jp2
d136fba5f9ab8e2bd9f92858a751879c
ff4bef81d00ee18dfbc73327fa31065c16d17418
48303 F20101118_AAAKYU takimoto_a_Page_067.pro
8804909b697b4323b0923b6000bd40e3
caec41b59aa1d10cdd57fbc65b43908529ddb956
6728 F20101118_AAALFD takimoto_a_Page_027thm.jpg
20ff15c618d75ed901486b731e6ddaed
1eccf49172719137c71c4b3c5a5ca03d5cd83099
96950 F20101118_AAALEO takimoto_a_Page_124.jp2
07e6adebd58ff5528325636fb17be0b1
a3336ffabfa1c566e4a791d73309c4e80bcac55d
36819 F20101118_AAAKZI takimoto_a_Page_081.jpg
a55d74dcaf47df1f6581e98b6233ac6c
f1e27f63dc4fc63ed97b06d5956e3f10351c8371
F20101118_AAAKYV takimoto_a_Page_122.txt
c3d964b19f6075a9343e4823611dfe82
cb1eb22c76f07f04303267c1065a871198dc5ea7
329 F20101118_AAALFE takimoto_a_Page_103.txt
f25354acfe1ca97c251a7165a449b35e
8bb87420dc1c98c0434901e03b79e6cc92d99936
F20101118_AAALEP takimoto_a_Page_120thm.jpg
4ee15c3fb43fa9491ef5f3bcf79110f3
d40cbd50185bca744c8d43cbcaea66a869801237
6661 F20101118_AAAKZJ takimoto_a_Page_118thm.jpg
53669afa51ea0f4427a6f64cb24733ee
b1a98b24c8e63d4e1edfd0fa7298b527fef8d81e
F20101118_AAAKYW takimoto_a_Page_152.tif
a2c8de923983c50a535fe83e00191492
3e56ad5f2f8a26535b5dbf8c9a147d40117ad854
F20101118_AAALEQ takimoto_a_Page_123.tif
2573e784a269f46457273505b7dddec5
3ca1bf0a41215864f90060bb1f626411d3e8d8b8
1714 F20101118_AAAKZK takimoto_a_Page_108.txt
87f4843f0dd6c014d669ec59761301a8
3f6a4181544182ee2332e6a5d8f245e58dfb2201
F20101118_AAALFF takimoto_a_Page_136.tif
458bd4f989db52276c35ff4fb39e1d7d
54ff128034e721c1d9baab613dd13e2220e7e468
F20101118_AAALER takimoto_a_Page_079.tif
e7b4baffef46607e2e42202746456e39
2b5d222e67e2f15f91483218e062954159f0d96e
2065 F20101118_AAAKZL takimoto_a_Page_043.txt
cdead4d3d883c2b7cc720cadbd760770
99dc06ba9542319b1d9fc9cfd4bb78fb377e2344
F20101118_AAAKYX takimoto_a_Page_065.tif
50cbfb5935e0ebd8dbb97eaaf41481c7
f48c5eb97620e8920b4657ff5f16148ef5b1f451
F20101118_AAALFG takimoto_a_Page_117.tif
9e066f5a6f8091e056721ce62342206c
890ce28d81cb72e219ac571aae26baba93f7c6ff
9403 F20101118_AAALES takimoto_a_Page_105.pro
1a21532539b28e582be2b8c6c7b0f340
6afcc210b2252f4694425d0b7ad7967c4dcb2377
F20101118_AAAKZM takimoto_a_Page_108.QC.jpg
dc085b1b39926e214ab3c24d0821559a
da3fd15cbef3ff38988dabb5c0a77a8f711bc497
F20101118_AAAKYY takimoto_a_Page_158.tif
8c842e584876fefefc3167e5751998d6
7633c0ec5a02c29b3ae36165cc6d9eba613be77d
F20101118_AAALFH takimoto_a_Page_115.tif
a1d51923645dc6cb385bd7b62b241bc8
cf4176a9bb293610f77ce737b9a9ea26c59fc14a
6261 F20101118_AAALET takimoto_a_Page_047thm.jpg
f1d8e3f162fb1e22766c6d64e93ac187
ad8e2cb84a99385816d59b1dafae84fe461e5501
29563 F20101118_AAAKZN takimoto_a_Page_109.jp2
27cad7e22adc0dcced209e758777407a
bf0bf6624022c3a4293cf9137bf98d0265a1b2b7
F20101118_AAALFI takimoto_a_Page_091.jp2
9c4109f32c520e9f87dcd9ad69117571
1cc6195562294fc7cc9d6e3ab6317249e08e74e4
7582 F20101118_AAALEU takimoto_a_Page_096thm.jpg
29d554f64d524459c5e2a903b23e3e6e
92936d192f25166be4edc204e7bd99127d84fc00
4096 F20101118_AAAKZO takimoto_a_Page_105thm.jpg
3b2288af806a216f0c2e979ceff3f1be
927c6679f37f1a98c3f65f120bd2cbc6bbfdd7dc
F20101118_AAAKYZ takimoto_a_Page_109.QC.jpg
dad8af97c6ad1270b75a3609f14c4102
fbd37438ebefd3cd8a3a37de797664748b88a122
7238 F20101118_AAALFJ takimoto_a_Page_065thm.jpg
a64ac0d660c87d18f71903048a8346b7
13c5dd3d719f9fc67e22d074191ada0493281311
93658 F20101118_AAALEV takimoto_a_Page_077.jp2
b637ba6f68a27033858608e922b22025
e7094238d2af19db04f7b0c0537e3dc69045098e
114493 F20101118_AAAKZP takimoto_a_Page_154.jp2
5105850d8aace23224fe5c2c59c0de40
b50443f2b214a0b02d9cdb085672a263be3f5622
51476 F20101118_AAALFK takimoto_a_Page_048.pro
e9448272382fddb8dd5bc261b1e4f4a4
ccf6c52fcb540e1b87aa10b420d7d305fa807639
51272 F20101118_AAALEW takimoto_a_Page_064.pro
0ad3c717d2dc544a1c8ebc31f746dec1
d7121e5ae0d1ca541cb3696200cf63e65f1b0d76
111252 F20101118_AAAKZQ takimoto_a_Page_122.jp2
2faabcd1d67577d694823c616fa854da
e7bfde1dc59493cab524d84a8983af319926548a
213707 F20101118_AAALFL UFE0021453_00001.mets
3f4c3dfad074de2fdf34cbda90c8c927
e69b97bbcde8a48f41d86cff222b7b6a41c9030f
27337 F20101118_AAALEX takimoto_a_Page_108.pro
95c6d36b3bc389df81ebb262467097c6
40f6db9f702b527e0230c72c07328b3eb7d1f646
82662 F20101118_AAAKZR takimoto_a_Page_171.jpg
f1115ef31f23a19b62d26b37b6eede7a
bcdce25320ee07b998dfa1c69cd4eace1988211f
74617 F20101118_AAALGA takimoto_a_Page_015.jpg
ddfbaa04bc815d017ce25adbf4b8379e
25125be55176ffc83fdd1ba4caacde8f8f463ef1
F20101118_AAALEY takimoto_a_Page_001.tif
5dd948dd4614e2f6e35d4f8d10e9309c
feadf4516404fa7aabe8cc414f2323971eb31adc
F20101118_AAAKZS takimoto_a_Page_098.tif
0f8b186c1111e64d0847142e47b75670
b9de6f0c50cfc8e2d1192efc0ee1cacbf3ce6e9d
80423 F20101118_AAALGB takimoto_a_Page_018.jpg
085912f10e553e17816543d8ffd542ac
537b124080842799f494538538abd4213febb5e0
F20101118_AAALEZ takimoto_a_Page_121.tif
40af8df201eb9ece0bb266109186be66
0c0484f056ad62274a9cb4a34265a08153e06899
8898 F20101118_AAAKZT takimoto_a_Page_116.pro
87c2384b8ea7dba7143620805f99ec53
5041ec8294fdc8567fcaf8c6039ec12e02b66760
13939 F20101118_AAALGC takimoto_a_Page_019.jpg
5545dd7d2e0540d91ec26fd51f66b0b3
fce2de7834c0c1773e0e1e299c407ab6005a7426
26059 F20101118_AAALFO takimoto_a_Page_001.jpg
a54faba8ed903d84889e28109888058d
3a6b4b1baa48ad2c843bef0280c7d07c21a08de0
113507 F20101118_AAAKZU takimoto_a_Page_054.jp2
c6493bd879fbf950e59f539b9c60ffba
5a0a179698393b6f82d46e759b7092268b576e8f
82733 F20101118_AAALGD takimoto_a_Page_021.jpg
6db5866a0bc096effd36302ba65c1a6e
b4b0386d9e773c3ec6067f142b425d139c6646a7
9866 F20101118_AAALFP takimoto_a_Page_002.jpg
c9360c6f0e26c96b856a8c060531d8f3
6bd0c795e8b15161fb13d2ca66cc411d6f259327
F20101118_AAAKZV takimoto_a_Page_042.tif
667610b68ef80acb13d191b66962580e
815828ff64e158b37d1617f05a2aebab786517a0
88765 F20101118_AAALGE takimoto_a_Page_026.jpg
144378d6c41c735e301409d0355aced9
1ec1268788c52f040dff9696b568a4a2f3af9363
10168 F20101118_AAALFQ takimoto_a_Page_003.jpg
1cb16f15362fcde0f519dfb18fb316d4
76210af6fd28c3c0931e36b8b501c1bac4e22b64
76609 F20101118_AAAKZW takimoto_a_Page_043.jpg
b2cc50eb10574aff06a67d93153b7d03
2529180107a00a2297fc5042c40e148cda1a9f50
64666 F20101118_AAALGF takimoto_a_Page_033.jpg
dd917d1c022382712093d96811fe2410
695f41462b4f65709a738452de7dc151616e4c48
74154 F20101118_AAALFR takimoto_a_Page_004.jpg
ea553026d96d8fc4260d813a4ad983a0
e59303e13b7214b36e50b3d0a5877ab16d998247
72460 F20101118_AAAKZX takimoto_a_Page_064.jpg
0f0687074d696ddddd9a9041c1594e75
b40c715da1c4a876215cc91f3597a5e2a3361969
18191 F20101118_AAALFS takimoto_a_Page_005.jpg
23c25de5fe3549f6ef33064ecdeeef58
9a71395cf2b3008128e676940b5eeda7611fabc6
6839 F20101118_AAAKZY takimoto_a_Page_011thm.jpg
2ec8ccbebc117ecc089e5ae86b149883
2a9426840acceaaf6ffe4d9f5cf68a36a2732d5a
67718 F20101118_AAALGG takimoto_a_Page_034.jpg
21fff983c9a6cbc99513d54f8dc5f4d0
c91138bea2377dcae86673219099b94eafbd5056
95705 F20101118_AAALFT takimoto_a_Page_007.jpg
b01e25342be0e44af9f298fa45533478
54031a9cdeeeb5b37b751dc15308cdd85758858a
426983 F20101118_AAAKZZ takimoto_a_Page_137.jp2
8ca6ff11ff72b2f8f2ec43e731731257
741a716063f4ba0e696814b3fceb06d9de252e86
73935 F20101118_AAALGH takimoto_a_Page_036.jpg
4fef019e62e4db0ce06cf93bb1b53348
9de49fcb44f6231b1aab637c4731c20136168b67
79654 F20101118_AAALGI takimoto_a_Page_039.jpg
0bc783d16cff51676f5c1a1a1b0e0b67
bcb22d0492e91dc46a583556c0103c2f4b2916a0
96937 F20101118_AAALFU takimoto_a_Page_008.jpg
2e303f3f423996bd39c4139230ce31b9
402c4ca990062a51e0505fdae50e8be98bd5b2d9
73041 F20101118_AAALGJ takimoto_a_Page_040.jpg
4ddf66c5dae9603bd4c830960a6f5541
18ed910debf7b7a5ff6cc01c274f86d6ed9524c2
35769 F20101118_AAALFV takimoto_a_Page_009.jpg
c5ca0496318006933b6a7f47395424ce
9ee1f17915cd5b0c6b57d40d034226c8054d6bdf
75633 F20101118_AAALGK takimoto_a_Page_041.jpg
736bb8f64caaf328243099cf6b02bee3
8da747ab760d758a728cbc3730edbdc85d5b501e
93216 F20101118_AAALFW takimoto_a_Page_011.jpg
9175d1f5e72f90217ab91746be9deccd
1a2916416e6ee23e8bff0eac718de1374d1c61ef
65225 F20101118_AAALHA takimoto_a_Page_070.jpg
d3388c5b7646e8d350182f65c952f673
c87484b2754802e1087235ae26caf0336172f0c1
76883 F20101118_AAALGL takimoto_a_Page_044.jpg
f97c25d8ca05558cf336cc55bfc4d9af
85567e51f82e1a51a22fd0253893500ddeb4eb0e
74699 F20101118_AAALFX takimoto_a_Page_012.jpg
97f4b6962cb1e1df4b09745dd7a90d47
0ac1837ff755c91b15b3fcba7dac0833a26dfc89
54545 F20101118_AAALHB takimoto_a_Page_071.jpg
99f661e1ccd0e3c3aefabbffebcb6f74
8afa7944298fb3865cdc7f4ca0d1ac17ab6ec055
72691 F20101118_AAALGM takimoto_a_Page_045.jpg
31c2e1d4d0aec0d86a2170f729b9c81d
3540931b10d5b665e408125eb5d38b1183b78ea1
72587 F20101118_AAALFY takimoto_a_Page_013.jpg
ebc72f009186afa0a3c1bf8cf9c6b550
94dbac09dbab6ce329e29d64b4a0ba5ee5eadffe
75674 F20101118_AAALHC takimoto_a_Page_073.jpg
a6073916f33e20261a956dee899d0548
9bb77390c05a4e35b79e9c11e0aa257d6606971b
68519 F20101118_AAALGN takimoto_a_Page_047.jpg
b484d7a7769db70510a5e2ef618f8718
58952cc3fceb7dcc4ebc878d6a97c301e46a15b0
62908 F20101118_AAALFZ takimoto_a_Page_014.jpg
f692fa66d51a7c6e4fd3cabc52e4386e
7c263a8bc296ee109a5e8c0714cb502a0fa4fb76
56873 F20101118_AAALHD takimoto_a_Page_079.jpg
46a9a62854bedd49bd3cec9588caa6b0
6023b38436ae6b11bc43dfcaacf8237090b09c03
82479 F20101118_AAALGO takimoto_a_Page_048.jpg
36ff1de5dc0da668f5d1d9f9cc85b5fb
950d9303835570b1be61a29e04a4b4e2bb4a7c1f
63935 F20101118_AAALHE takimoto_a_Page_080.jpg
e85f322bc564100d6657fe9bf94d0a01
1b4f470bb16b89b1aad759d020beeb2608808cd5
87989 F20101118_AAALGP takimoto_a_Page_050.jpg
82fdaca7cefec41b2faac38e74597433
cf91b1411eb1d485d79a178a57c587916b394ad5
35749 F20101118_AAALHF takimoto_a_Page_082.jpg
61927968bcec6f6d6909e8a0e3563610
55d12fadf20b0ea00955b6bedf70ae068ddde447
85173 F20101118_AAALGQ takimoto_a_Page_052.jpg
ccdefe63f952f48ac99313c7869cdb32
a30a66ab41b2418dab6f4ce486fd9e93b20e44a3
79065 F20101118_AAALHG takimoto_a_Page_083.jpg
22d8c7ee186367960c7b48b893b131c6
323159e95f1b4c6e75afbcfba8029e964783bdb1
73643 F20101118_AAALGR takimoto_a_Page_053.jpg
03ba2209c44019bbcae13ece2fdc7541
7b151d2af953c083d4e1a4ea70f71ac8ad894f48
74554 F20101118_AAALGS takimoto_a_Page_054.jpg
05f34a9bcbb49b6bf1ce095ed559b0f5
c64b16377106647042bba4dd87536fa9f5b3232c
79961 F20101118_AAALHH takimoto_a_Page_084.jpg
674f7b0ff0dba46d1bdf9ff48e7c1eda
79213437d4d5b5dc5c802ca4446146459f68bbb8
77059 F20101118_AAALGT takimoto_a_Page_055.jpg
d3f3128134c52f0577d3c427e52d0986
c9ecab762f607526e3bd67d328d3dca192047d88
74212 F20101118_AAALHI takimoto_a_Page_085.jpg
b11c70859e7f9c5536d81e91953869d6
5a12154709dda5f8db87704b3ae7393004ed8666
50255 F20101118_AAALGU takimoto_a_Page_060.jpg
65de26947a935c2a321d38245c6f95f0
8a452259bd1a26c76b22cbced32ba629074aa12e
69428 F20101118_AAALHJ takimoto_a_Page_086.jpg
4c718922febc0ee42b777c89f9475319
a98728dea99141a30c7b2d1887676a48fc4bc64f
69815 F20101118_AAALGV takimoto_a_Page_062.jpg
c3b844cfa2cd9e40a624097ff3604ff2
c8c6ab5c9ba61ff80e61bceef61ba959097efd15
82214 F20101118_AAALHK takimoto_a_Page_087.jpg
e09cc0c4040087a22bb0367a6ee4efb6
e66ae28069724c73d55b2b7cc0571c898da63beb
83928 F20101118_AAALGW takimoto_a_Page_063.jpg
8e5356a2d826f936501d847c080d911b
f9c5ae0a5a163c30daaa3cdbb1b464c59c393c19
81395 F20101118_AAALHL takimoto_a_Page_088.jpg
24b0bc3239d5a7b074e1b61a5fb27e49
953a2188e834350a97a732347e6b6aa44222c0ec
85695 F20101118_AAALGX takimoto_a_Page_066.jpg
07530e8cbf49605eedf22af060b83446
044677f7bdb1747158a3fa2ac6a3bae797f9df3e
73288 F20101118_AAALIA takimoto_a_Page_118.jpg
38988017d1730bedd9c576bc8e6f65a3
cb98c349f2aff784069dab455923a6349acba9e4
85273 F20101118_AAALHM takimoto_a_Page_091.jpg
f07c9f53fd793b16a4ddd65f75ea6cd9
342d165b944ad20beb4bcebd029ea29858bb0df6
77411 F20101118_AAALGY takimoto_a_Page_067.jpg
2949814b830493fdc1fc401f0b96ba76
697f655ce81241a7329517c41400cc17a8f83b35
73138 F20101118_AAALIB takimoto_a_Page_119.jpg
24ca636b64934f0cf40f190c8a1620ba
6a9587a357d5ba9b8da1a06851ac22643c17fb0d
84087 F20101118_AAALHN takimoto_a_Page_092.jpg
1e7d8778ee2b89bbfd5e42d7a40b5a01
75711fe49ff9016c6362bac396e778361ea54d78
67657 F20101118_AAALGZ takimoto_a_Page_069.jpg
ffd5b0ac938db06e021094def7ba2697
be47ba0e5f254bae6725bab164518e6a92bb873e
84764 F20101118_AAALIC takimoto_a_Page_120.jpg
bd4f8f673a0cf18308012bca492bb02b
fed0836884cda76d0d63a03cc79b949e0e290ce4
85911 F20101118_AAALHO takimoto_a_Page_094.jpg
76911270a36c75129ff8161ec6c54b90
51efac5bedb701f427ee554a507b99a3ec882855
74816 F20101118_AAALID takimoto_a_Page_122.jpg
cdd15b17a0864d3bbc3243e42d39e669
739348c7cff9893277e2b0675daae79cfabb9d69
83619 F20101118_AAALHP takimoto_a_Page_095.jpg
3b56dd6954160065c9b3c3f5e4c886b0
d46c0330dbeff2f0b43b83bd3f9a6080752ccd38
73180 F20101118_AAALIE takimoto_a_Page_123.jpg
dd7bbf2639e8177218fb887cdea04be7
7c54518a4336dcc340ba4c86a8c5c6de452497df
88026 F20101118_AAALHQ takimoto_a_Page_098.jpg
133733800525e7ff4d9e5295f507cbd8
5faddca280afcfe37430542d1c4fc4809c61a280
70773 F20101118_AAALIF takimoto_a_Page_126.jpg
4a7ce474918520b8dc1084e59772ff73
3c9e9dd4e00a602f3bcef595bc87651f151d8084
34080 F20101118_AAALHR takimoto_a_Page_101.jpg
ec41410923c4e6e79cacb1a135af2950
906c3228dc76595f32e436fe3e3432b8bc60378d
79186 F20101118_AAALIG takimoto_a_Page_130.jpg
d9c12980e9e3eeaa4e547246d7a49105
e857d4a29b260232b03e1e86dcfee36f2defc3c5
42482 F20101118_AAALHS takimoto_a_Page_103.jpg
492a05e1bb18a561ff8756ba7be4fb95
b6a3366ec6c62993277f04d54789a19fcb3f934e
85512 F20101118_AAALIH takimoto_a_Page_131.jpg
012ff78e8a53e12d9e195dcf45594049
5d43325af6d7a1970452a2ed464aba22ee6b8146
49977 F20101118_AAALHT takimoto_a_Page_104.jpg
2daa8ca8a70ba69d08e26d0cf70abe48
9d9744ca3172249daeaf1211da0eef0ca0df01fc
44522 F20101118_AAALHU takimoto_a_Page_105.jpg
98730a16a3aca3052113468d396a020a
b8f04b76d376a9e48091412675c4531526b76b97
78423 F20101118_AAALII takimoto_a_Page_135.jpg
70a60ad3e9f70feb6dc43c7fe1f86b5d
388ece49e685a49818e3540686999b945ce16669
48203 F20101118_AAALHV takimoto_a_Page_106.jpg
6d9bbfa5024d8629a333dc597f061666
5bb5cbc4905e94012e19742d799a24cc0a3afa98
86334 F20101118_AAALIJ takimoto_a_Page_136.jpg
1b66f109641ff5ec5b3c79dfbf4c845e
a5920d4d72f19e1b16cb9c88c0e23550df1e8631
24695 F20101118_AAALHW takimoto_a_Page_112.jpg
47658739371b564d5f375af1741b9eac
7c6e869c9e0187c334290fadecc84603a3f268b9
56990 F20101118_AAALIK takimoto_a_Page_138.jpg
be3c787ed791ecb107a86ab3893ba7e5
0aa3d02204d6bcb79bed5b9bbc051e0e54845eee
39105 F20101118_AAALHX takimoto_a_Page_113.jpg
2d2f77ea571166a10fd9318c801deb57
a87c2752edda5f3a4b20a2de6b7631a58af68bdb
58436 F20101118_AAALJA takimoto_a_Page_161.jpg
3583c3933b7628198b036e76b69c6292
9cc70a646567aab6dd317a5a230efc51cc1134da
39805 F20101118_AAALIL takimoto_a_Page_139.jpg
46c323d32f82c3d1c6ec26a714f4e837
9cd323328cdac3897e714bfef1daf160724b026b
24911 F20101118_AAALHY takimoto_a_Page_114.jpg
171ab00777ed01e5e637d5998ee8fc2f
882e022042dd42b46f24c1d1a08629161c6a0cea
28745 F20101118_AAALJB takimoto_a_Page_162.jpg
9aab3850dbad45d0ffe2a6d665c9dcda
1c6ff6e78e1c33c6548e45f93391a4bd8d7436bd
71179 F20101118_AAALIM takimoto_a_Page_140.jpg
45ae98bdb3d3186ac974906bb9dd2fb1
428cc3fb075b54545ce4dac283a86746cc8d157c
27958 F20101118_AAALHZ takimoto_a_Page_116.jpg
d8dd20ecc9d0fdde475ea5467d59f76a
2eb69eb5d46b95f87601e909390cfa1ecd130426
35349 F20101118_AAALJC takimoto_a_Page_163.jpg
8fdd5e529ec236ca827f8be07aca2c1f
431fe3d3dd8ff41c86bed0af37950db4de4d2eb3
32770 F20101118_AAALIN takimoto_a_Page_143.jpg
39645bb878ab6a61beef05b17bd3204b
081280faf806b6b37de1792202cee1a89a2698ed
51489 F20101118_AAALJD takimoto_a_Page_164.jpg
546ef2a40ce1805f04f4491be72534e3
984dee33f6387625ae921155a791dc2aa74271d0
34794 F20101118_AAALIO takimoto_a_Page_145.jpg
2b1402d9667f311a47634ceac1f6fd8b
66e275618198b8d172d738f8c51fe1e313b8a6bc
52931 F20101118_AAALJE takimoto_a_Page_165.jpg
fae9f758c467d8a4186ece5e21087efa
951b59c0ed2af3c0a4fac05f7b3ded88e486e2c0
36510 F20101118_AAALIP takimoto_a_Page_146.jpg
071364a02d42429bae27be62402c8b18
6fb7cdf44d4e1f99782845256ae8e6457a26ef32
50730 F20101118_AAALJF takimoto_a_Page_166.jpg
be4a0d6daaddcf13015fbfc6dbd95216
17b087a9485d4a97871520a122659fb1b5d6a8f8
69103 F20101118_AAALIQ takimoto_a_Page_148.jpg
2fbd95fdf385925aa976bbaab7842460
d826e539897330ad6e83200f7355ef84d2b27db0
37462 F20101118_AAALJG takimoto_a_Page_167.jpg
7f75292812eb86c0c5f621d8a6f387cf
0737531fe7487dd78c1c2f11233abc73495e02c5
77023 F20101118_AAALIR takimoto_a_Page_150.jpg
beb342e88e1272d103eede5153971567
b85e665dd5c1197bed2ebc6b2a6375b99c52c277
103634 F20101118_AAALJH takimoto_a_Page_172.jpg
153e58466bc72237a0b637b70e91184c
4ff9428b8b8840cd6e53907367edc89b267540be
76225 F20101118_AAALIS takimoto_a_Page_151.jpg
7319b9367b104c855c4fdb035631e9e1
1191631639f34ed7cd05599d7c9c5e6e6babcb5f
88748 F20101118_AAALJI takimoto_a_Page_176.jpg
b80fd13e7fbd15a93e3dcc1020203fab
13ea7906e987a24147b43e03d1309f8a0f93d36d
72326 F20101118_AAALIT takimoto_a_Page_152.jpg
d2e7d9e36284e6d77f64086dec0e20d1
0c84316ed51fd6365eb761e8d580878f779d671a
74890 F20101118_AAALIU takimoto_a_Page_153.jpg
3fee758515e1c8f6e3d75a7744edaa65
d03c9fd9141ad809dc087a7dfce0e6bf4166364c
84623 F20101118_AAALJJ takimoto_a_Page_178.jpg
32ed710076a64f065e53573d2aa3d2fc
1725f4b4587e632f494f3cecee8e4ddf8288bf2c
75261 F20101118_AAALIV takimoto_a_Page_154.jpg
3bc2740c8e3b6994cefad23919557cba
56a210d789a5d491e8133ee9889d9b02a6225513
117149 F20101118_AAALJK takimoto_a_Page_179.jpg
62f041bd9e5e7c72a6094c2d85b3beff
0335a2caa90e2c0440e09e227e6ac04992ce25c1
77944 F20101118_AAALIW takimoto_a_Page_155.jpg
a8c38bb70a90351b558c680e194e8fb8
fe125ba98a8456cb544593ab6b389dfd41b65ae3
1051908 F20101118_AAALKA takimoto_a_Page_022.jp2
a366cf0e5411853f54012292c505f9f5
543dd6f74907b172828738db8039ac394c5806f2
102859 F20101118_AAALJL takimoto_a_Page_180.jpg
cb1da9e611aec1c7675b6bc7188f919b
5f58a4dd2cb7f0aa738fdd5c02a80ff87ba1efe8
63179 F20101118_AAALIX takimoto_a_Page_156.jpg
97e4dcef5ecedbf8ba97fc4ce57b2dbf
573929b2d91eb86805ca4ed97e33f3e52527f028
112241 F20101118_AAALKB takimoto_a_Page_023.jp2
66365143a84e1e81f2db663eb4f61bd0
7b6032a8771940aacf70263db7c8838cd24b3db0
107563 F20101118_AAALJM takimoto_a_Page_182.jpg
b0dc63baa15c0ea4654af4a2fee1c792
524695b0c44e41b83dfa327558b15f2bfc183cc9
60060 F20101118_AAALIY takimoto_a_Page_157.jpg
6ff103017fcfd490cbe6ee30464c7c23
57e6c4d22a48ff3754e2cf8c9d298bda057cae78
1051942 F20101118_AAALKC takimoto_a_Page_026.jp2
2ce5e7655a927e0a25cb4acae0443293
afbbf0fe5b70b168586bcfe47d7b15eebe6e284d
55931 F20101118_AAALJN takimoto_a_Page_183.jpg
7263636cf1910d72f64ecb3e37c7a5ed
44ae037b21f6465339cd7e64e89fd149670d5d38
49478 F20101118_AAALIZ takimoto_a_Page_159.jpg
71449777786df882055d6c722a52b5da
1e6aa59b2cea2c33c2113cef76bdd340f4ab6bd4
111839 F20101118_AAALKD takimoto_a_Page_027.jp2
49a67b8343ecb45cd30048cf2243530d
4d52ae3e5ee15f4aee2bd946b35025267d3850a5
31138 F20101118_AAALJO takimoto_a_Page_184.jpg
2bb4cc0b3a0f4ed338c299135d63d3e6
3be7642934b41888e3a684b6f657d47ca2dc1661
94888 F20101118_AAALKE takimoto_a_Page_030.jp2
bad4626728b9c3a14a5072b2638de83f
89f98c224281d6ded603b1c6834cfda628962e12
28981 F20101118_AAALJP takimoto_a_Page_001.jp2
edcc31b9a9aa4b01c873c8d0e904fb98
39b24608c58fdb04459f275f5bde217a5830e94e
72867 F20101118_AAALKF takimoto_a_Page_031.jp2
d84829dd95f4d3e8381c306348c90ec6
db7ffeaad6e28b0fcd46e4020106f7813e4118fb
5039 F20101118_AAALJQ takimoto_a_Page_002.jp2
49d85d8b30811eb44e33f6222ff41ee4
f0eb70e50cd422f8f3fc8cc3df987257b81bc5f1
1051911 F20101118_AAALKG takimoto_a_Page_032.jp2
b8fced09b61d79ef34ee6452e9034668
960eb33fe6f0486e7236b0c395d68bba515d9869
5730 F20101118_AAALJR takimoto_a_Page_003.jp2
ba8a61144484af93fb3e7ec65e42acbf
b5a610885e9aac57b63af083ccfb8f880f9bb69e
F20101118_AAALKH takimoto_a_Page_034.jp2
9120a9169726f1cea35ea64aaf58fc42
9830db1ca64f0474e66cf440aa0579fba8f527c4
109566 F20101118_AAALJS takimoto_a_Page_004.jp2
1dd590b181737851ef58e4043b78d8be
f23e59d4c90ab74b1688b300c77757b30a97688a
1051951 F20101118_AAALKI takimoto_a_Page_035.jp2
22bd4fc752783e2c8e4caa1035abfd5b
3e56c240fbd6236850ae59230d10d18b96d5ee98
19871 F20101118_AAALJT takimoto_a_Page_005.jp2
e8299df770d1c017fc8784a3aed84d5a
403ad31510276507f23d586db3f44c9901e35f2c
120716 F20101118_AAALKJ takimoto_a_Page_037.jp2
d9d196791ac50b8d7d6046f47eb117cc
2d05b17a969b62174c450a2c5c6f37b6ff9016c9
1051972 F20101118_AAALJU takimoto_a_Page_007.jp2
5143054e8b2397f42d27191ef59cb18e
09efe8c221da448c8ef8adf7c1c393e204ded489
1051977 F20101118_AAALJV takimoto_a_Page_010.jp2
6ddf4790cd41992384aab0c971fcd3ad
c12a911d2676e07caac0447e4d5776194a85cc70
117289 F20101118_AAALKK takimoto_a_Page_038.jp2
2d4290077d0ab27a7eafabd277fe322d
ad2bbbf9a9f978f35364b616511f6d65ab47b29c
F20101118_AAALJW takimoto_a_Page_012.jp2
03c5fb8199f3de03257db6e778abdf2a
1e098dd2a040516e2f0290a55174d124ca628ea2
1051947 F20101118_AAALKL takimoto_a_Page_039.jp2
c960143c70c292cc18faa2918fd17c2d
c1eb44e90b3125957eb2246d75b57ba34cea4578
94034 F20101118_AAALJX takimoto_a_Page_014.jp2
0a8abc8bf9673334088eef96a33d2e0f
1956a70d720d6d0c84952aca660199067de1b228
1051952 F20101118_AAALLA takimoto_a_Page_067.jp2
b36d99a1ee38685eed09c918b7e7553d
2d3a8fee8c8c7fc10e710d1adac17ac26a46d41e
115236 F20101118_AAALKM takimoto_a_Page_041.jp2
05e35a92faff96e925314eabbd0c8b3f
2016a0cf9ff70e0aaf1f0eb0bc7d8997e73a92fb
122652 F20101118_AAALJY takimoto_a_Page_018.jp2
d13eaef62f78429feb1fdcd44d853aa5
92da625fed9f124524c9cd6a1cbaa968a582de73
115912 F20101118_AAALLB takimoto_a_Page_068.jp2
63605173397df6d18102d064f25c39f6
3c00d2bd958a25f0aeb1dc9c60aaf9a95e532be3
118661 F20101118_AAALKN takimoto_a_Page_044.jp2
7f706c9bfd5aac5468c6edcde3769954
14863f9566f1b60684bb10200b1cc27b0b5cba7f
F20101118_AAALJZ takimoto_a_Page_020.jp2
bce2a8655d53bd1705936ad5f951b5a7
9b23d0f24ea1ce173e20396f4f30a404ffe74d02
100564 F20101118_AAALLC takimoto_a_Page_069.jp2
91648aea3b786f71bbb84007aff72d89
706c3c891f70d838361e14bc62edf2d999bc1166
101907 F20101118_AAALKO takimoto_a_Page_047.jp2
70c0bd32b83a08e054b023c7b8e86b7f
93df7289127f816c79c4f5fc3895eee0fa9e2181
100234 F20101118_AAALLD takimoto_a_Page_070.jp2
2212df1fbc6defe16b37f7fd3a79d899
7ecbaff52e7e6af982d899e1c57cbdd13ae9e8de
F20101118_AAALKP takimoto_a_Page_048.jp2
6eb9e8a7d7cab6e19ad8ba16120bf875
edcb6773e0fc7f1197158897213ed8adbc2dc08e
79742 F20101118_AAALLE takimoto_a_Page_071.jp2
44dadf3ef89309a9abe47473eba76c6e
ad999d9a2ba1c635eb8a4acb0a62b8b5fe66d39e
117235 F20101118_AAALKQ takimoto_a_Page_049.jp2
db61ab354e16d8eca894d660f0e44c50
f41a565cbce204d8b3a91dc3dd9967b14b1dff8b
114476 F20101118_AAALLF takimoto_a_Page_073.jp2
7c3a555efdb2e2564671b22d066c272f
ef5af7239def15fbaf87909d73bd8829a1b19384
1051938 F20101118_AAALLG takimoto_a_Page_074.jp2
1265e88caa3ad2bb2864cad390ec5b72
df8d0177f0d11ca3933c8fb8ea144ee340948da5
1051917 F20101118_AAALKR takimoto_a_Page_051.jp2
36ce7b6eec8aeda2e8d5b5a9f9a3087d
2248599b6dabe3a2e3fe8a8472fe24ae7d377c20
84698 F20101118_AAALLH takimoto_a_Page_076.jp2
400a5fe09a9565ac7d9ad3da10de2d14
ce1b10d112a2c0d97883aa1bbf94d95d91dac8f5
F20101118_AAALKS takimoto_a_Page_052.jp2
eb5bc32a3bfe013fd714eabd4fe486ce
4b0609b6052f8cb289470c47dac1a940cbe282eb
1051950 F20101118_AAALLI takimoto_a_Page_078.jp2
31a1e4a14b0dd30267e689d3841724b1
ceec044ccaa08afcde030e61bcdf489844939ae0
113318 F20101118_AAALKT takimoto_a_Page_056.jp2
df1dd0be662908b6e9512ab05950db3a
fe4b3d402b4e3be253613903f6a80946ac408226
939297 F20101118_AAALLJ takimoto_a_Page_080.jp2
99ed6135aaab764c4067ece979700d71
7158d3a13efe5fe1d383a0715e984e8a8bf3a73b
113406 F20101118_AAALKU takimoto_a_Page_057.jp2
583abf5fc8604afa08cfb0c74c757c06
ae60bdad80d19b1f7bdefd8ce1b5d3e14f1fc088
496573 F20101118_AAALLK takimoto_a_Page_081.jp2
08f4fe3327830b3685bee6f5616eb3ea
b78e702bd1bed5e3f24f4469a7f0ff736295a320
60933 F20101118_AAALKV takimoto_a_Page_059.jp2
e5bdcc29f57eeefe5834cf2c5b6b90cf
d8324ea0aeb92ec9b5410818c73a10d70ff3d43a
74931 F20101118_AAALKW takimoto_a_Page_060.jp2
af15a0139b8f12f9c44db431046b3266
824bfa76137a1c95b46b816c5d20baea20c41e59
742910 F20101118_AAALMA takimoto_a_Page_107.jp2
a84a6cfae971a6f1c386de7e35b30dbd
e18f95bbd3be63f3720355ebdc75bba36b3a744a
122097 F20101118_AAALLL takimoto_a_Page_084.jp2
9e554460981311072107f0c352ed8ed2
c9d54da720db2617f0020a030805a352e1393659
118483 F20101118_AAALKX takimoto_a_Page_061.jp2
faf03dc6e46d4a2e0b06c6f48d175c59
0f3be7c474da3646e3eee871b76ad0506ec618e0
693240 F20101118_AAALMB takimoto_a_Page_108.jp2
8812973ddf9bd77e09aa6ad72c5e77dc
cf2c4e287e02c65f7cde1080ca301119b34edf6d
112268 F20101118_AAALLM takimoto_a_Page_085.jp2
20937557e9e2c8076cb0a2da6544b16e
b9b0952bfc7bbfffdb8a66aff6a2b0ca83264e63
107677 F20101118_AAALKY takimoto_a_Page_062.jp2
2fc974a0922082565aafb03321353d7e
71a656079a4f4809b6b9a5a4ed4701631f4bfe96
34287 F20101118_AAAKJA takimoto_a_Page_137.jpg
fd2137e3e9bc1c470db65ee792ed83f4
981c46dafba9213941a9070d63569e5973936700
342071 F20101118_AAALMC takimoto_a_Page_113.jp2
89a75a3e86b39c1f3aca21d9910f8e36
6af82a8109d742ff9656fa1c75a9e1a2a6370393
961827 F20101118_AAALLN takimoto_a_Page_086.jp2
6446ede8a89989206553a3c32fa079e6
eeecb390c6315bf78b3d6d944f33117dd02d3aae
1051971 F20101118_AAALKZ takimoto_a_Page_063.jp2
db3f389d2b4b5559576d823ab9ce954b
a5f35e25639b5cad43b131562e30e0fbc9101fc1
7368 F20101118_AAAKJB takimoto_a_Page_091thm.jpg
449eddc92aaa66e6c1cab93d28aee12b
0a9866139e529c59a88ead84cbcbb980307d4e7d
29813 F20101118_AAALMD takimoto_a_Page_114.jp2
601d68ccae968ea1ae65a95b9332c377
01f922a51834783304f163b6ebc9a151bb58f6b4
1051958 F20101118_AAALLO takimoto_a_Page_088.jp2
a11198ecb69fbed5d577c1c48df8c69f
eae3c867a8d131265b3ddae5d2e5c9eed4baeb86
75323 F20101118_AAAKJC takimoto_a_Page_023.jpg
f61ba8eca44a7d7354c0bc851d51ff90
03e0f4a9e932b4da7f9baa87dba1969a98c5528f
121962 F20101118_AAALME takimoto_a_Page_117.jp2
303d0f151ea02e9f9ace0b4513a77657
7648877ac210af7ad4561f907d8156612d5f38b8
91531 F20101118_AAALLP takimoto_a_Page_089.jp2
3328d15feb23ccecd64a10717e7e0c08
417f738b450dda89c442c083b07c9144e8cd7af5
22372 F20101118_AAAKJD takimoto_a_Page_107.pro
6d0c719dde86cde4719d81e2834f14f7
8c2afd68385e947b222dd55c014cac20c7417600
113712 F20101118_AAALMF takimoto_a_Page_118.jp2
16ec1f5b466338388e9c752192fc4683
8d1c8f08de4d862d1c400f27dddb0b64381763cf
1051910 F20101118_AAALLQ takimoto_a_Page_094.jp2
a7aa423c2b35719d42082700caefe0d3
be01a27682bf70cfdd829f827eda3271cdea3621
398671 F20101118_AAAKJE takimoto_a_Page_082.jp2
1974b9ef98a3fe21df1e13bce96b4ffb
bd8901ebd50670c2ea7661b429ccb879e9bb5647
110851 F20101118_AAALMG takimoto_a_Page_123.jp2
d72e566d5d2a38109e3fc501ecbf8070
eeb8a68918f8a18061f1f3e1c70385381e5b0b94
F20101118_AAALLR takimoto_a_Page_097.jp2
5c831f7b551c69d3c75437865760a93e
7486f0ee32427abe036e2a6cfce77b967de1fc96
F20101118_AAAKJF takimoto_a_Page_112.tif
73c3ee1e190efcd06aea16898874b63c
b60310e3ccaf3fdc7ff3d96a89396d8193bcc85b
117243 F20101118_AAALMH takimoto_a_Page_125.jp2
8d42dc456e7557e691ab05b0824ee837
11ac3bb2b680b71b70dcffab2d9bb58ffe35516d
F20101118_AAALLS takimoto_a_Page_098.jp2
dcd04c445a04fa75e32045f728c5fd5c
07309db1394c2bd4de7e91a0b1a34b083322e31e
1015 F20101118_AAAKJG takimoto_a_Page_159.txt
af760df7699985958d1f0f405aaec8ec
10ee8599e9e52e3b14301643767041f9d7ae0017
107402 F20101118_AAALMI takimoto_a_Page_126.jp2
88c9bd097a379d18d0508c8d887ae444
b215e75aa2799a78b143c45483015074bad0439b
118035 F20101118_AAALLT takimoto_a_Page_099.jp2
8dee77659c0d97cb82bd499660fb570c
8cc5db9a0e661a08c4b94acc1b0e643f01c09f4d
4019 F20101118_AAAKJH takimoto_a_Page_019.pro
6cbf23612adc894da3c1f9843678d3ef
fd0fbfd4860bd61c761ac9516e399f17a63ac8ec
115106 F20101118_AAALMJ takimoto_a_Page_127.jp2
6ebfb773246116da67d90a6785b53792
f031bad1cd52fa227f0dcc30227fc36ba2c5fc28
44393 F20101118_AAALLU takimoto_a_Page_101.jp2
c4f904ab8b1e1b4682957da933492b4f
414c9d57c1d8f009ce91adec7495acb002ae4470
836 F20101118_AAAKJI takimoto_a_Page_035.txt
606b922dad3b8c81c8e8671f05d6ac5e
271a01fb434297767133258443eabef8d2d7114d
1051957 F20101118_AAALMK takimoto_a_Page_129.jp2
eee93c09e27d21d1c357863596966ede
800e7ab224bf11b43fc4301e51001e10498fc9b8
70803 F20101118_AAALLV takimoto_a_Page_102.jp2
460c53c40083a1a9a1b25823c2769a68
b5181469715bf9ae5be7b35ed9c61ce3de50c30e
1401 F20101118_AAAKJJ takimoto_a_Page_183.txt
34f3ba4040b782767883cb06ed3cace9
d3b38e73e7855cf9167f8ff3b0f6b0fc53c49bc8
120542 F20101118_AAALML takimoto_a_Page_130.jp2
4c37169d976f9b7f5aa5d91614124264
74b87115ad3ab8154c59094faeeaf7676b24cb82
652737 F20101118_AAALLW takimoto_a_Page_103.jp2
b3d918b346dde79d6e1c6d69c6acfc28
cdb0760f7709d0249ec128215362e52b326ade44
F20101118_AAALLX takimoto_a_Page_104.jp2
98810b2c36fa23fc293be25658b2022a
7bb22feec63a526b31b887b7c5eed156586f0804
48754 F20101118_AAALNA takimoto_a_Page_158.jp2
db29b7cd0d7e6136955bd36f964c307c
06ff60c4454652c3b6759896aeb6ea8224ab9dc7







CARBON SEQUESTRATION POTENTIAL OF AGROFORESTRY SYSTEMS
IN THE WEST AFRICAN SAHEL:
AN ASSESSMENT OF BIOLOGICAL AND SOCIOECONOMIC FEASIBLITY






















By

ASAKO TAKIMOTO


A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL
OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT
OF THE REQUIREMENTS FOR THE DEGREE OF
DOCTOR OF PHILOSOPHY

UNIVERSITY OF FLORIDA

2007




































02007 Asako Takimoto


































To my parents and grandmother









ACKNOWLEDGMENTS

I am indebted and grateful for many individuals and organizations who contributed to this

study and my doctoral program. I thank my chair, Dr. P.K. Nair, for his dedication and guidance

throughout this process, and my committee, Dr. Nick Comerford, Dr. Janaki Alavalapati, Dr.

Tim Martin, Dr. Ted Schuur, and Dr. V.D. Nair, for their support and encouragement.

I recognize and express my sincere gratitude to the individuals and their institutions that

supported me during my doctoral studies: the School of Forest Resources and Conservation

(Cherie Arias, Sherry Tucker, Dr. Tim White), University of Florida International Center (Debby

Anderson), the Center for Tropical Conservation and Development of UF, the World

Agroforestry Centre (especially Dr. Bocary Kaya), the Fulbright Program, and the Joint

Japan/World Bank Graduate Scholarship Program (JJ/WBGSP).

At the Hieldwork in Segou, Mali, I received tremendous support and cooperation from the

farmers, field onfcers, and other collaborators. It was one of the most challenging times of my

life, and I could not go through without them. Thank you to Nicole Demers, Bayo Mounkoro,

Keita, Samake, and other officers in ICRAF Segou office, Kayo Sakaguchi, Takako Uchida, Mr.

Kiyoshi Sakai, and all the farmers in Segou who let me use their fields for data collections and

participated the survey.

I have greatly benefited from my friendship with colleagues in the agroforestry lab at UF.

I thank Solomon Haile, Alyson Dagang, Julie Clingerman, Sam Allen, Eddie Ellis, Brian Becker,

Joyce Leptu, David Howlett, Wendy Francesconi, Subrajit Saha, Shinjiro Sato, and Masaaki

Yamada, for the discussions and supports.

To my precious friends who have been an integral part of the many years of this process,

thank you Gogce Kayihan, Brian Daley, Elli Sugita, Mike Bannister, Jason and Karen Hupp,










Charlotte Skov, Chrysa Mitraki, Rania Habib, Maitreyi Mandal, Trina Hofreiter, Troy Thomas,

and my fiance, Nick Georgelis.

Last but not least, I express my most profound gratitude to my mother Ayuko Takimoto,

whose endless love and confidence in me made me come this far.












TABLE OF CONTENTS


page

ACKNOWLEDGMENT S .............. ...............4.....


LIST OF TABLES ........._.._ _..... ._ ._ ...............10....


LIST OF FIGURES ........._.. ..... ._ ._ ...............11....


AB S TRAC T ............._. .......... ..............._ 13...


CHAPTER


1 INTRODUCTION ................. ...............15......... .....


Back ground ..........._...__.......... ...............15.....
Rationale and Significance .............. ...............16....
Research Questions and Obj ectives ................. ...............17......... ....
Dissertation Overview ................ ...............18.................


2 THE WEST AFRICAN SAHEL: GENERAL LAND-USE AND AGROFORESTRY ........20


Description of the Region ................. ...............20................
Clim ate .............. ...............20....
Vegetati on ................. ...............21.................
Soil ................. ...... ....... ... ......... .... .... ...........22
Traditional Farming Systems and Agroforestry in the WAS .............. ....................2
Traditional Agroforestry Practices .............. ...............25....
Bush fallow/shifting cultivation ..........._..._ ......_._ ....._._ ...........2
Parkland system............... ...............25.
Improved Agroforestry Practices .............. ...............26....

3 LITERATURE REVIEW: CARBON SEQUESTRATION POTENTIAL OF
AGROFORESTRY SYSTEMS IN THE WEST AFRICAN SAHEL (WAS)..............._.._. ...36

Overvi ew ........._._.. ... .... ._._......... ._._..... .... .. ... ........3

C Sequestration as a Climate-Change-Mitigation Activity ........._._.... ......_._........36
Agroforestry for C sequestration .........._... ... ...............37_._......
Methodologies for C Sequestration Measurements ....._._._ ..... ... .__ ......_._..........3
Direct On-site Measurement............... ..............3
Inventory ............... .... ...............40.
Conversion and estimation ........._._ ...... .__ ...............40...
Indirect Remote Sensing Techniques .............. ...............42....
M odeling.................. ........... .. .... .......4
Default Values for Land/Activity Based Practices ....._._._ ..... ... .__ ........_.......44
Accounting Methods ........._._ ...... .... ...............44...












Approaches to Assessing C Sequestration Performance .............. ....................4
Fluxes of C and flow summation .............. ...............45....
Average changes in the stocks of C .............. ...............45....
Cumulative C storage .............. ...............46....
Other accounting methods ................. ...............46........... ....
Technical Problems and Uncertainties .............. ...............47....
Biomass C Sequestration .............. ...............48....
Studies in Various Ecoregions............... ...............4
Studies in West Africa............... ...............48.
Soil C Sequestration .............. .......... ............5
Studies of Soil C Stock and Dynamics............... ...............50
Soil C in the WAS .............. ...............52....
Socioeconomic Implications ................. ...............54.................
E conomi c Mod el s ................. ...............5.. 4..............
National/gl obal scale ................. ...............54.......... .....
Micro/site-specific scal e............... ...............55.
Feasibility in West Africa............... ...............56.


4 ABOVEGROUND AND BELOWGROUND CARBON STOCKS IN TRADITIONAL
AND IMPROVED AGROFORESTRY SYSTEMS IN MALI, WEST AFRICA .................61


Introducti on ................. ...............61.................
Materials and Methods .............. ...............62....

Study Area ................. ...............62.................
Republic of Mali ................. ...............63._._. ......
Segou region............... .. .. ....... .. ...... ...............6
Selected Land-use Systems for Field Data Collection .............. ...............64....
Parkland systems ........._.__....... .__. ...............64....
Improved agroforestry systems .............. ...............65....
Abandoned (degraded) land .............. ...............66....
Research Design .............. ...............66....
Data Collection ............... ... ...............67.......... ......
Biomass measurement ............._. ...._... ...............67....
Soil sampling............... ...............68
Carbon Stock Estimation ............._. ...._... ...............68....
Biomass C stock ............._. ...._... ...............69....
Soil C stock .............. ...............71....
Statistical Analysis .............. ...............71....
Results ........._.__........ _... ...... ...............72..
C Stock in Biomass and Soil .............. ...............72....
Total C Stock ........._.__....... .. ._. ...... ._.._ ............7
Relationship between Biomass C and Soil C .............. ...............73....
Discussion............... ...............7













5 SOIL CARBON SEQUESTRATION IN DIFFERENT PARTICLE-SIZE FRACTIONS
AT VARYING DEPTHS UNDER AGROFORESTRY SYSTEMS IN MALI ....................83


Introducti on ................. ...............83.................
Research Questions............... ...............8
Materials and Methods .............. ...............85....
Research Design .............. ...............86....
Soil Preparation and Analyses ................. ...............87................
Soil fractionation ............ _...... ._ ..... .............8
C isotopic ratio (13 /12C) measurement .............. ...............88....
Statistical Analysis .............. ...............89....
R results ............... .... ...............90....
Soil Characteristics ............ ..... .._ ...............90...
W hole Soil C .............. ...............9 1....
C in Soil Fractions ............ __...... ...._ ...............92...
Isotope Analysis of Whole Soil C .............. ...............93....
Isotope Analysis of C in Soil Fractions ...._ ......_____ .......___ ..........9
Relationships of Data Sets ............_ ..... ..__ ...............94..
Discussion............... ...............9


6 SOCIOECONOMIC ANALYSIS OF THE CARBON SEQUESTRATION
POTENTIAL OF IMPROVED AGROFORESTRY SYSTEMS IN MALI, WEST
AFRICA ................. ...............117................


Introducti on ................. ...............117................
Research Questions ................. ...............118................
M materials and M ethods .............. ... ........... ...............119.....
Social Survey of Fodder Bank Farmers ................. ...............119..............
Local M market Survey .............. ...............120....
Types of Analysis ................... ........... ...............121......
Cost-benefit analysis (CBA) .............. ...............121....
Sensitivity analysis............... ...............12
Risk m odeling .............. ...............128....
Results ................. ..... ........ .. ........ .... .... ..............2

Demographic Characteristics of Target Population .............. .... ........................129
Cost-Benefit Analysis: Best Guess Scenario of the Live Fence and the Fodder Bank .130
Sensitivity Analysis ................. ...............13. 2..............
Risk Modeling and Simulation ................. ...............132...............
D iscussion............... ..............13


7 SUMMARY AND CONCLUSIONS .............. ...............148....


C Sequestration Potential............... ...............14
Biophysical Potential ................. ...............148......... ......
Socioecomic Potential .............. ...............150....
Conclusions .............. ...............152....











Agroforestry Adoption for C sequestration in the Study Region .............. .....................15
Limiting Factors .............. ...............152....
Favorable Factors .............. ...............153....
Implications for Agroforestry ................. ...............154...............
Future Research .............. ...............155....

APPENDIX


A SOCIAL SURVEY QUESTIONNAIRE FOR FODDER BANK OWNERS ....................156

B COST BENEFIT ANALYSIS (CASH FLOW) OF LIVE FENCE .................. ...............163

C COST BENEFIT ANALYSIS (CASH FLOW) OF FODDER BANK .............. ..............167

LIST OF REFERENCES ............_...... ._ ...............171...

BIOGRAPHICAL SKETCH ............_...... ._ ...............184...










LIST OF TABLES


Table page

2-1 Common tree and shrub species found throughout the West African Sahel .....................29

2-2 Main productive functi on s of agroforestry parklands ................. .......... ................3 1

3-1 Summary of various biomass C measurement approaches used commonly in C
sequestration studies .............. ...............59....

3-2 Aboveground time-averaged C stock in different ecosystems and agroforestry
practices .............. ...............60....

4-1 Characteristics of the villages where the experimental plots were set up in Segou
region, M ali ................. ...............76.................

4-2 Characteristics of the experimental plots (three plots average) for five-selected land-
use systems in Segou region, Mali ................. ...............76...............

4-3 Estimated biomass C (above and below ground) stock values of each plot and three
plots average of five selected land-use systems............... ...............77

4-4 Total C stock (biomass C + soil C of different depth) of five selected land-use
system s. .............. ...............77....

5-1 Soil profile characteristics for plots of the five land-use systems used in the study at
Segou Region, M ali .............. ...............101....

5-2 613C ValUeS of whole soil and three fraction sizes from five studied land-use systems,
at Segou Region, M ali ................. ...............102...............

6-1 Demographic characteristics of the target population in Segou, Mali ................... ..........13 8

6-2 Net Present Value (NPV), Benefit Cost Ratio (BCR), and Internal Rate of Return
(IRR) of the live fence and the fodder bank proj ects in the three different scenarios
(without C sale, with C sale by the ideal accounting method, and with C sale by the
tonne-year accounting method) in Segou, Mali ................ ...............138..............

6-3 NPV sensitivity of the live fence proj ect and the fodder bank proj ect to the change of
an input variable in Segou, Mali ................ ...............139..............










LIST OF FIGURES


Fiare page

2-1 Map of West Africa with ecological zones and isohyetal lines............_. .........._._....32

2-2 Standardized annual Sahel rainfall (June to Octob er) from 1898 to 2004 ................... ......3 3

2-3 Seasonal landscape contrast of the WAS ................. ...............33........... ..

2-4 Distribution of soil orders (USDA soil taxonomy) in West Africa .............. ..................34

2-5 Parkland system in Segou, Mali............... ...............35..

2-6 Allowing the cattle to roam freely on the landscape during the dry season ......................35

4-1 Location map of A: Mali; B: Mali showing its land-locked nature: C: Map of
Segou region .............. ...............78....

4-2 Faidherbia albida parkland in Togo village .....__.....___ ..........._ ..........7

4-3 Vitellaria paradoxa~11~~1~~11~ parkland in Dakala village............... ...............79

4-4 Live fence system in Dougoukouna village ................. ...............80........... ..

4-5 Fodder bank in Dakala village .............. ...............80....

4-6 Abandoned land just outside of Diamaribougou village..........._.._.. ......._.._........._..81

4-7 Aboveground and belowground C stock per ha of five selected land-use systems........... 82

5-1 Soil sampling, Segou, Mali ................. ...............103.___ .....

5-2 Soil pits dug in plots of the five land-use systems studied in Segou region of Mali .......104

5-3 Model of soil organic matter dynamics .............. ...............105....

5-4 Whole soil C content of three depth classes (0 10 cm, 10 40 cm, and 40 100
cm) in different land-use systems in Segou, Mali............... ...............106.

5-5 Soil C content of three particle size fractions in three depth classes (0 10 cm, 10 -
40 cm, and 40 100 cm) under five land-use systems in Segou, Mali ...........................107

5-6 Whole soil C, divided into C3 plants (trees)- origin and C4 plants (crops)-origin, in
different soil layers up to 100 cm depth, in five land-use systems in Segou, Mali .........108

5-7 Soil C in three fraction sizes divided into C3 plants-origin and C4 plants-origin in
different soil particle-size fractions under different land-use systems in Segou, Mali....109










5-8 Linear regression between silt + clay content of soil and whole soil C content in three
depth classes across Hyve land-use systems in Segou region of Mali ............... ... ............1 12

5-9 Linear regression between silt and clay content of soil and C in soil particles of <53
Cpm in three soil-depth classes across Hyve land-use systems in Segou, Mali ................... 114

5-10 Linear regression between C derived from C3 plants and C in the large soil particles
(250 2000 Cpm) at 0 10 cm soil depth across Hyve land-use systems of Segou
region, M ali.................. ...............116................

6-1 Social survey with farmers in Segou, Mali ................. ...............140.............

6-2 Simulated NPV probability distribution of the live fence proj ect (without C sale).........140

6-3 Simulated net benefit (total costs total revenues in each year) of the live fence
project (without C sale)............... ...............141.

6-4 Simulated NPV probability distribution of the live fence proj ect (with C sale by the
ideal accounting method) ........... _... ......... ...............142..

6-5 Regression sensitivity analysis for NPV of the live fence proj ect (with C sale by the
ideal accounting method) ........... _... ......... ...............143..

6-6 Simulated NPV probability distribution of the fodder bank proj ect (without C sale).....144

6-7 Simulated net benefit (total costs total revenues in each year) of the fodder bank
project (without C sale)............... ...............145.

6-8 Simulated NPV probability distribution of the fodder bank proj ect (with C sale by
the ideal accounting method) ................. ...............146......... .....

6-9 Regression sensitivity analysis for NPV of the fodder bank proj ect (with C sale by
the ideal accounting method) ................. ...............147......... .....









Abstract of Dissertation Presented to the Graduate School
of the University of Florida in Partial Fulfillment of the
Requirements for the Degree of Doctor of Philosophy

CARBON SEQUESTRATION POTENTIAL OF AGROFORESTRY SYSTEMS
IN THE WEST AFRICAN SAHEL:
AN ASSESSMENT OF BIOLOGICAL AND SOCIOECONOMIC FEASIBILITY

By

Asako Takimoto

December 2007

Chair: P. K. Ramachandran Nair
Major: Forest Resources and Conservation

In recent years, carbon (C) sequestration potential of agroforestry systems has attracted

attention, especially following Kyoto Protocol's recognition of agroforestry as an option for

mitigating green house gasses. Although the possible benefits of agroforestry in carbon (C)

sequestration have been conceptually discussed, field measurements to validate these concepts

have not been undertaken to any significant extent. In addition to the traditional agroforestry

systems, improved practices and technologies are now being expanded into the dry regions such

as the West African Sahel for perceived benefits such as arresting desertification, reducing water

and wind erosion hazards, and improving biodiversity. Thus, it is imperative to investigate C

sequestration potential of agroforestry practices in these regions. My research hypothesizes that

the tree-based systems will retain more C in the systems both above- and below-ground than

tree-less land-use systems. By joining the C credit market, the landowners could sell the C

sequestered in their agroforestry systems.

My research consisted of three components. The first examined C (biomass + soil) stored

in five target land-use systems: two traditional parkland systems involving Faidherbia albida

and Vitellari~~~ll~~a 7~paradoxa trees as the dominant species, two improved agroforestry systems (live









fence and fodder bank), and land that is out of cultivation (abandoned or degraded) in the Segou

Region, Mali. The second component involved a study of soil C dynamics of these systems: the

extent of soil C storage/accumulation by trees and stability of the C accumulated were

investigated. In the third component, socioeconomic feasibility of the agroforestry systems was

examined in the context of C sequestration and C credit sale.

Research results show that the selected agroforestry systems have the potential for

sequestering more C both above- and belowground than in tree-less land-use systems, and that

the trees tend to contribute to storing more stable C in the soil. Among the selected land-use

systems, live fence and fodder bank are more suitable to start as agroforestry C sequestration

proj ects than the traditional parkland systems for smallholder farmers in the studied region.

Between the two improved systems, live fence has higher C sequestering potential per unit area

and is economically less risky than fodder banks. Adopting these systems on cultivated land

rather than on abandoned land is likely to sequester more C and be more profitable. Since

parklands are traditionally practiced, they are not likely to qualify as a new C sequestration

proj ect soon. Nevertheless, F. albida trees are more attractive than y. paradoxa trees in terms of

C sequestration potential.

These results can be used for development of recommendations and guidelines on selection

of land use-systems and species and their management, for planning successful C sequestration

proj ects in the West African Sahel.









CHAPTER 1
INTRODUCTION

Background

It is widely accepted that current global climate change or global warming is "the" most

serious environmental issue affecting human lives. Global warming refers to the increase in the

average temperature of the Earth's near-surface air and oceans in recent decades and its proj ected

continuation. It is brought about primarily by the increase in atmospheric concentrations of the

so-called greenhouse gases (GHGs). GHGs are components of atmosphere contributing to the

"green house effect," the process in which the emission of infrared radiation by the atmosphere

warms a planet's surface. The Intergovernmental Panel on Climate Change (IPCC), established

by the United Nations (UN) to evaluate the risk of climate change concludes in its most recent

report that "most of the observed increase in globally averaged temperatures since the mid-20th

century is very likely due to the observed increase in anthropogenic greenhouse gas

concentrations" (IPCC 2007). The Kyoto Protocol to the United Nations Framework Convention

on Climate Change (UNFCC) is the first and so far the largest international agreement to

stabilize GHG concentrations

Carbon dioxide (CO2) is a maj or GHG and its concentration build-up is accelerated by

human activities such as burning of fossil fuels and deforestation. One of the approaches to

reducing CO2 COncentration in the atmosphere, called biomass carbon (C) sequestration, is to

"store" it in forest and forest soils by trees and other plants through photosynthesis. This concept

became widely known because the Kyoto Protocol has an approach called Land Use, Land Use

Change and Forestry (LULUCF), which allows the use of C sequestration through afforestation

and reforestation as a form of GHG offset activities. The Marrakesh Accords in 2001

determined more detailed rules of LULUCF and added forest management, crop management,









grazing land management, and revegetation as LULUCF activities. This enables agroforestry to

be an activity of C sequestration under the Kyoto Protocol, and since then, C sequestration

potential of agroforestry systems has attracted attention from both industrialized and developing

countries (Albrecht and Kandji 2003; Makundi and Sathaye 2004; Sharrow and Ismail 2004).

This became particularly relevant because of an arrangement called Clean Development

Mechanism (CDM) under the Kyoto Protocol, which allows industrialized countries with a

GHGs reduction commitment to invest in mitigation proj ects in developing countries as an

alternative to what is generally more costly in their own countries. Since agroforestry is mostly

practiced by subsistence farmers in developing countries, there is an attractive opportunity for

those farmers to benefit economically from agroforestry if the C sequestered through

agroforestry activities are sold to developed countries; it will be an environmental benefit to the

global community at large as well.

Rationale and Significance

The IPCC Report (2000) estimates that 630 million ha of unproductive croplands and

grasslands could be converted to agroforestry worldwide, with the potential to sequester 0.391

Pg of C (1 Pg = petagram = 1015 g = 1 billion ton) per year by 2010 and 0.586 Pg C per year by

2040. The credibility of conceptual models and theoretical foundations of the possible benefits

of agroforestry in C sequestration have been suggested: agroforestry has C storage potential in its

multiple plant species and soil, high applicability in agricultural land, and indirect effects such as

decreasing pressure on natural forest or soil erosion (Nair and Nair 2003; Lal 2004a; Montagnini

and Nair 2004). Field measurements to validate these concepts and hypotheses, however, have

not been undertaken to a significant extent. Some studies of specific agroforestry practices

proved the potential of C sequestration and its benefits, such as the Indonesian homegarden

systems (Roshetko et al. 2002; Schroth et al. 2002). But very few such studies have been










reported regarding C sequestration potential of agroforestry systems in semiarid and arid regions.

In addition to already existing indigenous agroforestry systems, improved practices and

technologies are now being expanded into these dry regions for perceived benefits such as

arresting desertification, reducing water and wind erosion hazards, and improving biodiversity

(Droppelmann et al. 2000; Gordon et al. 2003). In this scenario, it is imperative that C

sequestration potential of agroforestry practices in these regions is investigated. Considering that

the ecological production potential of these dry ecosystems is inherently low compared to that of

"high-potential" areas of better climatic and soil conditions, the extent to which agroforestry

systems can contribute if at all to C sequestration in such regions is in itself an important

issue.

This study was conducted in Mali, situated in the West African Sahel (WAS), one of the

largest semiarid regions of the world. Considering the large extent of area of the region (approx.

5.4 million km2), results of studies of this nature are likely to have wide applicability; yet, such

studies have been rare, possibly because of the relative backwardness of the region in terms of

economic development and therefore research facilities and infrastructure. Needless to say, such

studies are important because of their relevance in the context of C credit sale under CDM. The

WAS is one of the most environmentally vulnerable and poorest areas in the world. If the

maj ority of the people who are subsistence farmers can receive even small amounts of C

payments through their agroforestry practices, it would be a substantial contribution to their

economic welfare and the overall development of the region. Thus, an analysis of the C

sequestration potential of various agroforestry practices (traditional and newly introduced) in the

region is timely.

Research Questions and Objectives

To address the issues discussed above, four research questions are raised:









1. How much C is stored in different agroforestry systems aboveground and belowground?

2. How do trees contribute to C storage in the soil, and how labile is this C?

3. What is the overall relative attractiveness of each of the selected agroforestry systems
considering its C sequestration potential in the context of its biological potential, economic
profitability, and social acceptability?

4. If carbon credit markets were introduced under CDM, would adoption of agroforestry
provide more profits to land owners? If yes, how much?

Dissertation Overview

This dissertation is presented in seven chapters. Following this introductory chapter

(Chapter 1), Chapter 2 describes the natural environment of the WAS, the study region, in terms

of its climate, vegetation, soil taxonomy etc. The region's land-use systems in general and

agroforestry systems in particular, are also described. Chapter 3 presents the literature review,

summarizing the methods used to estimate the C sequestration potential in agroforestry systems,

as well as the current state of knowledge on C sequestration potential in the WAS. The

possibilities and limitations in the region, current research trends, and future research needs are

also included. Chapter 4 presents the results of C stock measurements and a comparison of five

selected land-use systems (four agroforestry systems and one degraded land) in the Segou region,

Mali. Methodologies and results of measuring both biomass C and soil C are presented. Total C

storage of each system is compared and discussed. Chapter 5 examines soil C measurements in

more detail based on analyses of soil samples drawn from different depths from each of the five

selected land-use types, and discusses influence of trees and land management on soil C

sequestration and stability of soil C. Chapter 6 presents a socioeconomic feasibility analysis of

two improved agroforestry systems in the study region; results of cost/benefit and sensitivity

analysis are presented both with and without C sale scenarios. A risk assessment using a

simulation program gives insight into how introducing agroforestry in the study region might










economically affect local households. Chapter 7 gives a synthesis, conclusions and

recommendations for future research and development efforts.









CHAPTER 2
THE WEST AFRICAN SAHEL: GENERAL LAND-USE AND AGROFORESTRY

Description of the Region

The Sahel is a transition zone between the hyper-arid Sahara to the north and the more

humid savannas and woodlands to the south. The west part of the Sahel region (West African

Sahel: WAS) includes nine countries, who are members of the Interstate Committee for Drought

Control in Sahel (CILSS); these are Burkina Faso, Cape Verde, Gambia, Guinea Bissau, Mali,

Mauritania, Niger, Senegal, and Chad. The area covers about 5.4 million km2, with over 500

million inhabitants. Its vegetation mostly consists of bushes, herbs and small trees, and does not

offer year-round harvests.

The main characteristics of the WAS include: 1) irregular and little predictable rainfall; 2)

predominance of agriculture and animal husbandry: more than half of the inhabitants are farmers

and agriculture contributes more than 40 % to the Gross Domestic Product (GDP); and 3) high

demographic growth (around 3 %) and high urban growth (around 7 %) (USGS 2007).

Climate

The isohyet lines of the region are almost parallel to the latitude and divide the WAS into

three sub-groups: Sahelo-Saharan, Sahelian, and Sudano-Sahelian zones (Figure 2-1). Rainfall in

the region varies from 200 to 2500 mm per year with the vast maj ority of the region receiving

between 350 to 800 mm, and is characterized by year to year and decadal time-scale variability;

there were extended wet periods in 1905 09 and 1950 69, and extended dry periods in 1910 -

14 and 1968 1997 (Figure 2-2). The most recent drought that began in late 1960s caused the

severe famine in the 1970s. Since 1997, the rainfall recovered somewhat, but the annual rainfall

of the recent years was still below the pre-1970 level (= ~540mm), except 1994, 1999, and 2003

(Dai et al. 2004). Although the length of the rainy season varies with latitude and local









conditions, it is generally restricted to a period of three to five months from April to October.

During this period, there is an average of 24 rainfall events, 10 to 12 of which occur in August.

Rainstorms are rarely prolonged, usually lasting no more than one or two hours. Rainfall

intensities range from 5 to more than 50 mm per event (Gritzner 1988). The rainy season is

followed by an extended dry season where the vegetation cover changes drastically (Figure 2-3).

The monthly mean temperature of the region is 26 27 OC, with a monthly mean

maximum of 34 36 OC and monthly mean minimum 21 230C. Temperature abnormalities are

relatively low for the area as a whole (+0.7 OC to -0.6 OC), but may be greater in individual

places (Littmann 1991).

Vegetation

The WAS contains three generalized phytogeographical divisions corresponding to the

climate zones (Figure 2-1): (i) the northerly Sahelo-Saharan zone, or grass steppe, between the

100 and 200mm isohyets; (ii) the Sahel proper, or tree steppe, between the 200 and 400 mm

isohyets; (iii) the southerly Sudano-Sahelian borderlands, or shrub savannah, extending to the

800 mm isohyets.

Savanna plants are renowned for their well-developed root systems, penetrating deeply into

the soil. Herbaceous plants, mostly perennials, always have an extensive root system, often

forming a close mat of rootlets in the upper layers of the soil. Most of the roots are located

within the upper 30 cm of soil (Bourliere 1983). Grasses in the steppe grow in the very short

growing season (60 90 days) with narrow leaves in circles or basal rosettes. One of the most

common grass species throughout the WAS is Cenchrus biflorus. This prickly, short-lived grass

is the food of choice for the herds that graze throughout the Sahel. Mature grass has sharp

bristles; but ensiling softens them, so that it can also be used as silage (FAO 1991). Other

common grass species in steppe such as Schoenefeldia grcilis, Elionorus elegans, Borreria spp.,









are also used as fodders. In the south, where the savannah replaces the steppe, the tall perennial

grasses such as Andropzogon gavanus as well as annual grasses with long cycles such as

Pennisetum pzedicellatum, Andropzogon pzseudapzricus, and Diheteropzogon hagerupziiare are

common. These grasses grow rapidly up to 2.5 m in height, but natural bush fires control the

reserves. Some of these species are introduced as ornamental or fodder species in the US

(Pennisetum pedicellatum, called Kyasuma grass) and Australia (Andoropogon gayan~~~ggg~~us), and

because of their rigorous spread, they are invasive species.

Although most tree and shrub species are found both in steppe and savannah (Table 2-1),

the woody vegetation become more and more diverse and dense as one goes south. The trees in

the WAS are usually low-branched and may ramify from their base. Crowns are generally very

wide, and much more developed than the bole. The thickness of the bark has been interpreted as

affording protection against repeated bush fires. Spines and thorns on branches are also frequent,

which prevent reducing water loss through evaporation. It may afford some protection against

browsing by large mammals, but does not prevent foliage browsing.

Soil

Detailed information on the soil resource base of the WAS is inadequate for most research

purposes. In most countries, farm-level information and detailed soil maps are non-existent.

Still, in 1977, Food and Agricultural Organization (FAO) of United Nations (UN) and UN

Educational and Scientific Organization (UNESCO) formed soil map of Africa, by aggregating

specific soil mapping units to form soil regions that corresponded roughly to Africa's maj or

ecological regions. Natural Resources Conservation Service (NRCS) of the United States

Department of Agriculture (USDA) had a pedon database with more than 400 pedons from

Africa. With published national soil survey reports, NRCS translated the legend of the UN Soil

Map of the Africa into Soil Taxonomy Map (Figure 2-4).









The northern WAS, adj acent to rocky Sahara desert to the north, is dominated by Entisols

and in some parts by Aridisols. Most of the Entisols in the area have an aridic soil moisture

regime and are formed on sandy or loamy deposits. Psamments are present as Eingerings of

Sahara in zones with ustic or udic soil moisture regimes (Eswaran et al. 1996). Vertisols occur

locally in some places along the rift valley of the Niger River and around Lake Chad. At lower

latitudes within the WAS, Alfisols are extensively spread. In general, the wind-blown sand from

the Sahara desert has buried many of the former Oxisols and Alfisols/Ultisols; thus, soils in the

WAS characteristically have very sandy top soils and a low-activity clay subsoil.

In terms of soil quality for agricultural use, soil moisture stress is perhaps the overriding

constraint in much of the WAS. It is not only because of the low and erratic precipitation but

also of the ability of the soil to hold and release water. A large part of northern WAS (Entisols

and Aridisols) has low available water holding capacities (AWHC), <25 mm. And southern part

of the WAS is made up of soils with medium AWHC (24 100 mm), mainly Alfisols and

Ultisols. Salinity and alkalinity are other problems affecting agriculture. The extremely acid

soils, which are mainly the acid sulphate soils, occupy areas around the Niger delta. Some parts

of Alfisols (close to southern Ultisols) have acid surface and subsurface horizons, which,

coupled with the moisture stress conditions, makes these soils extremely difficult to manage for

productive use under low-input conditions. The annual additions of dust from the Sahara

brought by the Harmattan winds (a dry and dusty wind blowing south off the Sahara into the

Gulf of Guinea during the dry season) raise the pH and base saturation of the surface horizons;

although the changes are less acute than the eastern part of the Sahel where subsoil acidity is a

problem (Tiessen et al. 1991).









In addition to the moisture stress and alkalinity/acidity, there are several other soil-related

constraints common in the WAS contributing to low productivity. These include: 1) inherently

low nutrient storage capacities (cation exchange capacities, CECs) due to the low-activity

kaolintic clay minerals present or the overall low clay contents, 2) low equilibrium soil organic

matter levels due to intensive cultivation without adequate biomass return and high surface soil

temperatures, 3) the presence of large amounts of free aluminum and iron oxides which reduces

the availability of phosphate to plants (Gritzner 1988; de Alwis 1996)

Traditional Farming Systems and Agroforestry in the WAS

The traditional farming systems in the WAS are rain-fed, low external input operations.

Farmers use traditional agricultural methods: use of domestic wastes, farmyard manure, crop

rotations, and the incorporation of trees on farmlands. There is a considerable variety of crops

grown in Sahelian agricultural systems, including: grains, such as millet (Pennisetum glaucum),

sorghum (Sorghum bicolor), fonio (Digitaria exilis), rice (Oryza glaberrima and Oryza sative),

sesame (Sesamnum indicum), and safflower (Calrthamnus tinctorius); garden crops, such as

eggplant (Solan2um melongena), broad beans (Vicia faba), okra (Abelmoschus esculentus), carrots

(Daucus carota), chick-peas (Cicer arietinum), pigeon peas (Cajanus cajan), cowpeas (Vigna

unguiculata), ground nut (Arachis hypogaea), yams (Dioscorea spp.), calabash (Lagenaria

siceraria), leeks (Allium ampelopra~sum), melons (Cucurbitaceae Family), etc. Cultivated tree

crops including dates (Phoenix dactylifera), figs (Ficus spp.), lemons (Citrus spp.), mulberries

(M~orus spp.), and various gums (Acacia spp.) are also common (Gritzner 1988; ICRISAT 2007).









Traditional Agroforestry Practices

Bush fallow/shifting cultivation

Shifting cultivation refers to the land-management practice where a period of cropping

(cropping phase) is alternated with a period in which the soil is rested (fallow phase). This

system has been traditionally practiced in the WAS, as well as other tropical and semi-tropical

regions of the world (Nair 1993). First, the clearing is done using axes or machetes and only

herbaceous plants, saplings and undergrowth are cut. When the cut material is dried and burned,

the cleared area is planted with crops like yams, sorghum, millet, maize (Zea mays), and cassava

(Manihot esculenta). The land is cultivated for one to four years after which it returns to fallow.

The regrowth of natural vegetation rejuvenates the soil through nutrient cycling, addition of litter

and suppression of weeds (Ferguson 1983).

In general, the fallow phase is much longer than the cropping phase. However, recent

rapid population growth in the WAS countries (from 2.5 to 3.0 %) requires additional cultivated

land, often at the expense of fallow and pastureland. Over the years, the fallows became greatly

reduced both in area and duration, putting in j eopardy the return of vegetative cover for the

build-up of soil fertility (Kaya 2000).

Parkland system

Another traditional land-use system, sometimes overlapped with tree-combined fallow

system, is known as the 'agroforestry parklands' system. Parklands are generally understood as

landscapes in which mature trees occur scattered in cultivated or recently fallowed fields (Boffa

1999). Farmers grow crops around and underneath of the trees (Figure 2-5). These trees are

selectively left or regenerated by farmers because of the variety of functions (mostly non-timber

use) such as food and medicine (Table 2-2). Parkland trees can also contribute to temperature

amelioration and to prevention of soil erosion (Jonsson et al. 1999). Parklands occupy a vast









land area, representing a large part of the agricultural landscape under subsistence farming in the

WAS and it is the predominant agroforestry system. For example, the agroforestry parkland

system occupies about 90 % of the agricultural land area in Mali (Cisse, 1995), and in Burkina

Faso, parklands are found throughout settled zones where agriculture is practiced.

Parklands are most often characterized by the dominance of one or a few tree species.

Species composition is generally more diverse and variable, however, in areas located farther

away from villages and only occasionally cultivated. Common species in the WAS are Acacia

senegal, Adansoniadd~~~ddd~~~ddd~~ digiata, Anogeissus leiocarpus, Balan2ites aegyptiaca, Bombax costatum,

Bora~ssus aethiopum, Ceiba pentandra, Diospyros mespiliformis, Elaeis guineensis, Faidherbia

albida, Hyphaene thebaica, Lannea microcarpa, Parkia biglobosa, Sclerocarya birrea,

Tama~rindus indict, Vitellaria paradoxa, V~1~~11~11 itex doniana, and Ziziphus mauritiana (Table 2-2)

(Boffa 1999).

Improved Agroforestry Practices

The expansion of rain-fed agriculture results in soil erosion through the removal of

vegetative cover and physical disturbance. Wind and water erosion is extensive in many parts of

the WAS. Practically every country of Africa is prone to desertification, but the Sahelian

countries at the southern fringe of the Sahara are particularly vulnerable (Reich et al. 2001). Soil

nutrients are removed through crops, erosion, and leaching by rainfall, without replenishment by

additions or regeneration under natural fallow. Inappropriate tillage and cultural practice reduce

soil infiltration and retention of water, which further degrade the land (de Alwis 1996). Also,

deforestation accelerates the land degradation as trees and shrubs are cut to satisfy the

construction, fuel, and fodder requirements of the cultivators and their livestock. In the WAS,

farmers/pastoralists usually graze their animals in the open area without any control (Figure 2-6).

Degraded land spreads as these animals go further after eating the vegetation around the villages.









Consequently, forest and woodland areas are rapidly declining by an estimated 1.5 % per year on

average of West African countries (FAO 2000).

Prevention of land-degradation by controlled grazing and afforestation is often discussed

and tried sporadically throughout the WAS as proj ects, financed mainly by international donor

communities and agencies (Oba et al. 2000). However, local participation has often been short-

lived and management not successful because little consideration was given to why farmers keep

browsing the animals and do not protect or grow trees. Gradually, there has been a growing

awareness that trees be regarded as an integral component of an overall farming system and that

a complex decision-making environment with interdisciplinary interactions is needed (Boffa

1999).

Adoption of improved land-use systems such as agroforestry has been recommended and

tried for rehabilitation of the degraded soils in various parts of the WAS (Roose et al. 1999; Lal

2004a). No-till farming and improved fallow involving short-rotation woody and/or other

perennial species are increasingly studied. Improved fallow rests land from cultivation, as in

natural fallows, but the vegetation comprises planted and managed species of leguminous trees,

shrubs, and herbaceous cover crops. These vegetation and the roots are expected to reduce the

soil nutrient loss or even to replenish them both chemically and physically, and to sustain crop

production with shorter fallow period (Bationo et al. 2000; Kaya and Nair 2001). Farming

systems that promote organic manure inputs (including litters from woody plants) and tree-

cropping systems have also been tried (Breman and Kessler 1997). As such, agroforestry

practices involving incorporation of woody plants (both indigenous and exotic species) on

cultivated land as intercrops, fences, shelter belts, and/or fodder resources are recognized as a









maj or technique to ameliorate the spreading land degradation in the WAS. Details of the

improved agroforestry practices being introduced in the study region are described in Chapter 4.










Table 2-1. Common tree and shrub species found throughout the West African Sahel.


Botanic Description
One of the most common species in the
Sahel. Deep root system with feathers
leaves protecting barks from dry winds.
Species often seen in the WAS are A.
nilotica, A. tortillas, A. senegal, and A.
seyal.

This drought-and fire-resistant tree is found
throughout the Sahel. With trunks that are
often 10 15 m wide, it is one of the largest
trees (in terms of trunk width): it grows up
to 25 m high. In the dry season, the baobab
is completely without leaves, and because of
its distinguishable shape of branches that
look like roots, it is called the "upside down"
tree.


Species
Acacia spp.







Adansonia
digiata
(The baobab
tree)


Functional Use
The bark of most acacia produces tannins,
which are used in tanning leather. A.
senegal produces gum arabic, used in
pharmaceuticals and adhesives. Fruits are
sometimes consumed as condiments


The bark can be used for rope and cloth,
and the trunk, when hollowed out, as a
shelter. Fruits and leaves are food
sources; especially leaves are very
important vitamin source for the local
people.


Balanites Multi-branched, spiny shrub or tree up to 10
(,, e mot e,, m tall. Trunk is short and often branching
from near the base. Branches are armed
with stout yellow or green thorns up to 8 cm
long.


The fleshy pulp of both unripe and ripe
fruits is edible and eaten dried or fresh.
The fresh and dried leaves, fruits, and
sprouts are all eaten by livestock.


The branches are quite strong, and are a
useful material for building stools, beds,
tool handles, etc. A tea made by steeping
the leaves of C. micaranthum in boiling
water is a traditional tonic drink and a
decoction of the leaves is sometimes used
as a medication for malaria.

It is a valuable fodder tree for game and
domestic animals during dry season. The
seeds can be boiled and eaten, but first
the skin has to be removed. Also the pods
may be dried and ground into flour,
which is edible.

Leaves and roots are traditionally used to
treat different diseases, particularly
malaria and intestinal disorders.


Combretum
spp.


The genus comprises about 370 species of
trees and shrubs, 300 of which are native to
tropical and southern Africa. C. glutinosum
and C. micaranthum are common in the
WAS. They are bushes branching from
bases, 1-2m tall.


Faidherbia One of the fastest growing trees in the WAS.
albida It is deciduous and has the remarkable
phenolgy of leaves falling off in rainy
season and coming back in the dry season. It
can grow up to 30 m tall. Branching stems
and an erect to roundish crown.


Guiera
senegalensis


Perennial bush which is a maj or component
of disturbed parts of bushland in the WAS.
Also abundant on roadsides and fallowed
lands. The woody part is fragile.




































Vitellaria Occurs in a wide latitudinal belt between 5 o
paradoxa and 15 oN from Senegal to the Central
African Republic. The size of the mature
tree varies from 7 25 m. The bole is short,
3 4 m, sometimes up to 8 m with diameter
less than 1 m and with thick bark that
protects old trees from bush fires


Compiled from USDA plants database, FAO plants database, and other FAO documents.


Table 2-1. Continued.
Species Botanic Description


Parkia
biglobosa


Functional Use
Bark, leaves, flowers and pods have
innumerable medicinal and food
utilizations, the pods, in particular (husk
and pulp) are staple food for humans,
stored in households. Foliage contains
saponins, but is nevertheless considered
palatable to cattle, flowers are rich in
nectar and beehives are often placed on
the branches.

The fruit of the tree is used as fodder,
while the seeds are fermented to make a
protein-rich condiment. The products
from the hard wood, such as some
wooden farm implements, kitchen
utensils, and planks for construction, are
extensively traded. The tree is a good
source of firewood and charcoal. The
secondary roots are used as medicine.

The main product is shea butter (karite)
which is extracted from the seeds. It is
one of the most affordable and widely
used vegetable fats in the Sahel and plays
an important role in the economy of the
region. The timber is of good quality,
termite resistant, and generally very
durable, but is normally used only when
the tree has passed the fruit-bearing age.


Large tree (up to 20 m) with a dense
spreading crown, scaly and creviced grey-
brown bark. Rather slow growth, begins
fruiting after 8 years. Trees are usually
isolated.






Small to large leguminous spiny trees (4 -
20 m), with an open canopy and drooping
foliage. It thrives in arid soil and is resistant
to droughts, on occasion developing
extremely deep root systems.


Prosopis
africana










Table 2-2. Main productive functions of agroforestry parklands


Source: (Boffa 1999)


Parkland tree function
Browse


Examples
Pterocarpus erinaceus, Pterocarpus lucens, Balan2ites
aegyptiaca, Faidherbia albida, Acacia raddiana, Bauhinia
rufesenZs

Parkland products eaten when crops have failed. Young shoots of
Borassus aethiopum eaten as vegetables; fruits and leaves of Ficus
gnaphalocarpa and other Ficus species.

Butter extracted from Vitellaria paradoxa;11~~1~~11~ oil produced from
Balan2ites aegyptiaca, Parinari macrophylla, Lophira alata
and Elaeis guineensis.

Condiments served with staple cereals. Seeds of Parkia biglobosa,
Tamarindus indic, Adansonia digitata, and Ceiba pentandra leaves.

Borassus aethiopum (baskets, hats, furniture), fibers from Adansonia
digitata, Ficus ;li h hi, ,,ii and Ficus glumosa.

Faidherbia albida and, to a lesser degree, Prosopis africana
(Nitrogen-fixing) .

The sap of Elaeis guineensis, Borassus aethiopum and Hyphaene
thebat'ca is processed into wine.

Ziziphus spp., Anogeissus leiocarpus (firewood), Borassus
aethiopum (construction).


Famine food



Fat and oil production



Food complement


Handicrafts and clothing


Soil fertility


Wine production


Wood production


































Figure 2-1. Map of West Africa with ecological zones and isohyetal lines. The WAS consists of
Sahelo-Saharan, Sahelian and Sudano-Sahelian zones. Source: Famine Early
Warning Systems Network (http://www.fews.net/)


Sdhban SbhbBan I kd~nlan p Mrr
hhP1~5lharan ~ Swlan~S~hdian I Suinwn

























I III/I /I I II


r II


i 1 1 1 l i l
1890 1900 10 20 30


i l l
4s0 1950 60


l i i l l
70) 80


l I l
90 200


year


Figure 2-2. Standardized annual Sahel rainfall (June to October) from 1898 to 2004. The rainfall
data are converted to relative values (standardized) with respect to data from 1898
tol993, such that the mean and standard deviation of the series are 0 and 1,
respectively. Positive values (orange) are the years with rainfall more than the mean
of 1898 1993 data, and negative values (blue) are the years with less rainfall.
Source: Mitchell (2005)


A B


Figure 2-3. Seasonal landscape contrast of the WAS. Photos of the same site A) in the dry
season and B) rainy season. Source: USGS (http://edcintl. cr.usgs.gov/sahel .html).


'"r CT"''"il'"l'r









Soil Taxonomy Orders








L 1


I 8


Histaosls
Spodosols
Andisols
Oxisols
Vertisol~s
Anidis~ols
Ultisols
Molliso~ls
Alfisols
inceptisois
Enrtisols
Ounces, Pans
inlond Water


or Rocklands


Ociober 1996



Figure 2-4. Distribution of soil orders (USDA soil taxonomy) in West Africa. Source: Eswaran
et al. (1996)





























Figure 2-5. Parkland system in Segou, Mali. Trees are scattered in the cultivated land, and
protected for non-timber use. Ox-drawn plows are used to till the land to sow the
crops upon onset of rains. (Photographed by author)


?3;:' ;":""~i; ';;'
r*Il ..~ .


111


Figure 2-6. Allowing the cattle to roam freely on the landscape during the dry season after
seasonal crops have been harvested is a common feature of the WAS land-use system.
This often leads to overgrazing (photo from the Segou region, Mali). When the open
lands near the village are depleted of vegetation, farmers are forced to take the
animals further away from the village. (Photographed by author)









CHAPTER 3
LITERATURE REVIEW: CARBON SEQUESTRATION POTENTIAL OF AGROFORESTRY
SYSTEMS IN THE WEST AFRICAN SAHEL (WAS)

Overview

Carbon (C) sequestration has become a hotly debated and widely researched topic during

the recent past. Consequently, voluminous literature is available on the subject. The review in

this chapter is limited to issues that are most relevant to the present study. Following a general

overview of the topic, the chapter presents brief descriptions of various methodologies that are

currently recognized and/or debated for C measurement and accounting, although not all of these

were used in this study. Then, studies estimating C storage in agroforestry systems (both

biomass C and soil C) in the WAS and other ecoregions are presented. Given that the potential

of C sequestration cannot be fully evaluated without integrating both biophysical and

socioeconomic sides of acceptability, socioeconomic issues related to C sequestration activities

through agroforestry are also discussed.

C Sequestration as a Climate-Change-Mitigation Activity

The international response to climate change started in full with the establishment of the

United Nations Framework Convention on Climate Change (UNFCCC) in 1992. Five years

later, 159 countries signed a treaty called the Kyoto Protocol, which commits the 38 signatory

developed countries to reduce their collective greenhouse gas (GHG) emissions by at least 5%

compared to the 1990 level by the period 2008 2012. The agreement came into force on

February 16, 2005, following its ratification by Russia on November 18, 2004. As of April

2007, a total of 169 countries and other governmental entities have ratified the agreement. A

unique characteristic of the Kyoto protocol is that it allows the amount of CO2 sequestered by

forests to be counted towards emission targets.










Tropical forest conversion contributes as much as 25 % of net annual CO2 emiSSIOnS

globally (Palm et al. 2004). Removing this atmospheric C and storing it in the terrestrial

biosphere is, thus, one option for mitigating the emission of this GHG. A recent assessment of

Rose et al. (2007), referenced by Intergovernmental Panel on Climate Change (IPCC)'s newest

report, suggests that land-based mitigation agriculture, forestry, and biomass liquid and solid

energy substitutes can be cost-effective land mitigation options. And, it can contribute over the

century 94 to 343 Pg C equivalent of greenhouse gas emission abatement, which is 15 to 40

percent of the total abatement required for stabilization.

Agroforestry for C sequestration

Under the Kyoto Protocol's Article 3.3, further defined by Marrakesh Accord in 2001,

agroforestry was recognized as an option of mitigating GHGs. Since then, the C sequestration

potential of agroforestry systems has attracted greater attention from both industrialized and

developing countries. It is attractive because of its applicability to a large number of people and

areas currently in agriculture, as well as its perceived potential for reducing pressure on natural

forests. Also, Clean Development Mechanism (CDM), defined in Article 12 of the Protocol adds

the attractiveness, because the CDM provides for Annex I Parties (industrialized countries which

have emission reduction goals) to implement proj ect activities that reduce emissions in non-

Annex I Parties (developing countries), in return for certified emission reductions (CERs)

(UNFCCC 2007). Since agroforestry is traditionally and widely practiced in developing

countries, it is feasible/easy options for both developing and developed groups of countries to

start as mitigation proj ects under the CDM.

However, as stated by Makundi et al. (2004) and several others, estimating the amount of

C sequestered by agroforestry poses unique challenges. In addition to the complexity caused by

diverse factors such as climate, soil type, tree-planting densities, and tree management as well as










specific difficulties arising from requirements for monitoring, verification, leakage assessment

and the establishment of credible baselines, agroforestry estimations are beset by the problem of

estimating the area under agroforestry practices. Nevertheless, the IPCC (2000) estimated that

630 million ha of unproductive croplands and grasslands could be converted to agroforestry

worldwide, with the potential to sequester 391,000 Mg of C per year by 2010 and 586,000 Mg C

per year by 2040.

Although the credibility of conceptual models and theoretical benefits has been

demonstrated, C sequestration potential is still a little-studied characteristic of agroforestry

systems (Nair and Nair 2003). More studies examining how much C can be sequestered/stored

in various agroforestry systems around the world are needed. Several studies and reviews from

different regions of the world have discussed agroforesty' s benefits and limitations for C

sequestration (Schroeder 1994; Dixon 1995; Albrecht and Kandji 2003), but only very few deal

with comprehensive comparisons of different practices in each ecoregion.

Due to the difficult physical environment and lack of research infrastructure, agroforestry

systems in the WAS are one of the least documented topics regarding C sequestration potential.

Lal (1999) estimated the potential for sequestering C in the region was, as in most other

drylands, fairly low, between 0.05 0.3 Mg C ha' yr- The estimate, however, included a

variety of uncertainties related to future shifts in global climate, land-use and land cover, and the

poor performance of trees and crops on poor soils in the region.

In the WAS, impacts of population pressure, over-grazing and continuous drought are

causing severe land degradation. Consequently, biomass C stocks steadily decline within land-

use/land cover. Opportunities for C gains in the region are, thus, often discussed in the context

of agricultural fertility and sustainability of farming systems, which involve agroforestry such as









tree-crop-livestock integration and fallowing practices (Manlay et al. 2002; Woomer et al.

2004a).

Methodologies for C Sequestration Measurements

Efforts to accurately measure C in forests are gaining global attention as countries seek to

comply with agreements under the UNFCCC. Many methodologies have been put forth to

quantify the amount of C in forests (Beer et al. 1990; MacDicken 1997; Brown 1999), and are

best based on permanent sample plots laid out in a statistically sound designs. This is often quite

difficult in agroforestry systems and is one of the reasons why there are few studies that actually

measure the amount of C (Montagnini and Nair 2004). Practically, there are four possible

approaches to measuring the amount of C stored as a result of particular land management

practice; 1) Direct on-site measurements of biomass, soil C, or C flux, 2) Indirect remote sensing

techniques, 3) Modeling, 4) Default values for land/activity based practices (Table 3-1).

Most of these approaches were originally developed to estimate the amount of C in forest

stands. Several pilot proj ects are ongoing to ensure that C that is sequestered for the long term in

economically viable agroforestry systems is reliably measured. The factors that influence which

approach is used in a specific proj ect depends on technical availability, budget for the

measurement, and size of the land to be estimated. Since most of C mitigation proj ects are either

still in the pilot stage or implemented on a small scale, direct measurement approaches are most

commonly used and reported.

Direct On-site Measurement

Direct on-site measurement includes field sampling and laboratory measurements of total

C in the biomass and soil. These measurements (including inventory data used for the remote

sensing, modeling or default values) are in effect "snapshots" of C stored at the time of the









inventory. How to calculate/determine the amount of "sequestered" C over a certain period is

another issue, and discussed in the "Accounting Methods" section.

Inventory

In general, C in forest or agroforestry systems can be divided into four groups; 1)

Aboveground biomass, 2) Belowground biomass, 3) Soil C, 4) Litter fall/crop residue. Methods

to collect and calculate the sample data from proj ect sites have been standardized by many

reports and studies (MacDicken 1997; Roshetko et al. 2002).

Data for the four C categories are collected by timber cruising and sampling of herbaceous

vegetation, soil, and standing litter crop at sample plots (Shepherd and Montagnini 2001; Brown

2002; Tiepolo et al. 2002). Also, for existing forests, many tropical countries have at least one

inventory of all or part of their forest area that could be applied for agroforestry systems,

although many of the inventories are more than 10 years old and very few have repeated

inventories. Data from these inventories can be converted to biomass C depending on the level

of detail reported (Brown, 1997).

Conversion and estimation

For aboveground biomass, trees are divided by compartments: leaves, branches and trunks,

and measured in dry weight (Beer et al. 1990), because each compartment has unique C content

and decomposition rate. Although this is the most accurate method, these inventories are often

too time-consuming and costly.

Alternatively, biomass expansion factors or allometric biomass equations are often used,

because they require only stem wood information such as diameter at breast height (DBH).

These equations exist for practically all forests types of the world, especially in the temperate

zone (Sharrow and Ismail 2004). But, because of the very general nature of these equations, they

lack accuracy; they are, at best, approximations. For an agroforestry system, Shroeder (1994)









used a ratio of total aboveground biomass to stem wood biomass of 2. 15 derived from many

previous studies. Where tree-stocking density was high (>500 trees ha l) and the growth cycle or

rotation length was relatively long (>10 years), i.e., for conditions more similar to those for a

forest plantation, a ratio of 1.6 was used in the study to estimate total aboveground biomass.

Total C content is usually estimated based on the assumption that 45 to 50 % of branch and stem

dry biomass is C, and that 30 % of dry foliage biomass is C (Shepherd and Montagnini 2001;

Schroth et al. 2002).

Herbaceous vegetation and standing litter are also collected from sample plots and weighed

to calculate their C content. It is often assumed in inventories that this vegetation type

contributes little to the total biomass C of a forest and it is often ignored. However, the

contribution of herbaceous vegetations is often larger in agroforestry systems than in forests,

such as green manure from trees in natural systems. The amount of litterfall, pruning residues,

and crops largely depends on the season and rotation period (Beer et al. 1990). Thus, it is

difficult to estimate using general ratios as used in the stem biomass estimation.

For belowground C, it is divided into two main categories; root biomass, and soil C

(mainly organic matter). Although methods for measuring aboveground biomass are well

established, measurement of root biomass is difficult and time-consuming in any ecosystem and

methods are generally not standardized (Ingram and Fernandes 2001). A review of the literature

shows that typical methods include spatially distributed soil cores or pits for fine and medium

roots and partial to complete excavation and/or allometry for coarse roots. The distinction

between live and dead roots is generally not made and root biomass is usually reported as total.

Moreover, sampling depths are not standardized, yet the depth selected in a given study is

assumed to capture practically all roots (Brown 2002).









Root biomass is often estimated from root:shoot ratios (R/S). It can be calculated by

sample plot measurements, but there are also lists of reference data. A literature review by

Cairns et al. (1997) included more than 160 studies covering tropical, temperate and boreal

forests that reported both root biomass and aboveground biomass. The mean R/S based on these

studies was 0.26, with a range of 0. 18 (lower 25 % quartile) to 0.30 (upper 75 % quartile). The

R/S did not vary significantly with latitudinal zone (tropical, temperate, and boreal), soil texture

(fine, medium and coarse), or tree type (angiosperm and gymnosperm).

Soil C samples should be collected from each layer, dry-weighed and analyzed for its C

contents by recommended laboratory procedures. To calculate C stocks per unit area, the C

content in the soil is multiplied by the bulk density of the respective soil layer. By itself, C

sequestration in agricultural soils is expected to make only modest contributions globally (e.g., 3

- 6 % of total fossil C emissions) (Paustian et al. 1997). However, this amount can be

significantly varied through management such as fallow phase, erosion, tillage, or tree

incorporation.

Indirect Remote Sensing Techniques

Even where field measurement methodologies are established, agricultural/forestry

practices are inherently dispersed over a wide geographic area. Staffing costs for monitoring and

verification of land-use practices over such a wide area could prove to be cost prohibitive.

Because direct field measurements can be expensive, the use of indirect remote sensing

techniques is being considered. A range of remote data collection technologies is now available

including satellite imagery and aerial photo-imagery from low flying airplanes. Sensors that can

measure the height of the canopy or vertical structure will be needed along with the more

traditional sensors on Landsat or Spot satellites in order to improve the ability of remotely

sensing biomass (Brown 2002).










A promising advance in remote measurements of forest/agroforest biomass C is a scanning

lidar (a pulsed laser), a relatively new type of sensor that explicitly measures canopy height.

This sensor is able to monitor 98 % of the earth's closed canopy forests (Brown 2002). Another

promising advance in the remote sensing area, especially at smaller scales (thus, probably more

appropriate for agroforestry systems), couples dual-camera digital videos (wide-angle and zoom)

with a pulse laser profiler, data recorders, and differential GPS (geographical positioning system)

mounted on a single engine plane (Brown 2002). The plane flies aerial transects across the area

with several fixed altitude to take the images data, and these images are used to create 3D models

of the terrain. From these measurements, crown area, tree height, or number of stems per area of

agroforestry systems would be much more easily and accurately estimated.

Modeling

Since total direct inventory is site-specific and can be expensive, another way to lower the

cost of estimating C amount is developing a model. To date, several models have been

developed that simulate C budgets and fluxes at the level of the forest stands. These models

range from very detailed ecophysiological models used in climate impact assessment, to very

general empirical, descriptive models of C budgets within forest stands. None of these models

has been widely disseminated, and none of them accepted as a possible standard for C crediting

proj ects so far.

One of the most recognized and utilized models by various proj ects, including agroforestry

proj ects, is CO2FIX which was developed by researchers of Wageningen University,

Universidad Nacional Autonoma de Mexico, Centro Agronomico Tropical de Investigacion y

Ensenanza (CATIE), and European Forest Institute (EFI). This model is a user-friendly tool for

dynamically estimating the C sequestration potential of forest management, agroforestry and









afforestation projects. The model is a multi-cohort ecosystem-level model based on C

accounting of forest stands, including forest biomass, soils and products (Masera et al. 2003).

Another common methodological approach to estimating mitigation potential more broadly

is known as comprehensive mitigation assessment process (COMAP). The COMAP model

requires the proj section of land-use scenarios for both a baseline and for a mitigation case. It

requires data on a per hectare basis on C sequestration in vegetation, detritus, and forest

products, soils and also on GHG emission avoidance activities (Makundi and Sathaye 2004).

Default Values for Land/Activity Based Practices

This approach is the broadest, nation-level approach, which uses default values for certain

land-based activities for estimating C storage. A land-use based accounting system would focus

on the changes in C stocks on managed lands during a defined time period (Dixon et al. 1994a).

Default values would be assigned to a particular tract of land based upon county or regional level

research on the average sequestration likely to result from specific agricultural or conservation

measures in that area. Various values could be assigned to such broad land management

activities as forest, cropland, or grazing management. Under this approach, field measurements

of C storage changes in individual fields would not be necessary. Land-use monitoring can be

readily measured by remote sensing techniques, eliminating the need for many field inspectors.

However, field plots may need to be set up, representing the average or a range of conditions for

the entire proj ect area, and used as a reference to provide actual estimates to increase the

accuracy of large-scale proj ects.

Accounting Methods

In order to assert that agroforestry systems are an important C sequestration method, the

amounts measured in agroforestry systems must result in long-term changes in terrestrial C

storage and CO2 COncentrations in the atmosphere (Masera et al. 2003). Thus, the time frame









and C accounting methods are very important; but they are also often controversial issues at

international negotiations of climate change.

Approaches to Assessing C Sequestration Performance

Fluxes of C and flow summation

Balancing the annual flux from a source of emissions by uptake in a forestry/agroforestry

proj ect is conceptually the simplest way of providing offsets. In this approach, offsets are

'delivered' to the C credit market on an annual basis, according to the emissions avoided,

relative to the proj ect baseline. However, since fluxes of C associated with forestry/agroforestry

are irregular, it may be difficult to match the uptake by a particular project to an industrial source

of emissions.

Furthermore, national or international authorities must assign permanent C storage status

to proj ect areas, such that the owners are liable for any emissions, as well as eligible for credits.

Without such status C might be accumulated in the growing phase of the forestry cycle, only to

be lost at the end of rotation (Tipper and De Jong 1998).

Average changes in the stocks of C

The pilot phase of most sequestration proj ects is assessed on the basis of the long-term

average increase in the stocks of terrestrial C relative to the baseline (Kursten and Burschel

1993; van Noordwijk et al. 2002), expressed as tC according to the equation: Average net C

storage (tC) = C(C stored in project C stored in baseline) in tC / n (years)

The stock change method calculates the difference in C stocks between a proj ect and its

baseline at a given point in time. A key advantage of both methods is that it focuses on the

sustainability of changing the stock of C stored in vegetation and soils. However, long-term C

storage is not easily defined, and there may be considerable argument over the assumptions

about risks and possible future changes in management. The timing of the emission reduction









relative to the emissions that are supposed to be offset may be problematic, since there may be a

time lag of years to decades between the establishment of the offset proj ect and actual uptake of

the C.

Cumulative C storage

The cumulative storage approach is based on an understanding of C cycle dynamics and

radiative forcing of the atmosphere. The total warming effect of a given emission is determined

by the cumulative presence of GHG in the atmosphere; in other words the product of

concentration and time. In the case of CO2, terrestrial and oceanic sinks take up C previously

emitted, over time. Assuming the dynamics of the C cycle remain stable, most CO2 emitted at

the present will be absorbed within 100 years, and the cumulative radiative forcing produced by

this emission will be proportional to the area under the depletion curve, expressed in tC.years.

Calculation of this area provides an estimate of the cumulative C storage that would be required

to offset an emission of 1 tC at the present time. This method avoids both the necessity of

making questionable assumptions about the long-term balance of C in forests/agroforests, and

the practical difficulties of implementing flux-based incentive systems. However, international

agreements on the conversion factor for tC.years per tC emission and the time limit for crediting

the effect of a given proj ect are required (Tipper and De Jong 1998).

Other accounting methods

In addition to these relatively simple conventional methods, alternative approaches have

been proposed to better address the temporal dimension of C storage, such as equivalence-

adjusted average storage, stock change crediting with ton-year liability adjustment, equivalence-

factor yearly crediting, equivalence-delayed full crediting, and ex-ante ton-year crediting (De

Jong 2001). Most of these are based on adopting a two-dimensional measurement unit that

reflects storage and time, i.e., ton-C year. The general concept of the ton-year approach is in the










application of a factor to convert the climatic effect of temporal C storage an equivalent

(equivalent factor) amount of avoided emissions.

Technical Problems and Uncertainties

There are a numb er of shortcomi ngs of conventi onal m ethod s for e sti mati ng/accounti ng

the C in a system that need to be considered. These include the uncertainties related to future

shifts in global climate, land-use and land cover, the poor performance of trees and crops, varied

environments, pests and diseases such as nematodes. For example, the amount of C remaining

belowground at the end of the tree rotation, and the amount of C sequestered in products created

from the harvested wood, including their final disposition are often not included in the

accounting methods discussed above (Johnsen et al. 2001). Oren et al. (2001) reported that after

an initial growth spurt, trees grew more slowly and did not absorb as much C from the

atmosphere as expected. They concluded that assessment of future C sequestration should

consider the limitations imposed by soil fertility as well as interactions with nitrogen deposition.

In addition to these uncertainties, there are some concerns about the impacts of

agroforestry in other GHGs. The wide-scale use of woody legumes, which is common in

agroforestry systems, might result in release of nitrous oxide (N20)(Choudhary et al. 2002),

although it does not seem to be as strong an impact as N- fertilization (Mosier et al. 2004). N20

is known to have a global warming potential 200 300 times higher than that of CO2. Similarly,

pasture and rice paddy cultivation in agroforestry systems can produce significant quantities of

methane (CH4), another strong GHG (20 60 times higher impact than CO2), on a global scale

(Dixon, 1995).









Biomass C Sequestration


Studies in Various Ecoregions

The amount of C sequestered in an agroforestry system depends largely on the nature of

the system put in place, the structure and function of which are determined by environmental and

socioeconomic factors (Albrecht and Kandji. 2003). Other factors influencing C storage include

tree species and system management (Delaney and Roshetkol998; Roshetko et al. 2002). Palm

et al. (2004) compared the amount of C stored (above-ground) in different ecological systems

(Table 3-2). To compare the rotation of the different land-use systems, time-averaged C of each

system was used.

C stocks in the vegetation of the primary forests averaged 300 Mg C ha- and that of

logged or managed forests ranged from a high of 228 Mg C ha-l in Cameroon to a low of 93 Mg

C ha-l in Indonesia. Time-averaged aboveground C for the different land-uses ranged from 50 -

90 Mg C ha-l in long-fallow shifting cultivation and complex agroforestry systems, to 30 60

Mg C ha-l in simple agroforestry systems and most tree plantations and medium-fallow

rotations. These are considerably larger than those for annual crops or pastures.

Studies in West Africa

In a review of C sequestration in tropical agroforestry systems, Albrecht and Kandji (2003)

estimated that agrosilvicultural system could sequester 29 53 Mg C ha-l in humid tropical

Africa. A case study in Cameroon (humid west and central Africa) showed that the cacao

(Theobroma cacao) agroforest is superior to the alternative food crop production system (slash-

and-burn), both in C sequestration and below- and above-ground bio-diversity. Total biomass in

cacao agroforest was 304 Mg ha- compared to crop fields (84 Mg ha- ) (Duguma et al. 2001).

Compared to C gains in the humid tropics, the benefits of agroforestry in the WAS, such as

parklands or improved fallow seem to be less. A simulation study in Senegal compared the C









gains after 25 years from protecting parkland systems (2.48 Mg C ha- ) with that of rotating

crops with Luecaena spp. fallow planting (6.35 Mg C ha- ) (Tschakert 2004). Although drylands

as whole are believed to provide a substantial opportunity for C offsets especially in soil C,

because of their large area (47.2 % of land in the world) and low human populations (Lal 2004b),

most studies in the Sahel region have concluded that the potential for C gains per unit area

through agroforestry is relatively low (Walker and Desankar 2004;Woomer et al. 2004b)

compared with other ecoregions.

In general, introducing trees into agricultural systems is expected to increase water and

nutrient availability, because trees can fix nitrogen, retrieve water and nutrients from below the

rooting zone of crops, and reduce water and nutrient losses from leaching and erosion (Buresh

and Tian 1997). This tree effect has been demonstrated in various agroforestry systems in the

semiarid region. However, this added value was lowest where it is most needed, in resource-

poor environments: the competition between woody plants and crops is strong (Kater et al. 1992;

Breman and Kessler 1997). Water constraints are the strongest limitations for C sequestration in

the WAS. Several local tree species such as Acacia tortilis, Guiera senegalensis, Pterocarpus

lucens have been planted in grasslands of the region for sequestering C, but their capacity to

grow has been shown to be constrained by moisture availability (Woomer et al. 2004a). The

capacity of exotic dryland tree species to afforest the WAS is also uncertain.

Since the moisture and nutrient levels of the study field are expected to be low, the tree

growth and the consequent C storing will not be extremely high, either, compared with more

moist parts of West Africa. However, the amounts of C sequestered as a result of specific land-

uses are mostly unknown in the WAS, thus, it is worthwhile to conduct the research to have a

reference data for future C sequestration proj ects.









Soil C Sequestration

Recently, research focused on C sequestration potential in terrestrial ecosystems has been

shifting from biomass C to soil C, because soil is recognized as an important storage (sink) for C

and studies estimating biomass have accumulated for various ecosystem (including

agroecosystem and plantation forests), while the dynamics of soil C is much less known.

Studies of Soil C Stock and Dynamics

The comparison study of C sequestration potential by Palm et al. (2004) (Table 3-2) also

measured soil C storage. On average, 45 Mg C ha-' were found in the forest systems studied (0

- 20 cm depth), and 80 100 % of that C storage in agroforestry systems; 80 % in pastures; 90 -

100 % in long-fallow cycles; 65 % in short-term fallows, and 50 % or less in annual crops and

degraded grasslands. Rosalina et al. (1997) reported a mean of 104 Mg C ha-l for home gardens

in North Lampung, Indonesia, and that 58 % of the stock is soil C. The soil C sampling depth of

these studies is usually on surface, up to 20cm. Although the surface soil is the major part of

which soil organic carbon (SOC) is found, it may not be deep enough to capture all the tree roots

influence on soil C (Jobbagy and Jackson 2000). But because of the labor intensity and the

relatively low soil C density, deeper soil C tends to be ignored.

Storage of soil C is rather easy to quantify and/or estimate, but detecting soil C flux

including its turn-over time is much more difficult and has not been studied much. Most current

models of soil organic matter (SOM) dynamics assume that equilibrium C stocks are linearly

proportional to C inputs, i.e. there are no assumptions of soil C saturation. Six et al. (2002a)

questioned the validity of this assumption for proj ecting longer term SOM dynamics, and

developed the proposition that physiochemical characteristics inherent to soils define the

maximum protective capacity of SOM pools. Methodologies such as fractionation and C isotope

measurements are being developed for quantifying and identifying the characteristics of soil C









dynamics, and are actively discussed in the soil science arena (Balesdent et al. 1998; Del Galdo

et al. 2003; Powers and Veldkamp 2005).

Soil fractionation: There are various ways to separate SOM into labile and recalcitrant

pools, and these methods rely on chemical, physical, or biological separation, and many of them

are used sequentially in analyses (McLauchlan and Hobbie 2004). Chemical fractionation

separates soil C into different resistance class to decomposition by using acid,

permanganate(KMnO4), or hot water, (Moody et al. 1997; Ghani et al. 2003). Physical

fractionation separates labile and recalcitrant fractions by either size or density. Sieving is used

to size differentiation and flotation with a dense liquid is usually used to measure light fraction

which is considered to be labile (Six et al. 1998). Biological separation uses microbes to

separate labile SOC from recalcitrant SOC under controlled temperature and moisture conditions,

assuming that microbes will mineralize the most labile C first, with recalcitrant C being

mineralized later (Alvarez and Alvarez 2000). With applying these methods, many examine the

impact of land-use change on soil C storage and dynamics. For example, it is possible to assess

how land-use rotation (including fallow) or change of management such as reduction of tillage

can effectively protect recalcitrant soil C, so that enhance soil C sequestration (Pikul. et al. 2007;

Zibilske and Bradford 2007).

13C isotope measurement: During photosynthesis, CO2 fixation of C3 plants discriminates

against the heavier isotope 13C more than do C4 plants, which result in different stable carbon

isotope composition (613C) 13 /12C ratio relative to that found in the PDB (Pee Dee belemnite),

for their plant material. This composition value of C3 plants is between -23 and -34"90, whereas

C4 plants ranges from -9 to -179Rb (Eleki et al. 2005). Negative values of 813C indicate that the

plant material is depleted in 13C COmpared with the PDB standard. Using this theoretical









expected difference between the measured 613C ValUeS, it is possible to calculate the proportion

of C4 derived material and C3 derived material in biomass or soil C (Balesdent et al. 1998). This

method has been used for soil C research to assess vegetation composition change (Dzurec et al.

1985) or to follow the dynamics (Harris et al. 2001). Mcdonagh et al. (2001) measured how

SOM from original vegetation (forest: C3 plants) were diminished after continuous cultivation of

maize (Zea nzays: C4 plants). In agroforestry system, Jonsson et al. (1999) used this method to

prove the positive influence of trees (C3 plants) on SOM increase at millet (Pennisetunt

glaucunt: C4 plants) cropland.

Soil C in the WAS

In the WAS, most of the soils have low activity clay, with low water retention and are

susceptible to soil erosion and compaction, as described in Chapter 2. Organic matter content of

these soils has been depleted due to overgrazing, agricultural mismanagement, deforestation and

overexploitation of the natural resources. As a result, soil organic C stock density in West Africa

is very low (4.2 4.5 kg C m-2), COmpared with the world average (10.9 11.6 kg C m-2), and

relatively lower even when compared to the average for Africa (6.4 6.7 kg C m-2) (Batj es

2001).

Soil degradation is a maj or obstacle for agricultural productivity and thus sustainable

development of the WAS. The possibility of enhancing C sequestration through improved soil

management has been discussed academically and at international workshops, as part of the

search for agroecosystem sustainability in the region. Among soil-nutrients studies in Africa, tree

integration into croplands is often recommended for soil amelioration (Onim et al. 1990; Tiessen

et al. 1991; Manlay et al. 2002). Kang et al. (1999) reported Grilicidia sepium and Leucaena

leucocephala increased surface soil organic C by 15 % compared to sole crops in a 12-year

hedgerow intercropping trial on a Nigerian Alfisol. Parkland system studies affirm that the soil









under the trees is richer in organic matter content and several cations compared to adj acent tree-

less sites (Kater et al. 1992; Jonsson et al. 1999).

Soil amelioration by land management in the WAS is, however, often met with limited

success when it comes to maintaining or increasing soil nutrient and C stocks. The potential for

C sequestration in a given soil and agroecological zone is proportional to the original reserves

present under undisturbed conditions. Breman and Kessler (1997) compared the added values of

woody plants in croplands or pastures between Sahel zone and wetter Sudan zone. They

concluded possibilities to improve the soil organic matter status are more limited in Sahel, due to

resource-poor environments and competition for water between woody plants and crops or

pasture. Because of the slow establishment of a woody plant community and the rapid turnover

of organic matter, a long "transition period" is necessary under the conditions in Sahel.

Improved fallow studies also suggest that a long period would be needed to amend soil physical

conditions of the highly degraded soil in the WAS (Buresh and Tian 1998; Ringius 2002; Kaya

and Nair 2004).

Soil C sequestration is not recognized as a mitigation means during the first commitment

period of the Kyoto Protocol (2008 2012), although political pressure to reverse this situation

has been growing. In his review of soil C sequestration in Africa, Ringius (2002) stated that sub-

Saharan Africa would not profit significantly from soil C sequestration under the Clean

Development Mechanism (CDM) as long as the land-use pressures due to a rapidly growing

population and poverty remain unsolved. Profitability of the soil C sequestration proj ect is

uncertain, since cost benefit studies of the sequestration activities have not been conducted.

There is a need to launch long-term (>10 yr) field experiments and pilot proj ects for soil C










sequestration as well as to develop a land resources information system in the WAS, geared

towards CDM and/or C sale.

Socioeconomic Implications

To analyze the socioeconomic feasibility of the agroforestry practices for mitigation

projects, analysis tools, i.e. models, are needed. Economic models of different scales used for

the studies in various ecoregions are summarized here. Although the number of studies is small,

the case studies and possibility of using these economic models in the WAS are also examined.

Economic Models

In most studies of C sequestration, agroforestry is regarded as one of the forest

management options for potential C sequestration. There are few studies specifically discussing

economic models of C sequestration in agroforestry systems; instead, models designed for

managed forests are usually applied (Masera et al. 2003). These economic models for

accounting C sequestration proj ects can be categorized into two different spatial scales.

National/global scale

Apart from the C sequestration potential per se of agroforestry systems, the potential for

realizing this assumed benefit depends largely on the availability of land which can be changed

to agroforestry from land with less C storage, such as agricultural Hields. Attempts to estimate

the global potential for increasing C sinks through land-use change had been conducted at the

global, national, and regional levels for more than a decade (Dixon et al. 1994b; Sathaye et al.

2001; Godal et al. 2003). These studies use simple integrated model structure, based on

biophysical and economic information. In this kind of large spatial scale, empirical model

schemes such as the Holdridge life zone system (LZS) can be used as a guideline for organizing

vegetation data (Pfaff et al. 2000). For economic factors such as the price of land, cost of land-

use change, and timber price, national census information are generally available. Information









on land-use and trends of change, collected from national surveys or satellite/remote sensing

data, are integrated and the overall economic impacts are examined.

Results are presented in several different ways. Dixon (1995) presented the potential C

storage (over 50 year rotation) and initial proj ect financial costs for agroforestry systems for

ecoregions of selected nations in terms of $/Mg C. Some studies estimate total investment cost

in actual dollar amounts for developed countries to achieve their reduction goals (Baron and

Lanza 2000, Godal et al. 2003), and others estimate each country's investment in $ ha- or

internal rate of return (IRR) (%) (Dixon 1995; Sathaye et al. 2001).

Micro/site-specific scale

At this level, more detailed economic estimation is conducted based on data from the

specific target (project) field. Various factors of benefits and costs are individually counted.

There are three different time frames for counting these costs and benefits of C sequestration

projects: point estimates, partial equilibrium estimates, and general equilibrium approaches.

Many of the point-estimate studies provide undiscounted private costs and benefits of the

proj ect' s implementation in $ ha- These studies usually count only direct inputs and outputs,

including land cost. Estimating opportunity costs to compare with other land-use options is often

done (Tomich et al. 2002). Most of these studies reveal little about how costs might change

throughout the proj ect with time, or if the proj ect were to expand or be repeated. Thus, the

estimates tend to be biased towards the low end.

The Scolel Te proj ect, conducted in Mexico, is one of the few long-term and

comprehensive economic impact studies on this subject. It serves as an example agroforestry

proj ect for calculating the costs related to implementing a C sequestration project in rural

environments dominated by resource-poor small-scale farmers, who are expected to be maj or

players in agroforestry worldwide. The study accounts for costs of proj ect design, the time









required to explain to farmers the proj ect obj ectives, C related inventories, and the cost of

baseline setting, which are often ignored in similar estimate analysis (De Jong et al. 2004).

These equilibrium studies usually present the results as discounted costs in $/Mg C with long-

term average sequestered C as Mg C ha- .

For estimating much longer scales than one cycle of agroforestry rotation, computer-based

modeling methods are usually used. There are several models such as CO2FIX and

WaNULCAS for estimating the dynamics of C sequestration over decades to centuries and some

are applicable to agroforestry (Van Noordwijk et al. 2002; Masera et al. 2003). Wise and Cacho

(2005) used the WaNULCAS model for ecological estimates combined with their economic

model, including variables such as local discount rate, firewood price, and labor cost, and

simulated the long-term economic value of switching land-use from agricultural system to

agroforestry system in Indonesia. They include expected C prices into its economic analysis, and

presented net present values (NPV) in $ ha-l of several different setting (management options).

Agroforestry systems in temperate areas are usually analyzed in a very different way.

Compared with developing countries in the tropics, the C credit sale through forestation is not

likely to be economically feasible soon in the temperate area. Thus, C sequestration tends to be

considered as an environmental benefit (non-market value, or subj ect to receive the subsidies).

Studies to estimate the benefits of C sequestration using models that are generally used for these

environmental commodities seem to be the current research trends (Stainback and Alavalapati

2000; Alavalapati et al. 2004).

Feasibility in West Africa

West African countries' GHG emissions are currently negligible in global terms, due to the

low level of development and industrialization. As a result, emission reduction opportunities









remain few, and are mainly in lessening the negative impact on the climate resulting from land-

use change and deforestation.

For C offset projects, however, the risk of shifting from cropping to more C-beneficial

practices seems to be high, especially for subsistence farmers who have little lands. A study of C

sequestration through agroforestry in Senegal found that the costs for resource-poor farmers are

considerably higher than those of intermediate and richer farmers, because the former often lack

the necessary assets (land, labor, and animals) to switch from current to alternative practices

(Tschakert 2007).

Many African policy makers and financial institutions express little interest in controlling

GHG emissions or adapting to changes in climate. This attitude is based on their experience that,

in general, other, more local, environmental problems have more direct influence on their

populations than climate change. Senior government officials and most members of civil society

do not understand the climate issue very well (Denton et al. 2001). Many development

practitioners remain skeptical, arguing that C brokers, national ministries and local leaders rather

than needy rural populations will benefit from C proj ects.

An important challenge for the WAS countries lies in that they need to be more

"attractive" than the other African and developing countries in order to draw and hold

investments for C sequestration projects under CDM. As discussed above, the potential C gains

in the WAS through agroforestry has been considered to be unattractive. Synergies between

development and climate change response, however, can be an answer. Agroforestry projects,

which protect soils and result in C sequestration, also provide employment opportunities for local

farmers (Hardner et al. 2000). Soil C sequestration project through agroforestry could provide a

crucial link between three international conventions: the UN Framework Convention on Climate










Change (UNFCCC), the UN Convention to Combat Desertification (UNCCD), and the UN

Convention on Biodiversity (UNCBD). As such, incentives can come from a much broader area

such as development assistance, other multilateral agreements and sectoral policies on energy

and agriculture.










Table 3-1. Summary of various biomass C measurement approaches used commonly in C
sequestration studies.
Approaches Tools and methods for data collection Remarks
Direct on-site measurement Plot sampling, tree inventories. Regarded as most accurate
Using allometric equations, biomass and site-specific.
expansion factors, root:shoot ratios. Cost for inventory is high.

Indirect remote sensing Satellite imagery, aerial photo-imagery, Relatively larger scale.
techniques pulsed laser, dual digital camera. Technical availability can be
Field inventory for the reference data. an issue.
Cost-effective .

Modeling Ecophysiological study based, ecosystem Mainly used in academics or
based, or land-use change based. pilot projects so far.
Field inventory or data from national Needs many assumptions, but
surveys. can be applicable to various
situations .

Default values for Land-used change based. Most macro-scale approach.
land/activity based practices Focus on the changes in C stocks. Can be used not only for
Field plots for the reference data. forestry/agroforestry but also
other land-use.























































Numbers in parentheses are range of the mean value.
Source: Palm et al. (2004). Table II in page 149


Table 3-2. Aboveground time-averaged C stock in different ecosystems and agroforestry
practices. Timne-averag~e C stock< (Mg C ha- ) = (C stored in proj ect -C stored in
baseline) in Mg C ha- / n (years.)


Time-averaged C of
land-use system Mg C ha-l
306 (207 405)
294


Meta land-use systems


Country and specific land-use


Undisturbed forest


Indonesia
Pe ru

Brazil/Peru
Cameroon
Indonesia


Managed/logged forests


150 (123 -
228 (221
93.2 (51.9


185)
255)
- 134)


Shifting cultivation and crop-
fallows







Complex/extensive agroforests
Permanent

Rotational



Simple agroforests/ intensive
tree crop





Grasslands/crops


Cameroon
Shifting cultivation, 23yrs fallow
Bush fallow, 9.5 yrs
Brazil/Peru
Short fallow, 5yrs
Improved fallow, 5yrs
23 yrs fallow


Cameroon, Cacao
Indonesia Rubber
Cameroon, Cacao
Indonesia Rubber


77.0 (60.2 107)
28.1 (22.1 38.1)


6.86 (4.27
11.5 (9.50
93 (80.5 -


- 9.61)
- 13.4)
101)


88.7 (57.2 120)
89.2 (49.4 129)
61 (40 83)
46.2 (28.9 75.2)


Brazil/Peru
Coffee monoculture
Multistrata system
Cameroon, Oil Palm
Indonesia, Pulp trees

Brazil/Peru
Extensive pastures
Intensive pastures
Indonesia
Cassava/Imperata


11.0 (8.73
61.2 (47.5
36.4
37.2 (23.6


12.5)
74.7)

50.7)


2.85
3.06

<2









CHAPTER 4
ABOVEGROUND AND BELOWGROUND CARBON STOCKS IN TRADITIONAL AND
IMPROVED AGROFORESTRY SYSTEMS IN MALI, WEST AFRICA

Introduction

Agroforestry is a very common concept of traditional agricultural land-use in most of the

tropics. In the West African Sahel (WAS), the traditional systems such as "bush fallow" and

"parkland" systems involve integration of trees with agricultural crops. The trees provide

subsidiary (famine) food when crops fail by drought; can be sources of oil, wine or other

condiments, and are used for tools, fences or fodder (Boffa 1999). Also, trees can increase water

and nutrient availability through nitrogen-fixation, retrieval of water and nutrients from the

deeper layers of soil, and reduction of water and nutrient losses from leaching and erosion in the

semiarid region (Buresh and Tian 1997; Kang et al. 1999). As described in Chapter 2, parkland

agroforestry systems are currently the most prevalent land-use systems in the WAS. Other

agroforestry practices such as improved fallow, intercropping, tree fodder planting, and boundary

planting have been introduced, but these are still not widely adopted (Niang et al. 2002;

Levasseur et al. 2004).

Most of existing studies on the parkland systems are about the productivity of trees and/or

crops grown underneath, or about the interaction/competition of the trees and crops (Kater et al.

1992; Jonsson et al. 1999). Carbon (C) sequestration, defined by the United Nations Framework

Convention of Climate Change (UNFCCC) as "the process of removing carbon from the

atmosphere and depositing it in a reservoir," has not been a subj ect matter of studies in much of

the WAS region, let alone in parklands and other agroforestry systems of the region.

Nevertheless, it is widely accepted that environmental degradation resulting from long-term

drought and land-use change has adversely affected the terrestrial C stocks in the region (FAO

2000; Reich et al. 2001). Although Mali, where this study was conducted, signed off on the










Kyoto Protocol, there has been no pilot proj ect to document C sequestration and C credit sale in

the country.

Woomer et al (2004b) conducted a national scale C stock assessment in Senegal

(neighboring country of Mali), and found that there were opportunities for biological C

mitigation, but they were constrained by available knowledge and access to resources.

Compared with large-scale tree plantation, agroforestry is expected to be the most feasible

afforestation/reforestation proj ect that can be conducted by the maj ority of resource-limited land

users (farmers) in the WAS. Because of the scarcity of on-site information, it is important to

directly measure or estimate both biomass C and soil C stocks of various agroforestry systems.

Therefore the study reported in this chapter was undertaken with two research questions:

1. How do different agroforestry systems differ in their potential for C sequestration? How
much C is stored in the traditional and improved agroforestry systems, especially comparing
above-ground and below-ground?

2. What is the overall relative attractiveness of each of the selected agroforestry systems
considering them as biological C sequestration proj ects?

Materials and Methods

C sequestration potential of a specified proj ect is calculated by "C sequestered by the

proj ect" minus "C sequestered by the baseline (without the proj ect)". Since this study is not a

long-term proj ect, it was impossible to monitor both C accumulations by the proj ect

(agroforestry) and by non-proj ect land-use over the time. Instead, the differences of C stock

among selected land-use systems are assumed to represent the potential of C sequestration by the

land-use change.

Study Area

This research was done in Segou, Mali, in cooperation with the ICRAF (World

Agroforestry Centre) Field Station of Sahel Regional Programme.









Republic of Mali

Mali is a landlocked country with an area of 1.24-million km2; the Sahara desert occupies

60 % of it (Figure 4-1). Mali shares borders with seven countries: Mauritania, Algeria, Burkina

Faso, Ivory Coast, Guinea, Niger, and Senegal. It is a vast land of plains fed by two maj or

rivers, the Senegal River on its western edge and the Niger River flowing in a wide arc from

southwest to east. The population is 12.3 million with a growth rate 2.63 %, one of the highest

in the world (World Bank 2007). While most people live in rural areas, 1.2 million people live

in the capital city, Bamako; 90 % of people are Muslims. The maj or language is Bambara

(original language of a major ethnic group), although the official language is French.

Agriculture is the main source of livelihood for the people, with 80 % of people engaged in

agriculture or fishing (CIA 2007). Cotton is the main export product; gold and phosphate from

mines in the northern area are also traded. The per capital national income was US$ 380 in 2005.

Despite higher economic growth since 1994, Mali remains one of the world's poorest countries

- rated 174/177 in 2005, using the UNDP Human Development Index (World Bank 2007).

With the impact of current climate change and environmental degradation, the country is

vulnerable to drought and risks further desertification.

S~gou region

The City of Segou is Mali's second largest urban center, located on the Niger River about

300 km northeast of Bamako. It is the capital of Segou region, one of the eight administrative

regions of Mali (Figure 4-1). The region is located in the buffer zone of the Sahara, with 60 90

rainy days and 300 700 mm of rainfall annually (the rainfall intensity increasing from the north

to the south).

The Segou region has seven cercles (administrative sections) and 2,218 villages. The

population of the region is about 2 million with 0.3 million living in Segou city. Cotton










(Gossypium spp.) is the main crop (and product) of the region and the region is well known for it.

A large cotton mill, invested in by Chinese companies, is operating at the edge of the city. Rice

(Oryza glaberrima and Oryza sativa) is grown extensively in the irrigated area around the Niger

River (Republique du Mali 2005). Farmers commonly grow rain-fed pearl millet (Pennisetum

glaucum) and sorghum (Sorghum bicolor) as staple food crops.

Selected Land-use Systems for Field Data Collection

A preliminary survey was first conducted in July 2005 to identify the targeted land-use

systems and possible villages to locate on-farm plots. Five systems were selected: two parkland

agroforestry systems, two improved agroforestry systems, and an "abandoned" land (degraded

land) for comparison.

Parkland systems

The maj or land-use in the Segou region, as in most of other parts of the WAS, is parkland

agroforestry. Two parklands types are common: with Faidherbia albida or Vitellaria paradoxa~11~~1~~11~

as the dominant tree species. These two types occupy more than 60 % of cultivated land in

Segou region (personal communication, August 2005, with Director of Forestry Department,

Segou). Tree density is in the range of 20 to 30 trees/ha in both systems. Crops cultivated

underneath the trees include pearl millet and sorghum, sometimes intercropped with cowpea

(Vigna unguiculata) and/or banbarra groundnut (Vigna subterranea syn. Voandzeia

subterranea).

F. albida has a unique characteristic of "reverse" phenology or foliation (i.e. bearing leaves

during the hot dry season and dropping them before the rainy season), which is quite

advantageous for agroforestry: it reduces shading of crops grown underneath the tree and

possibly reduces competition for water between trees and crops, and enables farmers to grow

crops under the trees with practically very little reduction of cropped area in the intercropping









situation. It is also a nitrogen-fixing tree, and farmers use the foliage for both organic manure

and fodder. y. paradoxa is probably the most common parkland species in the WAS, known as

Karitd (in French) or Shea (in English). Farmers use the fat extracted from the nuts in multiple

ways, such as cooking oil, medicine, and cream for dry skin. This fat, called Karitd butter or

Shea butter, has recently become popular for cosmetic use in the western world. It has a natural

UV protection and moisturizing effect, and is therefore one of the "booming" products for

international cosmetic companies. ICRAF organized a program to study the characteristics of V.

paradoxa physiology for better production and to establish a network for local farmers to market

this newly developing commodity (ProKaritd 2007).

Improved agroforestry systems

To examine the possibility of implementing reforestation/afforestation proj ects by

agroforestry under the Kyoto Protocol for C sale, it is necessary to consider agroforestry systems

with higher tree density than that of parkland systems (where crown cover is about 20 %), or

abandoned land (crown cover is close to 0 %). This is because the definition of "forest" or

afforestationn" of Kyoto Protocol normally refers to higher tree density than parkland and taller

trees than bushes in abandoned land.

In Segou region, ICRAF carried out a study to identify agroforestry needs for the WAS in

general. The study indicated an overall shortage of fodder during the dry season, and that

farmers need to protect their fields, especially during the dry season when cattle roam freely (van

Duijl 1999; Figure 2-6). To address these problems, ICRAF has been introducing the improved

agroforestry technologies such as live fences and fodder banks.

Live fence refers to planting relatively fast-growing trees in very high density around field

plots, orchards, or cultivated land. Trees are planted along plot/field boundaries at 1 m intervals

in two lines 1.5 m apart thus giving a 3-m wide thick fence around the cultivated land. Five tree/










woody-perennial species are commonly used for live fence in the Segou region: Acacia nilotica,

Acacia senegal, Bauhinia rufescens, Law/sonia inernzis, and Ziziphus mauritiana. The protected

crops inside the fence are mainly cash crops such as cassava (Manihot esculenta), watermelon

(Citrullus lan2atus), calabash (Lagenaria siceraria), and groundnuts (Arachis hypogaea).

Fodder bank is a system of planting exotic and/or indigenous species suitable for animal

fodder in relatively high density. ICRAF introduced an exotic species, Gliricidia sepium, and

two indigenous fodder trees, Pterocarpus hecens and P. erinaceus, although these two species

did not grow well enough to be harvested in all the experimental plots. The common size of the

fodder bank is 0.25 ha (50 m by 50 m) framed in by live fence, and fodder trees are planted 2 m

by 1 m in lines. Most of the pilot fodder banks were established on previously cultivated land.

Abandoned (degraded) land

Land degradation is a very severe problem in Segou region and the extent of degradation

varies considerably from no vegetation with crusted surface to land covered by bushes and

grasses. In this study, lands somewhat vegetated with grasses and bushes were chosen for plots.

These lands were previously cultivated, but recently (within 10 years) abandoned because of the

lack of soil fertility. ICRAF is trying to introduce live fences and fodder banks to improve this

over-exploited land. In this scenario, the difference in the amount of C between these abandoned

lands and fodder bank/live fence systems would be the sequestration potential of the improved

agroforestry practices.

Research Design

Since it was impossible to find all the land-use systems in the same village, plots

representing each land-use system were set up in different villages in the Segou region (Table 4-

1). All the villages are within 30 km from the center of the city. For each land-use system

(treatment), three on-farm plots (replicates) that were as uniform as possible (size, understory










crops present, tree density, age, land-use history) were chosen. Villages for two parkland plots

were chosen because of relatively mature and uniform F. albida and y. paradoxa trees (Figure 4-

2, 4-3). Each plot was set inside a different farm. The village for live fence plots was chosen

because it has a group of farmers who participated in ICRAF's live fence program. Three

farmers' live fences with the same age and similar tree growth were selected (Figure 4-4).

Fodder bank plots were more difficult to find. Since fewer fodder banks were adopted and

maintained than live fences, only three comparable fodder banks were found in three different

villages (Figure 4-5). The village for abandoned land plots was chosen near the vast degraded

land spreading east of Segou city. All abandoned land plots were previously cultivated by

farmers (Figure 4-6).

Data Collection

Field data collection was conducted from August to September 2005.

Biomass measurement

The plot size was I ha for parkland systems, while it was the 'whole site' for improved

systems (about 0.25 ha or less), and 0.5 ha for abandoned land (Table 4-1).

Data recorded for aboveground biomass were:

* Species and number of trees in each plot
* Diameter at breast height (DBH) and/or diameter at the ground of each tree/bush
* Tree/bush height
* Crown size for bushes in abandoned land.

Regarding land-use history, age of traditional parkland and abandoned land systems were

difficult to estimate. According to owners of the plots, all parkland plots were at least 35 years

old, and the abandoned land plots had been "abandoned" for less than 10 years. All three live

fence plots were 8 years old (at the time of data sampling they were established in 1997), and

two of the three fodder bank plots were 9 years and one was 6 years old.









Soil sampling

Based on discussions with ICRAF researchers, three depth classes were determined for soil

sampling: 0 10 cm (surface soil), 10 40 cm (crop-root zone), and 40 100 cm (tree-root

zone). The average size tree in each plot was selected based on aboveground inventory data as

the center of the soil sampling area. Samples were taken from three horizontal distances from

trees in the two parkland systems and live fence system. At each horizontal distance, samples

were taken from four different points using an auger, and samples from each depth from these

points were well mixed as a composite sample before transferring them into bags. For the fodder

bank, where the bush/tree density was fairly uniform, and the abandoned land, plots, four random

points were chosen to make a composite sample of each depth. More details of soil sampling are

described in Chapter 5. Sampling for bulk density measurements were taken separately for each

depth and land-use using a 100 cm3 stainless steel cylinder. A soil pit (1 m depth) was made for

each land-use plot, and the cylinder was horizontally driven to the center of each depth class to

take the samples for bulk density determination. All samples (total 144 samples: 99 composite

samples and 45 bulk density samples) were air-dried and shipped to University of Florida for

analyses.

Carbon Stock Estimation

Amount of biomass C and soil C (C stock) were estimated respectively, as follows. Total

C stock (Mg C /ha) of each land-use system was calculated by adding biomass C stock and soil C

stock of each plot of each land-use system (all data on per ha basis). Live fences are

conventionally expressed in terms of length of rows. In this study, the "area" under live fence is

calculated based on 3 m width; but in practical terms, the area of the field "serviced" by the live

fence is important. Since live fences are along plot/field boundaries of unequal sizes, it is not

realistic to assign a standard row length per unit area (ha) of plot/field.









Biomass C stock

Since this study examines the potential of C sales under the Kyoto Protocol's Clean

Development Mechanism (CDM), methodologies used here for estimating biomass C stock are

based on the guideline published by UNFCCC (2006). Although species-specific allometric

equations are ideal, none was available for parkland trees from the study region. As an

alternative, the UNFCCC guideline recommends using the following general equations from

FAO (1997).

Tree biomass (kg) = exp (-2.134 + (2.530 InDBH(cm))) (n = 191, R2 = 0.97) (Eq. 4-1)

In the FAO (1997) paper, there are general equations suggested for drylands. However,

those equations were developed from much smaller sets of trees in India and Mexico, and their

DBH ranges are 3 to 30 cm. E. albida and y. paradoxa trees in parkland plots of this study

greatly exceed the diameter range of these general equations. The average DBH of trees in the

plots were about 59 cm for E. albida and 42 cm for y. paradoxa. Using the dryland general

equations may cause significant over estimation of the biomass. Thus, this study follows a

method proposed by Woomer et al. (2004a) in Senegal, using Equation 4-1. This equation is

also from FAO (1997) for higher rainfall area (1500 4000 mm), but covers the diameter ranges

of E albida and y. paradoxa trees in this study.

There are two options for estimating the biomass of the five live fence and one fodder bank

species. One is following UNFCCC's guideline, using a general equation for areas with <900

mm annual rainfall. The tree sizes are within this equation's DBH limits (3 30 cm). The

equation is:

Tree biomass (kg) = 10 ^'(-0.535 + loglo(nxDBH(cm)2/4)) (R2 = 0.94) (Eq. 4-2)









The UNFCCC guidelines suggest using these equations when no local species allometric

equations are available.

A second option is to use the equations developed from Acacia tortillas and Acacia

ruficiens in Northwest Kenya (Coughenour et al. 1990). These are:

Loglo (g mass) -2.26+3.98 loglo (mm stem diameter) (Eq. 4-3)
R2 =0.98 (stem diameter<15.7 mm)

Log to (g mass)- -0.68 +2.66 loglo (mm stem diameter) (Eq. 4-4)
R2 =0.98 (stem diameter>15.7 mm)


Although Northwest Kenya is not in the WAS, its climatic condition is much more similar

to that of the study area than to the area where the UNFCCC guideline's equation was developed.

Gonzalez (2001) used these Acacia spp. equations in his research at various parts of Senegal.

These two options were both tried in this study, and the results of estimated biomass C are

compared later.

For abandoned land plots, equations for Guiera senegalensis used by Seghieri et al (2005)

in Niger were adopted. G. senegalensis is the most dominant shrub species in the abandoned

land plots, and the equation was originally developed in fallows of Mali (Cisse 1980, Franklin

and Hiernaux 1991).

Foliage mass of each stem of each shrub: Blstem (g),
Basal circumference of the stem: Cstem (cm):
Blstem =1.09xCstem (all stems of n=20 shrubs, R2 =0. 82, P<0. 001) (Eq. 4-5)
Stem wood dry mass: Bwstem (kg):
Bwstem =0.0037xCstem (36 stems among n=15 shrubs, R2 =0.90, P<0.001) (Eq. 4-6)
Leaf and wood masses (Blstem and Bwstem) were then aggregated for each multi-stemmed shrub.

To calculate the amount of C in the biomass, C fraction rate of 0.5 is suggested in the

UNFCCC guideline. Belowground biomass is also estimated by using the suggested root/shoot










ratios, which are 0.25 for trees and 0.5 for abandoned land bushes. Biomass C stock was

calculated by adding aboveground biomass C and belowground biomass C.

Soil C stock

Soil C stock was estimated from the samples taken at each land-use's plots. Sub-samples

were taken from the 99 air-dried composite samples and ground. Soil C percentages of the sub-

samples (% of C weight in whole soil weight) were measured by the dry combustion method on

an automated Flash EA 1112 NC elemental analyzer (Thermo Fisher Scientific, Inc.). Then, the

soil C percentage data of each land-use of each depth class was converted to the amount of C per

ha basis with using bulk density data.

Statistical Analysis

Analysis of variance (ANOVA) by SAS PROC MIXED procedure and Turkey-Kramer

multiple comparison test were conducted to compare the C stocks of different land-use systems

and soil depth. The linear model shown below was used.

y, = pu L, e,

y, is the C concentration in land-use i,

pu is the population mean,

L, is the land-use (treatments), i = FA, VP, LF, FB, and AL.

FA: F.albida parkland, VP: E~paradoxa parkland, LF: live fence, FB: fodder bank, and

AL: abandoned land.

e, is the random variable error within the experiment.

Linear correlation was also tested to examine the relationship of biomass C and soil C

stock.












C Stock in Biomass and Soil

The two parklands selected for the study were similar in tree density and the dominance of

maj or tree species (Table 4-2). E. albida trees were generally larger and taller than the y.

paradoxa trees in all parkland plots. Although live fence and fodder bank plots had large

numbers of trees per ha, these were proportionally calculated numbers based on small plots. The

real size of live fence plots was 0.088 ha on average (293 m length x 3 m width) with 327 trees,

and that of fodder bank plots was 0.24 ha on average with 145 G. sepium trees.

The soil C specific comparisons will be presented in detail in Chapter 5; but estimates of

soil C stock of each depth are presented, along with biomass C stock per ha basis of each system

(Figure 4-7). Statistical comparison by ANOVA showed that abandoned land had larger soil C

stock than the other four systems in every depth, although the significance varied with depth.

Estimated biomass C values of live fence and fodder bank plots from UNFCCC guideline

equations and those from Acacia spp. equations developed in Kenya were significantly different

(t-test, p<0.01) (Table 4-3). However, when each set of estimation was compared with other

three systems by ANOVA, the results were the same. The ranking of systems in order of

biomass C stock was: E. albida parkland > K. paradoxa parkland > Live fence > Fodder bank

>Abandoned land. However, the last three systems were not significantly different (Tukey-

Kramer test) even when analyzed separately.

Total C Stock

Total C (aboveground biomass C + soil C) stock of each system was calculated and

compared at three different soil depth ranges. The order among the systems and the significance

of difference varied with the depth of soil (Table 4-4). Overall, E. albida parkland had the

largest total C stock, and was significantly different from the other four systems. y. paradoxa


Results









parkland had the second largest total C stock, while the difference between other systems

became less significant as deeper soil C stock was included. Also as deeper soil was taken into

account, abandoned land had more C stock relative to the other systems.

Relationship between Biomass C and Soil C

All the possible combinations of biomass C stock data and soil C stock data across the five

land-use systems were tested for their relationship:

* Biomass C stock data and soil C stock data (0-10cm),
* Biomass C stock data and soil C stock data (0-40cm),
* Biomass C stock data and soil C stock data (0-100cm),
* Biomass C stock data and soil C stock data (10-40cm), and
* Biomass C stock data and soil C stock data (40-100cm).
None of the regression was significant.

Discussion

In terms of total C stock per ha, traditional agroforestry systems (parklands) are a larger

storage than improved agroforestry systems (live fence and fodder bank) or abandoned land.

Although the improved agroforestry systems are relatively young, they are not likely to store as

much as the parklands at the end of their 25 year rotation. Because branches of fodder bank trees

are annually pruned to control their height (<4 m), and live fence trees are planted in such a high

density that woody biomass accumulation per tree will be comparatively less. However, having

a large C stock does not necessarily mean having a large C sequestration potential. Traditional

parklands are very stable (long-standing), and so is the C stored. Farmers in the area are unlikely

to increase the tree density of parklands to match the UNFCCC's "forest plantation" criteria for

sequestering more C for sale, because it will produce a negative impact on crop growth. Also it

is very difficult to convert nutrient-poor abandoned land to parklands, since silvicultural methods

are not established for these species (parkland trees are mostly natural regeneration), and

abandoned land is not fertile enough to grow crops underneath.









On the other hand, introducing live fences and/or fodder banks into cultivated land or

abandoned land can sequester C by increasing the tree biomass, but the extent would vary largely

by the baseline and accounting method used. Biomass C sequestered by live fence planting is a

straightforward "addition" in the C equation for the site, since they are normally established on

the tree-less cultivated land (the baseline is nearly 0). The potential of fodder banks, however,

depends on initial plot condition. As in the situation for live fence, when the cultivated land is

converted to fodder bank, most of C sequestered by the fodder trees can be counted. However,

establishing fodder banks on abandoned land, as ICRAF or local government is trying to

promote, may actually result in net loss of C stock during the initial stage, because the biomass

from bushes and grasses in the abandoned land has to be removed at the time of establishment,

and it may take years for fodder trees to accumulate an amount equal to the original biomass.

Further investigations are needed on temporal C dynamics of these systems.

Soil C is not considered in the calculations of the Kyoto Protocol for its first commitment

period (2008-2012). When, rather than if, soil C is taken into account, determining baseline soil

C will be another challenge to determine and compare the C sequestration potentials of land-use

systems. Results suggest that soil-sampling depth makes a large difference in estimating the

amount of C stored per area basis, as well as the potential for C sequestration (Table 4-4). To

compare and discuss the C sequestration potential of different land-use or different ecoregions, it

will be very important to standardize sampling depth. Several studies in Africa reported that

planting trees for C sequestration will not immediately retain soil C equal to the baseline level

nor increase it in the short term (Kaya and Nair 2001; Walker and Desanker 2004). Introducing

live fences or fodder banks may increase the biomass C in the system, but may not increase soil

C. Soil C sequestration potential will be discussed in more detail in the next chapter.










Regarding the biomass estimation methodology, two sets of allometric equations were

used for live fence and fodder bank. The values calculated from the two methods are

significantly different from each other (UNFCCC's general equation is much more conservative

than Acacia species equations from Kenya). However, comparing each method's values with

other three land-use systems showed similar results: both live fence and fodder bank have not

(perhaps not yet) stored significant amounts of C compared with bushes of abandoned land. This

is partly because of the young age of the two systems (6 to 9 years old). The UNFCCC guideline

suggests applying the general equation only if it is impossible to Eind/establish local allometric

equations. The Acacia species equations are not exactly "local"; but they are from environments

more similar to the studied area than were the general equations. Considering the difference

between these two methods, developing the local allometric equations is likely to increase the

profits from C credit sale when the C market is introduced in the area. Obviously, substantial

research efforts are warranted in this area.












Elevation
Land-use Name of the village Position Size of the plot
(m)

Faidherbia albida Togo N. 13.35, W. 6.31 300 1 ha
parkland

Vitellaria .paradoxa Dakala N. 13.32, W. 6.23 297 1 ha
parkland

Live fence Dougoucouna N. 13.37, W.6.37 298 294m (average)

Fodder bank' Dakala N. 13.32, W. 6.23 297 0.25 ha
Siguila N. 13.28, W. 6.21 305 0.25 ha
Banankoroni N. 13.35, W. 6.38 293 0.22 ha

Abandoned land Diamaribougou N. 13.36, W. 6.19 298 0.5 ha
10ne fodder bank plot from each village

Table 4-2. Characteristics of the experimental plots (three plots average) for five-selected land-
use systems in Segou region, Mali.
Number of
DBH (cm) Height (m) tre 1h' Species composition

Faidherbia albida 59.4 (1.8) 13.0 (0.9) 21 (5.3) Average 88.6% Faidherbia.
parkland albida dominance

Vitellaria paradoxa 41.7 (5.9) 9.9 (0.9) 20 (0.6) Average 80.6% Vitellaria
parkland paradoxa dominance

Live fence 2.5 (0.5) 2.5 (0.4) 3720 (882) Average 67.6% Acacia.
nilotica

Fodder bank 2.2 (0.5) 20(.) 588 (277) Gliricidia sepium average
only

Abandoned land 2.8 (0.6) 1.3 (0.4) 46 (30) Average 47.5% Guiera
diameter at ground senegalensis and 39.5%
Combretum micranthum


Table 4-1. Characteristics of the villages where the experimental plots were
region, Mali.


set up in Segou


Note: Numbers in parentheses are standard deviations.
standing trees in the plot.


Tree dominance means the percentage among the










Table 4-3. Estimated biomass C (above and below ground) stock values of each plot and three
plots average of five selected land-use systems. Two sets of values from different
allometric equations are shown for live fence and fodder bank systems, which are
significantly different in t-test (values of UNFCCC equations < values of Acacia spp.
equations).
Faidherbia Vitellarial~~~~~11111~~~~ Live fence Fodder bank Abandoned
albida paradoxa land
parkland parkland UNFCCC' Acacia' UNFCCC' Acacia?
(Mg C ha ')

Plot A 51.4 24.2 3.2 5.9 2.1 4.8 0.8

Plot B 55.7 16.5 3.0 4.3 1.8 2.7 0.4

Plot C 54.8 26.6 7.8 14.8 2.7 4.9 1.0

Average 54.0 22.4 4.7 8.3 2.2 4.1 0.7

a b c c3 c c3 c
a, b, c: Mean separation by Tukey-Kramer's multiple comparison test at p<= 0.05). 'Estimation from the
UTNFCCC guideline's equations. 2Estimation from Acacia spp. equations developed in Northern Kenya.
SValues from UNFCCC equations and Kenyan equations were separately compared with other three
systems .

Table 4-4. Total C stock (biomass C + soil C of different depth) of five selected land-use
systems.
Total C stock (Mg C ha ')
More Ct Less C
1 2 3 4 5

Biomass + 0-10cm soil C FA 59.8 a VP 27.7 b LF 9.8 c AL 7.9 c FB 4.8 c

Biomass + 0-40cm soil C FA 70.8 a VP 37.1 b AL 24.7 bc LF 17.7 c FB 14.0 c

Biomass + 0-100cm soil C FA 87.3 a AL 56.9 b VP 49.8 b FB 35.6 b LF 28.7 c
FA: Faidherbia albida parkland, VP: Vitellaria paradoxa~11~~1~~11~ parkland, LF: Live fence, FB: Fodder
bank, AL: Abandoned land. (a, b, c: Mean separation across land-use systems by Tukey-
Kramer's multiple comparison test at p<= 0.05).
Data source: Biomass C values of live fence and fodder bank are from UNFCCC equations.


















Ic ~~~r;--

:


~f~ ;
i-i
.s~--, L.~
~


-I ~~
L"


Figure 4-1. A: Location map of Mali; B: Map of Mali showing its land-locked nature: C: Map of
Segou region (The sign refers to the city of Segou).






















Figure 4-2. Faidherbia albida parkland in Togo village. The tree leaves are shed at the
beginning of the rainy season; but they return at the beginning of the dry rainy season.
The understory crop is pearl millet (Pennisetum glaucum). (Photographed by author)


Figure 4-3. Vitellaria paradoxa~11~~1~~11~ parkland in Dakala village. The trees have wide canopies, and
leaves remain during the rainy season. Farmers plant crops (in this photo, pearl
millet) beneath the trees, often very close to the trunk. (Photographed by author)


"~-~-~-~-~14

..
;1~.





























Figure 4-4. Live fence system in Dougoukouna village. Relatively fast-growing (mostly thorny)
trees are planted around the crop field to protect crops from free-roaming animals.
The trees are planted in two lines (1.5m apart). The outside line trees shown in the
photo are mostly Acacia nilotica. (Photographed by author)


Figure 4-5. Fodder bank in Dakala village. Gliricidia sepium trees are planted at 2 m by 1 m
spacing in lines. Towards the end of the dry season when other fodder sources such
as fresh grasses or crop residue are scarce, farmers harvest branches of the trees, dry
and feed them to their domestic animals. (Photographed by author)





























Figure 4-6. Abandoned land just outside of Diamaribougou village. The land was cultivated
until less than 10 years ago. The surface soil is eroded leading to formation of a hard
surface pan. Only certain bushes such as Guiera senegalensis and Combretum
micranthum can survive on this type of degraded land. (Photographed by author)














O Biomnass C 0 Soil C 0 10 cm

2 Soil C 10 40 cm I Soil C 40 100 cm


Ab ovegroun d
biomass


40


S20


0


S20


40


60


Lve
fence


Fodder
bank


Aban don ed
land


I I IGround level
b ::: a
bI a


a Soil


-r Faidhrbia
albida
-parkland


Vitala~riaa
pardoxa:
parkl and


Figure 4-7. Aboveground and belowground C stock per ha of five selected land-use systems.
Biomass C is shown above the x-axis, and soil C is shown below the axis with three
soil depth classes. Values of live fence and fodder bank are from UNFCCC
equations.









CHAPTER 5
SOIL CARBON SEQUESTRATION IN DIFFERENT PARTICLE-SIZE FRACTIONS AT
VARYING DEPTHS UNDER AGROFORESTRY SYSTEMS IN MALI

Introduction

The measurement of carbon (C) content is part of the basic soil analysis procedure.

Inventory data on soil C content is available in most of the WAS countries. However, to discuss

soil C sequestration as one of the options for mitigation of atmospheric CO2, the stability of soil

C (how well C is "captured" inside the soil) has to be considered. In other words, the soil C that

goes back to the atmosphere after decomposition within a month of its deposition, and that stays

in the soil for decades should not be counted as similar in terms of C credits. Characteri stics

such as the stability of soil C are very controversial issues in estimating and accounting

methodologies (Ingram and Fernandes 2001; Garcia-Oliva and Masera 2004). Also, soil-

sampling depth for these accounting procedures needs to be deeper than for normal soil analysis.

The conventional soil C studies of agricultural systems mostly focus on soil organic matter in the

surface layer of 20 cm for the interests of soil fertility. But sampling of deeper soil horizon is

necessary in efforts to understand the extent of soil C protection and characteristics of various

soil C forms, especially the systems involving deep rooting plants such as trees (Jobbagy and

Jackson 2000).

In general, soil C dynamics regarding C sequestration have not yet been well studied,

although recent technological development and interests towards climate mitigation activities are

contributing to an increased number of this type of studies (Post et al. 2000; Sun et al. 2004).

Still, these studies are limited even in developed countries, and not easily available in the

research- resource limited area such as the studied region or Africa in general. The studies of

this nature that have been conducted so far have been in natural environment such as forest

stands, tundra, or grasslands, probably due to the relatively stable dynamics of soil properties










(Richter et al. 1999; Schuur et al. 2001). And, soil C studies in agricultural croplands have

mostly been in the context of soil productivity management (Phillips et al. 1993; Beare et

al.1994; Alvalez et al. 1995; Blair et al. 1995; Franzluebbers et al. 1995; Rhoton 2000), although

recently more and more soil C studies are considering agricultural soil as C sinks, a mechanism

which removes CO2 from the atmosphere (Smith et al. 1998, Duiker and Lal 1999, Lal 2004b)

Soil C studies in agroforestry systems have been few. Interactions between crops and trees

and the relatively short-term rotation of land management make such studies more complicated

and challenging compared to single-species agricultural and forestry systems. Existing studies in

agroforestry systems measure soil organic matter (SOM) content with other soil nutrients (Kang

et al. 1999; Makumba et al. 2006). Those studies discussed whether trees have positive (e.g.,

nitrogen fixing) or negative (e.g., competition for light, nutrients, or water) impacts on crop

production. In the WAS, parkland trees were found to increase soil C around trees (Jonsson et

al. 1999), and an improved fallow system (planting Gliricida sepium during the non-cropping

phase) was found to increase soil C on the surface compared with natural grass fallow (Kaya and

Nair 2001). These studies support the expectation that agroforestry systems would enhance soil

C sequestration, but there is still little information about trees' influence on C in deeper soil and

stability of various forms of soil C sequestered by trees.

In this study, organic C in soil is assumed to be "protected" from further decomposition in

three ways: 1. physically stabilized, or protected through microaggregation (microaggregate-

associated soil C), 2. intimate association with silt and clay particles (silt- and clay- associated

soil C), and 3. biochemically stabilized through the formation of recalcitrant soil organic matter

compounds (non-hydrolysable soil C) (Six et al. 2002). There are some other ways such as Al or

Fe- SOM complexes, C accumulation resulting from anaerobic conditions, and transfer to









subsoil by colloidal or soluble C; but they do not seem to occur significantly under the soil and

climatic conditions of the study region (Tan et al. 2004; Nierop et al. 2007; Zinn et al. 2007).

The turnover time for physically protected C (type 1 and 2) is estimated to be 50 1000 years;

for biochemically protected C (type 3), it is 1000 3000 years. The turnover time for less stable

C within macroaggregates is 5 50 years, and for other types C, such as the litter fraction, it is

0. 1 20 years (Batjes 2001). To differentiate the types of soil C, physical fractionation is the

common initial step. The dynamics of soil C in each fraction size can be further investigated by

13C isotopic ratio measurement, which distinguishes between C derived from plants that follow

C3 photosynthetic pathways (all trees) and those that follow C4 pathways (most warm-season

graminaceous plants: in this study pearl millet, Pennisetum glaucum, and sorghum, Sorghum

bicolor). This method has been used for studying the impact of land-use change on soil C and

for comparing the C dynamics in different land-use systems (Balesdent et al. 1998; Potvin et al.

2004).

Research Questions

In this scenario, the present study was undertaken based on the premise that compared with

agricultural and tree-less systems, agroforestry systems will help store more C in soil and offer

better stability of stored C in deeper soil layers due to presence of deep-rooted trees. Specific

research questions are:

1. Do trees contribute to soil C storage in the selected agroforestry systems, and how stable is
the stored C?

2. What is the relative attractiveness of each of the selected agroforestry systems or land-use
change in terms of its soil C sequestration potential?

Materials and Methods

The study was conducted in the seven selected villages of Segou region, Mali, West

Africa. The details of the site and the selected land-use systems (treatments) are described in










Chapter 4. In each land-use system, three on-farm plots (replicates) were chosen for soil

sampling.

Research Design

Soil samples were taken from different distances from trees. In the two parkland systems,

three horizontal distances for soil sampling were chosen:

* Near (bottom of) the tree
* 3 m (about half the crown radius) from the trunk
* 10 m from the trunk (outside of the crown)

The average size tree in each plot was selected based on aboveground inventory data as the

center of the sampling area. Soil samples were taken from four directions (north, south, west,

east) around the tree and mixed before putting in the sample bag (Figure 5-1).

Three horizontal distances for sampling live fence plots were:

* Near (bottom of) the tree
* 1 m (inside) from planted line (root influence zone)
* 3 m (inside) from planted line (outside the crown and rootzone)

Live fences are either rectangular or polygonal shapes; four sampling points on different

sides were randomly chosen. Samples away from the tree line were taken inside the fence,

because outside of the fence were often paths or borders of the cultivated land.

Since fodder bank trees are evenly planted (2m x m) inside the plots and shrubs are

randomly grown in abandoned land plots, horizontal differentiation of sampling was not taken at

these two systems. In each plot, samples were taken from four randomly selected points, and

mixed well to form the composite sample.

Sampling depths at each horizontal distance were as described in Chapter 4: 0 10 cm

(surface soil), 10 40 cm (crop root zone), and 40 100 cm (tree-root zone). This was based on









the expectation that the amount of C content would differ by depth class depending on the

presence or absence of tree roots and tillage.

In summary, the numbers of soil samples are:

2 (Parkland systems) x 3 (horizontal dist.) x 3 (depth) x 3 (replicates) = 54
1 (Live fence) x 3 (horizontal dist.) x 3 (depth) x 3 (replicates) = 27
1 (Fodder bank) x 1 (horizontal dist.) x 3 (depth) x 3 (replicates) = 9
1 (Abandoned land) x 1(horizontal dist.) x 3(depth) x 3(replicates) = 9
Total = 99

Soil Preparation and Analyses

Samples were all air-dried and passed through a 2 mm sieve (except samples for bulk

density measurement) at the field station in Segou. There is no visible O horizon or surface

litter, and therefore no analysis was done for that layer (Woomer et al. 2004) (Figure 5-2). Soil

samples were brought back from Mali to University of Florida in October 2005 for analysis.

Samples for bulk density measurement for each depth class were separately collected at

each plot with a 100 cm3 cylinder. Wet weight and air-dry weight were measured in the field.

Samples were oven-dried and analyzed for particle size distribution (USDA Soil Survey Lab

Method) and pH at the University of Florida, Soil and Water Science Department laboratory.

Sub-samples were taken from the 99 air dried samples and ground. Soil C content (g C kg' soil)

of the sub-samples was measured by the dry combustion method on an automated Flash EA 1 112

NC elemental analyzer (Thermo Fisher Scientific, Inc.).

Soil fractionation

Soil samples were fractionated into three aggregate size classes (2000 250 Cpm, 250 53

Cpm, and <53 Cpm) by wet sieving, following the method of Six et al. (2002). A sub-sample of

100 g of the composite soil sample was submerged in deionized water as disruptive forces of

slaking for about 5 minutes prior to placing it on top of a 250 Cpm sieve. The sieving was done

manually by moving the sieve up and down approximately 50 times in 2 minutes. The fraction









remaining on the top of the sieve was collected in a hard plastic pan, oven-dried at 65 oC and

weighed. Water plus soil <250 Cpm were poured through a 53 Cpm sieve and the same sieving

procedure repeated. The recovery of mass soil fractions after overall wet sieving procedure

ranged from 97 to 99% of the initial soil mass. Sub-samples for each soil fraction (99 samples x

3 fraction size = 297 samples) were then ground, and C contents were measured by the same dry

combustion instrument as described for whole soil C. Soil C in the large (L) fraction (2000 -

250 Cpm) contains fairly new coarse/fine particulate organic matter (POM) C, although there is

other forms of "protected" C not fully separated by wet sieving. The medium (M) fraction (250

- 53 Cpm) contains both less protected C (within fine POM) and protected C (microaggregate

protected POM C and silt + clay associated C). C in the small (S) fraction (<53 Cpm) contains the

protected form (silt + clay protected C or non-hydrolysable C), although there are less stable

forms of C in the size class, too (Six et al. 1998, Six et al. 2000). Unprotected C involves the

youngest form of SOM, and through the process of aggregate formation, adsorption/desorption,

and condensation/complexation, soil C becomes older and more stable (SOM dynamic model

Figure 5-3).

C isotopic ratio (13C/12C) measurement

613C ValUeS of soil samples (whole soil and fractionated soil) were measured by Thermo

Finnigan MAT Delta Plus XL mass spectrometer (Thermo Fisher Scientific, Inc.). C isotope

ratios are presented in 8-notation:

613C = [(RSample RStd)/RStd] x 103 (Eq. 5-1)

Where RSample is the 13/ 12C ratio of the sample, and RStd is the 13/ 12C ratio of the Vienna

Pee Dee Belemnite (VPDB) standard.

Relative proportions of soil C derived from C4 plants material versus C3 plants material

was estimated by mass balance (Balesdent and Mariotti 1996):










C4 plants contribution (6 6Tr) / (6Cr 6Tr) (Eq. 5-2)
Where 6 is the 613C ValUe Of a given sample, SCr is the average 613C ValUe Of C4 plants

tissue (-13 %o), and 6Tr is the average 613C ValUe Of C3 plants (-27 %o). In the studied land-use

systems, trees and bushes in abandoned land are C3 plants, and crops grown underneath the

parklands around the live fence are C4 plants (sorghum and millet) as well as the presumed

previous vegetation (crops) of fodder bank and abandoned land.

Statistical Analysis

Analysis of variance (ANOVA) was used to estimate the variance components. The linear

models were applied to the soil C concentration data. Modell was applied to all five land-use

systems, and model 2 was applied to three land-use systems (two parklands and live fence) that

have distance information. The linear models were:

M od el 1: y ykI = pu + L, + D, + Fk I+ L *D II L *Fzk + L *Izi + D *F k + D *IJi + F *IkI

L*"D*FyJk + L*"D*I,i + D*F*17ki + L*"D*F*IykI+ eskI (Eq. 5-3)

Model 2: y ykin2 = pu + L, + D, Fk I~ Tnz+ L*"D II+ L*~Fzk + L*Izi1 + D*~F k + D*~IJi + F*IkI

SL *T ,,, + D *T,,, + F*"Tkn2+ ITI + L*D *Fyk + L*D *I,i + D *F*17ki + L*D *T,,,,+

L*~F*Tzkn + L*~I*Ti,~ + D*F*Tykn2 + D*~I*Ti,I, F*I*Tkin2 + L*~D*F*Iy~kI + L*~D*F*Tykn2

L*D*I*Tyi,, + D*F*I*Tykin 2 L*D*F*I*Tykin2 eykin (Eq. 5-4)

y ylkin is the C concentration in land-use i, at depth of j, fraction size of k, isotopic ratio of 1,

distance of m.

pu is the population mean,

L, is the land-use (treatments), i = FA, VP, LF, FB, and AL.

FA: F. albida parkland, VP: K. paradoxa parkland, LF: live fence, FB: fodder bank, and

AL: abandoned land.

D, is the depth, j= 1 3










1: 0 10 cm, 2: 10 40 cm, 3: 40 100 cm.

Fk is the fraction size, k = L, M, S

L: large fraction (2000 250 Cpm), M: medium fraction (250 53 Cpm), S: small fraction

(<53 Cpm)

It is the isotopic ratio, l = C3, C4

C3: C3 plants origin C, C4: C4 plants origin C

T,, is the distance, m = n, m, f

n: near the tree, m: middle of the canopy, f: far from the tree

eykln is the random variable error within the experiment.

Model 1 and model 2 including all the possible interactions between the factors were run

using PROC MIXED procedure of SAS. Interactions that were not significant were dropped

from the model. The models that were biologically and statistically significant are presented in

the results section. To further examine the interactions, data were sorted (PROC SORT

procedure) with certain factors fixed, and tested again using ANOVA. Based on the outcome of

the ANOVA, factors and other soil characteristics (e.g. percentage of sand, silt, and clay) were

tested for their relationships using linear regression.

All statistical tests were considered significant when p<0.05 unless otherwise specified.

Results

Soil Characteristics

Soils in the sample plots are mostly sandy loam or loamy sand (Table 5-1). Soil colors

varied from whitish or dark gray to reddish brown in different plots, but all are categorized as

Haplustalfs by the regional survey (Doumbia 2000). Abandoned-land soil was extremely hard to

sample with an auger because bedrock was found in some places at less than 1 m depth (Figure

5-2). Most of the time, silt or clay was clearly observed in 70 80 cm depths. Content (g kg









soil) of sand, silt and clay were compared statistically over the five land-use systems.

Abandoned land had lower sand content (530 694 g kg-l soil) and higher content of silt and

clay (306 470 g kg-' soil) than the other four systems (sand: 715 935 g kg-' soil, silt + clay:

65 285 g kg-l soil) (Table 5-1). Soils of the four systems in each depth class were not different

in the particle size content.

Whole Soil C

Whole soil C content across all the systems varied from 1 6 g C kg-l soil (Figure 5-4).

Two parklands and live fence had three sampling locations (0, 3, and 10 m from tree base for

parklands; 0, 1, and 3 m from tree lines for live fence). Only the surface soil (0 10 cm) of live

fence showed a difference between the "near tree" and the two zones more distant from the tree,

but other depth classes and two parklands plots did not show difference by horizontal distance

from trees, although the trend of "near tree" > "outside crown" was observed in the surface soil

of both parkland systems. C content decreased with soil depth for all land-use systems except

the fodder bank, where the surface soil (0 10 cm) had less whole soil C than lower depths.

The whole soil C data of five land-use systems were compared statistically using two

factorial (land-use and depth) ANOVA (model 1). Both land-use and depth factors, as well as

land-use~depth interaction were significant for that variables. By Tukey-Kramer multiple

comparison test, abandoned land had higher C content than the other four systems, but the other

four systems were not different from each other. C content was different by depth: 0 10 cm >

10 40 cm > 40 100 cm across all treatments.

Among the data for the two parklands and live fence plots, horizontal distance was another

"factor" (model 2). Land-use and depth factors were significant, but distance was not

significant, although showing the trend (p=0.0884), in the three-way factorial (land-use, depth,

and distance) ANOVA. When land-use factor and distance factor were examined in the fixed









depth class, the distance factor was still not significant in any depth class. However, at 10 40

cm and 40 100 cm classes, C content was higher for F. albida parkland than in the case of the

live fence.

C in Soil Fractions

Carbon-fraction contents [large (L): (250 2000 Cpm), and medium (M): (53 250 Cpm)] in

different systems were mostly not different among each other, and ranged from 1 to 2 g C kg-

soil (except in fodder bank, where it was from 0 to 1 C kg-l soil) (Figure 5-5). Small (S) fraction

(<53 Cpm) C content did not change much from 0 10 cm to 10 40 cm depth in all systems. In

the live fence treatment, C in the 0 10 cm depth contained more L fraction (1.8 g C kg-l soil)

than the other two size fractions (less than 0.7 g C kg-l soil), whereas the fodder bank treatment

had very low C content of L and M fractions in that soil layer (0.6 g C kg-l soil).

Data of C in the three fractions were analyzed by three-factorial ANOVA (land-use, depth,

and size) (model 1). All three factors were significant, as well as three combinations of two-

factor interactions. Results of multiple comparisons among land-use systems were the same as

for the whole soil C data; abandoned land had higher C content than the other four systems,

which did not differ among each other. Depth class comparison also showed the same result: 0 -

10 cm > 10 40 cm > 40 100 cm. S fraction and L fraction C were both significantly higher in

content than M fraction C when three depth class data are combined. The significance varied

when each depth class was separately tested, but M fraction C content was always the lowest.

When each fraction size data were tested separately, land-use and depth were significant for all

fraction sizes.

Distance from the tree was the only factor that was not significant in four-factorial

ANOVA (land-use, depth, size, and distance) for three systems (two parklands and live fence:

model 2). Interactions of four factors and three factors including distance were not significant.









Further sorting and testing showed that the distance factor was significant in only M fraction C at

the 0 10 cm depth, where "near tree" was higher in content than "outside crown."

Isotope Analysis of Whole Soil C

The measured 813C ValUeS of each depth class of each land-use systems varied from -23.9

to -15.1 (Table 5-2). Based on the values and the mass balance calculation, whole soil C data

was separated into that originating from C3 plants (trees) and C4 plants (crops) (Figure 5-6).

"Near tree" data and "outside crown" data are presented side by side for two parklands (Figure 5-

6 A, B, C, D) and live fence (Figure 5-6, E, F). In the figure, C of tree origin was found more in

surface soil and near the tree, although when they were tested statistically, there was no

significant difference between "near tree" and "outside crown" data except for that of live fence

at the 0 10 cm depth. Fodder bank did not have much C of C3-origin, even with trees growing

in the plots. On abandoned land, C4-origin C was the major form of C, and, as mentioned

earlier, the soil C content was higher in this system compared with other systems.

Three-factorial (land-use, depth, and isotopic ratio) ANOVA was conducted among the

five land-use systems (model 1). All factors were significant, and three-factor interaction and

two-factor interactions including isotopic ratio were also significant. C3-origin C and C4-origin

C were then tested separately using the "SORT" procedure. Land-use was not a significant

factor among C3-origin C data, but depth was. For C4-origin C, both land-use and depth had

significant effect: abandoned land had higher content than other four systems with parklands

higher than the improved systems, and deeper depth had less C content.

Four-factorial (land-use, depth, distance, and isotopic ratio) ANOVA was used to test

differences among the two parklands and live fence systems (model 2). Distance was again not a

significant factor while all others were. Four-factor interaction, as well as three- and two-factor

interactions including distance was significant, suggesting distance was somewhat influential for









C content. When only C3-origin C data was tested separately, land-use was not significant but

depth and distance were. Multiple comparison tests showed that C3-origin C content from "near

tree" and "half crown" were higher than that from "outside crown" throughout the three systems.

Among C4-origin C, land-use and depth were significant factors but distance was not.

Isotope Analysis of C in Soil Fractions

The measured 813C ValUeS of three fraction sizes varied from -25.4 to -15.7 (Table 5-2).

Data of C content in the three size fractions were also separated into those with C originating

from C3 and C4 plants (Figure 5-7). Four-factorial ANOVA (land-use, depth, size, isotopic

ratio) was used to test differences among the Hyve land-use systems (model 1). Land-use, depth,

and size were significant factors, but isotopic ratio was not. Four-factor interaction was not

significant, but all two-factor interaction combinations including isotopic ratio were significant

(p<0.01). When C3-origin C was tested separately, land-use was not significant while size and

depth were. All three were significant factors among C4-origin C data. When data were sorted

by the fraction size, C3-origin C was significantly more than C4-origin C in the L fraction, while

C4-origin C was more than C3-origin C in the S fraction.

Among the two parklands and live fence data sets, five-factorial ANOVA (land-use, depth,

size, isotopic ratio, and distance) was conducted (model 2). Land-use, size and depth were

significant, but isotopic ratio and distance were not. All combinations of three-factor

interactions including isotopic ratio and distance were significant, so was the two-factor

interaction of distance and isotopic ratio. Distance became a significant factor ("near tree" >

"outside crown") for C3-origin C data sets, while it was not for C4-origin C.

Relationships of Data Sets

Linear relationships were tested between C content data sets and other soil characteristics

of the samples. The S fraction (<53 Cpm) percentage in whole soil had significant relationship









(R2=0.60) with the S size C (contains protected C) content. However, this relationship was not

seen for M and L fractions; the more fraction percentage of M or L size in the soil does not mean

the more soil C of those sizes. Also, the regression between silt + clay content and whole soil C

content was strong, especially in the 10 40 cm soil depth (Figure 5-8).

Sand-, silt-, clay-, or silt + clay contents did not have strong relationship with L fraction C

or M fraction C. Although the regression between silt + clay content and S fraction C was poor

(R2=0.16) in the total data set, it became more pronounced when the data set was divided by

depth class. R2 ValUeS between silt + clay and S fraction C were 0.53 at 0 10 cm, 0.44 at 10 -

40 cm, and 0.67 at 40 100 cm (Figure 5-9). Soil pH or bulk density did not show any strong

relationship with C in whole soil or in each of the three fractions in any depth or for all depths

combined.

Percentages of C3-origin C or C4-origin C did not have a significant relationship with

whole soil C content. There was a strong relationship between C3-origin C content and L

fraction C content (R2=0.67) throughout the data set. The relationship was stronger at 0 10 cm

soil (R2=0.72) when data sets were divided by depth (Figure 5-10), but were not significant in

deeper depths. The relationship between C3-origin C and S fraction C content was also observed

in the 40 100 cm depth (R2=0.45).

Discussion

Contrary to expectations, C content in all soil depths was higher in the "abandoned" land

than in any of the four agroforestry systems (Figure 5-4), although the significance varied

depending on the depth class. Judging from the observation that the abandoned land soil had

significantly more silt and clay fractions than those of soils under the agroforestry systems

(Table 5-1), it seems that the whole soil C content was directly related to the silt and clay

contents of the soil. In general, soil organic C content is known to correlate positively with the









amount of non-crystalline clays (Powers and Schlesinger 2002). Indeed, the whole soil C

content was related to silt + clay content in all three depth classes (strongest in 10 40 cm)

(Figure 5-8). Silt + clay content also had strong relationship with S fraction C (<53 Cpm), the

strongest being in 40 100 cm (Figure 5-9), but not as well as with whole soil C. This suggests

that silt and clay are mainly associated with soil C in <53 Cpm size, especially in deep soil, but

they are also associated with larger fraction size form of soil C. Based on the data points on

Figure 5-9, abandoned land data do not seem to follow this relationship well, while they follow

the relationship better in Figure 5-8. This could be because silt and clay are more involved in

forming larger than 53 Cpm fraction size C in abandoned land compared with other systems.

After being abandoned for a few years (less than 10: see Chapter 4), the land probably was

subj ected to extensive erosion that took away aggregates and sandy particles from the surface

layer. The remaining soil was higher in silt and clay and formed a hard surface pan, which

prevented further disturbance or leaching. The maj ority of soil C in the abandoned land was of

C4 plant origin (Figure 5-6), suggesting that the stored C in abandoned land was mainly from the

previous land-use (land cultivated with C4 plants) and was well protected.

The other four land-use systems were not significantly different in terms of the soil

characteristics (pH, bulk density, particle size). However, as seen in the soil pit photo (Figure 5-

2), the color was quite different in each soil pit, suggesting soil variations among plots of same

land-use systems in the same village. Still, soil C content variations among these soils can be

explained more as a consequence of the influence of trees and land management than caused by

soil characteristics as in the case of abandoned land. Among the two parklands and live fence

systems, C content was expected to be: near tree > under the crown > outside the crown, as

reported in a similar study in the parkland system in South Mali, which showed the significant









difference between near tree and outside the crown in 0 20 cm (although not significant in 20 -

40 cm) (Kater et al. 1992). However, in this study, the tree effect (distance factor) was only

significant in the live fence but not in the two parkland systems (the trends of "near tree" >

"outside crown" were observed in both parklands) (Figure 5-4). From the field observation, this

seems to be because of the frequent tillage (a couple of times during the growing season) in the

parklands. Many studies reported that tillage caused significant loss of soil organic C especially

in the surface (Gebhart et al. 1994; Campbell et al. 1996; Six et al. 1998). Tillage using animal

traction is done very close to the trunks of parkland trees where tree density is low, and this

accelerates the decomposition of organic matter in the top soil, and thus reduces soil C

accumulation. Live fence trees are planted in high density and their thorny branches spread,

making it almost impossible to do tillage near the tree lines. C content around the live fence tree

lines is higher due to the higher litter inputs from trees (Figure 5-6). The positive correlation of

C3 (tree)-derived C and L fraction (contains new SOM) C in the 0 10 cm soil (Figure 5-10)

also suggests that the recent planting of live fence trees and the input of litter have already

contributed to the accumulation of C in the topsoil.

The reason why fodder bank systems did not have much topsoil C (Figure 5-4) could also

be explained by the management style. First, the land is tilled before planting the trees, which

causes the loss of aggregated C (L and M fraction C). Then, crops are not grown inside the

fodder bank after tree planting, and most of the tree leaves and branches are taken away as

fodder. The low litter input after establishment also causes the low density of L and M fraction

C (Figure 5-5). Since S fraction C contains well-protected C, its content in fodder banks was not

affected much by the tillage, and stayed similar with other systems in all depths.









Long-term influence of tree presence was observed in parklands data. S fraction C content

(involving well protected C) was higher in deeper soil in parklands compared with live fences or

fodder banks (Figure 5-5). This is probably because of the long-term inputs of litter and tree root

biomass in the parkland system, compared to the other two systems (live fence and fodder bank)

that represent land that was treeless (only crops) until six to nine years ago. Also, the distance

factor is significant among two parklands and live fence in M fraction C. M fraction C contains

various forms of SOM including microaggregate-associated C, which means trees contribute to

increasing the protected form of SOC. This suggests that trees help increase not only litter input

and the content of unprotected C, but also facilitate a variety of protection to soil C from

unprotected to protected state.

The content of C4-origin C was higher than that of C3-origin C when the whole data

(whole treatment, whole depth) was tested. The tendency of higher amount of C4-origin C in

deeper soil layer and/or in S fraction was also observed (Figure 5-6, 5-7). In the studied land-use

systems, trees and bushes in abandoned land are C3 plants, whereas crops grown underneath the

parklands and around the live fence as well as the presumed previous vegetation (crops) of

fodder bank and abandoned land are C4 plants (sorghum and millet). Moreover, there are other

isotopic variation/bias that could be considered in the use of the mass balance equation (Eq.5-2).

The 613C of plant (the main source of SOM) is known to vary depending on species and

environmental factors or CO2 COncentration in the atmosphere (Tieszen 1991; Marino et al.

1991). These differentiations are relatively small compared with the large difference caused by

the different photosynthetic pathway. Another considerable isotopic composition change is

related to SOM decay. Over the decomposition process, 613C ValUe Of soil organic C tends to

increase (less negative) (Balesdent and Mariotti 1996). The study using 613C ValUe for tracing









the vegetation change in East African savannah suggests that using the mass balance approach to

interpret soil profiles might lead to an under-estimation of past C3 plant abundance (Gillson et al.

2004). Thus, the C3-origin C, especially in deeper soil tends to be underestimated, and this

might be one of the reasons that C3-origin C in the deeper soil (tree root contribution) was not

observed as much as expected. It is also probably due to the higher inputs of belowground

biomass from crops in the sampling depth. Aboveground crop residue is usually taken away as

fodder in fodder bank, but crop roots are annually left in the soil and become the source of soil

organic C. Crop roots are also expected to have a faster decomposition rate than tree roots that

contain higher proportion of lignin and other substances that slow down the decomposition

process. In addition, sampling depth (1 m) might not be enough to see more tree roots influence

than crop roots influence. Most of Mali's soil research sets 40 cm as the crop root influence

limit, but a study found a significant amount of sorghum roots close to or deeper than 60cm in a

similar climatic condition (Jones et al. 1998). Tree roots, especially in semiarid areas, are

expected to go even deeper than 1 m (Jeltsch et al. 1996).

Overall, soil organic C content in the studied systems were of relatively lower magnitude

(1 to 6 g C kgl soil), than in agriculture or agroforestry systems of other ecoregions. This is

possibly due, at least partly, to the rapid decomposition of organic C, which is known to be

facilitated by the high temperature and low silt and clay contents (Schimel et al. 1994; Hassink

1997). The lower amount of M fraction C than S and L fraction C (when tested with all

treatments data combined) also suggests that most of the litter inputs are decomposed rapidly so

that little is going to the process of becoming a more protected form of C. In many situations, it

is probably best for farmers to allow the maj ority of the residues to be eaten by cattle in these

systems rather than attempt to build soil organic matter.









A conservative estimate of soil C sequestration potential through addition of organic

matter such as plant litter and animal waste to these agricultural soils is in the range of 0. 1 0.3

Mg C ha-' yr' (Batj es 2004). ICRAF is trying to promote live fences and fodder banks for land

amelioration and counts C sequestration potential as one of the potential benefits. However, it is

important to address the possibility of causing net loss of soil C while converting abandoned land

into live fences or fodder banks in this study region at the initial stage, because of the tillage

factor at establishment. And, because the subsequent land-use practices provide low levels of

litter input, especially in fodder banks, it may take a long time to regain the initial loss of soil C.










Table 5-1. Soil profile characteristics for plots of the five land-use systems used in the study at
Segou Region, Mali.
Depth Sand Silt Clay HBulk density
(cm) (gkg-'soil) (g kg-'soil) (g kg-'soil) ~(g cm )

Faidherbia albida 0 10 865 69 66 6.7 1.42
Parkland 10 40 805 81 114 6.3 1.50
40 100 715 86 199 5.9 1.50

Vitellaria paradoxa 0 10 857 86 57 6.6 1.48
Parkland 10 40 809 96 95 5.9 1.38
40 100 798 82 120 5.6 1.47

Live fence 0 10 935 22 43 5.8 1.44
10 40 900 22 78 5.3 1.51
40 100 846 31 123 5.0 1.39

Fodder bank 0 10 830 72 98 5.2 1.42
10 40 797 91 112 5.3 1.46
40 100 752 95 153 5.5 1.55

Abandoned land 0 -10 694 129 177 5.4 1.36
10 40 576 164 260 5.3 1.21
40 100 530 164 306 4.9 1.43












Table 5-2. 613C ValUeS of whole soil and three fraction sizes from five studied land-use systems,
at Segou Region, Mali. (all values are average of three replicates)


Whole soil Fraction
Land-use system Depth (cm) 250 2000 ptm 53 250 ptm <53 ptm


Faidherbia albida
parkland


near tree


10
-40
-100
10
-40
-100
10
-40
-100

10
-40
-100
10
-40
-100
10
-40
-100

10
-40
-100
10
-40
-100
10
-40
-100


-21.5
-18.7
-16.6
-21.8
-17.5
-15.6
-19.5
-16.9
-15.1

-20.7
-18.8
-18.7
-20.6
-17.8
-17.8
-18.7
-17.6
-16.8

-23.9
-20.1
-17.1
-22.5
-19.3
-16.9
-20.9
-17.9
-16.3

-18.2
-17.9
-17.4

-16.3
-15.7
-16.4


-22.4
-20.3
-18.8
-22.9
-20.7
-18.2
-20.9
-21.4
-20.0

-23.1
-23.3
-22.7
-22.7
-19.9
-20.9
-20.5
-19.2
-19.6

-25.4
-21.1
-19.2
-23.3
-22.2
-19.2
-22.2
-18.9
-19.7

-20.4
-21.3
-22.9

-16.8
-16.9
-18.0


-20.3
-20.2
-18.6
-21.8
-19.7
-17.2
-20.0
-20.6
-18.6

-21.1
-20.1
-20.8
-20.4
-18.3
-18.8
-18.1
-18.9
-20.8

-23.9
-19.8
-18.0
-22.4
-19.3
-18.6
-18.8
-19.1
-18.9

-21.2
-20.4
-18.7

-18.5
-16.5
-16.2


-21.2
-18.6
-18.1
-21.5
-18.3
-17.3
-19.3
-17.3
-15.9

-21.4
-20.1
-19.2
-20.6
-17.8
-18.3
-19.3
-19.0
-18.9

-22.6
-20.3
-17.7
-23.1
-18.8
-17.7
-20.8
-17.7
-16.8

-19.1
-17.4
-16.9

-16.4
-15.7
-16.8


3 m from tree



10 m from tree


Vitellaria paradoxa
parkland


near tree


3 m from tree



10 m from tree


Live fence


near tree


1 m from tree line



3 m from tree line


Fodder bank




Abandoned land


0 -10
10 40
40 100

0 -10
10 40
40 100

























Figure 5-1. Soil sampling, Segou, Mali. The samples were drawn with an auger from the defined
soil depths and horizontal distances from trees; each sample was a composite of four
sub-samples drawn from different points within a plot. (Photographed by author)









9 -~'; ~ C


B


D E


Figure 5-2. Soil pits dug in plots of the five land-use systems studied in Segou region of Mali.
The red stick is marked (in black) at 10 cm intervals. A) Faidherbia albida parkland.
B) Vitellaria paradoxa~11~~1~~11~ parkland. C) Live fence. D) Fodder bank. E) Abandoned land.
(Photographed by author)















U n prote ctecd
,soil C
Litter quality


I


Ph ysicallIy
protected
soil C


BiochemicallSy
protected
soil C


Microaggregate- Silt- and clay-
associated soil C associated soil C

Condensation/complexation

N on- hyd ro lyza ble
soil C


Figure 5-3. Model of soil organic matter dynamics
Source: Six et al. 2002. Figure 3 in page 163.














0
O

10 -

20 -

,30

40



60 -

70 -


90 .

10 -


Total C (g C kg' soil)
1234567


II


Total C (g C kg~' soil)
01234567


Total C (g C kg' soil)
01234567















+near tree
wroot intiuence zone
outside root zone


20

,30

A ~40


60


70
near treeBO

-half crown90

outside crown 10


-


1-: ti

-



-

- I : 4


B E


+near tree
w half crown
outside crovn


Total C (g C kg soi)
0 1 2 34 5 7
', ,


Total C (qC kg-' sol)
o 1 2 J 4 5 B 7 T


10

20






R 50


60





100


0





30

40

50 E

60

70

BO

90

100


Figure 5-4. Whole soil C content of three depth classes (0 10 cm, 10 40 cm, and 40 100
cm) in different land-use systems in Segou, Mali: A) Faidherbia albida parkland, B)
Vitellaria paradoxa~11~~1~~11~ parkland, C) Live fence, D) Fodder bank, and E) Abandoned
land. Range of the each depth value is 95 % confidence level. Depth indicated is the
mid-point of sampled depth.










g C kg ~'soil
01 2 34


g C kg-' soil




B 2
1730
S40

60
70
. +250 -2000 plm BO
-E53 -250 plm 90
<53 lm
100


g C kg' soil
S1 23 4




j~;Tt~C


O

30
40

60
70

BO

90
100


-+250 -2000 plm
-e53 20 plm
<53 plm


-st250 2000 lm
-4E53 10 lm
<53 lm


g C kg-1 soil





E








-5 1OCl


-<63 plm


Sg C kq-' sil 4







- 25 20 l


- 5 j l




-<63 lm


O
10
20

40
.1 4
50
S60
70
80
90
100


O
10
20


r 40
0..0
250

70
80
90
100


Figure 5-5. Soil C content of three particle size fractions in three depth classes (0 10 cm, 10 -
40 cm, and 40 100 cm) under five land-use systems in Segou, Mali. A) Faidherbia
albida parkland, B) Vitellaria paradoxa~11~~1~~11~ parkland, C) Live fence, D) Fodder bank, and
E) Abandoned land. Range of the each depth value is 95% confidence level. Depth
indicated is the mid-point of sampled depth.











(q C kp-1 soil)
O 1 23 45 6



A

H C4
C3


(g C kg~ Soil)
0123456





H C4
C3


(g C kg sil






H C4
C3



0123(g C kg soil)


(g C kg soil)






H C4
I C3


(g C kg soil)
0123456



D

MC4
I C3









HC4
H C3


(g C kg soil)
0123456





SC4
m C3


(cm)

0-10

10 -40

40 100




(cm)

O-10

10 -40

40 -100




(cm)
O-10

10 -40

40 100



(c m)
S- 10

10 -40

40 100


(cm)
O- 10

10 -40

40 100



(cm)

O- 10

10 -40

40 -100




(cm)
O-10

10 -40

40 100


(c m)
S- 10

10 40

40 100


, ,


SC4


Figure 5-6. Whole soil C, divided into C3 plants (trees)- origin and C4 plants (crops)-origin, in
different soil layers up to 100 cm depth, in five land-use systems in Segou, Mali. A)
Faidherbia albida parkland, near tree trunk, B) Faidherbia albida parkland, outside
crown, C) Vitellaria paradoxa~11~~1~~11~ parkland, near tree trunk, D) Vitellaria paradoxa~11~~1~~11~
parkland, outside crown, E) Live fence, near trees, F) Live fence, 3m away from tree
lines, G) Fodder bank, and H) Abandoned land.


, ,










Figure 5-7. Soil C in three fraction sizes divided into C3 plants-origin and C4 plants-origin in
different soil particle-size fractions under different land-use systems in Segou, Mali.
A) Faidherbia albida parkland, near tree trunk, B) Faidherbia albida parkland,
outside crown, C) Vitellaria paradoxa~11~~1~~11~ parkland, near tree trunk, D) Vitellarial~~~~~11111~~~~
paradoxa parkland, outside crown, E) Live fence, near trees, F) Live fence, 3m away
from tree lines, G) Fodder bank, and H) Abandoned land.











(g C kg"' soil)
O 1 2 3


(g C kg'' soil)
1 2 3


(p m) O


(p m)


m
-
m
m
m
M
m
m
M
m


I I


I





i


I I


250 2000
53 250
<63
S260 2000
53 250
o- <63
250 2000
53 250
<63


250 2UO
63 2j0
63
S260 2U0
5 3 2j0


250 2UO
63 2j0
63


m C3


m C4
m C3


(g C kg-' soil)
O 1 2 3


(g C kg-' soil)
0 1 2 3


(p m)


(p m)


I I


260 2U0
63 2j0
63
E' 250 2UO0
--53 250
8 63
250 2UO
53 250
8 63


250 2000
53 250
<53
E' 250 2000
-c 250


0 250 -2000
58 250
8 <53


IIC4
II C3


m C4


m
-
m


m
M


I
I



































(gC kg'l soil)
O 1 2 3


(g C kg soil)


O 1 2 3


(p m)


(p m)


S250 --1300
.- 53 250


c, 250-2~000
63 250
<53


Iil


250 -2000
SB 250


E' 250 -2000
--53 250


c, 250-2000
SB 250
<53


iggI


m C4
a C3


m C4
a C3


(gC kg-' soil)


(g C kg soil)


O 12 3


O 12 3


(prm)


(prm)


I I


250 -2000
CD
53 250
ca <53
E' 250 -2000
4-I53 -250
oE'- <53
c,250 -2000
53 250
<53


lill
-
lill
11111111
111
1111
-
iggggggggg
15551
-
lill
1555555555


250 2000
53 250
ca <53
i 20 2000

53 <53

C, 250-23000
CD
"[ 53 250
CD
S<653


SC4
m c3


MC3


lilIM


111111111
lilllli










Figure 5-8. Linear regression between silt + clay content of soil and whole soil C content in
three depth classes across five land-use systems in Segou region of Mali. The three
data points, one each in each depth class, marked by circles around them, refer to one
of the fodder bank plots the data from which were quite inconsistent with those from
the other fodder bank plots as well as all the other treatments; these points were
therefore considered as outliers and excluded from the regression.











0 10 cm


y I=0.01x + 2.03
06 R~ = 0.63 +

M + FA
O VP


..; ::: AL


0 100 200 301 400 500
silt + clay (g kg sail)



10 -40 cm


v= 0.01 x+ 0.79
Rr = 0. 87 :

20 I FA

~ 2 x:FB
::t AL


0 100 200 300 400 500
silt + clay (g kgl sail



40 100 cmn


y= 0. 01 x+ 3.1.03


53 + FA
u O VP
r2 Z O A LF
W x FB
31- m ::AL


0 1o 00 00 3QD 400. 500 600
slt + clay ,g kg go9










Figure 5-9. Linear regression between silt and clay content of soil and C in soil particles of <53
Cpm in three soil-depth classes across five land-use systems in Segou, Mali. The three
data points, one each in each depth class, marked by circles around them, refer to one
of the fodder bank plots the data from which were quite inconsistent with those from
the other fodder bank plots as well as all the other treatments; these points were
therefore considered as outliers and excluded from the regression.













O- 10 cm

y = 0.005 x + 0.54
c3 R 2 = 0.53


E -, I M + FA
cl VP

:+ AL


O 100 200 300 400 500
silt + clay (g kg-' soil)


1 0- 40cm

3 y = 01.003J x + 03.66
SR2 = 0.44



I? o + FA
t 1 o VP
A LF
o I xr FB
:t AL

a 200 400 600
silt + clay (g kg-' soil)


40 10 cm

3 y = 0.002 x + 0.17
2' = 0.67








a 200 400 600
silt + clay (g kg soil)












y = 0.64 x + 0.26
R2 = 0.72


3

o
v,

r
o
w


I FA
O VP
A LF
X FB
m AL


0 1


2 3
C3 plant origin C (g C


4
kgl soil)


5 6


Figure 5-10. Linear regression between C derived from C3 plants and C in the large soil
particles (250 2000 Cpm) at 0 10 cm soil depth across five land-use systems of
Segou region, Mali.









CHAPTER 6
SOCIOECONOMIC ANALYSIS OF THE CARBON SEQUESTRATION POTENTIAL OF
IMPROVED AGROFORESTRY SYSTEMS IN MALI, WEST AFRICA

Introduction

The success in the implementation of any proj ect for greenhouse gas (GHG) mitigation

through agricultural means will depend on the farmers' willingness to participate in the proj ect.

This is particularly so in a region such as this study site where the vast maj ority of inhabitants

rely on the outputs from their crop fields and animals for subsistence and cash income rarely

exists in their household budgets. These farmers might be encouraged to plant trees in their

croplands for potential carbon (C) benefits (cash payments), considering that agroforestry is a

recognized GHG mitigation strategy according to the Kyoto Protocol. Several maj or reasons

have been recognized as favoring introduction of C sequestration benefits into smallholders'

agroforestry practices in developing countries. One is that this sequestration service does not

need to be transported, thus, it can benefit people in remote areas, many of whom are poor.

Secondly, there are no quality differences: a molecule of C is the same wherever it is located; so

the problem often faced by smallholders in not being able to achieve the quality required by

international markets in agricultural commodities does not apply here (Cacho et al. 2003a).

Furthermore, even small amounts of additional income would make a great difference for these

subsistence farmers who have practically no opportunity to make such additional cash income.

The political environment is also favorable for enhancing smallholders' involvement in

GHG mitigation proj ects. The United Nations Framework Convention on Climate Change

(UNFCCC) included development, equity, and sustainability as conditions to be met when

setting its principles for stabilizing GHG concentrations through mitigation policy (UNFCCC

2007). Large-scale adoption of C sequestration activities by agroforestry in African countries

could contribute to these obj ectives through biodiversity conservation, rural employment, and









soil amelioration (Breman 1997; Tschakert 2007). The World Agroforestry Centre (ICRAF)

announced that they were confident that establishment of agroforestry, especially in degraded

land, would qualify and play an important role under the Clean Development Mechanism (CDM)

of Kyoto Protocol (ICRAF 2007).

A few maj or problems exist, however, in the context of promoting agroforestry practices

by smallholders for entering CDM market. For example, we need to examine cost per unit of C

sequestration since there are many other options such as emission reduction or sequestration by

large-scale monoculture plantation (De Jong et al. 2004). Furthermore, based on the feasibility

studies, appropriate technical and political assistance should be provided so that smallholder of

agroforestry owners will not always be less competitive than other mitigation proj ects options.

Thus, socioeconomic feasibility of the improved systems is an important consideration in the

context of agroforestry implementation for GHG mitigation, and that is the scope of this chapter.

Under the Kyoto Protocol, only newly sequestered C as a result of the proj ect is

recognized as tradablee" C; the traditional agroforestry systems such as parklands are, thus, not

likely to be counted as C sequestration projects. The improved agroforestry systems that are

currently being introduced in the study region can be qualified for tradable C credits. Therefore,

the target agroforestry systems for this study are live fence and fodder bank in Segou region,

Mali (detailed system description in Chapter 4).

Research Questions

1. What is the relative attractiveness of the two improved agroforestry systems (live fence and
fodder bank) considering their C sequestration potential, economic profitability, and social
acceptability?

2. If C credit markets were introduced under the CDM of Kyoto Protocol, would adoption of
agroforestry provide more profits to land owners? If yes, how much?









Materials and Methods

The World Agroforesty Centre (ICRAF) conducted monitoring surveys for farmers

implementing live fences and fodder banks after introduction of the systems (Hamer et al. 2005,

van Duijl 2000). Data from these studies as well as databases from ICRAF research station were

used for this study. Furthermore, field surveys were conducted during February March 2006

(the dry season after the harvests when farmers were less busy with agricultural activities) to

collect additional data necessary for the analysis.

The target population was composed of farmers living in the Segou region who had

adopted live fences and/or fodder banks with assistance of ICRAF. A comprehensive cost-

benefit study of live fences had already been conducted for ICRAF by van Dorp et al. (2005).

Also, the need and social acceptability of live fences and fodder banks had been discussed in

several previous studies (van Duijl 1999; Levasseur 2003; Yossi et al. 2005). The information

about the fodder bank implementation was much more scarce than that of live fence. Thus, the

survey focused on collecting more data for fodder banks, specifically data to conduct the cost-

benefit analysis (CBA) equivalent to the existing live fence study, as well as data such as the

price of timber and non-timber products from both live fences and fodder banks to conduct risk

simulation analysis.

Social Survey of Fodder Bank Farmers

The questionnaire was designed based on that of the live fence survey (Annex A),

following the protocol of the Institutional Review Board of University of Florida (Protocol #

2005-U-1023). The structured questionnaire consisted of open-ended and/or close-ended

questions of 14 sections, asking for information about materials and labor used for managing and

harvesting fodder banks as well as related benefits from the implementation.









Twenty-two farmers from 13 different villages in the Segou region were interviewed (the

live fence survey was conducted on 18 owners from 15 different villages). The language used

was Bambara, the most common local language in Mali, although the questionnaire was made in

French. A translator (French Bambara), an agronomist who had conducted social survey for

ICRAF, was hired to communicate with interviewees (Figure 6-1). To ensure his

survey/interviewing skill, an experienced ICRAF officer went through the survey questionnaire

with him before the real survey started, and made him practice the follow-up explanations in case

farmers did not understand the questions.

The maj ority of fodder banks were 0.25 ha (50 m 50 m) in area, since that was the

default recommendation of ICRAF. Some interviewed farmers turned out to have larger or

smaller sizes by the time of the survey, due to the success or failure of the management. All the

labor data and other costs were converted to per 0.25 ha basis before taking the mean. The live

fence study was based on the average live fence row-length, 291 m (van Dorp et al. 2005).

Because the live fence and/or fodder bank installation was at least a couple of years ago,

farmers seemed to have difficulties recalling the installation costs, especially labor (days and

people) needed for planting and management. Also, it was very difficult to estimate the amounts

of products harvested such as fodder and fruits. The sizes of the bags farmers were using to

collect the harvests varied. Direct measurements of the bag size and the fodder weight (air-

dried) were conducted at several villages to reduce the estimation variability.

Local Market Survey

There are three local markets inside the city of Segou where most of the farmers in the

villages go to buy/sell their products and equipment. Price data were collected from all markets,

although some products such as fodder were not sold in all the markets. The average price of

each item was used for the analysis.









Types of Analysis

Cost-benefit analysis (CBA)

CBA involves weighing the total expected costs against the total expected benefits of one

or more actions in order to choose the best or most profitable proj ect, taking time into

consideration (Campbell and Brown 2003). In this case, the "project" refers to whether it is

profitable or not for farmers to start a live fence and/or a fodder bank. To appraise these

proj ects, three decision-rules were used: net present value (NPV); benefit cost ratio (BCR), and

internal rate of return (IRR).

The NPV of a proj ect simply expresses the difference between the discounted present

value (PV) of future benefits and the discounted present value of future costs. In other words,

that NPV is the sum of revenues in each year, y, discounted to year 0 minus the sum of costs in

each year discounted to year 0.



NPV =PV(Benefits) PV(Costs)= i:=O r (1 r)B )C (Eq. 6-1)


By: Proj ect benefits (revenues) of a given year y
C,: Proj ect costs of a given year y
r: Discount rate/interest rate
n: Proj ect life, years

According to the NPV guideline, a proj ect is acceptable (profitable) if NPV is zero or

greater. Projects with a negative NPV are unacceptable (Klemperer 1996).

BCR is another way of determining whether the proj ect should be accepted or rej ected as

an investment. It is the present value of benefits divided by the present value of costs.

i:BY
PV(Benefits) v 0 (1+ r)Y
BCR = ~-(Eq. 6-2)
PV(Costs) i: C,
Y= O (1 + r)Y









When PV benefits equals to PV costs, the BCR is 1, and NPV is 0. Also, if PV benefits

exceed PV costs, BCR must be greater than 1, and if PV costs exceed PV revenues, BCR<1.

Thus, according to the BCR decision-rule, proj ects are acceptable when the BCR is 1 or greater,

and unacceptable if BCR<1 (Klemperer 1996).

The IRR of the proj ect is the discount rate at which the NPV becomes "O" in the NPV

formula (Eq. 6-1).

B C,
= (Eq. 6-3)
(1 +1RR)Y = (1+1IRR )

The IRR is the rate of return earned on funds invested in a proj ect. The equation 6-3 also

says that the IRR is the interest rate at which PV benefits equals PV costs. A project is

acceptable if its IRR is equal to or greater than the minimum acceptable rate of return

(Klemperer 1996). In this study's case, however, the farmers will not have a specific acceptable

rate of return. So, the rate can be recognized as "acceptable" if it is greater than the interest rate

(when farmers take a loan from a local bank or financial institution).

Several basic budgets were available for calculating the above three decision-rules, such as

whole-farm budget, enterprise budget, partial budget and cash flow budget. Each budget is

specific in its application, and the partial budgets were used in this study. Partial budget is used

to evaluate the economic effect of minor adjustments in some portion of the business. Many

changes that do not require a complete reorganization are possible in a farming business. Given

a fixed set of resources, a farmer can employ these resources in more than one way in response

to changes in product price levels, cropping patterns, or carrying capacity. Partial budgets are

useful to evaluate changes such as expanding an enterprise (e.g. a crop), alternative enterprise,

and different production practices (Dalsted and Gutierrez 2007). Because introducing a live

fence and/or a fodder bank has limited impacts on the costs and returns of a farmer' s budget due









to their small sizes, the partial budget was appropriate to use. Partial budgeting is based on the

principle that a small change in a farming business will have one or more of the following

effects: 1. Eliminate or reduce some costs, 2. Eliminate or reduce some benefits, 3. Cause

additional costs to be incurred, and 4. Cause additional returns to be received (Dalsted and

Gutierrez 2007). The net effects, i.e. NPV, BCR, and IRR, can be calculated from those four

components. Only the changes in costs and benefits that resulted directly from starting a live

fence and/or a fodder bank were collected/extracted from the survey data and taken to account in

the analysis.

The proj ect cycle was set to 25 years. This is the expected rotation time for both live fence

and fodder bank tree species (personal communication with Dr. Bocary Kaya, 2006). The

growth curve was estimated based on the available data from biomass measurement. A discount

rate of 15 % was used, drawing on the information about standard interest rates applied by the

several local institutions for micro-credits (available to local farmers) in the study region (van

Dorp et al. 2005). Data collection and analysis were done with local currency, FCFA. It is

called CFA (Communaut Financiaire Africaine) franc, which is fixed against the euro at E 1 =

655.96 FCFA in 2006. When the results are shown in US dollar for the reference, exchange rate

of US$ 1 = 550 FCFA (average exchange rate during the field survey) was used.

Cost structure: Cost of seedlings was calculated following the method of Traore et al.

(2003): an aggregation of the cost of seeds and the labor cost needed to grow seedlings in the

nursery. The average size of the fodder bank was 0.25 ha (50 m*50 m) the length of live-fence

rows was 200 m (50 m*4 with 800 trees) around to protect the fodder trees. Thus, planting and

maintaining these trees were included in the costs of fodder bank.










To protect the newly planted trees, dead fences (made from dead bush brunches) were

needed for the initial three years. Branches were obtained free of charge in the wild; no cash

costs were involved. To estimate the material cost of the dead fence, the volume of branches and

the price farmers would be willing to pay on the market were asked, and recalculated to the

standard size. The total average tool cost per farmer per year for the average size of live fence

was estimated 1,000 FCFA ($1.82) (van Dorp et al 2005). Since the fodder bank requires more

use of tools based on the data for required labor, the cost was set at 1,500 FCFA ($ 2.73) per

year.

To estimate the labor costs, farmers were asked for the average wage rate they pay for a

hired labor. The most common daily labor wage (7 hours work) was 750 FCFA ($ 1.36), ranging

between 500 and 1,000 FCFA ($ 0.91 $ 1.82) depending on the season. The respondents of the

survey were also asked if they actually hired labor to install their fodder banks. The large

maj ority of them did not; instead they used their family members including children, or

exchanged the labor with neighbors.

Labor tasks are divided into:

1. Obtaining seedlings (or seeds)

2. Planting

3. Watering

4. Collecting materials for the dead fence

5. Constructing the dead fence around the live fence for protecting the seedlings (first three
years)

6. Maintenance of the live fence/fodder bank (weeding, replanting, pruning etc.)

7. Collecting products from the live fence and the fodder bank

8. Marketing live fence products (bringing to local market and selling)

9. Harvesting the timber/fuelwood at the end of the rotation










From the survey, the time farmers spent on each of the above tasks were estimated for

every year, and converted to the standard size and an average or median value was taken for the

CBA analysis. Median values were used when the sample population had extreme outliers. In

the cash flow chart, the labor time was calculated to monetary value, using the average labor

wage (Appendix B, C).

Benefit structure: Yields from live fence trees are:

* Acacia nilotica: Fruits for tanning agent for the treatment of leather and traditional
medicines are sold in local markets. Branches are used as material for dead fences.

* Acacia senegal: Bark is used as a traditional medicine, although it is not easily harvested,
and not sold in market.

* Bauhinia rufescens: Leaves can be used as medicine, but not sold in the market.

* Law/sonia inernzis: Leaves are transformed into powder to be used for the dying of hands
and feet of women (cosmetic use), and highly valued in local markets.

* Ziziphus nzauritiana: Fruits are edible, but mostly for home consumption.

Benefits from these products were calculated in monetary value with local market price in

van Dorp study (2005). That data were used in this study's cash flow.

From the third year, farmers started harvesting the fodder (branches and leaves). Since the

fodder tree, Gliricidia sepium is an exotic species and had not been commonly used in the study

region, no market value was available for its fodder; none of the farmers interviewed had sold its

fodder in the market. Thus, the expected price of G. sepium fodder, if it is sold in the market,

was asked for in the survey, and the average of the answers was used to estimate the fodder

value. In addition, fodder bank provides another source of revenue, i.e. the saved time. The

owners were asked how many people and days they used to spend looking for fodder in the wild

before the fodder bank installation, as well as after they started harvesting from their fodder

banks. The difference is the saved time/labor, which they can use for other activities. The saved









time was then converted to monetary value using the labor wage, and counted as benefits of the

fodder bank in the cash flow.

Timber harvests at the end of rotation period (25 years) were also estimated by the

proj ected growth lines. The local market prices of timber and fuelwood were used to estimate

the monetary values. Eighty percent of trees planted in a live fence and a fodder bank is

assumed to produce a small log per tree, which can be sold in the local market at 700 1,500

FCFA ($ 1.27 $ 2.73) per one cart (about 40 logs). The rest of trees and all branches, foliage

etc. (about 40% of the total expected biomass) were assumed to be sold as fuelwood which is

4,000-6,000 FCFA ($ 7.27 $ 9.10) per one cart (250 300 kg) (personal communication with

ICRAF officers and local merchants, 2006).

C sale: Price of C varies quite largely at the international market. This study used the

average price for C emission trading in 2006, $42 per Mg C (World Bank 2006). Transacti on

costs [which are the costs of arranging a contract (i.e. C sequestration proj ect and consequent C

credit sale) and monitoring and enforcing the contract, as opposed to production costs

(implementation costs of the proj ect)] were considered to be 0 in this cash flow. This is because

the transaction costs of agroforestry proj ects for C sequestration are usually covered by the third

party such as the proj ect' s trust fund (Scolel Te 2007). The trust fund deals with C buyers

(companies or individuals in developed countries) for the trade, monitors the proj ect

performance, and provides the C payment and technical assistance to farmers.

The payment method of C credit is also a long debated issue in the negotiation of the

Kyoto Protocol and related meetings as discussed in Chapter 3. Two maj or payment methods

(Cacho et al. 2003b) were tried in this study for comparison.









Ideal accounting method: In this method, payments for C sequestration occur as the service

is provided and a debit occurs when C is released (i.e. by fire or harvest). Farmers annually

receive the payment according to the amount of C sequestered in their proj ect' s fields (live

fence/fodder bank). The full debit at harvest means that the total amount of C credits sale

received during the life of the proj ect (live fence and/or fodder bank) are paid back to the

investor by farmers.

Tonne-year accounting method: Although the ideal accounting system is "ideal" for the

land owners (i.e. farmers), it is risky for investors because they are not sure the proj ect will last

until the end of the rotational age. The tonne-year method does not require redemption of C

credits upon harvest, because the payment occurs based only on the 'equivalent' amount of

permanently avoided emissions during a given year (Moura-Costa and Wilson 2000). This

method has the advantage that no guarantee is needed if the proj ect will last a required number of

years, as the annual payments are adjusted by the equivalent factor. This is a more favorable

method for the investors, and politically popular (Hardner et al 2000). In this study, the

equivalent factor of 0.0215 (Cacho et al. 2003a) was used. Farmers annually receive C credit

payment only equivalent to "the amount of C sequestered in each year 0.0215", but there is no

payment back to the investors at the end of the proj ect.

These two methods were separately incorporated into the cash flow of both a live fence

project and a fodder bank project (see Appendix B, C). The decision rules (NPV, BCR, and

IRR) were calculated in three different cash flows: 1) Cash flow without C sale, 2) Cash flow

with C sale (ideal accounting), and 3) Cash flow with C sale (tonne-year accounting).

Sensitivity analysis

The calculation of NPV, BCR, or IRR in the CBA described above is based on the best

guess scenario where all variables of costs and benefits included in a cash flow are "most likely"









values such as an average of a data set. However, the future is uncertain: we do not know with

certainty what the future values of a proj ect' s costs and benefits will be. Sensitivity analysis is

the simple process of establishing the extent to which the outcome of the benefit-cost analysis is

sensitive to the assumed values of the inputs used in the analysis (Campbell and Brown 2003).

In this study, sensitivity of NPV to the change of maj or five input variable were tested and

presented in the results section. The tested variables were: 1) discount rate, 2) seedling cost, 3)

labor price (wage), 4) yield of harvests, and 5) C credit price. All variables except the discount

rate were changed +/- 50 % from the best guess scenario to compare which variable would affect

the NPV most. Discount rate was changed only +/- 5% because it was unreasonable to assume

the local discount rate to change largely (such as 50 %).

Risk modeling

Risk analysis refers to the identification and description of the nature of uncertainty

surrounding the proj ect variables using probability distributions. When there is no appropriate

information on the expected range of values of input variable (= risks), only sensitivity analysis

can be done to observe the uncertainty of output proj section. However, if some expectations are

available for the occurrence of the variability of the input variable, it is possible to conduct the

risk analysis. Each input variable has a range of possible values; high, medium, low; or,

maximum, mean, minimum. Risk modeling describes the likelihood of occurrence of these input

variables within the given range (probability distribution). The probability distribution for the

output (NPV) will then depend on the aggregation of probability distributions for these

individual input variables into a joint probability distribution.

The NPV probability distribution was simulated by a computer program called @RISK'

(Palisade Corporation). The program performs a simulation known as Monte Carlo analysis,

whereby the NPV of the proj ects is recalculated over and over again, each time using a different,









randomly chosen, set of values of input variables. The random selection of values is based on

the characteristics of each input variable's probability distribution. In this study, there was

insufficient information for the distribution of each four input variable (labor wage, seedling

cost, yield, and C credit price). Campbell and Brown (2003) recommended the use of a

triangular or "three-point" distribution for this kind of analysis. This is the distribution described

by a high, low and best-guess estimate, which provide the maximum, minimum, and modal

values of the distribution respectively. Each input variable' s distribution was set based on the

surveys data, observation, and personal communication with ICRAF field officers.

Labor wage distribution was likely to be between 500 and 1,000 FCFA (0.91-1.82 US$),

while 750 FCFA ($ 1.36)/man/day was the most prevalent labor wage. Seedling cost distribution

was assumed to be -50 % to +50 % from the best guess scenario. Harvests or yield distribution

was assumed to be 0 % to +50 % from the best guess scenario. C price was assumed to have a

range of $ 3.7 to $ 99 per Mg C, which was the minimum and maximum price traded in

international C market in 2006 (World Bank 2006). Mean price was $ 42/Mg C as used in the

best guess scenario.

Results

Demographic Characteristics of Target Population

Demographic characteristics (Table 6-1) were not based on data collected in the social

survey conducted in this study. However, the information on live fence farmers was already

available in the previous studies of ICRAF (Levasseur 2003; van Dorp et al. 2005). The fodder

bank owners who were interviewed in this study were mostly in the same village or neighboring

villages with similar conditions, and therefore their demographic characteristics were considered

to be similar. Average household size is 27.7 persons, consisting of 6.8 male members, 6.7

female members and 14.2 children. An active household member is a person actively









contributing to agricultural activities (male 4.9, female 5.2, children 5.7 on average). Non-active

members are generally sick or elderly, or children under 10 years old (9.2 on average). Also,

about 10 % of the household members have migrated from the village, usually for temporary

labor in the town of Segou or other urban areas, to work or study. The average area of cultivated

land owned by a household is 15.3 ha. The main enterprise is millet, occupying more than 50 %

of total cultivating area. Other major crops include sorghum (2.3 ha), rice (2.1 ha) and

groundnut (0.9 ha). On average, each household possesses 3.6 work oxen (for agricultural labor

such as tillage), 5.8 cows (for breeding purposes), 2.1 donkeys, and other small livestock

including sheep, goats, and poultry.

Cost-Benefit Analysis: Best Guess Scenario of the Live Fence and the Fodder Bank

Based on the collected data, all costs and revenues (benefits) consisting of cash flow (year

0 to 25) were put into the spreadsheet. The cash flows of the live fence proj ect and the fodder

bank project in the best guess scenario are shown in Appendix B, C. The net benefit, (total

revenues total costs) of each year, was negative at the beginning, and turned positive from the

second year for both projects. Among components of costs, labor cost was the largest. Total

labor cost throughout the project year converted to present value was 60,738 FCFA ($ 110.4) for

the 291 m live fence proj ect and 94,589 FCFA ($ 172.0) for the 0.25 ha fodder bank proj ect.

Labor cost was high in the first three years for the live fence proj ect compared with the rest of

the proj ect term, because of the initial management such as construction of dead fence for

protecting seedlings. The fodder bank proj ect needed more consistent management practices

(labor) than for the live fence proj ect due to the fodder-tree management such as weeding and

pruning. Seedling cost was a relatively large component of the costs on net cash flow of both

proj ects, since it was initial investment (at year 0) and was not discounted to calculate the present

value.









When the components of revenues were examined, a big difference between the two

proj ects' cash flows was the revenue from the saved time of the fodder bank. Before the fodder

bank installation, farmers had to graze animals almost everyday for quite a long time during the

dry season. Since the survey data showed that this grazing time/labor a farmer could save by the

fodder bank was considerably high, the revenue of this component became the significant

difference between the live fence and the fodder bank. Other revenue components, timber and

fuelwood sale occurred at the end of the proj ect year, thus, the revenues from them were

discounted largely when converted to the present values. Another revenue component, C sale,

was added to the cash flow with the ideal accounting method and the tonne-year method

separately. The amount paid in US$ was converted to FCFA, and put into the cash flows.

Three decision rules (NPV, BCR, and IRR) for three different conditions (No C sale, C

sale with the ideal accounting method, and C sale with the tonne-year accounting method) were

calculated (Table 6-2). C sale by the ideal accounting method significantly changed all three

decision rules. NPV of the live fence was 52,802 FCFA ($ 96.0) without C sale, and it increased

to 60,465 FCFA ($ 109.9) with C sale by the ideal accounting method. NPV of the fodder bank

was 87,319 FCFA ($ 158.8) without C sale, and 96,394 FCFA ($179.3) with C sale by the ideal

accounting method. BCR and IRR also increased (economically more profitable) with C sale.

However, C sale by the tonne-year method did not increase the three decision rules much from

those without C sale. In NPV, only 172 FCFA ($0.3) increase for the live fence and 204 FCFA

($0.3) increase for the fodder bank were observed, compared with NPV without C sale. The C

sale profits by the tonne-year method were too small to make a change of BCR and IRR values

for both live fence and fodder bank proj ects.









Sensitivity Analysis

Sensitivity analysis was conducted, changing five major input variables separately: when

one variable is changed, others are not changed (Table 6-3). Two scenarios, with or without C

sale, were tested to see the NPV sensitivity. The ideal accounting method was used for

calculating the NPV of "with C sale", since the CBA results (Table 6-2) showed that the tonne-

year accounting method hardly changed the NPV or other two values from "No C sale" scenario.

When the discount rate was changed from 15 %, the NPV changed greatly in both live

fence and fodder bank projects. Seedling costs change (+/- 50 %), on the other hand, did not

fluctuate the NPV much compared with other variables in both proj ects. Labor price changes

(+/- 50%) affected the NPV of the live fence proj ect and the fodder bank proj ect differently,

causing a large change in the NPV of live fence while causing very little in the NPV of fodder

bank. This is because the labor wage variable was used for calculating both costs (labor) and

benefits (revenues from the saved time) of the fodder bank proj ect cash flow. The variable,

which has the largest impact on NPV values, was yield (harvest of fodder, live fence products

and timber). When the yield was tested with +/-50 %, the range of NPV was largest in both live

fence and fodder bank proj ects. The NPV values became even negative (meaning: the proj ect is

economically unacceptable) for the live fence proj ect when yield is -50 % from the best guess

scenario. C price change (+/- 50 %) did not change the NPV of both projects largely, suggesting

C price is not an influential factor to change the proj ects' profitability.

Risk Modeling and Simulation

The risk simulation program @RISK was run based on the best guess scenario cash flow

(shown in Appendix B, C) with the maj or four variable' s range described in the Materials and

Methods section. The mean of the NPV distribution of the live fence project without C sale was

28,730 FCFA ($ 52.2), and the 90 % confidence range was from -50,178 FCFA ($ -91.1) to









96,546 FCFA ($ 175.7) (Figure 6-2). The chance of the NPV being negative (meaning: the

proj ect is economically not acceptable) was 26.3 8 %. The net cash flow, each year' s total

revenue minus total costs, of the project was also simulated (Figure 6-3). This shows the

probability distribution of the net benefit in each year from year 0 to 25. According to this

simulation, the net benefit will be positive with 95 % likelihood from the third year of the

proj ect. Then the net benefit is likely to be stable, and will increase significantly because of the

harvests of timber and firewood at the end of the rotation.

C sale was added to the live fence project cash flow, and the risk simulation was run again.

It was conducted only with the ideal accounting method, because C sale by tonne-year

accounting method changed NPV very little from that of "No C sale" scenario. Adding C sale

changed the NPV probability distribution. The mean NPV of the distribution increased to 36,058

FCFA ($ 65.6), and 90 % confidence range was from -52,713 FCFA ($ -95.8) to 110,069 FCFA

($ 200. 1) (Figure 6-4). The chance of the NPV becoming negative is 24.47%, slightly less than

that without C sale. The two probability distributions (with or without C sale) were found to be

significantly different, when compared using t-test (p<0.01). The @RISK program also

conducted a regression sensitivity analysis, which is able to show how each input variable is

influential for the NPV simulation (Figure 6-5). Yield has a positive as well as the largest impact

among the input variables. Both labor wage and seedling cost had negative coefficients (when

the variable increases, the NPV would decreases), but relatively small extent. C price, although

positive, had the smallest influence on the NPV proj section.

Mean of the NPV distribution of the fodder bank proj ect without C sale was 63,153 FCFA

($ 1 14.8), and its 90 % confidence range was from -53,3 86 FCFA ($ -97. 1) to 161,3 13 FCFA ($

293.3) (Figure 6-6). The chance that the NPV becomes negative (economically not acceptable)









was 19. 15 %. Probability distribution of the net benefit of each proj ect year (0 to 25) was

simulated (Figure 6-7). The net benefit turned positive in 95 % confidence from the fourth year

of the project. The distribution is much more largely spread from the mean than the same

simulation of the live fence (Figure 6-3).

C sale was added to the fodder bank cash flow and the simulation was run again. The NPV

probability distribution of the fodder bank project with C sale (by the ideal accounting method)

is shown in Figure 6-8. The mean NPV became 63,289 FCFA ($115.1), slightly more than that

of "without C sale" simulation. The 90 % confidence range was from -54,305 FCFA ($ -98.7) to

159,301 FCFA ($ 289.6), and the chance of NPV being negative was 19.12 %. The distributions

of "with C sale" and "without C sale" were compared; they were not significantly different in t-

test. The sensitivity regression (Figure 6-9) showed again that the yield was the most influential

and positive factor for the fodder bank NPV simulation. C price remained to be relatively small

and positive variable. Seedling cost and labor wage were both negative variables, same as the

results of the live fence proj ect, but the impact of labor wage was much smaller in the fodder

bank simulation. This is because labor wage variable was used for calculating both labor costs

and benefits (the saved time) of the fodder bank proj ect, while it was used only for calculating

labor costs in the live fence proj ect.

Discussion

Overall, C sale seemed to increase the profitability of both live fence and fodder bank

systems for farmers. However, if the tonne-year accounting method is applied, the amount of C

payment per farmer will be too little; it will not be attractive for farmers to participate the C sale

program. Even with the ideal accounting system, the benefits from C sale will not be the maj or

part of the farmer' s income. The live fence or the fodder bank proj ect can provide multiple

benefits, and the best guess scenario shows it is likely to be profitable without C sale. If farmers










can gain the C payment without changing the use of the live fence or the fodder bank and

without paying some of the transaction costs, which were the assumptions of this analysis, the

payment does increase expected NPV. And there is no reason for farmers not to participate the C

sale program, if it exists. As mentioned in the beginning of this chapter, even if the amount of

cash income is very small (in the perspective of C credit buyers in developed countries) it will

make a big difference to the economic situation and well-being of the farmers in the studied

region. For investors, these agroforestry proj ects might not be very attractive regarding the

amount of C credits they can purchase. However, the price used in the simulation was the same

as in any large-scale C mitigation project. In addition, contributing to the socioeconomic

development of communities in Africa could provide an "environmental friendly" image to the

companies/corporations, who are often blamed as responsible for large amounts of GHG

emission; this image value may add more attractiveness for the companies to invest in this type

of C sequestration project. The added C sale increased the mean of the simulated NPV

significantly for the live fence proj ect, but not for the fodder bank proj ect, which suggests that C

sale is likely to have more economic impacts for the live fence owners than for the fodder bank

owners.

In both the best guess scenario analysis and the risk simulation analysis, yield was the most

influential and uncertain factor among the input variables. According to the survey data, farmers

who had larger trees and more harvests tended to spend more time for watering and weeding

during the initial years of the proj ect. Thus, proper training for the initial year treatment could

significantly increase the expected yield for both live fence and fodder bank. On the other hand,

the annual precipitation, which influences the tree growth greatly, varies largely in the study

region, and it risks the expected yield. Future climate shifts is unknown, but it would change the









amount of labor for the management (especially watering) required for each proj ect, which will

change the proj ect' s attractiveness.

Another issue is that the assumed labor wage (750 FCFA/man/day) is a maj or factor

affecting the proj ect' s profitability. From the interviews, it was clear that the rate used in the

analysis was prevalent in the region but the occasions when farmers were hired for the manual

labor and received this amount of wages were rather very few, for example during busy farming

seasons such as harvesting. Thus, the real opportunity cost of the labor may be considerably less

than the assumption, and therefore the proj ect' s cost could be substantially lower.

As shown in the NPV probability distribution of the simulations (Figure 6-2, 6-4, 6-6, 6-8),

the mean NPV of each distribution was much smaller than that of the best guess scenario (Table

6-2). The best guess scenario's NPVs were at the highest peak of each probability distribution.

It means that the risk and uncertainty of the proj ect were somewhat taken into account in the

simulations, and suggests that evaluating the proj ect' s profitability only by the best guess

scenario may overestimate the proj ect' s expected profitability.

Both the best guess scenario and the risk simulation suggest that the fodder bank proj ect

has larger expected profits than the live fence proj ect, although the scale of these two systems are

not same and cannot be compared as two options. In reality, much more farmers adopted the live

fence than the fodder bank in the region. This is probably because live fences already existed

somewhat in the form of traditional live fences or dead fences, and farmers do not need large

parcels of land or extra labor inputs. The fodder bank is a rather new concept that introduces

exotic species and requires larger areas of land and extra labor. These factors seem to be the

heavy burden for farmers, especially for relatively poor ones. In addition, the probability

distribution of the fodder bank proj ect was more horizontally spread than that of the live fence










proj ect (Figure 6-2, 6-6), and the net benefit flow of the fodder bank had wider probability range

than that of the live fence (Figure 6-3, 6-7). This suggests that the fodder bank has a higher

chance of NPV fluctuation. Overall, the fodder bank seems to be a high risk, high return proj ect

than the live fence. The majority of the farmers in the region are subsistence oriented and are

expected to be very risk averse. Profitability may not be the first consideration in their adoption

process. Other factors, such as water or labor availability and the presence of fodder in the open

land nearby might be the more important factors in the adoption of the fodder bank, if they are to

be promoted.










Table 6-1. Demographic characteristics of the target population in Segou, Mali.


Cattle possession per household
Work oxen 3.6
Cows 5.8
Donkeys 2.1
Sheep 4.5
Goats 4.7
Poultry 21.7
Horses 0.1
Actively working in agricultural activities. "'Temporarily moving out from the village. Data
from van Dorp et al. 2005.

Table 6-2. Net Present Value (NPV), Benefit Cost Ratio (BCR), and Internal Rate of Return
(IRR) of the live fence and the fodder bank proj ects in the three different scenarios
(without C sale, with C sale by the ideal accounting method, and with C sale by the
tonne-year accounting method) in Segou, Mali.
Live Fence Fodder bank

No C Ideal Tonne-year No C Ideal Tonne-year
sale accounting accounting sale accounting accounting

NPV (FCFA) 52,802 60,465 52,974 87,319 96,394 87,523

BCR 1.53 1.60 1.53 1.67 1.74 1.67

IRR 25.5% 27.3% 25.5% 29.5% 31.4% 29.5%


Average household size (Number of people)
Active' male
Active female
Active children
Non-active male
Non-active female
Non-active children
Migrated
Total

Cultivating area (ha)
Millet (Pennisetum glaucum)
Sorghum (Sorghum bicolor)
Rice (Orvza glaberrima and Oryza sativa)
Groundnut (Arachis :!pp yn d l,
Chickpea (Cicer arietinum)
Cassava (M~anihot esculenta)
Fonio (Digitaria exilis)
Watermelon (Cucuribitaceae) and other fruits
Vegetable and others
Total


4.9
5.2
5.7
0.7
1.4
7.1
2.7
27.7


6.4
2.3
2.1
0.9
0.6
0.6
0.6
0.9
0.9
15.3











Table 6-3. NPV sensitivity of the live fence proj ect and the fodder bank proj ect to the change of
an input variable in Segou, Mali.
Live fence Fodder bank

No C sale With C sale No C sale With C sale
-------- FCFA ------

Base 52,802 60,465 87,319 96,394


Discount rate -5 % 109,367 118,391 166,434 177,304
Discount rate +5 % 20,999 27,478 43,094 50,670

Seedling cost -50 % 68,829 76,492 101,844 110,919
Seedling cost +50 % 36,775 44,438 72,794 81,869

Labor price -50 % 83,171 87,002 88,750 93,287
Labor price +50 % 22,433 26,264 85,888 90,425

Yield of harvests +50 % 129,330 140,825 149,624 163,236
Yield of harvests 50 % -23,727 -19,894 25,014 19,551

C price +50 % 64,297 100,931
C price 50 % 56,633 91,856


Source: Base values are from the best guess scenario cash flow. NPV values of "with
from ideal accounting method scenario.


C sale" are






















































Figure 6-2. Simulated NPV probability distribution of the live fence project (without C sale).
The distribution is likelihood (y-axis) of the proj ect' s NPV (x-axis): the worst
scenario can be less than -100,000 FCFA in NPV, and the best scenario can be close
to 150,000 FCFA in NPV. The peak of the distribution is most likely (best guess)
scenario of the proj ect.


I


Figure 6-1. Social survey with farmers in Segou, Mali. Based on the ICRAF database, all
farmers who have at least once harvested fodder from the fodder bank were
interviewed. The survey was conducted in Bambara (local language) and translated
to French through the interpreter (man with a jacket in the photos).


in
-150 -100 -50 0 50 100
N PV (FCFA i n tho~usan ds)


100


1 5%


96.5


46


-5 0. 107 8





























Prclject year (O 25)
A-Mean,*1-1sD II -951u

Figure 6-3. Simulated net benefit (total costs total revenues in each year) of the live fence
project (without C sale). Mean value of each year' s probability distribution is shown
in the yellow line, red range is plus minus 1 standard deviation from the mean, and
green range is the 5 to 95 % likelihood of the value.




























141































L596


5-
4-






-1 U


I I I II I
-50 0 50 1 U 1 50 2U3

N PV (FCFA i n thousands)


-52. 71 33


11 Q C699


Figure 6-4. Simulated NPV probability distribution of the live fence project (with C sale by the
ideal accounting method). The distribution is likelihood (y-axis) of the proj ect' s NPV
(x-axis): the worst scenario can be less than -75,000 FCFA in NPV, and the best
scenario can be close to 150,000 FCFA in NPV. The peak of the distribution is most
likely (best guess) scenario of the proj ect.
















Yield fractuation


.972


-.1 64 Daily labo r wage



-.133 Seedline Cost






-1 -0.75 -0.5 -0.25 0 0.25 0.5 0.75 1
Standard b~ coefficients



Figure 6-5. Regression sensitivity analysis for NPV of the live fence project (with C sale by the
ideal accounting method). Standard b coefficients show how these input variables are
related to the results (NPV). Positive (negative) value means the input variable
positively (negatively) affect the NPV, and the absolute value represents the extent of
the influence.





























NPV (FCFA in thousands)


Mear-F631 52.5


CO


-150 -1


-50


0 50 1CO 150 2U3


1 61.31 34


-53.3JB6


Figure 6-6. Simulated NPV probability distribution of the fodder bank project (without C sale).
The distribution is likelihood (y-axis) of the proj ect' s NPV (x-axis): the worst
scenario can be less than -100,000 FCFA in NPV, and the best scenario can be up to
200,000 FCFA in NPV. The peak of the distribution is most likely (best guess)
scenario of the proj ect.











6C1


CO

-2



LL








Project year (O 25)
5 =Mean,+1/-1 SD -95%,5%


Figure 6-7. Simulated net benefit (total costs -total revenues in each year) of the fodder bank
project (without C sale). Mean value of each year' s probability distribution is shown
in the yellow line, red range is plus minus 1 standard deviation from the mean, and
green range is the 5 to 95 % likelihood of the value.

































145





01

-150 -100 -50 0 50 100 150 200 250
NPV (FCFA i n thousands)

-54.3051 1 59.30093

Figure 6-8. Simulated NPV probability distribution of the fodder bank project (with C sale by
the ideal accounting method). The distribution is likelihood (y-axis) of the project' s
NPV (x-axis): the worst scenario can be close to -100,000 FCFA in NPV, and the
best scenario can be up to 200,000 FCFA in NPV. The peak of the distribution is
most likely (best guess) scenario of the proj ect.




























Daily labo r wage


Stan dard b~ coeffi cients


Figure 6-9. Regression sensitivity analysis for NPV of the fodder bank proj ect (with C sale by
the ideal accounting method). Standard b coefficients show how these input variables
are related to the results (NPV). Positive (negative) value means the input variable
positively (negatively) affect the NPV, and the absolute value represents the extent of
the influence.









CHAPTER 7
SUMMARY AND CONCLUSIONS

This dissertation study examined the carbon (C) sequestration potential of maj or

agroforestry practices in Segou Region, Mali, of the West African Sahel (WAS), and analyzed

the feasibility and socioeconomic characteristics of the selected agroforestry systems in the

context of C sequestration services. The selected systems were two traditional parkland

agroforestry systems with Faidherbia albida or Vitellaria paradoxa~11~~1~~11~ as the dominant tree species,

two newly introduced (improved) agroforestry systems (live fence and fodder bank), and a so-

called "abandoned" (degraded) land. The research revolved around four maj or questions.

1. How much C is stored in different agroforestry systems aboveground and belowground?

2. Do trees contribute to store C in soil, and how stable is that C?

3. What is the overall relative attractiveness of each of the selected agroforestry systems in
terms of its C sequestration potential?

4. If C credit markets were available, would adopting agroforestry provide more profits to
land owners?

C Sequestration Potential

Biophysical Potential

The selected agroforestry systems proved to have potentials for sequestering more C both

above- and belowground than the tree-less cultivated land in the study region. However, the

estimated amounts of C stored in these systems and sequestered after the systems' establishment

are quite variable depending on the baseline (without proj ect) status as well as the accounting

method used.

The two traditional parklands store significant amounts of C in the biomass C. Especially,

the large F. albida trees (average DBH 59.4 cm, height 13 m) store considerable amounts of C.

However, parklands are not likely to be considered for C sequestration proj ects anytime soon,









because Kyoto Protocol currently admits only C sequestered as a result of newly implemented

mitigation proj ects, and traditional land-use systems such as parklands and forest conservation -

sustaining parklands are not recognized as emission-reduction activities under the Protocol at

least until 2012.

On the other hand, improved agroforestry systems (live fence and fodder bank) were found

to have a better chance to be recognized as C sequestering activities than parkland systems.

Because these systems are newly introduced, most of the biomass stored in the systems can be

counted as "sequestered" C credits, although their potentials as C sequestration proj ects were not

as high as expected. C sequestration potential of a land-use system has to be expressed on a unit-

area basis for a given period of time. From that perspective, some improved agroforestry

systems (live fence and fodder bank, in this study) do not rank high because of the nature of their

planting configurations and/or management requirements. Live fence trees are densely planted

along rows such that the individual trees are not "allowed" to grow fully; moreover, the fence

rows are on plot boundaries and therefore the area occupied or influenced by a fence row in

relation to the total area of the plot it borders is low. As far as the fodder banks are concerned,

the fodder trees that are frequently harvested for their leafy biomass cannot obviously be

expected to store large quantities of biomass C. Therefore, the absolute amounts of C stored in

these systems per unit area would not be as large as that for, say, parklands. While the amounts

of biomass C stored (calculated from general allometric equations following UNFCCC

guidelines) were 54.0 and 22.4 Mg C ha-l respectively for 40-year or older stands ofF. albida

and K paradoxa parkland systems, the amounts were 4.7 Mg C ha-l for a 8-year-old stand of live

fence and 2.2 Mg C ha-l for 6 9 -year-old stand of fodder bank.









Another issue is that the initial C loss (both in biomass and soil) resulting from land

clearing and tillage for facilitating tree establishment in these improved practices is expected to

be significant; this loss may not be compensated by the planted trees any time soon, given their

slow growth rates owing to poor soil fertility and adverse climatic conditions. Therefore,

introducing these improved systems in abandoned land for land amelioration, as the World

Agroforestry Centre (ICRAF) is promoting, may not make a significant contribution to net C

sequestration in the near term; indeed it is likely to cause net negative C balance in the initial

stage of implementation.

Currently soil C is not considered to be tradable, but the relative portion of soil C in the

studied systems turned out to be comparatively large. For example, the percentages of soil C (0

- 100 cm) in total C (biomass C + soil C 0 100 cm) stock of the studied agroforestry systems

were 38 % in F. albida parkland, 55 % in y. paradoxa parkland, 84 % in live fence, and 94 % in

fodder bank. This cannot be ignored when the potential for long-term storage is considered.

Soil fractionation studies and isotopic ratio measurements showed that tree litter tends to

increase unprotected, relatively new C on the surface soil. In the deeper soil, the parklands that

have had trees in the system for a long time were likely to hold more protected C than the newly

introduced live fence or fodder bank systems. Also, management practices such as tillage and

litter usage seemed to have a large influence on soil C accumulation.

Socioecomic Potential

The cost-benefit analysis suggested that live fence and fodder bank were likely to be

profitable for farmers, whether with or without C sale. C sale changed the profitability: $ 13.9

more in net present value (NPV) of average-size live fence (291 m), and $ 20.5 more in NPV of

average size fodder bank (0.25 ha). These estimations are based on the assumptions of 25-year

rotation, no transaction costs on the farmers, and an accounting method ideal to C sellers










(farmers); all of these assumptions are subj ect to change. With the accounting method that is in

favor for the investors, the expected profits from C sale in the same model produced an increase

in NPV of only about $ 0.3 in both systems. Even in the local currency with farmer' s monetary

values, $ 0.3 increase in NPV is almost nothing for a 25-year proj ect. Thus, it is clear that the

accounting method is a very strong factor to determine whether C sale through agroforestry

should be introduced to the region.

Also, sensitivity analysis and risk analysis showed that C price did not have a major

influence on changing the cost and benefit flow of both systems. It would contribute to the

increase of profitability but had relatively small effect compared with other major variables such

as yield, labor cost, and seedling cost. On the other hand, tree growth (yield) had a strong

influence on the proj ect' s profitability. Regression sensitivity analysis showed that the effect of

yield was 10 times or more strong than that of other factors. Since natural environment such as

rainfall and pests greatly affects tree growth, the uncertainty (risk) regarding yield (whether tree

would grow expectedly or not) is quite difficult to control and is a maj or discouraging factor for

applying the improved agroforestry in general, whether with or without C sale.

Regarding relative attractiveness of live fence and fodder bank, it was difficult to compare

because the scale of the proj ect (the land needed) was different in the simulation of two systems.

The fodder bank proj ect that needs about three times more land (and correspondingly higher

labor cost) than live fence proj ect showed larger range of expected NPV ($ -98.7 to $ 289.6 in

fodder bank vs. $ -95.8 to $ 65.6 in live fence). Considering that improved agroforestry systems

require "additional" work for farmer' s cultivation practices and that their resources (land, money

for buying seeds, etc.) are very limited, live fence would be easier and less risky proj ect for them

to implement.









Conclusions

Regarding the overall attractiveness of the selected land use systems, live fence and

fodder bank are more suitable to start as agroforestry C sequestration proj ects than the parkland

systems for small-scale farmers in the studied region. Between the two improved systems, live

fence has higher C sequestering potential per unit area and is less risky than fodder banks. This

situation could change, however, depending on tree management and conditions that affect tree

growth. Adopting these systems on cultivated land rather than on abandoned land is likely to

sequester more C and be more profitable. Since parklands are traditionally practiced, they do not

qualify as a new C sequestration project. Nevertheless, F. albida trees are more attractive than

yK paradoxa trees in terms of C sequestration potential.

Agroforestry Adoption for C sequestration in the Study Region

Based on the findings summarized above and information acquired through fieldwork and

literature review, some factors that either limit or favor agroforestry adoption in the regions can

be identified.

Limiting Factors

With the current price range (and its large fluctuations) for C credits and the amount of C

potentially sequestered, the income from C sale is not likely to be a maj or source of income for

farmers in the WAS and therefore is not likely to be a strong incentive to start the live fence or

fodder bank. In addition, farmers are concerned about other factors as well, such as risks in

undertaking a new farming practice. Farmers might need some support such as technical and/or

material assistance to cover initial costs, and/or insurance or some kind of safety net in case the

trees die due to unexpected causes. As regards parklands, increasing the tree density is difficult

because parkland trees grow relatively slowly. Also, it is technically challenging since parkland

tree species rely on natural regeneration.










Soil C estimation is far more labor-intensive and costly than biomass C estimation, and

methods of measurements/accounting are still under discussion. Even in the same land use

system, the amount of soil C per unit area can vary depending on the depth of sampling and/or

the instruments used to measure C content. C storage is related to soil properties, which makes it

difficult to standardize soil C sequestration potential for any land use. Whether all C or only

protected C should be counted is another issue. If only protected C is to be counted, its method

and ease of determination would become an issue.

Based on farmer interviews and the researcher' s personal observations, it appeared that

relatively rich farmers were the ones who tried the improved agroforestry systems as ICRAF

recommended, and succeeded which is not different from the experience with many (or most)

agricultural development initiatives. In order to achieve poverty alleviation through C credit

sale, it is important that the poorest poor of the region can adopt the technology. Involving

farmers with little resources needs, naturally, extra support. Since C sale is not likely to provide

much income under current conditions, covering the cost of assistance and transaction costs for

C trade would be a large financial burden. Institutions such as international NGOs or

national/local governments will have to be encouraged to bear these costs.

Favorable Factors

Some of the successful live fence and fodder bank owners started their proj ects by

themselves without ICRAF's support. These successful projects had demonstration value too in

that farmers of other villages were interested in starting live fence and fodder bank by looking at

those pilot plots and came to request ICRAF's support in their villages. This strongly indicates

the local farmers' interests in the products and effects of live fence and/or fodder bank. As the

land degradation proceeds and more farm fields are abandoned, the ability of live fence and









fodder bank for protecting farmlands from free-roaming animals and producing fodder is

expected to make them even more attractive for farmers.

In the economic analysis, labor cost was converted to a monetary value using the local

labor wage. However, since there are very few employment opportunities for farmers, most

occurring only during the harvesting or planting season, the real opportunity cost (labor cost)

might be much lower than the assumed rate. This would lower the cost of live fence and fodder

bank proj ects than the model used in the analysis, and consequently, increase their profitability.

Thus, profitability of the improved system could be larger than the values shown in Chapter 6,

although the extent is unknown.

The situation or understandings of C sequestration proj ect as a mitigation activity is also

changing. Climate change is a major global issue these days. Both price and amount of traded C

are rising rapidly every year. After the first commitment period (2008 2012) of the Kyoto

Protocol, soil C may be counted for sequestration proj ects, which would increase the C sale

income for the studied agroforestry projects. Also, at the international negotiation regarding

rules of the Kyoto Protocol, conservationists such as the Nature Conservancy and researchers

point out the importance of the "forest conservation" effect preventing CO2 emiSSion from

deforestation and suggest the conservation cost to be shared internationally. Thus, in future,

the system steadily storing certain amount of C, such as parkland systems, might also be

recognized as mitigation proj ects.

Implications for Agroforestry

Economic benefits of establishing the improved agroforestry practices were clearly found

in the studied region. Various social and environmental benefits were also found, such as

increasing soil organic matter and preventing soil erosion through introducing trees in the

agricultural practices (in both traditional and improved agroforestry). Some of the non-









marketable benefits such as C sequestration are not directly perceived by the individual farmer.

From C credit payment, farmers can at least receive some rewards for the environmental service

they provide (internalize the non-market values). In this sense, C credit sale is an option to

charge for many of the non-market services agroforestry systems provide. When the C sale

through CDM becomes more popular in the future, agroforestry systems will definitely have

potential to promote economic development of subsistence farmers as well as environmental

improvement in developing countries.

Future Research

For the biophysical aspects of the C sequestration proj ect study, there is a strong need for

more studies on soil C dynamics. One of the reasons why soil C is not recognized as tradable C

currently is the lack of information. To start the small-scale C sequestration projects, such as

agroforestry, it is necessary to develop guidelines similar to the biomass C estimation guideline

by UNFCCC. Conducting direct soil C measurement for each small-scale project will be too

costly for the project to be attractive. Establishing guidelines or default values would, however,

be quite challenging and controversial, both academically and politically.

In order to administer agroforestry proj ects for C sequestration, an organization such as a

proj ect trust fund is needed to bear the transaction costs such as costs of monitoring and

certifying the C sequestered, providing technical assistance and C payments, and selling

accumulated C credits to the buyers at the international C market. It will be worthwhile to

launch such a new pilot proj ect, the type of which has never been attempted in the study region

or entire Africa, to understand a trust fund' s responsibilities including designing the proj ect,

explaining to farmers the project obj ectives and providing technical/material assistance,

conducting inventories related to C, and setting the baseline. Indeed, such a research project

would be essential for promoting C sequestration through agroforestry in the WAS.



































1.3 Si vous avez achete les semences ou les plants, quels etaient les cotts?
Unite Prix par unite Quantite Cott total
Semences
Plants

1.4 Outils utilises pour installer et maintenir les banque fourrageres:
Note : Noter tous les outils utilises pour l'installation et l'entretien de les banque fourrageres, pas
les outils uniquement utilises pour le travail champ~tre agricole. (preferer le prix de achete, mais
si difficile, noter quel annee).
Outils(entrer Nombre Prix d'outil Valeur total Nombre
codes): d'outils (prix de d'annees
marched en F d'utilisation
CFA)


APPENDIX A
SOCIAL SURVEY QUESTIONNAIRE FOR FODDER BANK OWNERS


Questionnaire #:


Date:


Duree approximative:


QUESTIONS SUR L'INSTALLATION DES BANQUES FOURRAGERES

1.1 Methode d'installation:
(Entrer code)
1: Semi-direct
2: Plants a racines nues
3: Plants en pots
4: Autres
9: Pas de reponse


1.2 Comment vous avez obtenu les semences ou les plants?


(Entrer code)
1: Cultives en pepiniere par le paysan
2: Don d'une structure d'encadrement
3: Achete d'un autre paysan / au marched


4: Don d'un autre paysan
5: Autres
9: Pas de reponse


Houe
Hache
Pioche
Coupe-coupe
Pelle


6: Piquet/Piquasse
7: Brouette
8: Arrosoir
9: Charrue
10: Multiculteur


11: Charrette
12: Barre a mine
13: Autres
99: Pas de repons










1.5 Si vous utilisez les mannuvres pour installer ou entretenir les banques fourrageres, comment
est-ce qu'ils sont remuneres: en argent, en entraide, en nature?

1.6 D'une fagon generale, quel est le taux journalier pour la main d'muvre salariee (par
personne/j our)?
Note: Specifier si le repas est compris dans le taux.




1.7 Personnes impliquis dans les diff~rentes activists pour 1'installation et
1'entretien de les banques fourrageres (pendant les premiidres trois anndes):
Notes:- Expliquer comment vous Stes arrive au nombre de personne/jours.
-Preciser si ce sont les membres de l'UPA ou bien de la main d'muvre salaries.
-Preciser l'annee dont on parle (par example An 3: 1999; An 4: 2000 etc.).

Etpe1 Obtenir les plants ou les semences (PAS la production des plants en pepiniere!)
Personnes impliquees No. de personnel/ jours
(H/F/E): (heures) An 1:




Note :




Etape 2: Transplanter les plants ou semences au champ (inclure la haie vive)
Personnes impliquees No. de personnel/ jours
(H/F/E): (heures) An 1:




Note :




Etape 3: Arroser les banques fourrageres et la haie vive (apres l'installation)
Personnes impliquees No. de personnel/ No. de personnel/ No. de personnel/
(H/F/E): ours (heures) jours (heures) jours (heures) -
Anl:. An 2: An 3:




Note :














Eae4: Chercher du materiel pour la haie morte autour de la haie vive
Personnes impliquees No. de personnel/ No. de personnel/ No. de personnel/
(H/F/E): jours (heures) jours (heures) jours (heures) -
Anl:. An 2: An 3:





Note :




Etape 5: Construire la haie morte autour de la haie vive
Personnes impliquees No. de personnel/ No. de personnel/ No. de personnel/
(H/F/E): jours (heures) jours (heures) jours (heures) -
Anl:. An 2: An 3:





Note :




Etape 6: Entretien des banques fourrageres et la haie vive (suivi, boucher les spaces etc. SANS
recolte)
Personnes impliquees No. de personnel/ No. de personnel/ No. de personnel/
(H/F/E): jours (heures) jours (heures) jours (heures) -
Anl:. An 2: An 3:





Note :
An 4. 5. 6.... (si possible)










1.8 Utilisation des products des banques fourrageres et la haie vive pendant la derniere annee:
Espece Produit Utilisation de Unite de Poids d'une Production
(entrer produit (entrer recolte (par unite (en annuelle (en
codes) codes) ex. 1 sac) kg.) unites)
G.Sepium


P. Lucens


Produits :
1: Feuilles 2: Branches 3: Bois 4: Frits 5: Ecorces 6: Racines 7: Fleures 8: Semences 9: Pas
de reponse
Utilisation:
1: Alimentation 2: Medicaments 3: Bois de chauffe 4: Bois de service
5: Parre beautye) 6: Fourrage 7: Tannage 8: Autres 9: Pasde reponse

1.9 Distribution des products des banques forrageres et la haie vive pendant la derniere annee (en
nombre d'unites).
Espece Produit (voir Auto- Dons Vente Reserve
ci-dessus consommati (en unites) (en unites) (en unites)
pour les on (en
codes) unites)
G. Sepium


P. Lucens


I I I I I









1.10 Les prix et la valeur de la vente au marched (en F CFA pour 2006)
Note: Si le produit se ne vend pas au marched, estimer le prix.
Espece Produit (voir Prix Prix Prix Valeur de
ci-dessus pour minimum maximum moyen la vente
les codes) (par unite) (par unite) total (en
2006)
G. Sepium


1.11 Personnes impliques dans les differentes activities pour la recolte, la transformation et la
vente des products de les banques fourrageres ou/et la haie vive (a partir de la troisieme
ann~ee
Notes:- Specifier pour les differents espece
-Expliquer comment vous Stes arrive au nombre de personne/jours
(heures);
-Preciser si ce sont les membres de l'UPA ou bien de la main d'oeuvre salaries.
-Preciser l'annee dont on parle (par example An 3: 1999; An 4: 2000 etc.)
Etape 1: Collecte des products de les banques forrageres
Personnes No. de No. de No. de No. de No. de No. de
impliquees P/J(H) P/J(H) P/J(H) P/J(H) P/J(H) P/J(H)
(H/F/E) An 3: An 4: An 5: An 6: An 7: An 8:
G. sepium :






Note :
(An. 9. 10. si pssible)


Etape 2: Transformation des products de les banques forrageres
Personnes No. de P/J No. de P/J No. de P/J No. de P/J No. de P/J No. de P/J
impliquees An 3: An 4: An 5: An 6: An 7: An 8:
(H/F/E)
G. Sepium:






Note :










(An. 9. 10. si possible)
Etape 3: Vente des products de les banques forrageres
Personnes No. de P/J No. de P/J No. de P/J No. de P/J No. de P/J No. de P/J
impliquees An 3: An 4: An 5: An 6: An 7: An 8:
(H/F/E)
G. Sepium:






Note :




1.12 Quelle est la distance du champ jusqu'au marched ou vous vendez les products de les
banqlues forrageres (et la haie vive, si quelauechose) ?


1.13 Quels sont les moyens de transport utilises pour vendre les products des banques
forrageres au marched?
Note: Noter tous les moyens de transport utilises pour la vente des products de les banques
forrageres, PAS les moyens de transport uniquement utilises pour le travail champ~tre agricole.
Moyen de Nombre Prix (en CFA) Montant total Nombre
transport (entrer d' annees
codes): d'utilisation


Nom du marched


Distance (entrer codes)


1: 0-5 km
2: 5-10 km
3: 10-20 km
4: 20-50 km
5: >50 km
9: Pas de reponse


1: Charrette
2: Bicyclette/mobylette
3: Vehicule
4: Autres
9: Pas de reponse










1. 14 Personnes impliquis dans le activity pour le rasembler forragfe ligneux de
champs ou brousse:
Notes:- Expliquer comment vous Stes arrive au nombre de personne/jours;
Preciser si ce sont les membres de l'UPA ou bien de la main d'oeuvre salaries.
Personnes impliquees No. de personnel/ jours
(H/F/E): (heures) An. 2005






Note :




Si possible. avant commencer les banques forrageres
Personnes impliquees No. de personnel/ jours
(H/F/E): (heures) An.




Note :













Table of Key Variables
No. Price Cost (CFA)
Exchange rate US Dollar 550
Size of the live fence 291m
Discount rate 15%
Material Costs
Seedling (A. nilotica) 582 29.0 16878
Seedling (Z. mauritiana) 146 28.0 4074
Seedling (A. senegal) 146 25.9 3768
Seedling (B. rufescens) 146 25.9 3768
Seedling (L. inermis) 146 24.5 3565
TOTAL 32054
Agriculutral equipment (every year) 1000
Labor Costs
Daily labor wage 1 750 750

Revenues
Yields from live fence products (after 3rd year) 27054
Price of fuelwood (CFA/kg) 18 18
Price of timber (CFA/log) 931 37 34454
Price of C ($, FCFA) kg C 0.042 23


APPENDIX B
COST BENEFIT ANALYSIS (CASH FLOW)


OF LIVE FENCE












ITEM/YEAR
Material costs
Labor costs
Obtaining & planting
seeds/seedlings
Watering plants
Collecting material for dead
Constructing dead fence
Maintenance of live fence
Collecting products from live
Marketing products from live
Harvesting
ITOLLL Labor costs


0 1 2 3 4 5 6 7 8
33054 1000 1000 1000 1000 1000 1000 1000 1000



7,500
1,875
6,375 6,000 3,000
4,500 3,000 1,875
1,875 1,875 2,625 2,625 2,625 2,625 2,625 2,625 2,625
1,500 1,500 1,500 1,500 1,500 1,500 1,500
375 375 375 375 375 375 375

22,125 10,875 9,375 4,500 4,500 4,500 4,500 4,500 4,500


TCYEAL COSTS
pv cost
Revenues
Yields fromnlive fence products
Yields from fuelwood
Yields from timber

ITOLLL PEllEFUES
pv revenue
14et benefit (cash fow)

]Discount factor
Present value
11PV
IRR
BCR

Ideal accounting system
C storage (kg)
Csale (FCFA)
14et cash fow
NPV
IRR
pv revenue
BCR


55179 11875 10375 5500
55179 10326 7845 3616


5500 5500 5500
3145 2734 2378


5500 5500
2068 1798


552 27054 27054 27054 27054 27054 27054 27054




0 552 27054 27054 27054 27054 27054 27054 27054
0 480 20457 17788 15468 13451 11696 10171 8844
-55179 -11323 16679 21554 21554 21554 21554 21554 21554

1 0.86957 0.75614 0.65752 0.57175 0.49718 0.43233 0.37594 0.3269
-55179 -9846.1 12611.7 14172.1 12323.6 10716.1 9318.39 8102.95 7046.04
52802
25.5%
1.53



0 70 70 70 70 70 50 50 50
0 1617 1617 1617 1617 1617 1155 1155 1155
-55179 -9706 18296 23171 23171 23171 22709 22709 22709
60465
27.3%
0 1886.09 21679.4 18851.6 16392.7 14254.6 12195.5 10604.8 9221.57
1.60



0 35 35 35 35 35 25 25 25
-55179 -11288 16714 21589 21589 21589 21579 21579 21579
52974
25.5%
0 51C'.231 20483 17811.3 15488.1 13467.9 11706.9 10179.9 8852.12
1.53


Tonne-year accounting
C sale (FCFA)
14et cash fow
11PV
IRR
pv revenue
BCR











ITE1VI/EAR
Material costs
Labor costs
Obtaining & planting
seeds/seedlings
Watering plants
Collecting material for dead
Constructing dead fence
Maintenance of live fence
Collecting products frcanlive
Marketing products from live
Harvesting
TOTAL Labor costs


9 10 11 12 13 14 15 16 17
1000 1000 1000 1000 1000 1000 1000 1000 1000








2,625 2,625 2,625 2,625 2,625 2,625 2,625 2,625 2,625
1,500 1,500 1,500 1,500 1,500 1,500 1,500 1,500 1,500
375 375 375 375 375 375 375 375 375

4,500 4,500 4,500 4,500 4,500 4,500 4,500 4,500 4,500

5500 5500 5500 5500 5500 5500 5500 5500 5500
1563 1360 1182 1028 894 777 676 588 511


IOTALLENDSTS
pv cost


Revenues
Yiehis front 1ive fence products 27054 27054 27054 27054 27054 27054 27054 27054 27054
Yields from fuelwood
Yields from timber


IOTALLIUEVENTIES
py revenue
Net benefit (cash fow)


27054 27054 27054 27054 27054 27054 27054 27054 27054
7690 6687 5815 5057 4397 3824 3325 2891 2514
21554 21554 21554 21554 21554 21554 21554 21554 21554

0.28426 0.24718 0.21494 0.18691 0.16253 0.14133 0.12289 0.10686 0.09293
6126.99 5327.82 4632.89 4028.6 3503.13 3046.2 2648.87 2303.36 2002.92


Discount factor
Present value
NPV
IRR
BCR


Ideal accounting system
C storage (kg)
C sale (FCFA)
Net cash fow
NPV
IRR
pv revenue
BCR

Tonne-year accounting
C sale (FCFA)
Net cash fow
NPV
IRR
py revenue
BCR


50 50 30 30 30 30 30 7 7
1155 1155 693 693 693 693 693 161.7 161.7
22709 22709 22247 22247 22247 22247 22247 21716 21716



8018.76 6972.83 5964.03 5186.11 4509.66 3921.45 3409.95 2908.4 2529.04


25 25 15 15 15 15 15 3 3
21579 21579 21569 21569 21569 21569 21569 21557 21557



7697.49 6693.47 5818.28 5059.37 4399.45 3825.61 3326.62 2891.49 2514.34





ITEM/YEAR
Material costs
Labor costs
Obtaining & planting
seeds/seedlings
Watering plants
Collecting material for dead
Constructing dead fence
Maintenance of live fence
Collecting products from live
Marketing inoducts from live
Harvesting
113TAL Labor costs

TOTAL COSTS
pv cost
Revenues


18 19 20 21 22 23 24 25
1000 1000 1000 1000 1000 1000 1000 1000








2,625 2,625 2,625 2,625 2,625 2,625 2,625 2,625
1,500 1,500 1,500 1,500 1,500 1,500 1,500 1,500
375 375 375 375 375 375 375 700
9,375
4,500 4,500 4,500 4,500 4,500 4,500 4,500 14,200

5500 5500 5500 5500 5500 5500 5500 15200
444 386 336 292 254 221 192 462


Yields frounlive fence products 27054 27054 27054 27054 27054 27054 27054 27054
Yields from fuelwood 5760
Yields from timber 34454


TOTAL, REVENUES
pyrevenue
Net benefit (cash flow)


27054 27054 27054 27054 27054 27054 27054 67268.4
2186 1901 1653 1437 1250 1087 945 2043
21554 21554 21554 21554 21554 21554 21554 52068

0.08081 0.07027 0.0611 0.05313 0.0462 0.04017 0.03493 0.03038
1741.67 1514.5 1316.96 1145.18 995.807 865.92 752.974 1581.71


1Discount factor
Present vadue
NPV
IRR
BCR


Ideal accounting system
Cstonage (kg)
C sale (FCFA)
Net cash fow
NPV
IRR
pv revenue
BCR

Tonne-year accounting
C saee(FCFA)
Net cash fove
NPV
IRR
pv revenue
BCR


7 7 7 3 3 3 3 -477
161.7 161.7 161.7 69.3 69.3 69.3 69.3 -11019
21716 21716 21716 21623 21623 21623 21623 41050



2199.17 1912.32 1662.89 1441.08 1253.11 1089.66 947.533 1708.73


3 3 3 1 1 1 1 1
21557 21557 21557 21555 21555 21555 21555 52070



2186.38 1901.2 1653.22 1437.48 1249.98 1086.94 945.164 2043.5













No. Price Cost (CFA)
Exchange rate US Dollar 550
Size of the fodder bank 200 m perimeter
Discount rate 15%
Material Costs
Seedling (A. nilotica:) 400 29.0 11600
Seedling (Z. nmauritiana:) 100 28.0 2800
Seedling (A. senegall) 100 25.9 2590
Seedling (B. rufescens) 100 25.9 2590
Seedling (L. inernzis) 100 24.5 2450
Seedling (G. sepium ) 200 35.1 7020
TOTAL 29050
Agriculutral equipment (every year) 1500
Labor Costs
Daily labor wage 1 750 750

Revenues
Yields from live fence products (after 3rd year) 18594
Price of fuelwood (CFA/kg) 18 18
Price of timber (CFA/log) 800 37 29600
Price of C ($, FCFA) kg C 0.042 23


APPENDIX C
COST BENEFIT ANALYSIS (CASH FLOW) OF FODDER BANK











ITEM/YEAR 0 1 2 3 4 5 6 7 8
Material costs 30050 1000 1000 1000 1000 1000 1000 1000 1000


Labor costs
Obtaining & planting
seeds/seedlings
Watering plants
Collecting material for dead
Constructing dead fence
Maintenance
Collecting products frona live fence
Collecting fodder
Marketing products
Harvesting
ITYTAL Labor costs

ITOTAL COSTS
pv cost
Revenues
Yields front 1ive fence products
Yields front foder
Labor thnesaved
Yields front fuelwood
Yields front timber

ITOTAL REVIR4CES
py revenue
Net benefit (cash fow)

Discount factor
Present value
NWV
IRR
BCR

Ideal accounting systenI
C storage (kg)
C sale (FCFA)
Net cash fow
NWV
IRR
pv revenue
BCR


8946
2384
4,381 4,124 2,062
3,093 2,062 1,289
5,786 5,909 6,169 6,429 6,429 6,429 6,429 6,429 6,429
1031 1031 1031 1031 1031 1031 1031
2679 2679 2679 2679 2679 2679
375 375 375 375 375 375 375

24590 12095 10925 10513 10513 10513 10513 10513 10513
94589
54640 13095 11925 11513 11513 11513 11513 11513 11513
54640 11387 9017 7570 6583 5724 4977 4328 3764

379 18594 18594 18594 18594 18594 18594 18594
3,500 4,000 4,000 4,000 4,500 4,500
16875 19286 19286 19286 19286 19286




0 379 18594 38969 41880 41880 41880 42380 42380
0 330 14060 25623 23945 20822 18106 15932 13854
-54640 -12716 6668 27456 30366 30366 30366 30866 30866

1 0.86957 0.75614 0.65752 0.57175 0.49718 0.43233 0.37594 0.3269
-54640 -11057 5042 18053 17362 15097 13128 11604 10090
87319
29.5%
1.67



0 78 78 78 78 78 64 64 64
0 1801.8 1801.8 1801.8 1801.8 1801.8 1478.4 1478.4 1478.4
-54640 -10914 8470 29258 32168 32168 31845 32345 32345
96394
31.4%
0 1896.68 15422 26807.5 24974.9 21717.3 18744.8 16487.8 14337.2
1.74



0 39 39 39 39 39 32 32 32
-54640 -12677 6707 27495 30405 30405 30398 30898 30898
87523
29.5%
0 363.583 14088.9 25648.3 23966.9 20840.8 18119.4 15944 13864.3
1.67


Tonne-year accounting
C sale (FCFA)
Net cash flow
NWV
IRR
py revenue
BCR











ITEM/YEAR 9 10 11 12 13 14 15 16 17
Nhtterial costs 1000 1000 1000 1000 1000 1000 1000 1000 1000
Labor costs
Obtaining & planting
seeds/seedlings
Watering plants
Collecting material for dead
Constructing dead fence
Maintenance 6,429 6,429 6,429 6,429 6,429 6,429 6,429 6,429 6,429
Collecting products fomnlive fence 1031 1031 1031 1031 1031 1031 1031 1031 1031
Collecting fodder 2679 2679 2679 2679 2679 2679 2679 2679 2679
Marketing products 375 375 375 375 375 375 375 375 375
Harvesting
TOTAL Labor costs 10513 10513 10513 10513 10513 10513 10513 10513 10513


ITOTAL COSTS
pv cost
Revenues
Yiekis from live fence products
Yields frainfoder
Labor thnesaved
Yields from fuelwood
Yields from timber

ITOTAL REVENUES
pv revenue
Net benefit (cash fkow)

Discount factor
Present value
NPV
IRR
BCR


11513 11513 11513 11513 11513 11513 11513 11513 11513
3273 2846 2475 2152 1871 1627 1415 1230 1070

18594 18594 18594 18594 18594 18594 18594 18594 18594
4,500 5,000 5,000 5,000 5,000 5,000 5,000 5,000 5,000
19286 19286 19286 19286 19286 19286 19286 19286 19286




42380 42880 42880 42880 42880 42880 42880 42880 42880
12047 10599 9217 8014 6969 6060 5270 4582 3985
30866 31366 31366 31366 31366 31366 31366 31366 31366

0.28426 0124718 0.21494 0.18691 0.16253 0.14133 0.12289 0.10686 0.09293
8774 7753 6742 5863 5098 4433 3855 3352 2915


Ideal accounting system
C storage (kg)
C sale (FCFA)
Net cash fow
NPV
IRR
pv revenue
BCR

Tonne-year accounting
C sale (FCFA)
Net cash fow
NPV
IRR
pv revenue
BCR


64 64 41 41 41 41 41 21 21
1478.4 1478.4 947.1 947.1 947.1 947.1 947.1 485.1 485.1
32345 32845 32314 32314 32314 32314 32314 31852 31852



12467.2 10964.6 9420.24 8191.51 7123.05 6193.96 5386.05 4634.15 4029.7


32 32 20 20 20 20 20 10 10
30898 31398 31387 31387 31387 31387 31387 31377 31377



12055.9 10607 9221.04 8018.3 6972.43 6062.98 5272.16 4583.43 3985.59











ITE1V/YEAR 18 19 20 21 22 23 24 25
Matedial costs 1000 1000 1000 1000 1000 1000 1000 1000
Labor costs
Obtaining & planting
seeds/seedlings
Watering plants
Collecting material for dead
Constructing dead fence
Maintenance 6,429 6,429 6,429 6,429 6,429 6,429 6,429 6,429
Collecting products roin live fence 1031 1031 1031 1031 1031 1031 1031 1031
Collecting fodder 2679 2679 2679 2679 2679 2679 2679 2679
Marketing inoducts 375 375 375 375 375 375 375 700
Harvesting 11330
TOTAL Labor costs 10513 10513 10513 10513 10513 10513 10513 22168


TCOTAZLCOSTS
pv cost


11513 11513 11513 11513 11513 11513 11513 23168
930 809 703 612 532 463 402 704


Revenues
Yields front live fence products 18594 18594 18594 18594 18594 18594 18594 18594
Yields from foder 5,000 5,000 5,000 5,000 5,000 5,000 5,000 5,000
Labor thnesaved 19286 19286 19286 19286 19286 19286 19286 19286
Yields front fuehvood 7560
Yields front timber 29600


TOTAL, REVENUES
pv revenue
Net benefit (cash flow)


42880 42880 42880 42880 42880 42880 42880 80040
3465 3013 2620 2278 1981 1723 1498 2431
31366 31366 31366 31366 31366 31366 31366 56871

0.08081 0.07027 0.0611 0.05313 0.0462 0.04017 0.03493 0.03038
2535 2204 1916 1667 1449 1260 1096 1728


]Discount factor
Present ralue
NPV
IRR
BCR


Ideal accounting system
C stmrage (kg)
C sale (FCFA)
Net cash ilow
NPV
IRR
pv revenue
BCR

Tonne-year accounting
C sale (FCFA)
Net cash ilow
NPV
IRR
pv revenue
BCR


21 21 21 6 6 6 6 -624
485.1 485.1 485.1 138.6 138.6 138.6 138.6 -14414
31852 31852 31852 31505 31505 31505 31505 42457



3504.08 3047.03 2649.59 2285.58 1987.46 1728.23 1502.81 1993.54


10 10 10 3 3 3 3 3
31377 31377 31377 31369 31369 31369 31369 56874



3465.73 3013.68 2620.59 2278.38 1981.2 1722.78 1498.07 2431.5










LIST OF REFERENCES


Alavalapati, J.R.R., R.K. Shrestha, G.A. Stainback, and J.R. Matta. 2004. Agroforestry
development: An environmental economic perspective. Agrofor. Syst. 61-62: 299-310.

Albrecht, A. and S.T. Kandji. 2003. Carbon sequestration in tropical agroforestry systems. Agric.
Ecosyst. Environ. 99:15-27.

Alvarez R., R.A. Diaz, N. Barbero, O.J. Santanatoglia, and L. Blotta. 1995. Soil organic carbon,
microbial biomass and CO2-C production from three tillage systems. Soil Tillage Res. 33:
17-28.

Alvarez, R. and C. R. Alvarez. 2000. Soil organic matter pools and their associations with carbon
mineralization kinetics. Soil Sci. Soc. Am. J. 64:184-189.

Balesdent, J. and A. Mariotti. 1996. Measurement of soil organic matter turnover using 13C
natural abundance. p.83-112. In T.W. Boutton and S.I. Yamasaki (ed.) Mass spectrometry
of soils. Marcel Dekker, New York.

Balesdent, J., E. Besnard, D. Arrouays, and C. Chenu. 1998. The dynamics of carbon in particle-
size fractions of soil in a forest-cultivation sequence. Plant Soil 201:49-57.

Baron, R. and A. Lanza. 2000. Kyoto commitments: macro and micro insights on trading and the
Clean Development Mechanism. Integrated Assessment 1:137-144.

Bationo, A. and B.R. Ntare. 2000. Rotation and nitrogen fertilizer effects on pearl millet, cowpea
and groundnut yield and soil chemical properties in a sandy soil in the semi-arid tropics,
West Africa. J. Agric. Sci.(Cambridge) 134:277-284.

Batj es, N.H. 2004. Estimation of soil carbon gains upon improved management within croplands
and grasslands of Africa. Environ. Dev. Sust. 6: 133-143.

Batj es, N.H. 2001. Options for increasing carbon sequestration in West African soils: An
exploratory study with special focus on Senegal. Land Deg. Dev. 12:131-142.

Beare, M. H. M. L. Cabrera, P. F. Hendrix, and D. C. Coleman. 1994. Aggregate-protected and
unprotected organic matter pools in conventional- and no-tillage soils. Soil Sci. Soc. Am.
J. 58:787-795.

Beer, J., A. Bonnemann, W. Chavez, H.W. Fassbender, A.C. Imbach, and I. Martel. 1990.
Modelling agroforestry systems of cacao (Theobroma cacao) with laurel (Cordia
alliodora) or poro (Erythrina poeppigiana) in Costa Rica. Agrofor. Syst. 12:229-249.

Blair, G.J., R.D.B. Lefroy, and L. Lisle. 1995. Soil carbon fractions based on their degree of
oxidation, and the development of a carbon management index for agricultural systems.
Aust. J. Agric. Res. 46:1459-1466.










Boffa, J. M. Agroforestry parklands in sub-Saharan Africa 1999. FAO Conservation Guides 34.
FAO, Rome, Italy.

Bouliere, F. 1983. Tropical Savannas. Elsevier Scientifie Publishing Co. New York, NY.

Breman, H. and J.J. Kessler. 1997. The potential benefits of agroforestry in the Sahel and other
semi-arid regions. Euro. J. Agron. 7:25-33.

Brown, S. 1997. Estimating Biomass and Biomass Change of Tropical Forests: a Primer. FAO,
Rome, Italy.

Brown, S. 1999. Guidelines for Inventorying and Monitoring Carbon Offsets in Forest-Based
Projects. Winrock International, Arlington, VA.

Brown, S. 2002. Measuring carbon in forests: current status and future challenges. Environ.
Pollut. 116:363-372.

Buresh, R.J. and G. Tian. 1997. Soil improvement by trees in sub-Saharan Africa. Agrofor. Syst.
38:51-76.

Cacho, O.J., G.R. Marshall, and M. Milne. 2003a. Smallhoder agroforestry projects: potential for
carbon sequestration and poverty alleviation. ESA Working Paper No.03-06. FAO.
www. fao. org/es/esa

Cacho, O.J., R.L. Hean, and R.M. Wise. 2003b. Carbon-accounting methods and reforestation
incentives. Aust. J. Agric. Res. Econ. 47:153-179.

Cairns, M.A., S. Brown, E.H. Helmer, and G.A. Baumgardner. 1997. Root biomass allocation in
the world's upland forests. Oecologia (Berlin) 111:1-11.

Campbell C.A., B.G. McConkey, R.P. Zentner, F. Selles, and D. Curtin. 1996. Tillage and crop
rotation effects on soil organic C and N in a course-textured Typic Haploboroll in
southwestern Saskatchewan. Soil Tillage Res. 37: 3-14.

Campbell, H.A. and P.C. Brown. (ed.) 2003. Benefit-cost analysis: Financial and economic
appraisal using spreadsheets. Cambridge University Press, Port Melbourne, Australia.

Choudhary, M.A., A. Akramkhanov, and S. Saggar. 2002. Nitrous oxide emissions from a New
Zealand cropped soil: tillage effects, spatial and seasonal variability. Agric. Ecosyst.
Environ. 93:33-43.

CIA. 2007. The World Factbook: Mali.[Online] Available at
https://www. cia. gov/library/publications/the-world-factboo/esm.html (verified 5 Jul.
2007). CIA, Washington, DC.

Cissee, M.I. 1980. Production fourragre de quelques arbres saheliens: relations entire la biomasse
foliaire maximale et divers parametresphysiques. p.203-208. In H.N. Le Houerou. (ed.) Les
fourrages ligneux en Afrique, L'etat actuel des connaissances. CiPEAA, Addis Abeba.










Cisse, M.I. Les parcs agroforestiers au Mali. 1995. Etat des connaissances et perspectives pour
leur amelioration. Rapport AFRENA. ICRAF, Nairobi, Kenya.

Coughenour, M.B., J.E. Ellis, and R.G. Popp. 1990. Morphometric relationships and
developmental patterns of Acacia tortilis and Acacia reficiens in Southern Turkana, Kenya.
Bull. Torrey Bot. Club 117:8-17.

Dai, A., P.J. Lamb, K.E. Trenberth, M. Hulme, P.D. Jones, and P. Xie. 2004. The recent Sahel
drought is real. Int. J. Climat. 24:1323-1331.

Dalsted, N.L. and P.H. Gutierrez. 2007. Partial budgeting. [Online]. Available at
http ://www.ext.colostate.edu/PUB S/farmmgt/03 760.html (verified 5 Jul. 2007). Colorado
State University Extension-Agriculture, Fort Collins, CO.

De Alwis, K.A. Recapitalization of soil productivity in sub-Saharan Africa. 1996. FAO
Investment Center, Rome, Italy.

De Jong, B.H., S.O. Gaona, S.Q. Montalvo, E.E. Bazan, and N.P. Hemnandez. 2004. Economics
of agroforestry carbon sequestration: A case study from southern Mexico. Chap.4. In
J.R.R. Alavalapati and D.E. Mercer (ed.) Valuing agroforestry systems. Kluwer Academic
Publishers, Netherlands.

De Jong, B.H.J. 2001. Uncertainties in estimating the potential for carbon mitigation of forest
management. For. Ecol. Manage. 154:85-104.

Del Galdo, I., J. Six, A. Peressotti, and M.F. Cotrufo. 2003. Assessing the impact of land-use
change on soil C sequestration in agricultural soils by means of organic matter
fractionation and stable C isotopes. Glob. Chan. Bio. 9:1204-1213.

Delaney, M. and J. Roshetko. 1999. Field test of carbon monitoring methods for home gardens in
Indonesia p.45-5 1. hz Field Tests of Carbon Monitoring Methods in Forestry Proj ects.
Forest Carbon Monitoring Program. Winrock Intemnational, Arlington, VA.

Denton, F., Y. Sokona, and J.P. Thomas. Climate change and sustainable development strategies
in the making: What should West African countries expect? 2001. OECD Climate Change
and Development. Environnement et Developpement du Tiers Monde (ENDA-TM),
Dakar, Senegal.

Dixon, R.K. 1995. Agroforestry system: sources or sinks of greenhouse gases? Agrofor. Syst.
31:99-116.

Dixon, R.K., S Brown, R.A. Houghton, M.C. Solomon, M.C. Trexler, and J. Wisniewski. 1994a
Carbon pools and flux of global forest ecosystems. Science (Washington, DC) 263:185-
190.

Dixon, R.K., J.K. Winjum, K.J. Andrasko, J.J. Lee, and P.E. Schroeder. 1994b. Integrated land-
use systems: assessment of promising agroforest and alternative land-use practices to
enhance carbon conservation and sequestration. Clim. Change 27:71-92.










Doumbia, O. Final Report on the Soil Resources of the Villages Covered by the "JICA-SEGOU
Project" 2000. Segou, Mali.

Droppelmann, K.J., J. Lehmann, J.E. Ephrath, and P.R. Berliner. 2000. Water use efficiency and
uptake patterns in a runoff agroforestry system in an arid environment. Agrofor. Syst.
49:223-243.

Duguma, B., J. Gockowski, and J. Bakala. 2001. Smallholder cacao (Theobroma cacao Linn.)
cultivation in agroforestry systems of West and Central Africa: challenges and
opportunities. Agrofor. Syst. 51:177-188.

Duiker, S.W. and R. Lal. 1999. Crop residue and tillage effects on carbon sequestration in a
Luvisol in central Ohio. Soil Tillage Res. 52, 73-81.

Dzurec, R.S., T.W. Boutton, M.M. Caldwell, and B.N. Smith. 1985. Carbon isotope ratios of soil
organic matter and their use in assessing community composition changes in Curlew
Valley, Utah. Oecologia (Berlin) 66:17-24.

Eleki, K., R.M. Cruse, and K.A. Albrecht. 2005. Root segregation of C3 and C4 species using
carbon isotope composition. Crop Sci 45:879-882.

Eswaran, H., R. Almaraz, E. Berg, and P. Reich. 1996. An Assessment of the Soil Resources of
Africa in Relation to Productivity. [Online] Available at
http ://soils.usda.gov/use/worldsoils/papers/afia.html (verified 5 Jul. 2007). NRC S,
Washington, DC.

FAO. 2000. Global Forest Resources Assessment 2000. FAO Forestry Paper 140. Rome, Italy.

FAO. 1997. Estimating biomass and biomass change of tropical forests. FAO Forestry Paper
134. Rome, Italy.

FAO. 1991. Feeding dairy cows in the tropics. FAO Animal Production and Health Paper 86.
Rome, Italy.

Ferguson, W. 1983. Integrating crops and livestock in West Africa. FAO Animal Production and
Health Paper 41. Rome, Italy.

Franklin, J. and P. Hiernaux. 1991. Estimating foliage and woody biomass in Sahelian and
Sudanian woodlands using a remote sensing model. Int. J. Remote Sens.12:1387-1404.

Franzluebbers, A.J., F.M. Hons, and D.A. Zuberer. 1995. Tillage-induced seasonal changes in
soil physical properties affecting soil CO2 evolution under intensive cropping. Soil Tillage
Res. 34: 41-60.

Garcia-Oliva, F. and O.R. Masera. 2004. Assessment and measurement issues related to soil
carbon sequestration in Land-Use, Land-Use Change, and Forestry (LULUCF) proj ects
under the Kyoto Protocol. Clim. Change 65:347-364.










Gebhart, D.L., H.B. Johnson, H.S. Mayeux, and H.W. Polley. 1994. The CRP increases soil
organic carbon. 49: 488-492. J. Soil Water Conserv.

Ghani, A., M. Dexter, and K.W. Perrott. 2003. Hot-water extractable carbon in soils: a sensitive
measurement for determining impacts of fertilisation, grazing and cultivation. Soil Biol.
Biochem. 35:1231-1243.

Godal, O., Y. Ermoliev, G. Klaassen, and M. Obersteiner. 2003. Carbon trading with imperfectly
observable emissions. Environ. Res. Econ. 25:151-169.

Gonzalez, P. 2001. Desertifieation and a shift of forest species in the West African Sahel. Clim.
Res. 17:217-228.

Gordon, J.E., W.D. Hawthorne, G. Sandoval, and A.J. Barrance. 2003. Trees and farming in the
dry zone of southern Honduras II: the potential for tree diversity conservation. Agrofor.
Syst. 59: 107-117.

Gritzner, J.A. 1988. The West African Sahel. University of Chicago, Committee on
Geographical Studies, Chicago, IL.

Hamer, A., S. Franzel, B. Mounkoro, A. Niang, and C.O. Traore. 2005. Fodder banks in Mali.
ICRAF, Bamako, Mali.

Hardner, J.J., P.C. Frumhoff, and D.C. Goetze. 2000. Prospects for mitigating carbon, conserving
biodiversity, and promoting socioeconomic development obj ectives through the clean
development mechanism. Mitig. Adapt. Strat. Glob. Change 5:61-80.

Harris, D., R.H. William, and C. van Kessel. 2001. Acid fumigation of soils to remove
carbonates prior to total organic carbon or CARBON-13 isotopic analysis. Soil Sci. Soc.
Am. J. 65:1853-1856.

Hassink, J. 1997. The capacity of soils to preserve organic C and N by their association with clay
and silt particles. Plant Soil 191:77-87.

ICRAF. 2007. ICRAF Position on the Clean Development Mechanism in the Land-Use, Land-
Use Change and Forestry Sector. [Online] Available at
http ://www.worldagroforestry. org/climatechange/documents/ICRAFPositionoCMpdf
(verified 5 Jul. 2007) Nairobi, Kenya.

ICRISAT. Crops. 2007. [Online] Available at http://www.icrisat.org/ (verified 5 Jul. 2007).

Ingram, J.S.I. and E.C.M. Fernandes. 2001. Managing carbon sequestration in soils: concepts
and terminology. Agric. Ecosys. Environ. 87: 111-117.

IPCC. 2007. Climate Change 2007: Mitigation of Climate Change. Working Group III
contribution to the Intergovernmental Panel on Climate Change, Fourth Assessment
Report. Bangkok, Thailand.










IPCC. 2000. Land use, Land-use Change, and Forestry. A Special Report of the IPCC.
Cambridge University Press Cambridge, UK.

Jeltsch, F., S.J. Milton, W.R.J. Dean, and N. van Rooyen. 1996. Tree spacing and coexistence in
semiarid savannas. J. Ecol. 84:583-595.

Jobbagy, E.G. and R.B. Jackson. 2000. The vertical distribution of soil organic carbon and its
relation to climate and vegetation. Ecol. Applic. 10:423-436.

Johnsen, K.H., D. Wear, R. Oren, R.O. Teskey, F. Sanchez, R. Will, J. Butnor, D. Markewitz, D.
Richter, T. Rials, H.L. Allen, J. Seller, D. Ellsworth, C. Maier, G. Katul, and P.M.
Dougherty. 2001. Meeting global policy commitments: Carbon sequestration and southern
pine forests. J. For. 99:14-21.

Jones, M., M.L. Sinclair, and V.L. Grime. 1998. Effect of tree species and crown pruning on root
length and soil water content in semi-arid agroforestry. Plant Soil. 201:197-207.

Jonsson, K., C.K. Ong, and J.C.W. Odongo. 1999. Influence of scattered Nere and Karite trees
on microclimate, soil fertility and millet yield in Burkina Faso. Exp. Agric. 35:39-53.

Kang, B.T., F.E. Caveness, G. Tian, and G.O. Kolawole. 1999. Longterm alley cropping with
four hedgerow species on an Alfisol in southwestern Nigeria effect on crop performance,
soil chemical properties and nematode population. Nutr. Cycl. Agroecosyst. 54:145-155.

Skater, L.J.M., S. Kante, and A Budelman. 1992. Karite (Vitellaria paradoxa)1~~1~~1~~1 and nere(Parkia
biglobosa) associated with crops in South Mali. Agrofor. Syst. 18:89-105.

Kaya, B. 2000. Soil fertility regeneration through improved fallow systems in southern Mali.
Ph.D. diss.Univ. of Florida, Gainesville, FL.

Kaya, B. and P.K.R. Nair. 2004. Dynamics of Particulate Organic Matter following biomass
addition from fallow-improvement species in southern Mali. Agrofor. Syst. 60:267-276.

Kaya, B. and P.K.R. Nair. 2001. Soil fertility and crop yields under improved-fallow systems in
southern Mali. Agrofor. Syst. 52: 1-11.

Klemperer, W.D. (ed.) 1996. Forest resource economics and finance. McGraw-Hill, Inc, USA.

Kursten, E. and P. Burschel. 1993. CO2-mitigation by agroforestry. Water Air Soil Pollut.
70:533-544.

Lal, R. 2004a. Carbon sequestration in dryland ecosystems. Environ. Manag. 33:528-544 .

Lal, R. 2004b. Soil carbon sequestration impacts on global climate change and food security.
Science (Washington, DC) 304:1623-1627.










Lal, R. 1999. Global carbon pools and fluxes and the impact of agricultural intensification and
judicious land use. p. 44-52. hz Prevention of Land Degradation, Enhancement of Carbon
Sequestration and Conservation of Biodiversity through Land Use Change and Sustainable
Land Management with a Focus on Latin America and the Caribbean. FAO, Rome, Italy.

Levasseur, V., M. Djimde, and A. Olivier. 2004. Live fences in Segou, Mali: an evaluation by
their early users. Agrofor. Syst. 60:131-136.

Littmann, T. 1991. Rainfall, temperature, and dust storm anomalities in the African Sahel.
Geogr. J 157:136-160.

MacDicken, K.G. (ed.) 1997. A guide to monitoring carbon storage in forestry and agroforestry
projects. Winrock Intemnational Institute for Agricultural Development, Forest Carbon
Monitoring Program. Arlington, VA.

Makumba, W., B. Janssen, O. Oenema, F.K. Akinnifesi, D. Mweta, and F. Kwesiga. 2006. The
long-term effects of a gliricidia-maize intercropping system in Southemn Malawi, on
gliricidia and maize yields, and soil properties: Nutrient management in tropical
agroecosystems. Agric. Ecosyst. Environ. 116:85-92.

Makundi, W.R. and J.A. Sathaye. 2004. GHG Mitigation potential and cost in tropical forestry -
relative role for agroforestry. Environ. Dev. Sust. 6:235-260.

Manlay, R.J., J.L. Chotte, D. Masse, J.Y. Laurent, and C. Feller. 2002. Carbon, nitrogen and
phosphorus allocation in agro-ecosystems of a West African savanna: III. Plant and soil
components under continuous cultivation. Agric. Ecosyst. Environ. 88:249-269.

Marino, B.D. and M.B. McElroy. 1991. Isotopic composition of atmospheric CO2 inferred from
carbon in C4 plant cellulose. Nature 349:127.

Masera, O.R., J.F. Garza-Caligaris, M. Kanninen, T. Karjalainen, J. Liski, G.J. Nabuurs, A.
Pussinen, B.H.J. de Jong, and G.M.J. Mohren. 2003. Modeling carbon sequestration in
afforestation, agroforestry and forest management proj ects: the CO2FIX V.2 approach.
Ecol. Modell. 164:177-199.

McDonagh, J.F., T.B.Thomsen, and J. Magid. 2001. Soil organic matter decline and
compositional change associated with cereal cropping in southern Tanzania. Land Deg.
Dev. 12:13-26.

McLauchlan, K.K. and S.E. Hobble. 2004. Comparison of labile soil organic matter fractionation
techniques. Soil Sci. Soc. Am. J. 68:1616-1625.

Mitchell, T. 2005. Sahel rainfall index (20-10N, 20W-10E), 1898 2004. [Online] Avaliable at
http://jisao.washington. edu/data~sets/sahel/index2 .html#values (verified 5 Jul. 2007).
University of Washington, Seattle, WA.

Montagnini, F. and P.K.R. Nair. 2004. Carbon sequestration: An underexploited environmental
benefit of agroforestry systems. Agrofor. Syst. 61-62:281-295.










Moody, P.W., S.A. Yo, and R.L. Aitken. 1997. Soil organic carbon, permanganate fractions, and
the chemical properties of acidic soils. Aust. J. Soil Res. 35:1301-1308.

Mosier, A., R. Wassmann, L. Verchot, J. King, and C. Palm. 2004. Methane and nitrogen oxide
fluxes in tropical agricultural soils: Sources, sinks and mechanisms. Environ. Dev. Sust.
6:11-49.

Moura-Costa, P. and C. Wilson. 2000. An equivalence factor between CO2 avoidedemissions
and sequestration description and applications in forestry. Mitig. Adapt. Strat. Glob.
Change 5:51-60.

Nair, P. K. R. and V.D. Nair. 2003. Carbon storage in North American agroforestry systems. In:
Kimble, J., Heath, L.S., Birdsey, R.A., and Lal, R. (eds). The Potential of U.S. Forest Soils
to Sequester Carbon and Mitigate the Greenhouse Effect, pp. 333-346. CRC Press LLC,
Boca Raton, FL.

Nair, P.K.R. (ed.) 1993. An Introduction to Agroforestry. Kluwer Academic Publishers,
Dordrecht, Netherlands.

Niang, A., M. Djimde, B. Kaya, E.G. Bonkoungou, and M. Macalou. 2002. Paper presented at
Improving the quantity and quality of dry season fodder availability in the Sahel. Presented
at the Regional Agroforestry Conference. Pretoria, South Africa. May 20-24, 2002.

Nierop, K.G.J., F.H. Tonneijck, B. Jansen, and J.M. Verstraten. 2007. Organic matter in volcanic
ash soils under forest and paramo along an Ecuadorian altitudinal transect. Soil Sci. Soc.
Am. J. 71:1119-1127.

Oba, N., N.C. Stenseth, and W.J. Lusigi. 2000. New perspectives on sustainable grazing
management in arid zones of sub-Saharan Africa. Bioscience 50:3 5-51.

Onim, J.F.M., M. Mathuva, K. Otieno, and H.A. Fitzhugh. 1990. Soil fertility changes and
response of maize and beans to green manures of leucaena, sesbania and pigeonpea.
Agrofor. Syst. 12:197-215.

Oren, R., D.S. Ellsworth, K.H. Johnsen, N. Phillips, B.E. Ewers, C. Maier, K.V.R Schafer, H.
McCarthy, G. Hendrey, S.G. McNulty, and G.G Katul. 2001. Soil fertility limits carbon
sequestration by forest ecosystems in a CO2-enriched atmosphere. Nature (London)
411:469-472.

Palm, C.A, T. Tomich, M. Van Noordwijk, S. Vosti, J. Alegre, J. Gockowski, and L. Verchot.
2004. Mitigating GHG emissions in the humid tropics: Case studies from the Alternatives
to Slash-and-Burn Program (ASB). Environ. Dev. Sust. 6:145-162.










Palm, C.A., P.L. Woomer, J. Alegre, L. Arevalo, C. Castilla, D.G. Cordeiro, B. Feigl, K. Hairiah,
J. Kotto-Same, A. Mendes, A. Moukam, D. Murdiyarso, R. Njomgang, W.J. Parton, A.
Ricse, V. Rodrigues, S.M. Sitompul, and M van Noordwijk. 1999. Carbon sequestration
and trace gas emissions in slash and burn and alternative land use in the humid tropics.
ASB Climate Change Working Group Final Report, Phase II. ASB Coordination Office,
ICRAF, Nairobi, Kenya.

Paustian, K., O. Andren, H.H. Janzen, R. Lal, P. Smith, G. Tian, H. Tiessen, M. van Noordwijk,
and P. L. Woomer. 1997. Agricultural soils as a sink to mitigate CO2 emissions. Soil Use
and Manage. 13: 230-244.

Pfaff, A.S.P., S. Kerr, R.F. Hughes, S. Liu, G.A. Sanchez-Azofeifa, D. Schimel, J. Tosi, and V.
Watson. 2000. The Kyoto protocol and payments for tropical forest: An interdisciplinary
method for estimating carbon-offset supply and increasing the feasibility of a carbon
market under the CDM. Ecol. Econ. 35:203-221.

Phillips, D.L., P.D. Hardin, V.W. Benson, J.V. Baglio. 1993. Nonpoint source pollution impacts
of alternative agricultural management practices in Illinois: A simulation study. J. Soil
Water Consev. 48: 449-457.

Pikul, J.L., S. Osborne, M. Ellsbury, and W. Riedell. 2007. Particulate organic matter and water-
stable aggregation of soil under contrasting management. Soil Sci. Soc. Am. J. 71:766-776.

Post, W. M. and K.C. Kwon. 2000. Soil carbon sequestration and land-use change: processes and
potential. Glob. Change Biol. 6:317-327.

Potvin, C., E. Whidden, and T. Moore. 2004. A case study of carbon pools under three different
land-uses in Panama. Clim. Change 67:291-307.

Powers, J.S. and E. Veldkamp. 2005. Regional variation in soil carbon and 13C in forests and
pastures of northeastern Costa Rica. Biogeochem. 72:3 15-336.

Powers, J.S. and W.H. Schlesinger. 2002. Relationships among soil carbon distributions and
biophysical factors at nested spatial scales in rain forests of northeastern Costa Rica.
Geoderma 109:165-190.

ProKarite. Proj et d'Appui Technique a la Filliere Karite 2007. [Online] Available at
http:.//www.prokarite. org/index-eng.html (verified 5 Jul.2007). Bamako, Mali.

Reich, P.F., S.T. Numbrem, R.A. Almaraz, and H. Eswaran. 2001. Land resource stresses and
desertification in Africa. hz E.M. Bridges, I.D. Hannam, L.R. Oldeman, F.W.T. Pening de
Vries, S.J. Scherr, and S. Sompatpanit (eds.). Responses to Land Degradation. Proc. 2nd.
International Conference on Land Degradation and Desertification, Khon Kaen, Thailand.
Oxford Press, New Delhi, India.

Republique du Mali. 2005. Minitere du Plan et de L amenagement du Territoire and Ministere de
L agriculture de L elevage et de la Peche. Enquete Agricole de Conjoncture Campagne
2003/2004. Bamako, Mali.










Rhoton, F.E. 2000. Influence of time on soil response to no-till practices. Soil Sci. Soc. Am. J.
64: 700-709.

Richter, D.D., D. Markewitz, S.E. Trumbore, and C.G. Wells. 1999. Rapid accumulation and
turnover of soil carbon in a re-establishing forest. Nature (London) 400:56-58.

Ringius, L. 2002. Soil carbon sequestration and the CDM: Opportunities and challenges for
Africa. Clim. Change 54:471-495.

Roose, E., V. Kabore, and C. Guenat. 1999. Zai Practice: A west African traditional
rehabilitation system for semiarid degraded lands, a case study in Burkina Faso. Arid Soil
Res. Rehabil. 13:343-355.

Rosalina, U., Setiabudhi, and A.E. Putra. Vegetation analysis and database management system
in Lampung and Jambi. 1997. ICRAF Southeast Asia Regional Office, Bogor, Indonesia.

Rose, S., H. Ahammad, B. Eickhout, B. Fisher, A. Kurosawa, S. Rao, K. Riahi, and D. van
Vuuren. 2007. Land in climate stabilization modeling: Initial observations. [Online]
Available at http://www. stanford. edu/group/EMF/proj ects/group21 /Landuse.pdf (verified 5
Jul. 2007). Stanford Energy Modeling Forum (EMF), Stanford University, Stanford, CA.

Roshetko, J.M., M. Delaney, K. Hairiah, and P Purnomosidhi. 2002. Carbon stocks in Indonesian
homegarden systems: Can smallholder systems be targeted for increased carbon storage?
Am. J. Alt. Agric. 17:138-148.

Sathaye, J.A., W.R. Makundi, K. Andrasko, R. Boer, N.H. Ravindranath, P. Sudha, S. Rao, R.
Lasco, F. Pulhin, O. Masera, A. Ceron, J. Ordonez, X. Deying, X. Zhang, and S. Zuomin.
2001. Carbon mitigation potential and costs of forestry options in Brazil, China, India,
Indonesia, Mexico, the Philippines and Tanzania. Mitig. Adapt. Strat. Glob. Change
6:185-211.

Schimel, D.S., B.H. Braswell, E.A. Holland, R. McKeown, D.S. Ojima, T.H. Painter, W.J.
Parton, and A.R. Townsend. 1994. Climatic, edaphic, and biotic controls over storage and
turnover of carbon in soils. Glob. Biogeochem. Cycl. 8:279-293.

Schroeder, P. 1994. Carbon storage benefits of agroforestry systems. Agrofor. Syst. 27:89-97.

Schroth, G., S.A. D'Angelo, W.G. Teixeira, D. Haag, and R. Lieberei. 2002. Conversion of
secondary forest into agroforestry and monoculture plantations in Amazonia: consequences
for biomass, litter and soil carbon stocks after 7 years. For. Ecol. Manag. 163:131-150.

Schuur, E.A.G. 2001. The effect of water on decomposition dynamics in mesic to wet Hawaiian
montane forests. Ecosystems 4:259-273.

Scolel Te. 2007. [Online] Available at http://www.eccm.uk.com/scolelte/ (verified 5 Jul. 2007).
Edinburgh, UK.










Seghieri, J., M. Simier, A. Mahamane, P. Hiernaux, and S. Rambal. 2005. Adaptative above-
ground biomass, stand density and leaf water potential to droughts and clearing in Guiera
senegalensis, a dominant shrub in Sahelian fallows (Niger). J. Trop. Ecol. 21:203-213.

Sharrow, S.H. and S. Ismail. 2004. Carbon and nitrogen storage in agroforests, tree plantations,
and pastures in western Oregon, USA. Agrofor. Syst. 60:123-130.

Shepherd, D. and F. Montagnini. 2001. Above ground carbon sequestration potential in mixed
and pure tree plantation in the humid tropics. J. Trop. For. Sci. 13:450-459.

Six, J., R.T. Conant, E.A. Paul, and K. Paustian. 2002a. Stabilization mechanisms of soil organic
matter: Implications for C-saturation of soils. Plant Soil 241:155-176.

Six, J., P. Callewaert, S. Lenders, S. De Gryze, S.J. Morris, E.G. Gregorich, E.A. Paul, and K.
Paustian. 2002b. Measuring and understanding carbon storage in afforested soils by
physical fractionation. Soil Sci. Soc. Am. J. 66:1981-1987.

Six, J., R. Merckx, K. Kimpe, K. Paustian, and E.T. Elliott. 2000c. A re-evaluation of the
enriched labile soil organic matter fraction. Eur. J. Soil Sci. 51: 283-293.

Six, J., E.T. Elliott, K. Paustian, and J.W. Doran. 1998. Aggregation and soil organic matter
accumulation in cultivated and native grassland soils. Soil Sci. Soc. Am. J. 62:1367-1377.

Smith, P., D.S. Powlson, M.J. Glending, and J.U. Smith. 1998. Preliminary estimates of the
potential for carbon mitigation in European soils through no-till farming. Glob. Change
Bio. 4: 679-685.

Stainback, A.G. and J.R.R. Alavalapati. 2002. Economic analysis of slash pine forest carbon
sequestration in the southern U.S. J. For. Econ. 8:105-117.

Sun, O.J., J. Campbell, B.E. Law, and V. Wolf. 2004. Dynamics of carbon stocks in soils and
detritus across chronosequences of different forest types in the Pacific Northwest, USA.
Glob. Chan. Biol.10:1470-1481.

Tan, Z.X., R. Lal, N.E. Smeck, and F.G. Calhoun. 2004. Relationships between surface soil
organic carbon pool and site variables. Geoderma 121: 187-195.

Tiepolo, G., M. Calmon, and A.R. Feretti. 2002. Paper presented at measuring and monitoring
carbon stocks at the Guaraquecaba Climate Action Proj ect, Parana, Brazil. Proceedings of
the International Symposium on Forest Carbon Sequestration and Monitoring. 11-15
November 2002, Taipei, Taiwan. Taiwan Forestry Research Institute Winrock
International .

Tiessen, H., H.K. Hauffe, and A.R. Mermut. 1991. Deposition of Harmattan dust and its
influence on base saturation of soils in northern Ghana. Geoderma 49:285-299.

Tieszen, L.L. 1991. Natural variations in the carbon isotope values of plants: implications for
archaeology, ecology and paleoecology. J. Archaeol. Sci. 18: 227.










Tipper, R. and B.H. De Jong. 1998. Quantifieation and regulation of carbon offsets from
forestry: comparison of alternative methodologies, with special reference to Chiapas,
Mexico. Commonwealth For. Rev. 77:219-228.

Tomich, T.P., H. de Foresta, R. Dennis, Q. Ketterings D. Murdiyarso, C. Palm, F. Stolle,
Suyanto, and M. van Noordwijk. 2002. Carbon offsets for conservation and development
in Indonesia? Am. J. Alt. Agric. 17:125-137.

Traore, C.O., F. Place, and A. Niang. 2002. Evaluation economique des couts de production des
plants d'especes haies vives a la pepiniere de Segou. Mali. ICRAF, Bamako, Mali.

Tschakert, P. 2004. Carbon for farmers: Assessing the potential for soil carbon sequestration in
the Old Peanut Basin of Senegal. Clim. Change 67:273-290.

Tschakert, P. 2007. Environmental services and poverty reduction: Options for smallholders in
the Sahel: Making carbon sequestration work for Africa's rural poor Opportunities and
constraints. Agric. Syst. 94:75-86.

UNFCCC. 2006. Revised simplified baseline and monitoring methodologies for selected small-
scale afforestation and reforestation proj ect activities under the clean development
mechanism. [Online] Available at
http://cdm .unfecc.int/UserManagement/FileStorage/CDM FAMAII6AX6KGW5GBB
7M6AI98UD3W59X4 (verified 5 Jul.2007). Bonn, Germany.

UNFCCC 2007. [Online] Available at http://unfecc.int/2860.php (verified 5 Jul.2007). Bonn,
Germany.

USGS. 2007. International Program Sahel Land Use. [Online] Available at
http://edcintl .cr.usgs.gov/sahel .html (verified 5 Jul. 2007). Reston, VA.

Van Dorp, M., B. Mounkoro, S. Soumana, C.O. Traore, S. Franzel, F. Place, and A. Niang. 2005.
Economic analysis of improved live fences as an agroforestry technology as compared to
traditional live fences and dead fences in the Segou Region, Mali. ICRAF, Bamako, Mali.

Van Duijl, E. 2000. Feeding livestock in the dry season: towards identifying potential adopters
for fodderbanks in Segou, Mali. Draft Report. ICRAF, Bamako, Mali.

Van Duijl, E.C. 1999. Characterisation of potential adopters for live fences in Segou, Mali.
ICRAF, Bamako, Mali.

Van Noordwijk, M., S. Rahayu, K. Hairiah, Y.C. Wulan, A. Farida, and B. Verbist. 2002.
Carbon stock assessment for a forest-to-coffee conversion landscape in Sumber-Jaya
(Lampung, Indonesia): from allometric equations to land use change analysis. Science
China 45:75-86.

Walker, S.M. and P.V. Desanker. 2004. The impact of land use on soil carbon in Miombo
Woodlands of Malawi. For. Ecol. Manag. 203:345-360.










Wise, R. and O. Cacho. 2005. Tree-crop interactions and their environmental and economic
implications in the presence of carbon-sequestration payments. Environ. Modell. Software
20:1139-1148.

Woomer, P.L., L.L. Tieszen, G. Tappan, A. Toure, and M. Sall. 2004a. Land use change and
terrestrial carbon stocks in Senegal. J. Arid Environ. 59:625-642.

Woomer, P.L., A. Toure, and M. Sall. 2004b. Carbon stocks in Senegal's Sahel transition zone. J.
Arid Environ. 59:499-510.

World Bank. 2007. Mali Country Brief. [Online] Available at www.worldbank.org/mali (verified
5 Jul. 2007). Washington, DC.

World Bank. 2006. State and trends of the carbon market 2006. World Bank, Washington DC.

Yossi, H., B. Kaya, C.O. Traore, A. Niang, I. Butare, V. Levasseur, and D. Sanogo. 2005. Les
haies vives au Sahel: Etat des connaissances et reccommandations pour la recherche et le
developpement. ICRAF Programme Regional Sahel, Bamako, Mali.

Zibilske, L.M. and J.M. Bradford. 2007. Soil aggregation, aggregate carbon and nitrogen, and
moisture retention induced by conservation tillage. Soil Sci. Soc. Am. J. 71:793-802.

Zinn, Y.L., R. Lal, J.M. Bigham, and D.V.S. Resck. 2007. Edaphic controls on soil organic
carbon retention in the Brazilian Cerrado: Texture and mineralogy. Soil Sci. Soc. Am. J.
71: 1204-1214.









BIOGRAPHICAL SKETCH

Asako Takimoto was born in Ashiya, Hyogo, Japan in 1975. She graduated from the

Kyoto University in Kyoto, Japan, in 1998 where she earned her B.Sc. in Agricultural Science

and Forestry. In 1997, she received the Rotary Intemnational Scholarship and entered the

graduate program of the Nicholas School of the Environment, Duke University, where she

finished her Master of Forestry. After graduation in 1999, she returned to Japan and worked for

the Japan International Cooperation Agency. In 2003, she received the Fulbright Scholarship to

start her PhD study in agroforestry at University of Florida.





PAGE 1

1 CARBON SEQUESTRATION POTENTIAL OF AGROFORESTRY SYSTEMS IN THE WEST AFRICAN SAHEL : AN ASSESSMENT OF BIOLOGICAL AND SOCIOECONOMIC FEASIBILITY By ASAKO TAKIMOTO A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSI TY OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY UNIVERSITY OF FLORIDA 2007

PAGE 2

2 2007 Asako Takimoto

PAGE 3

3 To my parents and grandmother

PAGE 4

4 ACKNOWLEDGMENTS I am indebted and grateful for many individu als and organizations who contributed to this study and my doctoral program. I thank my chair, Dr. P.K. Nair, for his dedication and guidance throughout this process, and my committee, Dr. Nick Comerford, Dr. Janaki Alavalapati, Dr. Tim Martin, Dr. Ted Sc huur, and Dr. V.D. Nair, for their support and encouragement. I recognize and express my sincere gratitude to the individuals and their institutions that supported me during my doctoral studies: the School of Forest Resources and Conservation (Cherie Ari as, Sherry Tucker, Dr. Tim White), University of Florida International Center (Debby Anderson), the Center for Tropical Conservation and Development of UF, the World Agroforestry Centre (especially Dr. Bocary Kaya), the Fulbright Program, and the Joint Jap an/World Bank Graduate Scholarship Program (JJ/WBGSP). At the fieldwork in S gou, Mali, I received tremendous support and cooperation from the farmers, field officers, and other collaborators It was one of the most challenging times of my life, and I c ould not go through without them. Thank you to Nicole Demers, Bayo Mounkoro, Keita, Samake, and other officers in ICRAF S gou office, Kayo Sakaguchi, Takako Uchida, Mr. Kiyoshi Sakai, and all the farmers in S gou who let me use their fields for data colle ctions and participated the survey. I have greatly benefited from my friendship with colleagues in the agroforestry lab at UF. I thank Solomon Haile, Alyson Dagang, Julie Clingerman, Sam Allen, Eddie Ellis, Brian Becker, Joyce Leptu, David Howlett, Wend y Francesconi, Subrajit Saha, Shinjiro Sato, and Masaaki Yamada, for the discussions and supports. To my precious friends who have been an integral part of the many years of this process, thank you Gogce Kayihan, Brian Daley, Elli Sugita, Mike Bannister, Jason and Karen Hupp,

PAGE 5

5 Charlotte Skov, Chrysa Mitraki, Rania Habib, Maitreyi Mandal, Trina Hofreiter, Troy Thomas, and my fianc Nick Georgelis. Last but not least, I express my most profound gratitude to my mother Ayuko Takimoto, whose endless love and confidence in me made me come this far.

PAGE 6

6 TABLE OF CONTENTS page ACKNOWLEDGMENTS ................................ ................................ ................................ ............... 4 LIST OF TABLES ................................ ................................ ................................ ......................... 10 LIST OF FIGURES ................................ ................................ ................................ ....................... 11 ABSTRACT ................................ ................................ ................................ ................................ ... 13 CHAPTER 1 INTRODUCTION ................................ ................................ ................................ .................. 15 Background ................................ ................................ ................................ ............................. 15 Rationale and Significance ................................ ................................ ................................ ..... 16 Research Questions and Objectives ................................ ................................ ........................ 17 Dissertation Overview ................................ ................................ ................................ ............ 18 2 THE WEST AFRICAN SAHEL: GENERAL LAND USE AND AGROFORESTRY ........ 20 Description of the Region ................................ ................................ ................................ ....... 20 Climate ................................ ................................ ................................ ............................ 20 Vegetation ................................ ................................ ................................ ........................ 21 Soil ................................ ................................ ................................ ................................ ... 22 Traditional Farming Systems and Agroforestry in the WAS ................................ ................. 24 Traditional Agroforestry Practices ................................ ................................ .................. 25 Bush fallow/shifting cultivation ................................ ................................ ............... 25 Parkland system ................................ ................................ ................................ ........ 25 Improved Agroforestry Practices ................................ ................................ .................... 26 3 LITERATURE REVIEW: CARBON SEQUESTRATION POTENTIAL OF AGROFORESTRY SYSTEMS IN THE WEST AFRICAN SAHEL (WAS) ....................... 36 Overview ................................ ................................ ................................ ................................ 36 C Sequestration as a Climate Change Mitigation Activity ................................ ............. 36 Agroforestry for C sequestration ................................ ................................ ..................... 37 Methodologies for C Sequestration Measurements ................................ ................................ 39 Direct On site Measurement ................................ ................................ ............................ 39 Inventory ................................ ................................ ................................ .................. 40 Conversion and e stimation ................................ ................................ ....................... 40 Indirect Remote Sensing Techniques ................................ ................................ .............. 42 Modeling ................................ ................................ ................................ .......................... 43 Default Values for Land/Activity Based Practices ................................ .......................... 44 Accounting Methods ................................ ................................ ................................ ............... 44

PAGE 7

7 Approaches to Assessing C Sequestration Performance ................................ ................. 45 Fluxes of C and flow summation ................................ ................................ ............. 45 Average changes in the stocks of C ................................ ................................ ......... 45 Cumulative C storage ................................ ................................ ............................... 46 Other accounting methods ................................ ................................ ........................ 46 Technical Problems and Uncertainties ................................ ................................ ............ 47 Biomass C Sequestration ................................ ................................ ................................ ........ 48 Studies in Various Ecoregions ................................ ................................ ......................... 48 Studies in West Africa ................................ ................................ ................................ ..... 48 Soil C Sequestration ................................ ................................ ................................ ............... 50 Studies of Soil C Stock and Dynamics ................................ ................................ ............ 50 Soil C in the WAS ................................ ................................ ................................ ........... 52 Socioeconomic Implications ................................ ................................ ................................ ... 54 Economic Models ................................ ................................ ................................ ............ 54 National/global scale ................................ ................................ ................................ 54 Micro/site s pecific scale ................................ ................................ ........................... 55 Feasibility in West Africa ................................ ................................ ................................ 56 4 ABOVEGROUND AND BELOWGROUND CARBON STOCKS IN TRADITIONAL AND IMPROVED AGROFORESTRY SYSTEMS IN MALI, WEST AFRICA ................. 61 Introducti on ................................ ................................ ................................ ............................. 61 Materials and Methods ................................ ................................ ................................ ........... 62 Study Area ................................ ................................ ................................ ....................... 62 Republic of Mali ................................ ................................ ................................ ....... 63 Sgou region ................................ ................................ ................................ ............. 63 Select ed Land use Systems for Field Data Collection ................................ .................... 64 Parkland systems ................................ ................................ ................................ ...... 64 Improved a groforestry s ystems ................................ ................................ ................ 65 Abandoned (degraded ) land ................................ ................................ ..................... 66 Research Design ................................ ................................ ................................ .............. 66 Data Collection ................................ ................................ ................................ ................ 67 Biomass measurement ................................ ................................ .............................. 67 Soil sampling ................................ ................................ ................................ ............ 68 C arbon Stock Estimation ................................ ................................ ................................ 68 Biomass C stock ................................ ................................ ................................ ....... 69 Soil C stock ................................ ................................ ................................ .............. 71 Statistical Analysis ................................ ................................ ................................ .......... 71 Results ................................ ................................ ................................ ................................ ..... 72 C Stock in Biomass and Soil ................................ ................................ ........................... 72 Total C Stock ................................ ................................ ................................ ................... 72 Relationship between Biomass C and Soil C ................................ ................................ .. 73 Discussion ................................ ................................ ................................ ............................... 73

PAGE 8

8 5 SOIL CARBON SEQUESTRATION IN DIFFERENT PARTICLE SIZE FRACTIONS AT VARYING DEPTHS UNDER AGROFORESTRY SYSTEMS IN MALI .................... 83 Introduction ................................ ................................ ................................ ............................. 83 Research Questions ................................ ................................ ................................ ................. 85 Materials and Methods ................................ ................................ ................................ ........... 85 Research Design ................................ ................................ ................................ .............. 86 Soil Preparation and Analyses ................................ ................................ ......................... 87 Soil fractionation ................................ ................................ ................................ ...... 87 C isotopic ratio ( 13 C / 12 C) measurement ................................ ................................ ... 88 Statistical Analysis ................................ ................................ ................................ .......... 89 Results ................................ ................................ ................................ ................................ ..... 90 Soil Characteristics ................................ ................................ ................................ .......... 90 Whole Soil C ................................ ................................ ................................ ................... 91 C in Soil Fractions ................................ ................................ ................................ ........... 92 Isotope Analysis of Whole Soil C ................................ ................................ ................... 93 Isotope Analysis of C in Soil Fractions ................................ ................................ ........... 94 Relationship s of Data Sets ................................ ................................ ............................... 94 Discussion ................................ ................................ ................................ ............................... 95 6 SOCIOECONOMIC ANALYSIS OF THE CARBON SEQUESTRATION POTENTIAL OF I MPROVED AGROFORESTRY SYSTEMS IN MALI, WEST AFRICA ................................ ................................ ................................ ................................ 117 Introduction ................................ ................................ ................................ ........................... 117 Research Question s ................................ ................................ ................................ ............... 118 Materials and Methods ................................ ................................ ................................ ......... 119 Social Survey of Fodder Bank Farmers ................................ ................................ ......... 119 Local Market Survey ................................ ................................ ................................ ..... 120 Types of Analysis ................................ ................................ ................................ .......... 121 Cost benefit analysis (CBA) ................................ ................................ .................. 121 Sensitivity analy sis ................................ ................................ ................................ 127 Risk modeling ................................ ................................ ................................ ........ 128 Results ................................ ................................ ................................ ................................ ... 129 Demographic Characteristic s of Target Population ................................ ...................... 129 Cost Benefit Analysis: Best Guess Scenario of the Live Fence and the Fodder Bank 130 Sensitivity Anal ysis ................................ ................................ ................................ ....... 132 Risk Modeling and Simulation ................................ ................................ ...................... 132 Discussion ................................ ................................ ................................ ............................. 134 7 SUMMA RY AND CONCLUSIONS ................................ ................................ ................... 148 C Sequestration P otential ................................ ................................ ................................ ...... 148 Biophysical Potential ................................ ................................ ................................ ..... 148 Socioecomic Potential ................................ ................................ ................................ ... 150 C onclusions ................................ ................................ ................................ ................... 152

PAGE 9

9 Agroforestry Adoption for C sequestration in the Study Region ................................ ......... 152 Limiting Factors ................................ ................................ ................................ ............ 152 Favorable Factors ................................ ................................ ................................ .......... 153 Implications for Agrofores try ................................ ................................ ............................... 154 Future Research ................................ ................................ ................................ .................... 155 APPENDIX A SOCIAL SURVEY QUESTIONNAIRE FOR FODDER BANK OWNERS ...................... 156 B COST BENEFIT ANALYSIS (CASH FLOW) OF LIVE FENCE ................................ ..... 163 C COST BENEFIT ANALYSIS (CASH FLOW) OF FODDER BANK ................................ 167 LIST OF REFERENCES ................................ ................................ ................................ ............. 171 BIOGRAPHICAL SKETCH ................................ ................................ ................................ ....... 184

PAGE 10

10 LIST OF TABLES Table page 2 1 Common tree and shrub species found throughout the West African Sahel ..................... 29 2 2 Main productive functions of agroforestry parklands ................................ ........................ 31 3 1 Summary of various biomass C measurement approaches used commonly in C sequestration studies ................................ ................................ ................................ .......... 59 3 2 Aboveground time averaged C stock in different ecosystems and agroforestry prac tices ................................ ................................ ................................ ............................. 6 0 4 1 Characteristics of the villages where the experimental plots were set up in Sgou region, Mali ................................ ................................ ................................ ........................ 76 4 2 Characteristi cs of the experimental plots (three plots average) for five selected land use system s in Sgou region, Mali ................................ ................................ ..................... 76 4 3 Estimated biomass C (above and below ground) stock values of each plot and th ree plots average of five selected land use systems ................................ ................................ 77 4 4 Total C stock (biomass C + soil C of different depth) of five selected land use systems. ................................ ................................ ................................ .............................. 77 5 1 Soil profile characteristics for plots of the five land use systems used in the study at Sgou Region, Mali ................................ ................................ ................................ ......... 101 5 2 13 C value s of whole soil and three fraction sizes from five studied land use systems, at Sgou Region, Mali ................................ ................................ ................................ ...... 102 6 1 Demographic characteristics of the target population in Sgou, Mali ............................. 138 6 2 N et Present Value (N PV ) B enefit Cost Ratio (B CR ) and Internal Rate of Return ( IRR ) of the live fence and the fodder bank projects in the three different scenarios (without C sale, with C sa le by the ideal accounting method, and with C sale by the tonne year accounting method) in Sgou, Mali ................................ ............................... 138 6 3 NPV sensitivity of the live fence project and the fodder bank project to the chan ge of an input variable in Sgou, Mali ................................ ................................ ...................... 139

PAGE 11

11 LIST OF FIGURES Figure page 2-1 Map of West Africa with ecological zones and isohyetal lines .........................................32 2-2 Standardized annual Sahel rainfall (June to October) from 1898 to 2004 .........................33 2-3 Seasonal landscape contrast of the WAS ........................................................................... 33 2-4 Distribution of soil orders (USDA soil taxonomy) in West Africa ...................................34 2-5 Parkland system in Sgou, Mali.........................................................................................35 2-6 Allowing the cattle to roam freely on the landscape during the dry season ......................35 4-1 Location map of A: Mali; B: Mali showing its land-locked nature: C: Map of Sgou region ......................................................................................................................78 4-2 Faidherbia albida parkland in Togo village ......................................................................79 4-3 Vitellaria paradoxa parkland in Dakala village.................................................................79 4-4 Live fence system in Dougoukouna village .......................................................................80 4-5 Fodder bank in Dakala village ...........................................................................................80 4-6 Abandoned land just outside of Diamaribougou village....................................................81 4-7 Aboveground and belowground C stock per ha of five selected land-use systems ...........82 5-1 Soil sampling, Sgou, Mali ..............................................................................................103 5-2 Soil pits dug in plots of the five land-use systems studied in Sgou region of Mali .......104 5-3 Model of soil organic matter dynamics ...........................................................................105 5-4 Whole soil C content of three depth classes (0 10 cm, 10 40 cm, and 40 100 cm) in different land-use systems in Sgou, Mali ............................................................106 5-5 Soil C content of three particle size fractions in three depth classes (0 10 cm, 10 40 cm, and 40 100 cm) under five land-use systems in Sgou, Mali ...........................107 5-6 Whole soil C, divided into C3 plants (trees)origin and C4 plants (crops)-origin, in different soil layers up to 100 cm depth, in five land-use systems in Sgou, Mali .........108 5-7 Soil C in three fraction sizes divided into C3 plants-origin and C4 plants-origin in different soil particle-size fractions under different land-use systems in Sgou, Mali ....109

PAGE 12

12 5 8 Linear regression between silt + clay content of soil and whole soil C content in three depth classes across five land use systems in Sgou region of Mali ............................... 112 5 9 Linear regression betwe en silt and clay content of soil and C in soil particles of <53 depth classes across five land use systems in Sgou, Mali ................... 114 5 10 Linear regression between C derived from C3 plants and C in the large soil particles ( 250 2000 at 0 10 cm soil depth across five land use systems of Sgou region, Mali. ................................ ................................ ................................ ..................... 116 6 1 Social survey with farmers in Sgou, Mali ................................ ................................ ...... 140 6 2 Simulated NPV probability distribution of the live fence project (without C sale) ......... 140 6 3 Simulated net benefit (total costs total revenues in each year) of the live fence project (wit hout C sale) ................................ ................................ ................................ .... 141 6 4 Simulated NPV probability distribution of the live fence project (with C sale by the ideal accounting method) ................................ ................................ ................................ 142 6 5 Regression sensitivity analysis for NPV of the live fence project (with C sale by the ideal accounting method) ................................ ................................ ................................ 143 6 6 Simulated NPV probability distribution of the fodder bank proj ect (without C sale) ..... 144 6 7 Simulated net benefit (total costs total revenues in each year) of the fodder bank project (without C sale) ................................ ................................ ................................ .... 145 6 8 Simulated NPV probability distribution of the fodder bank project (with C sale by the ideal accounting method) ................................ ................................ ........................... 146 6 9 Regression sensitivity analysis for NPV of the fodder bank project (with C sale by the ideal accounting method) ................................ ................................ ........................... 147

PAGE 13

13 Abstract of Dissertation Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degr ee of Doctor of Philosophy CARBON SEQUESTRATION POTENTIAL OF AGROFORESTRY SYSTEMS IN THE WEST AFRICAN SAHEL : AN ASSESSMENT OF BIOLOGICA L AND SOCIOECONOMIC FEASIBILITY By Asako Takimoto December 2007 Chair: P. K. Ramachandran Nair Major: Forest Resource s and Conservation In recent years, carbon (C) sequestration potential of agroforestry systems has attracted attention an option for mitigating green house gasses. Although the possible benefits of agroforestry in carbon ( C) sequestration have been conceptually discussed, field measurements to validate these concepts have not been undertaken to any significant extent. I n addition to the traditional agroforestry systems, i mproved practic es and technologies are now being expanded into the dr y regions such as the West African Sahel for perceived benefits such as arresting desertification, reducing water and wind erosion hazards, and improving biodiversity Thus, it is imperative to investi gate C sequestration potential of agroforestry practices in these regions. M y research hypothesizes that the tree based systems will retain more C in the systems both above and below ground than tree less land use systems. By joining the C credit market the landowners c ould sell the C sequestered in their agroforestry systems. My research consisted of th ree components. The first examined C (biomass + soil) stored in five target land use systems: two traditional parkland system s involving Faidherbia a lbida and Vitell a ria paradoxa trees as the dominant species, two improved agroforestry systems (live

PAGE 14

14 fence and fodder bank ) and land that is out of cultivation ( abandoned or degraded) in the S gou Region, Mali. The second component involved a study of so il C dynamics of these systems : the extent of soil C storage/accumulation by trees and stability of the C accumulated were investigated. In the third component, socioeconomic feasibility of the agroforestry systems was examined in the context of C sequest ration and C credit sale. Research results show that t he selected agroforestry systems have the potential for sequestering more C both above and belowground than in tree less land use systems, and that the trees tend to contribute to storing more stable C in the soil. A mong the selected land use systems, live fence and fodder bank are more suitable to start as agroforestry C sequestration projects than the traditional parkland systems for smallholder farmers in the studied region Between the two impro ved systems, live fence has higher C sequestering potential per unit area and is economically less risky than fodder bank s. Adopting these systems on cultivated land rather than on abandoned land is likely to sequester more C and be more profitable. Sinc e parklands are traditionally practiced they are not likely to qualify as a new C sequestration project soon Nevertheless, F. albida trees are more attractive than V. paradoxa trees in terms of C sequestration potential. These results can be used for development of recommendations and guidelines on selection of land use systems and species and their management, for planning successful C sequestration projects in the West African Sahel.

PAGE 15

15 CHAPTER 1 INTRODUCTION Background It is widely accepted that c urrent global climate change or global warming is the most serious environmental issue affecting human lives. G lobal warming refers to the increase in the average temperature of the Earth's near surface air and oceans in recent decades and its projected continuation I t is brought about primarily by the increase in atmospheric concentrations of the so called greenhouse gases (GHGs). GHGs are components of atmosphere contributing to the green house effect, the process in which the emission of infrared radiation by the atmosphere warms a planet s surface. The Intergovernmental Panel on Climate Change (IPCC) established by the U nited N ations (UN) to evaluate the risk of climate change concludes in its most recent report that most of the observed increa se in globally averaged temperatures since the mid 20th century is very likely due to the observed increase in anthropogenic greenhouse gas concentrations (IPCC 2007). The Kyoto Protocol to the United Nations Framework Convention on Climate Change (UNFCC ) is the first and so far the largest international agreement to stabilize GHG concentrations Carbon dioxide (CO 2 ) is a major GHG and its concentration build up is accelerated by human activities such as burning of fossil fuels and deforestation. One of t he approaches to reducing CO 2 concentration in the atmosphere called biomass carbon (C) sequestration is to it in forest and forest soils by trees and other plants through photosynthesis. This concept became widely known because the Kyoto Protoc ol has an approach called Land Use, Land Use Change and Forestry (LULUCF), which allows the use of C sequestration through afforestation and reforestation as a form of GHG offset activities. The Marrakesh Accords in 2001 determined more detailed rules of LULUCF and added forest management, crop management,

PAGE 16

16 grazing land management, and revegetation as LULUCF activities. This enables agroforestry to be an activity of C sequestration under the Kyoto Protocol, and since then, C sequestration potential of agro forestry systems has attracted attention from both industrialized and developing countries (Albrecht and Kandji 2003 ; Makundi and Sathaye 2004; Sharrow and Ismail 2004 ). This became particularly relevant because of an arrangement called Clean Development Mechanism (CDM) under the Kyoto Protocol which allows industrialized countries with a GHGs reduction commitment to invest in mitigation projects in developing countries as an alternative to what is generally more costly in their own countries. Since a gro forestry is mostly practiced by subsist ence farmers in developing countries there is an attractive opportunity for those farmers to benefit economically from agroforestry if the C sequestered through agroforestry activities are sold to developed countr ies ; it will be an environmental benefit to the global community at large as well. Rationale and Significance The IPCC Report (2000) estimates that 630 million ha of unproductive croplands and grasslands could be converted to agroforestry worldwide, with the potential to sequester 0.391 Pg of C (1 Pg = petagram = 10 15 g = 1 billion ton ) per year by 2010 and 0.586 Pg C per year by 2040. The credibility of conceptual models and theoretical foundations of the possible benefits of agroforestry in C sequestration have been suggested: agroforestry ha s C storage potential in its multiple plant species and soil, high applicability in agricultural land, and indirect effects such as decreasing pressure on natural forest or soil erosion (Nair and Nair 200 3; Lal 2004 a ; M ontagnini and Nair 2004 ) Field measurements to validate these concepts and hypotheses, however, have not been undertaken to a significant extent. Some studies of specific agroforestry practices proved the potential of C sequestration and its benefits, s uch as the Indonesian homegarden systems ( Roshetko et al. 2002 ; Schroth et al. 2002 ). B ut very few such studies have been

PAGE 17

17 reported regarding C sequestration potential of agroforestry systems in semiarid and arid region s In addition to already existing i ndigenous agroforestry systems, improved practices and technologies are now being expanded into these dr y regions for perceived benefits such as arresting desertification, reducing water and wind erosion hazards, and improving biodiversity ( Droppelmann et al. 2000 ; Gordon et al. 2003 ). In this scenario, it is imperative that C sequestration potential of agroforestry practices in these regions is investigated. Considering that the ecological production potential of these dry ecosystems is inherently low co mpared to that of systems can contribute if at a l l to C sequestration in such regions is in itself an important issue. This study was conducted in Mali, s ituated in the West African Sahel (WAS) one of the largest semiarid regions of the world Considering the large extent of area of the region (approx. 5.4 million km 2 ), results of studies of this nature are likely to have wide applicability; yet, such stu dies have been rare possibly because of the relative backwardness of the region in terms of economic development and therefore research facilities and infrastructure. Needless to say, such studies are important because of their relevance in the context o f C credit sale under CDM. The WAS is one of the most environmentally vulnerable and poorest area s in the world. If the majority of the people who are subsisten ce farmers can receive even small amount s of C payments through their agroforestry practices, it would be a substantial contribution to their economic welfare and the overall development of the region. Thus, an analysis of the C sequestration potential of v arious agroforestry practices (traditional and newly introduced ) in the region is timely. R esearch Questions and Objectives To address the issues discussed above four research questions are raised :

PAGE 18

18 1. H ow much C is stored in different agroforestry systems aboveground and belowground ? 2. How do trees contribut e to C storage in the soil, and how labil e is th is C? 3. W hat is the overall relative attractiveness of each of the selected agroforestry systems considering its C sequestration potential in the context of its biological potential, economic profitability, and social acceptability? 4. If carbon credit market s were introduced under CDM, would adoption of agroforestry provide more profits to land owners? If yes, how much? Dissertation Overview This dissertation is presented in seven chapters. Following this introductory chapter (Chapter 1), Chapter 2 des cribes the natural environment of the WAS, the study region, in terms of its climate, vegetation, soil taxonomy etc T land use systems in general and agroforestry systems in particular, are also described. Chapter 3 presents the literature re view summarizing the method s used to estimate the C sequestration potential in agroforestry systems, as well as the current stat e of knowledge on C sequestration potential in the WAS The possibilities and limit ation s in the region, current research tren d s and future research needs are also included Chapter 4 pr esents the result s of C stock measurement s and a comparison of five selected land use systems (four agroforestry systems and one degraded land) in the S gou region, Mali. Methodologies and resu lts of measuring both biomass C and soil C are presented. Total C storage of each system is compared and discussed. Chapter 5 examines soil C measurements in more detail based on analyses of soil samples drawn from different depths from each of the five selected land use types, and discusses influence of trees and land management on soil C sequestration and stability of soil C Chapter 6 presents a socioeconomic feasibility analysis of two improved agroforestry systems in the study region ; results of cos t / benefit and sensitivity analysis are presented both with and without C sale scenarios A risk assessment using a simulation program gives insight in to how introducing agroforestry in the study region might

PAGE 19

19 economically affect local household s Chapter 7 gives a synthesis, conclusions and recommendations for future research and development efforts.

PAGE 20

20 CHAPTER 2 THE WEST AFRICAN SAHEL: GENERAL LAND USE AND AGROFORESTRY Description of the Region The Sahel is a transition zone between the hyper arid Sahar a to the north and the more humid savannas and woodlands to the south. The west part of the Sahel region (West African Sahel: WAS) includes nine countries wh o are members of the Interstate Committee for D rought C ontrol in Sahel (CILSS); these are Burkina Faso, Cape Verde, Gambia, Guinea Bissau, Mali, Mauritania Niger, Senegal, and Chad. The area covers about 5.4 million km 2 with over 500 million inhabitants. Its vegetation mostly consists of bushes, herbs and small trees and does not offer year round harvests. The main characteristics of the WAS include : 1) irregular and little predictable rainfall; 2) p redominance of a griculture and animal husbandry: more than half of the inhabitants are farmers and agriculture contributes more than 40 % to the Gros s Domestic Product (GDP) ; and 3) h igh demographic growth (around 3 %) and high urban growth (around 7 %) (USGS 2007 ) Climate The isohyet lines of the region are almost parallel to the latitude and divide the WAS into three sub groups: Sahelo S aharan, Sa helian, and Sudano Sahelian zones ( Figure 2 1 ). R ainfall in the region varies from 200 to 2500 mm per year with the vast majority of the region receiving between 350 to 800 mm, and is characterized by year to year and decadal tim e scale variability; there were extended wet periods in 1905 09 and 1950 69, and extended dry periods in 1910 14 and 1968 1997 ( Figure 2 2 ). The most recent drought that began in late 1960s caused the severe famine in th e 1970s Since 1997, the rainfall recovered somewhat, but the annual rainfall of the recent years was still below the pre 1970 level (= ~ 540mm), except 1994, 1999, and 2003 (Dai et al. 2004). Although the length of the rainy season varies with latitude a nd local

PAGE 21

21 conditions, it is generally restricted to a period of three to five months from April to October During this period, there is an average of 24 rainfall events, 10 to 12 of which occur in August. Rainstorms are rarely prolonged, usually lasting no more than one or two hours. Rainfall intensities range from 5 to more than 50 mm per event (Gritzner 1988). The rainy season is followed by an extended dry season where the vegetation cover changes drastically ( Figure 2 3 ) The monthly mean temperature of the region is 26 27 C with a monthly mean maximum of 34 36 C and monthly mean minimum 21 23 C Temperature abnormalities are relatively low for the area as a whole (+0.7 C to 0.6 C ), but may be greater in indivi dual places (Littmann 1991). Vegetation The WAS contains three generalized phytogeographical divisions corresponding to the climate zones ( Figure 2 1 ) : (i) the northerly Sahelo Saharan zone, or grass steppe, between the 100 and 2 00mm isohyets; (ii) the Sahel proper, or tree steppe, between the 200 and 400 mm isohyets; (iii) the southerly Sudano Sahelian borderlands, or shrub savannah, extending to the 8 00 mm isohyets Savanna plants are renowned for their well developed root syst ems, penetrating deeply into the soil. Herbaceous plants, mostly perennials, always have an extensive root system, often forming a close mat of rootlets in the upper layers of the soil. Most of the roots are located within the upper 30 cm of soil (Bourli ere 1983). Grasses in the steppe grow in the very short growing season (60 90 days) with narrow leaves in circles or basal rosettes. One of the most common grass species throughout the WAS is Cenchrus biflorus This prickly, short lived grass is the fo od of choice for the herds that graze throughout the Sahel. Mature grass has sharp bristles; but ensiling softens them, so that it can also be used as silage (FAO 1991). Other common grass species in steppe such as Schoenefeldia gracilis Elionorus elega ns Borreria spp.,

PAGE 22

22 are also used as fodders In the south, where the savannah replaces the steppe the tall perennial grasse s such as Andropogon gayanus as well as annual grasses with long cycles such as Pennisetum pedicellatum Andropogon pseudapricus a nd Diheteropogon hagerupii are are common. These grasses grow rapidly up to 2.5 m in height, but natural bush fires control the reserves. Some of the se species are introduced as ornamental or fodder species in the US ( Pennisetum pedicellatum, called Kyasu ma grass) and Australia ( Andoropogon gayanus ), and because of their rigorous spread, they are invasive species. Although most tree and shrub species are found both in steppe and savannah ( Table 2 1), t he woody vegetation beco me more and more divers e and dense as one goes south The trees in the WAS are usually low branched and may ramify from their base. Crowns are generally very wide, and much more developed than the bole. The thickness of the bark has been interpreted as affording protection against repeated bush fires. Spines and thorns on branches are also frequent, which prevent reducing water loss through evaporation. It may afford some protection against browsing by large mammals, but does not prevent foliage browsi ng. Soil D etailed information on the soil resource base of the WAS is inadequate for most research purposes. In most countries, farm level information and detailed soil maps are non existent. Still, in 1977, Food and Agricultural Organization (FAO) of United Nations (UN) and UN Educational and Scientific Organization (UNESCO) formed soil map of Africa, by aggregating specific soil mapping units to form soil regions that corresponded roughly to Africa's major ecological regions. Natural Resources Conser vation Service (NRCS) of the United States Department of Agriculture (USDA) had a pedon database with more than 400 pedons from Africa. With published national soil survey reports, NRCS translated the legend of the UN Soil Map of the Africa into Soil Taxo nomy Map ( Figure 2 4 )

PAGE 23

23 The northern WAS adjacent to rocky Sahara desert to the north, is dominated by Entisols and in some part s by Aridisols. Most of the Entisols in the area ha ve an aridic soil moisture regime and are formed on sandy or loamy deposits. Psamments are present as fingerings of Sahara in zones with ustic or udic soil moisture regimes (Eswaran et al.1996) Vertisols occur locally in some places along the rift valley of the Niger River and around Lake Chad. At lo wer latitudes within the WAS Alfisols are extensive ly spread In general, the wind blown sand from the Sahara desert has buried many of the former Oxisols and Alfisols/Ultisols ; thus, soils in the WAS characteristically have very sandy top soils and a lo w activity clay subsoil In terms of soil quality for agricultural use, soil moisture stress is perhaps the overriding constraint in much of the WAS It is not only because of the low and erratic precipitation but also of the ability of the soil to hold and release water. A large part of northern WAS (Entisols and Aridisols) has low available water holding capacities ( AWHC ) <25 mm And southern part of the WAS is made up of soils with medium AWHC (24 100 mm) mainly Alfisols and Ultisols. Salinity and alkalinity are other problem s affecting agriculture. The extremely acid soils, which are mainly the acid sulphate soils occupy area s around the Niger delta. Some part s of Alfisols (close to southern Ultisols) have acid surface and subsurface horizon s, which, coupled with the moisture stress conditions, makes these soils extremely difficult to manage for productive use under low input conditions. The annual additions of dust from the Sahara brought by the Harmattan winds ( a dry and dusty wind blowing south off the Sahara into the Gulf of Guinea during the dry season ) raise the pH and base saturation of the surface horizons; although the changes are less acute than the east ern part of the Sahel where subsoil acidity is a problem (Tiessen et al. 1991)

PAGE 24

24 In addition to the moisture stress and alkalinity /acidity, there are several other soil related constraints common in the WAS contributing to low productivity These include: 1 ) inherently low nutrient storage capacities (cation exchange capacities, CE Cs) due to the low activity kaolintic clay minerals present or the overall low clay contents, 2) low equilibrium soil organic matter levels due to intensive cultivation without adequate biomass return and high surface soil temperatures, 3) the presence of large amounts of free aluminum and iron oxides which reduces the availability of phosphate to plants ( Gritzner 1988 ; de Alwis 1996) Traditional Farming Systems and Agroforestry in the WAS The traditional farming systems in the WAS are rain fed low externa l input operations Farmers use traditional agricultural methods: use of domestic wastes, farmyard manure, crop rotations, and the incorporation of trees on farmlands There is a considerable variety of crops grown in Sahelian agricultural systems, inclu ding: grains, such as millet ( Pennisetum glaucum ) sorghum ( Sorghum bicolor ) fonio ( Digitaria exilis ) rice ( Oryza glaberrima and Oryza sative ) sesame ( Sesamum indicum ), and safflower ( Carthamus tinctorius ); garden crops, such as eggplant ( Solanum melong ena ), broad beans ( Vicia faba ), okra ( Abelmoschus esculentus ), carrots ( Daucus carota ), chick peas ( Cicer arietinum ), pigeon peas ( Cajanus cajan ), cowpeas ( Vigna unguiculata ), ground nut ( Arachis hypogaea ), yams ( Dioscorea spp.), calabash ( Lagenaria sicera ria ), leeks ( Allium ampeloprasum ), melons ( Cucurbitaceae Family ), etc. Cultivated tree crops includ ing dates ( Phoenix dactylifera ), figs ( Ficus spp .), lemons ( Citrus spp .), mulberries ( Morus spp .), and various gums ( Acacia spp .) are also common (Gritzner 1988 ; ICRISAT 2007 ).

PAGE 25

25 Traditional Agroforestry Practices Bush fallow/shifting cultivation Shifting cultivation refers to the land management practice where a period of cropping (cropping phase) is alternated with a period in which the soil is rested (fall ow phase). This system has been traditionally practiced in the WAS, as well as other tropical and semi tropical regions of the world (Nair 1993). First, t he clearing is done using axes or machetes and only herbaceous plants, saplings and undergrowth are cut. When the cut material is dried and burned the cleared area is planted with crops like yams, sorghum, millet, maize ( Zea mays ) and cassava ( Manihot esculenta ) The land is cultivated for one to four years after which it returns to fallow. The regr owth of natural vegetation rejuvenates the soil through nutrient cycling, addition of litter and suppression of weeds (Ferguson 1983) In general, the fallow phase is much longer than the cropping phase. However, recent rapid population growth in the WA S countries ( from 2.5 to 3.0 %) requires additional cultivated land, often at the expense of fallow and pastureland. Over the years, the fallows became greatly reduced both in area and duration, putting in jeopardy the return of vegetative cover for the b uild up of soil fertility (Kaya 2000). Parkland system Another traditional land use system, sometimes overlapped with tree combined fallow system, is Parklands are generally understood as landscapes in which mature trees occur scattered in cultivated or recently fallowed fields ( Boffa 1999). Farmers grow crops around and underneath of the trees ( Figure 2 5 ) These trees are selectively left or regenerated by farmers because of the variety of functions (mostly non timber use) such as food and medicine ( Table 2 2 ) Parkland trees can also contribute to temperature amelioration and to prevention of soil erosion (Jonsson et al 1999). Parklands occupy a v ast

PAGE 26

26 land area, representing a large part of the agricultural landscape under subsistence farming in the WAS and it is the predominant agroforestry system. For example, the agroforestry parkland system occupies about 90 % of the agricultural land area in M ali (Ciss, 1995) and in Burkina Faso, parklands are found throughout settled zones where agriculture is practiced. Parklands are most often characterized by the dominance of one or a few tree species. Species composition is generally more diverse and variable, however, in areas located farther away from villages and only occasionally cultivated. Common species in the WAS are Acacia senegal, Adansonia digiata Anogeissus leiocarpus Balanites aegyptiaca Bombax costatum Borassus aethiopum Ceiba penta ndra Diospyros mespiliformis Elaeis guineensis Faidherbia albida Hyphaene thebaica Lannea microcarpa Parkia biglobosa Sclerocarya birrea Tamarindus indica Vitellaria paradoxa Vitex doniana and Ziziphus mauritiana ( Tab le 2 2 ) ( Boffa 1999) Improved Agroforestry Practice s The expansion of rain fed agriculture results in soil erosion through the removal of vegetative cover and physical disturbance. Wind and water erosion is extensive in many parts of the WAS Practica lly every country of Africa is prone to desertification, but the Sahelian countries at the southern fringe of the Sahara are particularly vulnerable (Reich et al. 2001) Soil nutrients are removed through crops, erosion, and leaching by rainfall, without replenishment by additions or regeneration under natural fallow. Inappropriate tillage and cultural practice reduce soil infiltration and retention of water, which further degrade the land (de Alwis 1996). Also, deforestation accelerate s the land degrada tion as trees and shrubs are cut to satisfy the construction, fuel, and fodder requirements of the cultivators and their livestock. In the WAS, farmers/pastoralists usually graze their animals in the open area without any control ( Figure 2 6 ). Degraded land spreads as these animals go further after eating the vegetation around the villages.

PAGE 27

27 Consequently, forest and woodland areas are rapidly declining by an estimated 1 .5 % per year on average of West African countries (FAO 2000 ). Prevention of land degradation by controlled grazing and afforestation is often discussed and tried sporadically throughout the WAS as project s financed mainly by international donor communities and agencies (Oba et al. 2000) However, local particip ation has often been short lived and management not successful because little consideration was given to why farmers keep brow sing the animals and do not protect or grow trees. Gradually, there has been a growing awareness that trees be regarded as an int egral component of an overall farming system and that a complex decision making environment with interdisciplinary interactions is needed (Boffa 1999) Adoption of improved land use systems such as agroforestry has been recommended and tried for r ehabilita tion of the degraded soils in various parts of the WAS (Roose et al. 1999 ; Lal 2004 a ). No till farming and improved fallow involving short rotation woody and/or other perennial species are increasingly studied Improved fallow rests land from cultivation as in natural fallow s but the vegetation comprises planted and managed species of leguminous trees, shrubs, and herbaceous cover crops. These vegetation and the roots are expected to reduce the soil nutrient loss or even to replenish them both chemical ly and physically, and to sustain crop production with shorter fallow period (Bationo et al. 2000; Kaya and Nair 2001) Farming systems that promote organic manure inputs (including litters from woody plants) and tree cropping systems have also been tried (Breman and Kessler 1997). As such, agroforestry practices involving incorporation of woody plants (both indigenous and exotic species) on cultivated land as intercrops fences, shelter belts, and/or fodder resources are recognized as a

PAGE 28

28 major technique t o ameliorate the s preading land degradation in the WAS. Detail s of the improved agroforestry practices being introduced in the study region are described in Chapter 4.

PAGE 29

29 Table 2 1. Common tree and shrub species found throughout the W est African Sahel S pecies Botanic Description Functional Use Acacia spp. One of the most common species in the Sahel. Deep root system with feathery leaves protecting barks from dry winds. Species often seen in the WAS are A. nilotica, A. tortillas, A. senegal, and A. seya l. The bark of most acacia produces tannins, which are used in tanning leather. A. senegal produces gum arabic, used in pharmaceuticals and adhesives. Fruits are sometimes consumed as condiments Adansonia digiata ( The b aobab tree ) This drought and fire resistant tree is found throughout the Sahel. With trunks that are often 10 15 m wide, it is one of the largest trees (in terms of trunk width); it grows up to 25 m high. In the dry season, the baobab is completely without leaves, and because of its dis tinguishable shape of branches that look like roots, it is called the "upside down" tree. The bark can be used for rope and cloth, and the trunk, when hollowed out, as a shelter. Fruits and leaves are food sources; especially leaves are very important vi tamin source for the local people. Balanites aegyptiaca Multi branched, spiny shrub or tree up to l0 m tall. Trunk is short and often branching from near the base. Branches are armed with stout yellow or green thorns up to 8 cm long. The fleshy pulp of both unripe and ripe fruits is edible and eaten dried or fresh. The fresh and dried leaves, fruits, and sprouts are all eaten by livestock. Combretum spp. The genus comprises about 370 species of trees and shrubs, 300 of which are native to tropical and southern Africa. C. glutinosum and C. micaranthum are common in the WAS. They are bushes branching from bases, 1 2m tall. The branches are quite strong, and are a useful material for building stools, beds, tool handles, etc. A tea made by steeping the le aves of C. micaranthum in boiling water is a traditional tonic drink and a decoction of the leaves is sometimes used as a medication for malaria. Faidherbia albida One of the fastest growing trees in the WAS. It is deciduous and has the remarkable phenol gy of leaves falling off in rainy season and coming back in the dry season. It can grow up to 30 m tall. Branching stems and an erect to roundish crown. It is a valuable fodder tree for game and domestic animals during dry season. The seeds can be boile d and eaten, but first the skin has to be removed. Also the pods may be dried and ground into flour, which is edible. Guiera senegalensis Perennial bush which is a major component of disturbed parts of bushland in the WAS. Also abundant on roadsides and fallowed lands. The woody part is fragile. Leaves and roots are traditionally used to treat different diseases, particularly malaria and intestinal disorders.

PAGE 30

30 Table 2 1. Continued. Species Botanic Description Functional Use Parkia biglobosa Large tree (up to 20 m) with a dense spreading crown, scaly and creviced grey brown bark. Rather slow growth, begins fruiting after 8 years. Trees are usually isolated. Bark, leaves, flowers and pods have innumerable medicinal and food utilizations, the pods, in pa rticular (husk and pulp) are staple food for humans, stored in households. Foliage contains saponins, but is nevertheless considered palatable to cattle, flowers are rich in nectar and beehives are often placed on the branches. Prosopis africana Small to large leguminous spiny trees (4 20 m), with an open canopy and drooping foliage. It thrives in arid soil and is resistant to droughts, on occasion developing extremely deep root systems. The fruit of the tree is used as fodder, while the seeds are ferme nted to make a protein rich condiment. The products from the hard wood, such as some wooden farm implements, kitchen utensils, and planks for construction, are extensively traded. The tree is a good source of firewood and charcoal. The secondary roots are used as medicine. Vitellaria paradoxa Occurs in a wide latitudinal belt between 5 and 15 N from Senegal to the Central African Republic. The size of the mature tree varies from 7 25 m. The bole is short, 3 4 m, sometimes up to 8 m with diameter le ss than 1 m and with thick bark that protects old trees from bush fires The main product is shea butter (karit) which is extracted from the seeds. It is one of the most affordable and widely used vegetable fats in the Sahel and plays an important role in the economy of the region. The timber is of good quality, termite resistant, and generally very durable, but is normally used only when the tree has passed the fruit bearing age. Compiled from USDA plants database, FAO plants database, and other FAO docum ents.

PAGE 31

31 Table 2 2 Main productive functions of agroforestry parklands Parkland tree function Examples Browse Pterocarpus erinaceus, Pterocarpus lucens, Balanites aegyptiaca, Faidherbia albida, Acacia raddiana, Bauhinia rufescens Famine food Parkland p roducts eaten when crops have failed. Young shoots of Borassus aethiopum eaten as vegetables; fruits and leaves of Ficus gnaphalocarpa and other Ficus species. Fat and oil production Butter extracted from Vitellaria paradoxa ; oil produced from Balanites aegyptiaca, Parinari macrophylla, Lophira alata and Elaeis guineensis Food complement Condiments served with staple cereals. Seeds of Parkia biglobosa, Tamarindus indic, Adansonia digitata and Ceiba pentandra leaves. Handicrafts and clothing Borassus aethiopum (baskets, hats, furniture), fibers from Adansonia digitata, Ficus thonningii and Ficus glumosa Soil fertility Faidherbia albida and, to a lesser degree, Prosopis africana (Nitrogen fixing) Wine production The sap of Elaeis guineensis, Bora ssus aethiopum and Hyphaene thebaca is processed into wine. Wood production Ziziphus spp., Anogeissus leiocarpus (firewood), Borassus aethiopum (construction). Source: (Boffa 1999)

PAGE 32

32 Figure 2 1. Map of West Africa with ecological zones and isohyetal lines. The WAS consists of Sahelo Saharan, Sahelian and Sudano Sahelian zones Source: Famine Early Warning Systems Network ( http://www.fews.net/ )

PAGE 33

33 Figure 2 2. Standardized annual Sahel rainfall (June to October ) from 1898 to 2004. The rainfall data are converted to relative values (standardized) with respect to data from 1898 to1993, such that the mean and standard deviation of the series are 0 and 1 respectively Positive values (orange) are the years with ra infall more than the mean of 1898 1993 data, and negative values (blue) are the years with less rainfall. Sou r ce: Mi t chell (2005) ( A B Figure 2 3. Seasonal landscape contrast of the WAS. P hotos of the same site A) in the dr y season and B) rainy season. Source: USGS ( http://edcintl.cr.usgs.gov/sahel.html ).

PAGE 34

34 Figure 2 4. Distribution of soil orders (USDA soil taxonomy) in West Africa Source: Eswaran et al. (1996)

PAGE 35

35 Figure 2 5. P arkland system in S gou, Mali Trees are scattered in the cultivat ed land, and protected for non timber use. Ox drawn plows are used to till the land to sow the crops upon onset of rains ( Photographed by author) Figu re 2 6. Allowing the cattle to roam freely on the landscape during the dry season after seasonal crops have been harvested is a common feature of the WAS land use system. This often leads to overgrazing (photo from the S gou region, Mali ). When the open lands near the village are depleted of vegetation, farmers are forced to take the animals further away from the village. ( Photographed by author)

PAGE 36

36 CHAPTER 3 LITERATURE REVIEW : C ARBON SEQUESTRATION POTENT IAL OF AGROFORESTRY SYSTEMS IN THE WEST AFRICAN S AHEL (WAS) Overview Carbon (C) sequestration has become a hotly debated and widely researched topic during the recent past. Consequently, voluminous literature is available on the subject. The review in this chapter is limited to issues that are most rel evant to the present study. Following a general overview of the topic, the chapter presents brief descriptions of various methodologies that are currently recognized and/or debated for C measurement and accounting, although not all of these were used in t his study Then studies estimating C storage in agroforestry systems (both biomass C and soil C) in the WAS and other ecoregions are presented. Given that the potential of C sequestration cannot be fully evaluated without integrating both biophysical an d socioeconomic sides of acceptability s ocioeconomic issues related to C sequest ration activities through agroforestry are also discussed. C Sequestration as a Climate Change Mitigation Activity The international response to climate change started in fu ll with the establishment of the United Nations Framework Convention on Climate Change (UNFCCC) in 1992. Five years later, 159 countries signed a treaty called the Kyoto Protocol, which commits the 38 signatory developed countries to reduce their collecti ve greenhouse gas (GHG) emissions by at least 5% compared to the 1990 level by the period 2008 2012. The agreement came into force on February 16, 2005, following its ratification by Russia on November 18, 2004. As of April 2007, a total of 169 countri es and other governmental entities have ratified the agreement A unique characteristic of the Kyoto protocol is that it allows the amount of CO 2 sequestered by forests to be counted towards emission targets.

PAGE 37

37 Tropical forest conversion contributes as mu ch as 25 % of net annual CO 2 emissions globally ( Palm et al. 2004). Removing this atmospheric C and storing it in the terrestrial biosphere is thus, one option for mitigating the emission of this GHG. A recent assessment of Rose et al. (2007), reference d by Intergovernmental Panel on Climate Change (IPCC) s newest report, suggests that land based mitigation agriculture, forestry, and biomass liquid and solid energy substitutes can be cost effective land mitigation options And it can contribute ove r the century 94 to 343 Pg C equivalent of greenhouse gas emission abatement, which is 15 to 40 percent of the total abatement required for stabilization. Agroforestry for C sequestration Under the Kyoto Protocol s Article 3.3, further defined by Marrak esh Accord in 2001, a groforestry was recognized as an option of mitigating GHG s. Since then, the C sequestration potential of agroforestry systems has attracted greater attention from both industrialized and developing countries. It is attractive because of its applicability to a large number of people and area s currently in agriculture, a s well as its perceived potential for reducing pressure on natural forests. Also, Clean Development Mechanism (CDM), defined in Article 12 of the Protocol adds the attr activeness because the CDM provides for Annex I Parties (industrialized countries which have emission reduction goals) to implement project activities that reduce emissions in non Annex I Parties (developing countries) in return for certified emission re ductions (CERs) (UNFCCC 2007) Since agroforestry is traditionally and widely practiced in developing countries, it is feasible/easy options for both developing and developed groups of countries to start as mitigation projects under the CDM. However, as stated by Makundi et al. ( 200 4 ) and several others, estimating the amount of C sequestered by agroforestry poses unique challenges In addition to the complexity caused by diverse factors such as climate, soil type, tree planting densities, and tree mana gement as well as

PAGE 38

38 specific difficulties arising from requirements for monitoring, verification, leakage assessment and the establishment of credible baselines agroforestry estimations are beset by the problem of estimating the area under agroforestry prac tices. Nevertheless, the IPCC (2000) estimate d that 630 million ha of unproductive croplands and grasslands could be converted to agroforestry worldwide, with the potential to sequester 391,000 Mg of C per year by 2010 and 586,000 Mg C per year by 2040. Although the credibility of conceptual models and theoretical benefits has been demonstrated, C sequestration potential is still a little studied characteristic of agroforestry systems (Nair and Nair 2003). More studies examining how much C can be seques tered/stored in various agroforestry systems around the world are needed. S everal s tudies and reviews from different regions of the world have discussed agroforesty s benefits and limitations for C sequestration (Schroeder 1994; Dixon 1995; Albrecht and K andji 2003) but only very few deal with comprehensive comparisons of different practices in each ecoregion. Due to the difficult physical environment and lack of research infrastructure, agroforestry systems in the WAS are one of the least documented to pics regarding C s equestration potential. Lal (1999) estimated the potential for sequestering C in the region wa s, as in most other drylands, fairly low, between 0.05 0.3 Mg C ha 1 y r 1 The estimate, however, include d a variety of uncertainties relate d to future shifts in global climate, land use and land cover, and the poor performance of trees and crops on poor soils in the region. In the WAS, impacts of population pressure over grazing and continuous drought are causing severe land degradation. Co nsequently, biomass C stocks steadily decline within land use/land cover. Opportunities for C gains in the region are, thus, often discussed in the context of agricultural fertility and sustainability of farming systems, which involve agroforestry such as

PAGE 39

39 tree crop livestock integration and fallowing practices (Manlay et al. 2002; Woomer et al. 2004 a ). Methodologies for C Sequestration Measurements Efforts to accurately measure C in forests are gaining global attention as countries seek to comply with agre ements under the UNFCCC. Many methodologies have been put forth to quantify the amount of C in forests (Beer et al 1990; MacDicken 1997; Brown 1999), and are best based on permanent sample plots laid out in a statistically sound design s. This is often q uite difficult in agroforestry systems and is one of the reasons why there are few studies that actually measure the amount of C (Montagnini and Nair 2004) Practically, there are four possible approaches to measur ing the amount of C stored as a result of particular land management practice; 1) Direct on site measurements of biomass, soil C, or C flux, 2) Indirect remote sensing techniques, 3) Modeling, 4) Default values for land/activity based practices ( Table 3 1 ) Most of these approaches were originally developed to estimate the amount of C in forest stands. Several pilot projects are ongoing to ensure that C that is sequestered for the long term in economically viable agroforestry systems is reliably measured. T he facto rs that influence which approach is used in a specific project depends on technical availability budget for the measurement, and size of the land to be estimated. Since most of C mitigation projects are either still in the pilot stage or implemented on a small scale, direct measurement approaches are most commonly used and reported. Direct On site Measurement Direct on site measurement includes field sampling and laboratory measurements of total C in the biomass and soil. These measurements (including in ventory data used for the remote sensing, modeling or default values) are in effect snapshots of C stored at the time of the

PAGE 40

40 inventory. How to calculate/determine the amount of sequestered C over a certain period is another issue, and discussed in the Accounting Methods section. Inventory In general, C in forest or agroforestry systems can be divided into four groups; 1) Aboveground biomass, 2) Belowground biomass, 3) Soil C, 4) L itter fall /crop residue Methods to collect and calculate the sample data from project sites have been standardized by many reports and studies (MacDi c ken 1997; Roshetko et al. 2002). Data for the four C categories are collected by timber cruising and sampling of herbaceous vegetation, soil, and standing litter crop at s ample plots (Shepherd and Montagnini 2001; Brown 2002; Tie p olo et al 2002). Also, for existing forests, many tropical countries have at least one inventory of all or part of their forest area that could be applied for agroforestry systems, although many of the inventories are more than 10 years old and very few have repeated inventories. Data from these inventories can be converted to biomass C depending on the level of detail reported (Brown, 1997). Conversion and e stimation For aboveground biomass, tre es are divided by compartments: leaves, branches and trunks, and measured in dry weight (Beer et al 1990), because each compartment has unique C content and decomposition rate. Although this is the most accurate method, these inventories are often too ti me consuming and cost ly Alternatively, biomass expansion factors or allometric biomass equations are often used, because they require only stem wood information such as diameter at breast height (DBH). These equations exist for practically all forests t ypes of the world, especially in the temperate zone (Sharrow and Ismail 2004). But, because of the very general nature of these equations, they lack accuracy; they are, at best, approximations. For an agroforestry system Shroeder (1994)

PAGE 41

41 used a ratio of total aboveground biomass to stem wood biomass of 2.15 derived from many previous studies. Where tree stocking density was high (>500 trees ha 1 ) and the growth cycle or rotation length was relatively long (>10 years), i.e., for conditions more similar to those for a forest plantation, a ratio of 1.6 was used in the study to estimate total aboveground biomass. Total C content is usually estimated based on the assumption that 45 to 50 % of branch and stem dry biomass is C, and that 30 % of dry foliage biom ass is C (Shepherd and Montagnini 2001; Schroth et al. 2002). Herbaceous vegetation and standing litter are also collected from sample plots and weighed to calculate their C content. It is often assumed in inventories that this vegetation type contributes little to the total biomass C of a forest and it is often ignored. However, the contribution of herbaceous vegetations is often larger in agroforestry systems than in forests such as green manure from trees in natural systems. The amount of litterfall, pruning residues, and crops largely depends on the season and rotation period (Beer et al. 1990). Thus, it is difficult to estimate using general ratios as used in the stem biomass estimation. For belowground C, it is divided into two main categories; root biomass, and soil C (mainly organic matter). Although methods for measuring aboveground biomass are well established, measurement of root biomass is difficult and time consuming in any ecosystem and methods are generally not standardized (Ingram and Fernandes 2001) A review of the literature shows that typical methods include spatially distributed soil cores or pits for fine and medium roots and partial to complete excavation and/or allometry for coarse roots. The distinction between live and dead roots is generally not made and root biomass is usually reported as total. Moreover, sampling depths are not standardized, yet the depth selected in a given study is assumed to capture practically all roots (Brown 2002).

PAGE 42

42 Root biomass is often estimated f rom root:shoot ratios (R/S). It can be calculated by sample plot measurements, but there are also lists of reference data. A literature review by Cairns et al. (1997) included more than 160 studies covering tropical, temperate and boreal forests that rep orted both root biomass and aboveground biomass. The mean R/S based on these studies was 0.26, with a range of 0.18 (lower 25 % quartile) to 0.30 (upper 75 % quartile). The R/S did not vary significantly with latitudinal zone (tropical, temperate, and bo real), soil texture (fine, medium and coarse), or tree type (angiosperm and gymnosperm). Soil C samples should be collected from each layer, dry weighed and analyzed for its C contents by recommended laboratory procedures To calculate C stocks per unit area, the C content in the soil is multiplied by the bulk density of the respective soil layer. By itself, C sequestration in agricultural soils is expected to make only modest contributions globally (e.g. 3 6 % of total fossil C emissions) (Paustian et al. 1997) However, this amount can be significantly varied through management such as fallow phase, erosion, tillage or tree incorporation Indirect Remote Sensing Techniques Even where field measurement methodologies are established, agricultural/ forestry practices are inherently dispersed over a wide geographic area. Staffing costs for monitoring and verification of land use practices over such a wide area could prove to be cost prohibitive. Because direct field measurements can be expensive, th e use of indirect remote sensing techniques is being considered. A range of remote data collection technologies is now available including satellite imagery and aerial photo imagery from low flying airplanes. Sensors that can measure the height of the ca nopy or vertical structure will be needed along with the more traditional sensors on Landsat or Spot satellites in order to improve the ability of remotely sensing biomass (Brown 2002)

PAGE 43

43 A promising advance in remote measurements of forest/agroforest biom ass C is a scanning lidar (a pulsed laser), a relatively new type of sensor that explicitly measures canopy height. This sensor is able to monitor 98 % of the earth s closed canopy forests (Brown 2002). Another promising advance in the remote sensing are a, especially at smaller scales (thus, probably more appropriate for agroforestry systems), couples dual camera digital videos (wide angle and zoom) with a pulse laser profiler, data recorders, and differential GPS (geographical positioning system) mounted on a single engine plane (Brown 2002). The plane flies aerial transects across the area with several fixed altitude to take the images data, and these images are used to create 3D models of the terrain. From these measurements, crown area, tree height, o r number of stems per area of agroforestry systems would be much more easily and accurately estimated. Modeling Since total direct inventory is site specific and can be expensive, another way to lower the cost of estimating C amount is developing a model To date, several models have been developed that simulate C budgets and fluxes at the level of the forest stands. These models range from very detailed ecophysiological models used in climate impact assessment, to very general empirical, descriptive mo dels of C budgets within forest stands. None of these models ha s been widely disseminated, and none of them accepted as a possible standard for C crediting projects so far. One of the most recognized and utilized model s by various projects including ag roforestry projects is CO 2FIX which was developed by researchers of Wageningen University, Universidad Nacional Autonoma de Mexico, Centro Agronomico Tropical de Investigacion y Ensenanza (CATIE), and European Forest Institute (EFI). This model is a user friendly tool for dynamically estimating the C sequestration potential of forest management, agroforestry and

PAGE 44

44 afforestation projects. The model is a multi cohort ecosystem level model based on C accounting of forest stands, including forest biomass, soil s and products (Masera et al. 2003). Another common methodological approach to estimat ing mitigation potential more broadly is known as comprehensive mitigation assessment process (COMAP). Th e COMAP model requires the projection of land use scenarios fo r both a baseline and for a mitigation case. It requires data on a per hectare basis on C sequestration in vegetation, detritus, and forest products, soils and also on GHG emission avoidance activities (Makundi and Sathaye 2004). Default Values for Land/A ctivity Based Practices This approach is the broadest, nation level approach which uses default values for certain land based activities for estimating C storage. A land use based accounting system would focus on the changes in C stocks on managed lands during a defined time period (Dixon et al 1994 a ). Default values would be assigned to a particular tract of land based upon county or regional level research on the average sequestration likely to result from specific agricultural or conservation measure s in that area. Various values could be assigned to such broad land management activities as forest, cropland, or grazing management. Under this approach, field measurements of C storage changes in individual fields would not be necessary. Land use moni toring can be readily measured by remote sensing techniques, eliminating the need for many field inspectors. However, field plots may need to be set up, representing the average or a range of conditions for the entire project area, and used as a reference to provide actual estimates to increase the accuracy of large scale projects. Accounting Methods In order to assert that agroforestry systems are an important C sequestration method, the amount s measured in agroforestry systems must result in long term changes in terrestrial C storage and CO 2 concentrations in the atmosphere (Masera et al. 2003). Thus, the time frame

PAGE 45

45 and C accounting methods are very important ; but they are also often controversial issue s at international negotiations of climate change. Approaches to Assess ing C Sequestration Performance Fluxes of C and flow summation Balancing the annual flux from a source of emissions by uptake in a forestry/agroforestry project is conceptually the simplest way of providing offsets. In this approach offsets are to the C credit market on an annual basis, according to the emissions avoided, relative to the project baseline. However, since fluxes of C associated with forestry/agroforestry are irregular, it may be difficult to match the upt ake by a particular project to an industrial source of emissions. Furthermore, national or international authorities must assign permanent C storage status to project areas, such that the owners are liable for any emissions, as well as eligible for credi ts. Without such status C might be accumulated in the growing phase of the forestry cycle, only to be lost at the end of rotation (Tipper and De Jong 1998). Average changes in the stocks of C The pilot phase of most sequestration projects is assessed on the basis of the long term average increase in the stocks of terrestrial C relative to the baseline (Kursten and Burschel 1993; van Noordwijk et al. 2002), expressed as tC according to the equation: Average net C (C stored in projec t C s tored in baseline) in tC / n (y ea rs) The stock change method calculates the difference in C stocks between a project and its baseline at a given point in time. A key advantage of both methods is that it focuses on the sustainability of changing the stock of C stored in vegetation and soils. However, long term C storage is not easily defined, and there may be considerable argument over the assumptions about risks and possible future changes in management. The timing of the emission reduction

PAGE 46

46 relative to the emissions that are supposed to be offset may be problematic, since there may be a time lag of years to decades between the establishment of the offset project and actual uptake of the C. Cumulative C storage The cumulative storage approach is based on an understanding of C cycle dynamics and radiative forcing of the atmosphere. The total warming effect of a given emission is determined by the cumulative presence of GHG in the atmosphere ; in other words the product of concentration and time. In the case of CO 2 terrestrial and oceanic sinks take up C previously emitted, over time. Assuming the dynamics of the C cycle remain stable, most CO 2 emitted at the present will be absorbed within 100 years, and the cumulative radiative forcing produced by thi s emission will be proportional to the area under the depletion curve, expressed in tC.years. Calculation of this area provides an estimate of the cumulative C storage that would be required to offset an emission of 1 tC at the present time. This method avoids both the necessity of making questionable assumptions about the long term balance of C in forests/agroforests, and the practical difficulties of implementing flux based incentive systems. However, international agreements on the conversion factor f or tC.years per tC emission and the time limit for crediting the effect of a given project are required (Tipper and De Jong 1998) Other accounting methods In addition to these relatively simple conventional methods, alternative approaches have been propos ed to better address the temporal dimension of C storage, such as equivalence adjusted average storage, stock change crediting with ton year liability adjustment, equivalence factor yearly crediting, equivalence delayed full crediting, and ex ante ton year crediting (De Jong 2001). Most of these are based on adopting a two dimensional measurement unit that reflects storage and time, i.e., ton C year. The general concept of the ton year approach is in the

PAGE 47

47 application of a factor to convert the climatic eff ect of temporal C storage an equivalent (equivalent factor) amount of avoided emissions. Technical Problems and Uncertainties There are a number of shortcomings of conventional methods for estimating /accounting the C in a system that need to be considered These include the uncertainties related to future shifts in global climate, land use and land cover, the poor performance of trees and crops, varied environments, pests and diseases such as nematodes. For example, the amount of C remaining belowground a t the end of the tree rotation, and the amount of C sequestered in products created from the harvested wood, including their final disposition are often not included in the accounting methods discussed above (Johnsen et al. 2001). Oren et al. ( 2001 ) repor ted that after an initial growth spurt, trees grew more slowly and did not absorb as much C from the atmosphere as expected They concluded that assessment of future C sequestration should consider the limitations imposed by soil fertility as well as inte ractions with nitrogen deposition. In addition to these uncertainties, there are some concern s about the impacts of agroforestry in other GHGs. The wide scale use of woody legumes which is common in agroforestry systems, might result in release of nitr ous oxide ( N 2 O ) (Choudhary et al. 2002) although it does not seem to be as strong an impact as N fertilization (Mosier et al. 2004) N 2 O is known to have a global warming potential 200 300 times higher than that of CO 2 Similarly, pasture and rice pad dy cultivation in agroforestry systems can produce significant quantities of methane ( CH 4 ) another strong GHG (20 60 times higher impact than CO 2 ), on a global scale (Dixon, 1995).

PAGE 48

48 Biomass C Sequestration Studies in Various Ecoregion s The amount of C sequestered in an agroforestry system depends largely on the nature of the system put in place, the structure and function of which are determined by environmental and socioeconomic factors (Albrecht and Kandji 2003). Other factors influencing C storage include tree species and system management (Delaney and Roshetko 1998; Roshetko et al. 2002). Palm et al. (2004) compared the amount of C stored (above ground) in different ecological system s ( Table 3 2 ) To compare the rotati on of the different land use systems, time averaged C of each system was used. C stocks in the vegetation of the primary forests averaged 300 Mg C ha 1 and that of logged or managed forests ranged from a high of 228 Mg C ha 1 in Cameroon to a low of 93 M g C ha 1 in Indonesia. Time averaged aboveground C for the different land uses ranged from 50 90 Mg C ha 1 in long fallow shifting cultivation and complex agroforestry systems to 30 60 Mg C ha 1 in simple agroforestry systems and most tree plantation s and medium fallow rotations. The se are considerably larger than those for annual crops or pastures. Studies in West Africa In a review of C sequestration in tropical agroforestry systems, Albrecht and Kandji (2003) estimated that agrosilvicultural sys tem could sequester 29 53 Mg C ha 1 in humid tropical Africa. A case study in Cameroon (humid west and central Africa) showed that the cacao ( Theobroma cacao ) agroforest is superior to the alternative food crop production system (slash and burn), both i n C sequestration and below and above ground bio diversity. Total biomass in cacao agroforest was 304 Mg ha 1 compared to crop fields ( 84 Mg ha 1 ) (Duguma et al. 2001). Compared to C gains in the humid tropics, the benefits of agroforestry in the WAS, s uch as parklands or improved fallow seem to be less. A simulation study in Senegal compared the C

PAGE 49

49 gains after 25 years from protecting parkland systems (2.48 Mg C ha 1 ) with that of rotating crops with Luecaena spp fallow planting (6.35 Mg C ha 1 ) (Tscha kert 2004). Although drylands as whole are believed to provide a substantial opportunity for C offsets especially in soil C, because of their large area (47.2 % of land in the world) and low human populations (Lal 2004b) most studies in the S ahel region have concluded that the potential for C gains per unit area through agroforestry is relatively low (Walker and Desankar 2004;Woomer et al. 2004b) compared with other ecoregions In general, i ntroducing trees into agricultural systems is expected to increas e water and nutrient availability because trees can fix nitrogen, retrieve water and nutrients from below the rooting zone of crops and reduce water and nutrient losses from leaching and erosion (Buresh and Tian 1997) This tree effect has been demonstr ated in various agroforestry systems in the semiarid region. However, this added value was lowest where it is most needed, in resource poor environments: the competition between woody plants and crops is strong (Kater et al. 1992; Breman and Kessler 1997) Water constraints are the strongest limitations for C sequestration in the WAS. Several local tree species such as Acacia tortilis Guiera senegalensis Pterocarpus lucens have been planted in grasslands of the region for sequestering C, but their capa city to grow has been shown to be constrained by moisture availability (Woomer et al. 2004 a ). The capacity of exotic dryland tree species to afforest the WAS is also uncertain. Since the moisture and nutrient levels of the study field are expected to be low, the tree growth and the consequent C storing will not be extremely high, either, compared with more moist part s of West Africa. However, the amounts of C sequestered as a result of specific land uses are mostly unknown in the WAS, thus, it is worthwh ile to conduct the research to have a reference data for future C sequestration projects.

PAGE 50

50 Soil C Sequestration Recently, research focused on C sequestration potential in terrestrial ecosystems has been shifting from biomass C to soil C, because soil is r ecognized as an important storage (sink) for C and studies estimating biomass have accumulated for various ecosystem (including agroecosystem and plantation forests), while the dynamics of soil C is much less known. Studies of Soil C Stock and Dynamics T he comparison study of C sequestration potential by Palm et al. (2004) ( Table 3 2 ) also measured soil C storages. On average, 45 Mg C ha 1 were found in the forest systems studied (0 20 cm depth), and 80 100 % of that C st orage in agroforestry systems; 80 % in pastures; 90 100 % in long fallow cycles; 65 % in short term fallows, and 50 % or less in annual crops and degraded grasslands. Rosalina et al. (1997) reported a mean of 104 Mg C ha 1 for home gardens in North Lamp u n g, Indonesia, and th at 58 % of the stock is soil C. The soil C sampling depth of these studies is usually on surface, up to 20cm. Although the surface soil is the major part of which soil organic carbon (SOC) is found, it may not be deep enough to capt ure all the tree roots influence on soil C (Jobbagy and Jackson 2000). But because of the labor intensity and the relatively low soil C density, deeper soil C tends to be ignored. Storage of soil C is rather easy to quantify and/or estimate, but detect ing soil C flux including its turn over time is much more difficult and has not been studied much. Most current models of soil organic matter (SOM) dynamics assume that equilibrium C stocks are linearly proportional to C inputs, i.e. there are no assumpti ons of soil C saturation. Six et al. (2002 a ) questioned the validity of this assumption for projecting longer term SOM dynamics, and developed the proposition that physiochemical characteristics inherent to soils define the maximum protective capacity of SOM pools. Methodologies such as fractionation and C isotope measurements are being developed for quantifying and identifying the characteristics of soil C

PAGE 51

51 dynamics, and are actively discussed in the soil science arena (Balesdent et al. 1998; Del Galdo et al. 2003; Powers and Veldkamp 2005). Soil fractionation : There are various ways to separate SOM into labile and recalcitrant pools, and these methods rely on chemical, physical, or biological separation and many of them are used sequentially in analys es (McLauchlan and Hobbie 2004) Chemical fractionation separates soil C into different resistance class to decomposition by using acid, permanganate(KMnO 4 ), or hot water, (Moody et al. 1997; Ghani et al. 2003). Physical fractionation separates labile an d recalcitrant fractions by either size or density. Sieving is used to size differentiation and flotation with a dense liquid is usually used to measure light fraction which is considered to be labile (Six et al. 1998). Biological separation uses microbe s to separate labile SOC from recalcitrant SOC under controlled temperature and moisture conditions assuming that microbes will mineralize the most labile C first, with recalcitrant C being mineralized later (Alvarez and Alvarez 2000) With applying thes e methods, many examine the impact of land use change on soil C storage and dynamics. For example, it is possible to assess how land use rotation (including fallow) or change of management such as reduction of tillage can effectively protect recalcitrant soil C, so that enhance soil C sequestration (Pikul. et al. 2007; Zibilske and Bradford 2007). 13 C isotope measurement : During photosynthesis, CO 2 fixation of C 3 plants discriminates against the heavier isotope 13 C more than do C 4 plants, which result in different stable carbon isotope composition ( 13 C), 13 C/ 12 C ratio relative to that found in the PDB (Pee Dee belemnite) for their plant material. T his composition v alue of C 3 plants is between 23 and 34 whereas C 4 plants range s from 9 to 17 (Eleki et al 2005). Negative values of 13 C indicate that the plant material is depleted in 13 C compared with the PDB standard. Using th is theoretical

PAGE 52

52 expected difference between the measured 13 C value s, it is possible to calculate the proportion of C4 derived material and C3 derived material in biomass or soil C (Balesdent et al. 1998). This method has been used for soil C research to assess vegetation composition change (Dzurec et al. 1985) or to follow the dynamics (Harris et al. 2001). Mcdonagh et al. (2001) measured how SOM from original vegetation (forest: C3 plants) were diminished after continuou s cultivation of maize ( Zea mays : C4 plants). In agroforestry system, Jonsson et al. (1999) used this method to prove the positive influence of trees (C3 plants) on SOM increase at millet ( Pennisetum glaucum : C4 plants) cropland. Soil C in the WAS In th e WAS, most of the soils have low activity clay with low water retention and are susceptible to soil erosion and compaction as described in Chapter 2. Organic matter content of these soils has been depleted due to overgrazing, agricultural mismanagement deforestation and overexploitation of the natural resources. As a result, soil organic C stock density in West Africa is very low ( 4.2 4.5 kg C m 2 ), compared with the world average (10.9 11.6 kg C m 2 ), and relatively lower even when compared to th e average for Africa (6.4 6.7 kg C m 2 ) (Batjes 2001). S oil degradation is a major obstacle for agricultural productivity and thus sustainable development of the WAS The possibility of enhancing C sequestration through improved soil management has been discussed academically and at international workshops, as part of the search for agroecosystem sustainability in the region. Among s oil nutrients studies in Africa, tree integration into croplands is often recommended for soil amelioration (Onim et al. 19 90; Tiessen et al. 1991; Manlay et al. 2002). Kang et al. (1999) reported Grilicidia sepium and Leucaena leucocephala increased surface soil organic C by 15 % compared to sole crops in a 12 year hedgerow intercropping trial on a Nigerian Alfisol. Parklan d system studies affirm that the soil

PAGE 53

53 under the trees is richer in organic matter content and several cations compared to adjacent tree less sites ( Kater et al. 1992; Jonsson et al. 1999). Soil amelioration by land management in the WAS is, however, often met with limited success when it comes to maintaining or increasing soil nutrient and C stocks. The potential for C sequestration in a given soil and agroecological zone is proportional to the original reserves present under undisturbed conditions. Brem an and Kessler (1997) compared the added values of woody plants in croplands or pastures between Sahel zone and wetter Sudan zone. They concluded possibilities to improve the soil organic matter status are more limited in Sahel, due to resource poor envir onments and competition for water between woody plants and crops or pasture. Because of the slow establishment of a woody plant community and the rapid turnover of organic matter, a long transition period is necessary under the conditions in Sahel. Imp roved fallow studies also suggest that a long period would be needed to amend soil physical conditions of the highly degraded soil in the WAS (Buresh and Tian 1998; Ringius 2002; Kaya and Nair 2004). Soil C sequestration is not recognized as a mitigation means during the first commitment period of the Kyoto Protocol (2008 2012 ) although political pressure to reverse this situation has been growing. In his review of soil C sequestration in Africa, Ringius (2002) stated that s ub S aharan Africa would not profit significantly from soil C sequestration under the Clean Development Mechanism (CDM) as long as the land use pressures due to a rapidly growing population and poverty remain unsolved. Profitability of the soil C sequestration project is un certain, s ince cost benefit studies of the sequestration activities have not been conducted. There is a need to launch long term (>10 yr) field experiments and pilot projects for soil C

PAGE 54

54 sequestration as well as to develop a land resources information system in the WAS, geared towards CDM and/or C sale. Socioeconomic Implications To analyze the socioeconomic feasibility of the agroforestry practices for mitigation projects, analysis tools, i.e. models, are needed. Economic models of different scales used for the stu dies in various ecoregions are summarized here. Although the number of stud ies is small, the case studies and possibility of using these economic models in the WAS are also examined. Economic Models In most studies of C sequestration, agroforestry is reg arded as one of the forest management option s for potential C sequestration T here are few stud ies specifically discussing economic models of C sequestration in agroforestry systems; instead, models designed for managed forests are usually applied (Masera et al 2003). These economic models for accounting C sequestration projects can be categorized into two different spatial scales National/global scale Apart from the C sequestration potential per se of agroforestry systems, the potential for realizing t his assumed benefit depends largely on the availability of land which can be changed to agroforestry from land with less C storage such as agricultural field s Attempts to estimate the global potential for increasing C sinks through land use change had b een conducted at the global, national, and regional level s for more than a decade (Dixon et al 1994 b; Sathaye et al. 2001; Godal et al. 2003 ). T hese studies use simple integrated model structure based on biophysical and economic information In this ki nd of large spatial scale empirical model schemes such as the Holdridge life zone system (LZS) can be used as a guideline for organizing vegetation data (Pfaff et al 2000) For economic factors such as the price of land, cost of land use change, and tim ber price national census information are generally available. I nformation

PAGE 55

55 on land use and trend s of change, collected from national survey s or satellite/remote sensing data, are integrated and the overall economic impacts are examined. Results are pres ented in several different ways. Dixon (1995) presented the potential C storage (over 50 year rotation) and initial project financial costs for agroforestry systems for ecoregions of selected nations in terms of $/Mg C. Some studies estimate total invest ment cost in actual dollar amounts for developed countries to achieve their reduction goals (Baron and Lanza 2000, Godal et al 2003), and others estimate each country s investment in $ ha 1 or internal rate of return (IRR) (%) (Dixon 1995 ; Sathaye et al 2001). Micro/site s pecific scale At this level, more detailed economic estimation is conducted based on data from the specific target (project) field. Various factors of benefits and costs are individually counted. There are three different time frames for counting these costs and benefits of C sequestration projects: point estimates, partial equilibrium estimates, and general equilibrium approaches Many of the point estimate studies provide undiscounted private costs and benefits of the project s impl ementation in $ ha 1 These studies usually count only direct inputs and outputs including land cost. Estimating opportunity costs to compare with other land use option s is often done (Tomich et al. 2002). Most of the se studies reveal little about how c osts might change throughout the project with time or if the project were to expand or be repeated. Thus, the estimates tend to be biased towards the low end The Scolel T project, conducted in Mexico, is one of the few long term and comprehensive econo mic impact studies on this subject It serves as an example agroforestry project for calculat ing the costs related to implementing a C sequestration project in rural environments dominated by resource poor small scale farmers wh o are expected to be major player s in agroforestry worldwide. The study accounts for cost s of project design, the time

PAGE 56

56 required to explain to farmers the project objectives, C related inventories, and the cost of baseline setting, which are often ignored in similar estimate analys is (De Jong et al. 2004). These equilibrium studies usually present the results as discounted costs in $/Mg C with long term average sequestered C as Mg C ha 1 For estimating much longer scale s than one cycle of agroforestry rotation, computer based mode ling methods are usually used. There are several models such as CO2FIX and WaNULCAS for estimating the dynamics of C sequestration over decades to centuries and some are applicable to agroforestry ( Van Noordwijk et al. 2002 ; Masera et al 2003 ). Wise and Cacho (2005) used the WaNULCAS model for ecological estimates combined with their economic model including variables such as local discount rate, firewood price, and labor cost, and simulated the long term economic value of switching land use from agricu ltural system to agroforestry system in Indonesia. They include expected C prices into its economic analysis, and presented net present values (NPV) in $ ha 1 of several different setting (management options). Agroforestry systems in temperate area s ar e usually analyzed in a very different way. Compared with developing countries in the tropics, the C credit sale through forestation is not likely to be economically feasible soon in the temperate area Thus, C sequestration tends to be considered as an environmental benefit (non market value, or subject to receive the subsidies). Studies to estimate the benefits of C sequestration using models that are generally used for these environmental commodities seem to be the current research trends ( Stainback a nd Alavalapati 2000; Alavalapati et al. 2004). Feasibility in West Africa West African countries GHG emissions are currently negligible in global terms, due to the low level of development and industrialization. As a result, emission reduction opportunit ies

PAGE 57

57 remain few, and are mainly in lessening the negative impact on the climate resulting from land use change and deforestation. For C offset projects, however, the risk of shifting from cropping to more C beneficial practices seems to be high, especially for subsistence farmers who have little lands. A study of C sequestration through agroforestry in Senegal found that the costs for resource poor farmers are considerably higher than those of intermediate and richer farmers, because the former often lack t he necessary assets (land, labor, and animals) to switch from current to alternative practices (Tschakert 2007). Many African policy makers and financial institutions express little interest in controlling GHG emissions or adapting to changes in climate. This attitude is based on their experience that, in general, other, more local, environmental problems have more direct influence on their populations than climate change. Senior government officials and most members of civil society do not understand the climate issue very well (Denton et al. 2001) Many development practitioners remain skeptical, arguing that C brokers, national ministries and local leaders rather than needy rural populations will benefit from C projects. An important challenge for th e WAS countries lies in that they need to be more attractive than the other African and developing countries in order to draw and hold investments for C sequestration projects under CDM. As discussed above, the potential C gains in the WAS through agrof orestry has been considered to be unattractive. Synergies between development and climate change response, however, can be an answer. Agroforestry projects which protect soils and result in C sequestration, also provide employment opportunities for loca l farmers (Hardner et al. 2000) Soil C sequestration project through agroforestry could provide a crucial link between three international conventions: the UN Framework Convention on Climate

PAGE 58

58 Change (UNFCCC), the UN Convention to Combat Desertification (U NCCD), and the UN Convention on Biodiversity (UNCBD). As such, incentives can come from a much broader area such as development assistance, other multilateral agreements and sectoral policies on energy and agriculture.

PAGE 59

59 Table 3 1. Summary of various bi omass C measurement approaches used commonly in C sequestration studies. Approaches Tools and methods for data collection Remarks Direct on site measurement Plot sampling, tree inventories Using allometric equations, biomass expansion factors, root:shoo t ratios Regarded as most accurate and site specific. Cost for inventory is high. Indirect remote sensing techniques Satellite imagery, aerial photo imagery pulsed laser, dual digital camera. Field inventory for the reference data. Relatively larger s cale Technical availability can be an issue Cost effective. Modeling Ecophysiological study based, ecosystem based, or land use change based Field inventory or data from national surveys Mainly used in academics or pilot projects so far Needs man y assumptions, but can be applicable to various situations Default values for land/activity based practices Land used change based. Focus on the changes in C stocks. Field plots for the reference data Most macro scale approach. Can be used not only for forestry/agroforestry but also other land use.

PAGE 60

60 Table 3 2. Aboveground time averaged C stock in different ecosystems and agroforestry practices. Time average C stock (Mg C ha 1 (C stored in projec t C stored in baseline) in Mg C ha 1 / n (yea rs.) Meta land use systems Country and specific land use Time averaged C of land use system Mg C ha 1 Undisturbed forest Indonesia Peru 306 (207 405) 294 Managed/logged forests Brazil/Peru Cameroon Indonesia 150 (123 185) 228 (221 255) 93.2 (51.9 134) Shifting cultivation and crop fallows Cameroon Shifting cultivation, 23yrs fallow Bush fallow, 9.5 yrs Brazil/Peru Short fallow, 5yrs Improved fallow, 5yrs 23 yrs fallow 77.0 (60.2 107) 28.1 (22.1 38.1) 6.86 (4.27 9.6 1) 11.5 (9.50 13.4) 93 (80.5 101) Complex/extensive agroforests Permanent Rotational Cameroon, Cacao Indonesia Rubber Cameroon, Cacao Indonesia Rubber 88.7 (57.2 120) 89.2 (49.4 129) 61 (40 83) 46.2 (28.9 75.2) Simple agroforests/ i ntensive tree crop Brazil/Peru Coffee monoculture Multistrata system Cameroon, Oil Palm Indonesia, Pulp trees 11.0 (8.73 12.5) 61.2 (47.5 74.7) 36.4 37.2 (23.6 50.7) Grasslands/crops Brazil/Peru Extensive pastures Intensive pastures Indo nesia Cassava/Imperata 2.85 3.06 <2 Numbers in parentheses are range of the mean value. Source: Palm et al ( 2004 ) Table II in page 149

PAGE 61

61 CHAPTER 4 ABOVEGROUND AND BELO WGROUND CARBON STOCK S IN TRADITIONAL AND IMPROVED AGROFORESTR Y SYSTEMS IN MAL I, WEST AFRICA Introduction Agroforestry is a very common concept of traditional agricultural land use in most of the tropics. In the West African Sahel (WAS), the traditional systems such as bush fallow and parkland systems involve in tegration of tre es with agricultural crops. The trees provide subsidiary ( famine ) food when crops fail by drought ; can be sources of oil, wine or other condiments, and are used for tools, fences or fodder (Boffa 1999). Also, trees can increase water and nutrient availab ility through nitrogen fixation retriev al of water and nutrients from the deeper layer s of soil and reduction of water and nutrient losses from leaching and erosion in the semiarid region (Buresh and Tian 1997; Kang et al. 1999) A s described in C hapter 2, parkland agroforestry systems are currently the most prevalent land use systems in the WAS Other agroforestry practices such as improved fallow, intercropping, tree fodder planting, and boundary planting have been introduced but these are still not widely adopted (Niang et al. 2002; Levasseur et al. 2004) Most of existing studies on the parkland systems are about the productivity of trees and/or crops grown underneath, or about the interaction/competition of the trees and crops (Kater et al. 1992; Jonsson et al. 1999) C arbon (C) sequestration defined by the United Nations Framework Convention of Climate Change (UNFCCC) as has not been a subject matter of studi es in much of the WAS region, let alone in parklands and other agroforestry systems of the region. Nevertheless, it is widely accepted that environmental degradation resulting from long term drought and land use change has adversely affect ed the terrestri al C stocks in the region (FAO 2000; Reich et al. 2001). Although Mali, where this study was conducted signed off on the

PAGE 62

62 Kyoto Protocol, there has been no pilot project to document C sequestration and C credit sale in the country. Woomer et al (2004b) co nducted a national scale C stock assessment in Senegal (neighboring country of Mali), and found that there were opportunities for biological C mitigation, but they were constrained by available knowledge and access to resources. Compared with large scale tree plantation, agroforestry is expected to be the most feasible afforestation/reforestation project that can be conducted by the majority of resource limited land users (farmers) in the WAS. Because of the scarcity of on site information, it is importan t to directly measure or estimate both biomass C and soil C stocks of various agroforestry systems Therefore the study reported in this chapter was undertaken with two research questions: 1. How do different agroforestry system s differ in their potential f or C sequestration? H ow much C is stored in the traditional and improved agroforestry systems, e specially comparing above ground and below ground ? 2. W hat is the overall relative attractiveness of each of the selected agroforestry systems considering them as biological C sequestration project s ? Materials and Methods C sequestration potential of a specified project is calculated by C sequestered by the project minus C sequestered by the baseline (without the project) Since this study is not a long term project, it was impossible to monitor both C accumulations by the project (agroforestry) and by non project land use over the time. Instead, the difference s of C stock among selected land use systems are assumed to represent the potential of C sequestrati on by the land use change. Study Area This research was done in Sgou, Mali in cooperation with the ICRAF ( World Agroforestry Centre ) Field Station of Sahel Regional Programme.

PAGE 63

63 Republic of Mali Mali is a landlocked country with an area of 1.24 million k m 2 ; the Sahara desert occupies 60 % of it ( Figure 4 1 ) Mali shares borders with seven countries: Mauritania, Algeria, Burkina Faso, Ivory Coast, Guinea, Niger, and Senegal. It is a vast land of plains fed by two major rivers, the Senegal River on its western edge and the Niger River flowing in a wide arc from southwest to east The p opulation is 12.3 million with a growth rate 2.63 %, one of the highest in the world (World Bank 2007 ). While most people live in rural area s, 1. 2 million people live in the capital city, Bamako ; 90 % of people are Muslim s The major language is Bambara ( original language of a major ethnic group), although the official language is French Agriculture is the main source of livelihood for the peop le, with 80 % of people engaged in agriculture or fishing (CIA 2007) Cotton is the main export product ; gold and phosphate from mines in the north ern area are also traded. T he per capita national income was US$ 380 in 2005 Despite higher economic grow th since 1994, Mali remains one of the world's poorest countries rated 174/177 in 2005, using the UNDP Human Development Index (World Bank 2007). With the impact of current climate change and environmental degradation, the country is vulnerable to droug ht and risks further desertification S gou region The City of S gou is Mali s second largest urban center locat ed on the Niger River about 300 km northeast of Bamako. It is the capital of S gou region one of the eight administrative regions of Mali ( Figure 4 1 ) The region is locate d in the buffer zone of the Sahara, with 60 90 rainy days and 300 700 mm of rainfall annually (the rainfall intensity increasing from the north to the south ) The Sgou region has seven cercles ( administrative sections) and 2,218 villages. The p opulation of the region is about 2 million with 0.3 million living in S gou city. Cotton

PAGE 64

64 ( Gossypium spp.) i s the main crop (and product ) of the region and the region is well known for it A large cotto n mill invested in by Chinese companies is operating at the edge of the city. R ice ( Oryza glaberrima and Oryza sativa ) i s grown extensively in the irrigated area around the Niger River (R publique du Mali 2005). F armers commonly grow rain fed pearl m il let ( Pennisetum glaucum ) and sorghum ( Sorghum bicolor ) as staple food crops Select ed Land use Systems for Field Data Collection A p reliminary survey was first conducted in July 2005 to identify the targeted land use systems and possible villages to loc ate on farm plots. Five systems were selected : two parkland agroforestry systems, two improved agroforestry systems, and abandoned land (degraded land) for comparison. Parkland systems The major land use in the S gou region, as in most of other parts of the WAS, is parkland agroforestry. T wo parklands types are common: with Faidherbia albida or Vitellaria paradoxa as the dominant tree species. These two types occupy more than 60 % of cultivated land in S gou region (personal communication August 20 05, with Director of Forestry Department, S gou). Tree density is in the range of 20 to 30 trees/ha in both systems. Crops cultivated unde rneath the trees include pearl millet and sorghum sometimes intercropped with cowpea ( Vigna unguiculata ) a nd/or ban barra groundnut ( Vigna subterranea syn. Voandzeia subterranea ) F. albida has a during the hot dry season and dropping them before the rain y season ), which is quite advantageous for agroforestry: it reduces shading of crops grown underneath the tree and possibly reduces competition for water between trees and crops, and enables farmers to grow crops under the trees with practically very little reduction of cropped area in the int ercropping

PAGE 65

65 situation. It is also a nitrogen fixing tree, and farmers use the foliage for both organic manure and fodder. V. paradoxa is probably the most common parkland species in the WAS, known as Kari t (in French) or Shea (in English). Farmers use t he fat extracted from the nuts in multiple way s, such as cooking oil, medicine, and cream for dry skin. This fat, called Karit butter or Shea butter, has recently become popular for cosmetic use in the western world. It has a natural UV protection and m oisturizing effect and is therefore one of the booming products for international cosmetic companies. ICRAF organize d a program to study the characteristics of V paradoxa physiology for better production and to establish a network for local farmers to market this newly developing commodity (Pro Karit 2007 ). Improved a groforestry s ystems To examine the possibility of implementing reforestation/afforestation project s by agroforestry under the Kyoto P rotocol for C sale, it is necessary to consider agrof orestry systems with higher tree density than that of parkland systems ( where crown cover is about 20 %), or abandoned land (crown cover is close to 0 %). This is because the definition of forest or afforestation of Kyoto Protocol normally refers to h igher tree density than parkland and taller trees than bushes in abandoned land. In S gou region, ICRAF carried out a study to identify agroforestry needs for the WAS in general. The study indicated an overall shortage of fodder during the dry season, a nd that farmers need to protect their fields, especially during the dry season when cattle roam freely (van Duijl 1999 ; Figure 2 6 ). To address these problems, ICRAF has been introducing the improved agroforestry technologies such as live fence s and fodde r banks. Live fence refers to planting relatively fast growing trees in very high density around field plots, orchard s or cultivated land. Trees are planted along plot/field boundaries at 1 m intervals in two lines 1.5 m apart thus giving a 3 m wide th ick fence around the cultivated land F ive tree/

PAGE 66

66 woody perennial species are commonly used for live fence in the S gou region: Acacia nilotica Acacia senegal Bauhinia rufescens Lawsonia inermis and Ziziphus mauritiana The protected crops inside the fence are mainly cash crops such as cassava ( Manihot esculenta ), watermelon ( Citrull u s lanatus ) calabash ( Lagenaria siceraria ) and groundnuts ( Arachis hypogaea ) Fodder bank is a system of planting exotic and/or indigenous species suitable for animal fod der in relatively high density. ICRAF introduced an exotic species, Gliricidia sepium and two indigenous fodder trees, Pterocarpus lucens and P. erinaceus although these two species did not grow well enough to be harvested in all the experimental plots. The common size of the fodder bank is 0.25 ha (50 m by 50 m) framed in by live fence, and fodder trees are planted 2 m by 1 m in lines. Most of the pilot fodder banks were established on previously cultivated land. Abandoned (degraded ) land Land degra dation is a very severe problem in S gou region and the extent of degradation varies considerably from no vegetation with crusted surface to land covered by bushes and grasses. In this study, lands somewhat vegetated with grasses and bushes were chosen fo r plots. These lands were previously cultivated, but recently (within 10 years) abandoned because of the lack of soil fertility ICRAF is trying to introduce live fence s and fodder bank s to improve this over exploited land In this scenario, the differe nce in the amount of C between these abandoned lands and fodder bank/live fence systems would be the sequestration potential of the improved agroforestry practices. Research Design Since it was impossible to find all the land use systems in the same villag e, plots representing each land use system were set up in different villages in the S gou region ( Table 4 1 ). All the villages are within 30 km from the center of the city. For each land use system (treatment), three on farm plots (replicates) that were as uniform as possible (size, understory

PAGE 67

67 crops present tree density, age, land use history) were chosen. Villages for two parkland plots were chosen because of relatively mature and uniform F. albida and V. paradoxa trees ( Figure 4 2 4 3 ) Each plot was set inside a different farm. The village for live fence plots was chosen because it has a group of farmers who participated in ICRAF s live fence program. Three farmers live fences with the same age and similar tree growth were selected ( Figure 4 4 ) Fodder bank plots were more difficult to find. Since fewer fodder bank s were adopted and maintained than live fence s only three comparable fodd er banks were found in three different villages ( Figure 4 5 ) The village for abandoned land plots was chosen near the vast degraded land spreading east of S gou city. All abandoned land plots were previously cultivated by farm ers ( Figure 4 6 ) Data Collection Field data collection was conducted from August to September 2005. Biomass measurement T he plot size was 1 ha for parkland systems, wh site for improved systems (about 0. 25 ha or less), and 0.5 ha for abandoned land ( Table 4 1 ) D ata r ecorded for aboveground biomass were : Species and n umber of trees in each plot Diameter at breast height (DBH) and/or diameter at the ground of each tree/bush T ree /bush h eight Crown size for bushes in abandoned land. Regarding land use history, age of traditional parkland and abandoned land systems were difficult to estimate. According to owners of the plots, all parkland plots were at least 35 years old, and t he abandoned land plots less than 10 years. All three live fence plots were 8 years old (at the time of data sampling they were established in 1997), and two of the three fodder bank plots were 9 years and one was 6 years old.

PAGE 68

68 S oil sampl ing Based on discussions with ICRAF researchers, three depth classes were determined for soil sampling: 0 10 cm (surface soil) 10 40 cm (crop root zone) and 40 100 cm (tree root zone ). The average size tree in each plot was selected based on aboveground inventory data as the center of the soil sampling area S amples were taken from three horizontal distances from trees in the two parkland systems and live fence system At each horizontal distance samples were taken from four different p oints using an auger, and samples from each depth from these points were well mixed as a composite sample before transferring them i n to bags. F or the fodder bank where the bush/tree density was fairly uniform, and the abandoned land plots, f our random p oints were chosen to make a composite sample of each depth. More details of soil sampling are described in Chapter 5 Sampling for bulk density measurements were taken separately for each depth and land use using a 100 c m 3 stain less steel cylinder. A so il pit (1 m depth) was made for each land use plot, and the cylinder was horizontally driven to the center of each depth class to take the samples for bulk density determination All samples (total 144 samples: 99 composite samples and 45 bulk density sam ples) were air dried and shipped to University of Florida for analyses. C arbon Stock Estimation A m ount of biomass C and soil C (C stock) were estimated respectively, as follow s Total C stock (Mg C /ha) of each land use system was calculated by adding b iomass C stock and soil C stock of each plot of each land use system (all data on per ha basis). Live fences are conventionally expressed in terms of length of rows. calculated based on 3 m width; but in prac fence is important. Since live fences are along plot/field boundaries of unequal sizes, it is not realistic to assign a standard row length per unit area (ha) of plot/field.

PAGE 69

69 Biomass C stock Since this study examin es the potential of C sale s under the Kyoto Protocol s Clean Development Mechanism (CDM), methodologies used here for estimating biomass C stock are based on the guideline published by UNFCCC (2006). Although species specific allometric e quations are ideal, none was available for parkland trees from the study region. As an alternative, the UNFCCC guideline recommends using the following general equation s from FAO (1997) T ree biomass (kg) = exp ( 2.134 + (2.530 lnD BH(cm) )) (n = 191, R 2 = 0.97) (Eq. 4 1) In the FAO (1997) paper there are general equations suggested for drylands. However, th o se equations were developed from much smaller sets of trees in India and Mexico, and their DBH ranges are 3 to 30 cm. F. albida and V. parad oxa trees in parkland plots of this study greatly exceed the diameter range of these general equations. The average DBH of trees in the plots were about 59 cm for F. albida and 42 cm for V. paradoxa Using the dryland general equations may cause signific ant over estimation of the biomass. Thus, this study follows a method proposed by Woomer et al. (2004 a ) in Senegal, using Equation 4 1. This equation is also from FAO (1997) for higher rainfall area (1500 4000 mm), but covers the diameter ranges of F. albida and V. paradoxa trees in this study There are two options for estimating the biomass of the five live fence and one fodder bank species. One is following UNFCCC s guideline, using a general equation for areas with <900 mm annual rainfall. The tre e sizes are within this equation s DBH limits (3 30 cm). The equation is: T ree biomass (kg) = 10 ^ ( 0.535 + log 10 DBH(cm) 2 /4 )) (R 2 = 0.94) (Eq. 4 2)

PAGE 70

70 The UNFCCC guideline s suggest using these equations when no local species allometric equations are available. A s econd option is to us e the equations developed from Acacia tortillas and Acacia ruficiens in Northwest K enya (Coughenour et al. 1990). These are: Log 10 (g mass) = 2.26+3.98 log 10 (mm stem diameter) (Eq. 4 3) R 2 =0.98 (stem diameter<15.7 mm) Log 10 (g mass) = 0.68 +2.66 log 10 (mm stem diameter) (Eq. 4 4) R 2 =0.98 (stem diameter>15.7 mm) Although Northw est Kenya is not in the WAS, its climatic condition is much more similar to that of the study area than to the area where the UNFCCC guideline s equation was developed. Gonzalez (2001) used these Acacia spp. equations in his research at various parts of S enegal. These two options were both tried in this study, and the results of estimated biomass C are compared later. For abandoned land plots, equations for Guiera senegalensis used by Seghieri et al (2005) in Niger were adopted G. senegalensis is the mo st dominant shrub species in the abandoned land plots, and the equation was originally developed in fallows of Mali (Ciss 1980, Franklin and Hiernaux 1991). Foliage mass of each stem of each shrub: Bl stem (g), B asal circumference of the stem: C stem (cm): Bl stem = 1 09 C stem (all stems of n = 20 shrubs, R 2 = 0 82 P < 0 001) (Eq. 4 5) S tem wood dry mass: B w stem (kg): B w stem = 0 0037 C stem (36 stems among n = 15 shrubs, R 2 = 0 90 P < 0 001) (Eq. 4 6) Leaf and wood masses ( Bl stem and B w stem ) were then aggregated for each multi stemmed shrub. To calculate the amount of C in the biomass, C fraction rate of 0.5 is suggested in the UNFCCC guideline. Belowground biomass is also estimated by using the suggested root/shoot

PAGE 71

71 ratios, which are 0.25 for trees and 0.5 for abandoned land bushes. Biomass C stock was calculated by adding aboveground biomass C and belowground biomass C. Soil C stock Soil C stock was estimated from the samples taken at each land use s plots Sub samples were taken from the 99 air d ried composite samples and ground. Soil C percentages of the sub samples (% of C weight in whole soil weight) were measured by the dry combustion method on an automated Flash EA 1112 NC elemental analyzer (Thermo Fisher Scientific, Inc.). Then, the soil C percentage data of each land use of each depth class was converted to the amount of C per ha basis with using bulk density data. Statistic al Analysis Analysis of variance (ANOVA) by SAS PROC MIXED procedure and Turkey Kramer multiple comparison test we re conducted to compare the C stocks of different land use systems and soil depth. The linear model shown below was used. y i = + L i + e i y i is the C concentration in land use i is the population mean, L i is the land use (treatments), i = FA, VP, LF, FB, and AL. FA: F.albida parkland, VP: V.paradoxa parkland, LF: live fence, FB: fodder bank, and AL: abandoned land. e i is the r andom variable error within the experiment Linear correlation was also tested to examine the relationship of biomass C and soil C stock.

PAGE 72

72 Results C Stock in Biomass and Soil The t wo parklands selected for the study were similar in tree density and the d ominance of major tree species ( Table 4 2 ) F. albida trees were generally larger and taller than the V. paradoxa trees in all parkland plots. Although live fence and fodder bank plots ha d large number s of trees per ha, these were proportionally calculated numbers based on small plots. The real size of live fence plots was 0.088 ha on average (293 m length 3 m width) with 327 trees, and that of fodder bank plots was 0.24 ha on average with 145 G. sepium trees. The s oil C specific comparisons will be presented in detail in Chapter 5 ; but estimates of soil C stock of each depth are presented, along with biomass C stock per ha basis of each system ( Figure 4 7 ). Statistical comparison by ANOVA show e d that a bandoned land ha d larger soil C stock than the other four systems in every depth, although the significance varied with depth Estimated biomass C values of live fence and fodder bank plots from UNFCCC guideline equations and those from Acacia sp p. equations developed in Kenya were significantly different ( t test p <0.01) ( Table 4 3 ) However, when each set of estimation was compared with other three systems by ANOVA, the results were the same. The ranking of systems in order of biomass C stock was: F. albida parkland > V. paradoxa park la nd > Live fence > Fodder bank >Abandoned land. However, the last three systems were not significantly different ( Tukey Kramer test ) even when analyzed separately Total C Stock Tot al C ( aboveground biomass C + soil C) stock of each system was calculated and compared at three different soil depth ranges The order among the systems and the significance of difference var ied with the depth of soil ( Table 4 4 ). Overall, F. albida parkland ha d the largest total C stock and was significant ly differen t from the other four systems. V. paradoxa

PAGE 73

73 parkland ha d the second largest total C stock, while the difference between other systems became less significant as deeper soil C stock was included. Also as deeper soil was taken into account, abandoned land ha d more C stock relative to the other systems. Relationship between Biomass C and Soil C All the possible combination s of biomass C stock data and soil C stock data across the five land use systems were tested for their relationship: Biomass C stock data and soil C stock data (0 10cm), Biomass C stock data and soil C stock data (0 40cm), Biomass C stock data and soil C stock data (0 100cm), Biomass C stock dat a and soil C stock data (10 40cm), and Biomass C stock data and soil C stock data (40 100cm). None of the regression was significant. Discussion In terms of total C stock per ha traditional agroforestry systems (parklands) are a larger storage than im proved agroforestry systems (live fence and fodder bank) or abandoned land. Although the improved agroforestry systems are relatively young, they are not likely to store as much as the parklands at the end of their 25 year rotation Because branches of f odder bank trees are annually pruned to control their height (<4 m), and live fence trees are planted in such a high density that woody biomass accumulation per tree will be comparatively less However, h aving a large C sto ck does not necessar il y mean hav ing a large C sequestration potential. Traditional parklands are very stable (long standing) and so is the C stored Farmers in the area are unlikely to increase the tree density of parklands to match the UNFCCC s forest plantation criteria for seques tering more C for sale, because it will produce a negative impact on crop growth. Also it is very difficult to convert nutrient poor abandoned land to parklands, since silvicultural methods are not established for these species (parkland trees are mostly natural regeneration), and abandoned land is not fertile enough to grow crops underneath.

PAGE 74

74 On the other hand, introducing live fence s and/or fodder bank s into cultivated land or abandoned land can sequester C by increasing the tree biomass, but the extent would vary largely by the baseline and accounting method used Biomass C sequestered by live fence planting is a since they are normally established on the tree less cultivated land (the baseline is nearly 0). The potential of fodder bank s however, depends on initial plot condition As in the situation for live fence, w hen the cultivated land is converted to fodder bank most of C sequestered by the fodder trees can be counted. However, establ ishing fodder bank s on abandoned land, as ICRAF or local government is trying to promote, may actually result in net loss of C stock during the initial stage because the biomass from bushes and grasses in the abandoned land has to be removed at the time o f establishment, and it may take years for fodder trees to accumulate an amount equal to the original biomass. Further investigations are needed on temporal C dynamics of these systems. S oil C is not considered in the calculations of the Kyoto Protocol f or its first commitment period (2008 2012) When, rather than if, soil C is taken into account, determining b aseline soil C will be another challenge to determine and compare the C sequestration potentials of land use systems. Results suggest that soil s ampling depth makes a large difference in estimating the amount of C stored per area basis, as well as the potential for C sequestration ( Table 4 4 ) To compare and discuss the C sequestration potential of different land use o r different ecoregions it will be very important to standardize sampling depth. Several studies in Africa reported that planting trees for C sequestration will not immediately retain soil C equal to the baseline level nor increase it in the short term (K aya and Nair 2001; Walker and Desanker 2004). Introducing live fence s or fodder bank s may increase the biomass C in the system but may not in crease soil C. S oil C sequestration potential will be discussed in more detail in the next chapter.

PAGE 75

75 Regarding t he biomass estimation methodology, two sets of allometric equations we re used for live fence and fodder bank. The values calculated from the two methods are significantly different from each other ( UNFCCC s general equation is much more conservative than Acacia species equations from Kenya). However, comparing each method s values with other three land use systems showed similar results: both live fence and fodder bank have not (perhaps not yet) stored significant amount s of C compared with bushes of aban doned land. This is partly because of the young age of the two systems (6 to 9 years old) The UNFCCC guideline suggest s applying the general equation only if it is impossible to find/establish local allometric equations. The Acacia species equations ar e not exactly local but they are from environments more similar to the studied area than were the general equations. Considering the difference between these two method s developing the local allometric equations is likely to increase the profits from C credit sale when the C market is introduced in the area. Obviously, substantial research efforts are warranted in this area.

PAGE 76

76 Table 4 1. Characteristics of the v illages where the experimental plots were set up in S gou region, Mali Land use Name of t he village Position Elevation (m) Size of the plot Faidherbia albida parkland Togo N.13.35, W. 6.31 300 1 ha Vitellaria .paradoxa parkland Dakala N.13.32, W. 6.23 297 1 ha Live fence Dougoucouna N. 13.37, W.6.37 298 294m (average) Fodde r bank 1 Dakala N.13.32, W. 6.23 297 0.25 ha Siguila N.13.28, W. 6.21 305 0.25 ha Banankoroni N.13.35, W. 6.38 293 0.22 ha Abandoned land Diamaribougou N. 13.36, W. 6.19 298 0.5 ha 1 One fodder bank plot from each village Table 4 2. Characte ristics of the experimental plots (three plots average) for five selected land use system s in S gou region, Mali DBH (cm) Height (m) Number of trees (ha 1 ) Species composition Faidherbia albida parkland 59.4 (1.8) 13.0 (0.9) 21 (5.3) A verage 88.6% F aidherbia albida dominance Vitellaria paradoxa parkland 41.7 (5.9) 9.9 (0.9) 20 (0.6) A verage 80.6% V itellaria paradoxa dominance Live fence 2.5 (0.5) 2.5 (0.4) 3720 (882) Average 67.6% A cacia nilotica Fodder bank 2.2 (0.5) 2.0 (1.1) 5 88 (277) G liricidia sepium average only Abandoned land 2.8 (0.6) diameter at ground 1.3 (0.4) 46 (30) A verage 47.5% Guiera senegalensis and 39.5% Combretum micranthum Note: Numbers in parentheses are standard deviations. Tree dominance means the p ercentage among the standing trees in the plot.

PAGE 77

77 Table 4 3. Estimated b iomass C (above and below ground) stock values of each plot and three plots average of five selected land use systems. Two sets of values from different allometric equations are s hown for live fence and fodder bank systems, which are significantly different in t test (values of UNFCCC equations < values of Acacia spp. equations ). Faidherbia albida parkland Vitellaria paradoxa parkland Live fence UNFCCC 1 Acacia 2 Fodder bank UN FCCC 1 Acacia 2 Abandoned land (Mg C ha 1 ) Plot A 51.4 24.2 3.2 5.9 2.1 4.8 0.8 Plot B 55.7 16.5 3.0 4.3 1.8 2.7 0.4 Plot C 54.8 26.6 7.8 14.8 2.7 4.9 1.0 Average 54.0 22.4 4.7 8.3 2.2 4.1 0.7 a b c c 3 c c 3 c a, b, c: Mean separation by Tukey s multiple comparison test at p <= 0.05) 1 Estimation from the UNFCCC guideline's equations 2 Estimation from Acacia spp. equations developed in Northern Kenya 3 Values from UNFCCC equations and Kenyan equations w ere separately compared with other three systems Table 4 4. Total C stock (biomass C + soil C of different depth) of five selected land use systems. Total C stock (Mg C ha 1 ) More C Less C 1 2 3 4 5 Biomass + 0 10cm soil C FA 59.8 a VP 27.7 b LF 9.8 c AL 7.9 c FB 4.8 c Biomass + 0 40cm soil C FA 70.8 a VP 37.1 b AL 24.7 bc LF 17.7 c FB 14.0 c Bi omass + 0 100cm soil C FA 87.3 a AL 56.9 b VP 49.8 b FB 35.6 b LF 28.7 c FA: F aidherbia albida parkland, VP: V itellaria paradoxa parkland, LF: Live fence, FB: Fodder bank, AL: Abandoned land (a, b, c: Mean separation across land use systems by Tuk ey Kramer 's multiple comparison test at p <= 0.05) Data s ource: Biomass C values of live fence and fodder bank are from UNFCCC equations.

PAGE 78

78 A B Figure 4 1. A: Location map of Mali; B: Map of Mali showing its land locked nature: C: Map of S gou region ( T he sign refers to the city of S gou). S gou Niger River Bani Rive r C

PAGE 79

79 Figure 4 2. Faidherbi a albida parkland in Togo village. The tree leaves are shed at the beginning of the rainy season ; but they return at the beginning of the dry rainy season. The understory crop is pearl millet ( Pennisetum glaucum ) (Photographed by author) Figure 4 3 Vitellaria paradoxa parkland in Dakala village. The trees have wide canopies, and leaves remain during the rainy season. Farmers plant crops (in this photo, pearl millet) beneath the trees, often very close to the trunk (Photographed by author)

PAGE 80

80 Figure 4 4. Live fence system in Dougoukouna village. Relatively fast growing (mostly thorny) trees are planted around the crop field to protect crops from free roaming animals. The trees are planted in two lines (1.5m apart). The outside line trees s hown in the photo are mostly Acacia nilotica (Photographed by author) Figure 4 5. Fodder bank in Dakala village. Gliricidia sepium trees are planted at 2 m by 1 m spacing in lines. Towards the end of the dry season when other fodder sources such as fresh grasses or crop residue are scarce, farmers harvest branches of the trees, dry and feed them to their domestic animals. (Photographed by author)

PAGE 81

81 Figure 4 6. Abandoned land just outside of Diamaribougou village. The land was cultivated until l ess than 10 years ago. The surface soil is eroded leading to formation of a hard surface pan. Only certain bushes such as Guiera senegalensis and Combretum micranthum can survive on this type of degraded land. (Photographed by author)

PAGE 82

82 Figure 4 7. Aboveground and belowground C stock per ha of five selected land use systems. Biomass C is shown above the x axis, and soil C is shown below the axis with three soil depth classes. Values of live fence and fodder bank are from UNFCCC equations.

PAGE 83

83 CHAPT ER 5 SOIL CARBON SEQUESTRATION IN DIFFERENT PARTICL E SIZE FRACTION S AT VARYING DEPTHS UNDER AGROFORESTRY SYSTEMS IN MALI Introduction The measurement of carbon ( C ) content is part of the basic soil analysis procedure. Inventory data on soil C content is a vailable in most of the WAS countries. However, to discuss soil C sequestration as one of the options for mitigation of atmospheric CO 2 the stability of soil C ( how well C captured inside the soil) has to be considered. In other words, the soil C t hat goes back to the atmosphere after decomposition within a month of its deposition, and that stays in the soil for decades should not be counted as similar in terms of C credits. Characteristics such as the stability of soil C are very controversial iss ues in estimating and accounting methodologies (Ingram and Fernandes 2001; Garcia Oliva and Masera 2004) Also, soil sampling depth for these accounting procedures needs to be deeper than for normal soil analysis. The conventional soil C studies of agric ultural systems mostly focus on soil organic matter in the surface layer of 20 cm for the interests of soil fertility But sampling of deeper soil horizon is necessary in efforts to understand the extent of soil C protection and characteristics of various s oil C form s especially the systems involving deep rooting plants such as trees (Jobbagy and Jackson 2000) In general, soil C dynamics regarding C sequestration have not yet been well studied, although recent technological development and interests tow ards climate mitigation activities are contributing to an increased number of this type of studies (Post et al. 2000; Sun et al. 2004) Still, these studies are limited even in developed countries, and not easily available in the research resource limite d area such as the studied region or Africa in general. The studies of this nature that have been conducted so far have been in natural environment such as forest stands, tundra, or grasslands, probably due to the relatively stable dynamics of soil proper ties

PAGE 84

84 (Richter et al. 1999; Schuur et al. 2001) And s oil C studies in agricultural croplands have mostly been in the context of soil productivity management ( Phillips et al. 1993; Beare et al.1994; Alvalez et al. 1995 ; Blair et al. 1995; Franzluebbers et al. 1995 ; Rhoton 2000 ), although recently more and more soil C studies are considering agricultural soil as C sinks a mechanism which removes CO 2 from the atmosphere (Smith et al. 1998, Duiker and Lal 1999, Lal 2004 b ) Soil C studies in agroforestry syst ems have been few. Interactions between crops and trees and the relatively short term rotation of land management make such studies more complicated and challenging compared to single species agricultural and forestry systems Existing studies in agrofor estry systems measure soil organic matter (SOM) content with other soil nutrients (Kang et al. 1999; Makumba et al. 2006). Those studies discussed whether trees have positive ( e.g., nitrogen fixing) or negative ( e.g., competition for light, nutrients, or water) impacts on crop production. In the WAS, parkland trees were found to increase soil C around trees (Jonsson et al. 1999), and an improved fallow system (planting Gliricida sepium during the non cropping phase) was found to increase soil C on the sur face compared with natural grass fallow (Kaya and Nair 2001). These studies support the expectation that agroforestry systems would enhance soil C sequestration, but there is still little information about trees influence on C in deeper soil and stabilit y of various forms of soil C sequestered by trees. In this study, o rganic C in soil is assumed three ways: 1 physically stabilized, or protected through microaggregation (microaggregate associated soil C) 2. intimate association with silt and clay particles (silt and clay associated soil C), and 3 b iochemically stabilized through the formation of recalcitrant soil organic matter compounds (non hydrolysable soil C) (Six et al. 2002). There are some othe r ways such as Al or Fe SOM complexes, C accumulation resulting from anaerobic conditions, and transfer to

PAGE 85

85 subsoil by colloidal or soluble C ; but they do not seem to occur significantly under the soil and climatic conditions of the study region (Tan et al 2004; Nierop et al. 2007 ; Zinn et al. 2007) The turnover time for physically protected C (type 1 and 2) is estimated to be 50 1000 years ; for biochemically protected C (type 3) it is 1000 3000 years. The turnover time for less stable C within mac roaggregates is 5 50 years, and for other type s C such as the litter fraction it is 0.1 20 years (Batjes 2001). To differentiate the types of soil C, physical fractionation is the common initial step. The dynamics of soil C in each fraction size ca n be further investigated by 13 C isotopic ratio measurement, which distinguishe s between C derived from plants that follow C3 photosynthetic pathways (all trees) and those that follow C4 pathway s (most warm season graminaceous plants: in this study pearl m illet Pennisetum glaucum and sorghum Sorghum bicolor ) This method has been used for studying the impact of land use change on soil C and for comparing the C dynamics in different land use systems (Balesdent et al. 1998; Potvin et al. 2004). Research Questions In this scenario, the present study was undertaken based on the premise that compared with agricultural and tree less systems, a groforestry systems will help store more C in soil and offer better stability of stored C in deeper soil layers due t o presence of deep rooted trees Specific research questions are: 1. Do trees contribute to soil C storage in the selected agroforestry systems, and how stable is the stored C ? 2. What is the relative attractiveness of each of the selected agroforestry systems or land use change in terms of its soil C sequestration potential? Materials and Methods The study was conducted in the seven selected villages of S gou region, Mali, West Africa. The details of the site and the selected land use systems (treatment s ) are described in

PAGE 86

86 Chapter 4. In each land use system, three on farm plots (replicates) were chosen for soil sampling Research Design Soil s amples were taken from different distances from trees. In the two parkland systems, three horizontal distances for so il sampling were chosen : Near (bottom of) the tree 3 m ( about half the crown radius) from the trunk 10 m from the trunk ( outside of the crown) The average size tree in each plot was selected based on aboveground inventory data as the center of the sampl ing area Soil samples were taken from four direction s (north, south, west, east) around the tree and mixed before putting in the sample bag ( Figure 5 1 ). Three horizontal distances for sampling live fence plots were: Near (bot tom of) the tree 1 m ( inside) from planted line (root influence zone) 3 m (inside) from planted line ( outside the crown and rootzone ) Live fences are either rectangular or polygonal shapes; four sampling points on different sides were randomly chosen. Sa mples away from the tree line were taken inside the fence, because outside of the fence were often paths or borders of the cultivated land. Since fodder bank trees are evenly planted (2m 1m) inside the plots and shrubs are randomly grown in abandoned land plots, horizontal differentiation of sampling was not taken at these two systems. In each plot, samples were taken from four randomly selected points, and mixed well to form the composite sample Sampling depth s at each horizontal distance were as describ ed in Chapter 4 : 0 10 cm (surface soil) 10 40 cm (crop root zone) and 40 100 cm (tree root zone ). This was based on

PAGE 87

87 the expectation that the amount of C content would differ by depth class depending on the presence or absence of tree roots and til lage. In summary, the number s of soil samples are: 2 (P arkland systems) 3 (horizontal dist.) 3 (depth) 3 (replicates) = 54 1 (Live fence) 3 (horizontal dist.) 3 (depth) 3 (replicates) = 27 1 (Fodder bank) 1 (horizontal dist.) 3 (depth) 3 (replicates) = 9 1 (Abandoned land) 1 (horizontal dist.) 3 (depth) 3 (replicates) = 9 Total = 99 Soil Preparation and Analyses Samples were all air dried and passed through a 2 mm sieve (except samples for bulk density measurement) at the field station in S gou. There is no visible O horizon or surface litter and therefore no analysis was done for that layer ( Woomer et al 2004) ( Figure 5 2 ) Soil samples were brought back from Mali to University of Flo rida in October 2005 for analysis Samples for bulk density measurement for each depth class w ere separately collected at each plot with a 100 cm 3 cylinder. Wet weight and air dry weight were measured in the field. S amples were oven dried and analyzed fo r p article size distribution (USDA Soil Survey Lab Method) and pH at the University of Florida, Soil and Water Science Department laboratory Sub samples were taken from the 99 air dried samples and ground. Soil C content ( g C kg 1 soil ) of the sub sampl es w as measured by the dry combustion method on an automated Flash EA 1112 NC elemental analyzer (Thermo Fisher Scientific, Inc.). Soil f ractionation Soil samples were fractionated into three aggregate size classes (2000 250 m, 250 53 m, and <53 m) by wet sieving, following the method of Six et al. ( 2002) A sub sample of 100 g of the composite soil sample was submerged in deionized water as disruptive forces of slaking for about 5 min utes prior to placing it on top of a The sieving was done manually by moving the sieve up and down approximately 50 times in 2 minutes. The fraction

PAGE 88

88 remaining on the top of the sieve was collected in a hard plastic pan, oven dried at 65 o C and weighed. m were poured through a 53 procedure repeated. The recovery of mass soil fractions after overall wet sieving procedure ranged from 97 to 99% of the initial soil mass. S ub samples for e ach soil fraction (99 samples 3 fracti on size = 297 samples) were then ground and C contents were measure d by the same dry combustion instrument as described for whole soil C. Soil C in the large (L) fraction (2000 m) contains fairly new coarse/fine particulate organic matter (POM) C, although there is other forms of protected C not fully separated by wet sieving. The medium (M) fraction (250 m) contains both less protected C (within fine POM) and protected C (microaggregate protected POM C and silt + clay associated C). C in the small (S) fraction (<53 m) contains the protected form (silt + clay protected C or non hydrolysable C) although there are less stable forms of C in the size class, too (Six et al. 1998, Six et al. 2000) Unprotected C involves the youngest form of SOM and through the process of aggregate formation, adsorption/desorption, and condensation/complexation, soil C becomes older and more stable (SOM dynamic model Figure 5 3 ) C isotopic ratio ( 13 C / 12 C ) measurement 13 C values of soil samples (whole soil and fractionated soil) were measured by Thermo Finnigan MAT Delta Plus XL mass spectrometer (Thermo Fisher Scientific, Inc.) C isotope notation: 13 C = [(R Sample R Std )/R Std ] 10 3 ( Eq 5 1) Where R Sample is the 13 C/ 12 C ratio of the sample, and R Std is the 13 C/ 12 C ratio of the Vienna Pee Dee Belemnite ( VPDB ) standard. Relative proportions of soil C derived from C 4 plants material versus C 3 plants material was estimated by mass balance ( Balesdent and Mariotti 1996):

PAGE 89

89 C 4 plants contribution = ( Tr ) / ( Cr Tr ) (Eq. 5 2) Where is the 13 C value of a given sample, Cr is the average 13 C value of C 4 plants tissue ( Tr is the average 13 C value of C 3 plants ( In the studied l and use systems trees and bushes in abandoned land are C3 plants, and crops grown underneath the parklands around the live fence are C4 plants (sorghum and millet) as well as the presumed previous vegetation (crops) of fodder bank and abandoned land. St atistical Analysis Analysis of variance (ANOVA) was used to estimate the variance components. The linear models were applied to the soil C concentration data. Model1 was applied to all five land use systems, and model 2 was applied to three land use syst ems (two parklands and live fence ) that have distance information. The linear models were: Model 1: y ijkl = + L i + D j + F k + I l + L*D ij + L*F ik + L*I il + D*F jk + D*I jl + F*I kl + L*D*F ijk + L*D*I ijl + D*F*I jkl + L*D*F*I ijkl + e ijkl (Eq. 5 3) Model 2 : y ijklm = + L i + D j + F k + I l + T m + L*D ij + L*F ik + L*I il + D*F jk + D*I jl + F*I kl + L*T im + D*T jm + F*T km + I*T lm + L*D*F ijk + L*D*I ijl + D*F*I jkl + L*D*T ijm + L*F*T ikm + L*I*T ilm + D*F*T jkm + D*I*T jlm + F*I*T klm + L*D*F*I ijkl + L*D*F*T ijkm + L*D*I* T ijlm + D*F*I*T jklm + L*D*F*I*T ijklm + e ijklm (Eq. 5 4) y ijklm is the C concentration in land use i at depth of j fraction size of k isotopic ratio of l distance of m is the population mean, L i is the land use (treatments), i = FA, VP, LF, FB, a nd AL. FA: F. albida parkland, VP: V. paradoxa parkland, LF: live fence, FB: fodder bank, and AL: abandoned land. D j is the depth, j = 1 3

PAGE 90

90 1: 0 10 cm, 2: 10 40 cm, 3: 40 100 cm. F k is the fraction size, k = L, M, S L: large fraction ( 2000 m ), M: medium fraction ( 250 53 m), S: small fraction (<53 m ) I l is the isotopic ratio, l = C3, C4 C3: C3 plants origin C, C4: C4 plants origin C T m is the distance, m = n, m, f n: near the tree, m: middle of the canopy, f: far from the tree e ijklm is the random variable error within the experiment M odel 1 and model 2 including all the possible interactions between the factors were run using PROC MIXED procedure of SAS. Interactions that were not significant were dropped from the model. The models th at were biologically and statistically significant are presented in the results section. To further examine the interaction s data were sorted (PROC SORT procedure) with certain factors fixed, and tested again using ANOVA Based on the outcome of the ANO VA, factors and other soil characteristics (e. g percentage of sand, silt, and clay) were tested for their relationships using line ar regression All statistic al tests were considered significan t when p <0.05 unless otherwise specified. Results Soil Charac teristics Soils in the sample plots are mostly sandy loam or loamy sand ( Table 5 1 ) Soil colors varied from whitish or dark gray to reddish brown in different plots, but all are categorized as Haplustalfs by the regional surv ey (Doumbia 2000). Abandoned land soil was extremely hard to sample with an auger because bedrock was found in some places at less than 1 m depth ( Figure 5 2 ). Most of the time silt or clay was clearly observed in 70 80 cm d epths Content (g kg 1

PAGE 91

91 soil) of sand, silt and clay were compared statistically over the five land use systems. Abandoned land ha d lower sand content (530 694 g kg 1 soil) and higher content of silt and clay (306 470 g kg 1 soil) than the other four systems (sand: 715 935 g kg 1 soil, silt + clay: 65 285 g kg 1 soil) ( Table 5 1 ) Soils of the four systems in each depth class were not different in the particle size content Whole Soil C Whole soil C content across a ll the systems varied from 1 6 g C kg 1 soil ( Figure 5 4 ) Two parklands and live fence ha d three sampling locations ( 0, 3, and 10 m from tree base for parklands; 0, 1, and 3 m from tree lines for live fence ) Only the surfac e soil (0 10 cm) of live fence show ed a difference between the near tree and the two zones more distant from the tree but other depth classes and two parklands plots d id not show difference by horizontal distance from trees although the trend of nea r tree > outside crown was observed in the surface soil of both parkland systems C content decreased with soil depth for all land use systems except the fodder bank where the surface soil (0 10 cm) had less whole soil C than lower depths The whole soil C data of five land use systems were compared statistically using two factorial (land use and depth) ANOVA (model 1) Both land use and depth factors, as well as land use depth interaction were significant for that variables By Tukey Kramer multip le comparison test abandoned land ha d higher C con tent than the other four systems b ut the other four systems were not different from each other C con tent was different by depth: 0 10 cm > 10 40 cm > 40 100 cm across all treatments Among the d ata for the two parklands and live fence plots, horizontal distance was another factor (model 2) Land use and depth factors were significant but distance was not significant although showing the trend ( p =0.0884) in the three way factorial (land use, depth, and distance) ANOVA. When land use factor and distance factor were examined in the fixed

PAGE 92

92 depth class, the distance factor was still not significant in any depth class. However, at 10 40 cm and 40 100 cm class es C con tent was higher for F. al bida parkland than in the case of the live fence. C in Soil Fractions Carbon fraction contents [large (L): (250 m) and medium (M): (53 m) ] in different systems were most ly not different among each other and ranged from 1 to 2 g C kg 1 soil (except in fodder bank where it was from 0 to 1 C kg 1 soil) ( Figure 5 5 ). S mall (S) fraction (<53 m) C content d id not change much from 0 10 cm to 10 40 cm depth in all systems. In the live fence treatment, C in the 0 10 cm depth contained more L fraction (1.8 g C kg 1 soil) than the other two size fractions (less than 0.7 g C kg 1 soil) whereas the fodder bank treatment had very low C content of L and M fraction s in that soil layer ( 0.6 g C kg 1 soil) Data of C in t he three fractions were analyzed by three factorial ANOVA (land use, depth, and size) (model 1) All three factors were significant, as well as three combinations of two factor interactions. Results of multiple comparisons among land use systems were the same as for the whole soil C data; abandoned land ha d higher C con tent than the other four system s, which did not differ among each other. Depth class comparison also showed the same result: 0 10 cm > 10 40 cm > 40 100 cm S fraction and L fractio n C were both significantly higher in con tent than M fraction C when three depth class data are combined. The significance varied when each depth class was separately tested, but M fraction C content was always the lowest. When each fraction size data we re tested separately, land use and depth were significant for all fraction sizes. Distance from the tree was the only factor that was not significant in four factorial ANOVA (land use, depth, size, and distance) for three systems (two parklands and live fence : model 2 ). Interactions of four factors and three factors including distance were not significant.

PAGE 93

93 Further sorting and testing show ed that the distance factor was significant in only M fraction C at the 0 10 cm depth, where near tree was higher in con tent than outside crown Isotope Analysis of Whole Soil C The measured 13 C values of each depth class of each land use systems varied from 23.9 to 15.1 ( Table 5 2 ). Based on the values and the mass balance calcula tion, whole soil C data was separated into that originating from C 3 plants (trees) and C 4 plants (crops) ( Figure 5 6 ). Near tree data and outside crown data are presented side by side for two parklands ( Figure 5 6 A, B, C, D ) and live fence ( Figure 5 6, E, F ) I n the figure C of t ree origin was found more in surface soil and near the tree, although when they were tested statistically, there was no significant difference between near tree and outside crown data except for that of live fence at the 0 10 cm depth. Fodder bank d id not have much C of C 3 origin, even with trees growing in the plots. On abandoned land C 4 origin C was the major form of C and, as mention ed earlier, the soil C content was higher in this system compared with other systems Three factorial (land use, depth, and isotopic ratio) ANOVA was conducted among the five land use systems (model 1) All factors were significant, and three factor inte raction and two factor interactions including isotopic ratio were also significant. C 3 origin C and C 4 origin C were then tested separately using the SORT procedure. Land use was not a significant factor among C 3 origin C data, but depth was For C 4 o rigin C, both land use and depth had significant effect: abandoned land had higher content than other four systems with parklands higher than the improved systems, and deeper depth had less C content. Four factorial (land use, depth, distance, and isotop ic ratio) ANOVA was used to test differences among the two parklands and live fence systems (model 2) Distance was again not a significant factor while all others were Four factor interaction, as well as three and two factor interactions including dis tance was significant, suggesting distance was somewhat influential for

PAGE 94

94 C content When only C 3 origin C data was tested separately land use was not significant but depth and distance were Multiple comparison tests show ed that C 3 origin C content from near tree and half crown were higher than that from outside crown throughout the three systems. Among C 4 origin C, land use and depth were significant factors but distance was not. Isotope Analysis of C in Soil Fraction s The measured 13 C values of three fraction sizes varied from 25.4 to 15.7 ( Table 5 2 ). Data of C content in the three size fractions were also separated into those with C originating from C 3 and C 4 plants ( Figure 5 7 ). Four factorial ANOVA (land use, depth, size, isotopic ratio) was used to test differences among the five land use systems (model 1) Land use, depth, and size were significant factors but isotopic ratio was not. Four factor interaction was not significant, but all two factor interaction combinations including isotopic ratio were significant ( p <0.01). When C 3 origin C was tested separately land use was not significant while size and depth were. All three were significant factors among C 4 origin C data. Wh en data were sorted by the fraction size, C 3 origin C was significantly more than C 4 origin C in the L fraction, while C 4 origin C was more than C 3 origin C in the S fraction. Among the two parklands and live fence data sets, five factorial ANOVA (land use, depth, size, isotopic ratio, and distance) was conducted (model 2) Land use, size and depth were significant but isotopic ratio and distance were not. All combinations of three factor interactions including isotopic ratio and distan ce were signifi cant, so was the two factor interaction of distance and isotopic ratio. Distance bec ame a significant factor ( near tree > outside crown ) for C 3 origin C data sets, while it was not for C 4 origin C. Relationship s of Data Sets Linear relationship s were tested between C content data sets and other soil characteristics of the samples. The S fraction (<53 m) percentage in whole soil had significant relationship

PAGE 95

95 (R 2 =0. 60 ) with the S size C (contains protected C) con tent However, this relationship was not seen for M and L fraction s ; the more fraction percentage of M or L size in the soil does not mean the more soil C of those sizes. Also, t he regression between silt + clay content and whole soil C con tent was strong especially in the 10 40 cm soil dept h ( Figure 5 8 ). S and silt clay or silt + clay contents d id not have strong relationship with L fraction C or M fraction C. Although the regression between silt + clay content and S fraction C was poor (R 2 =0. 16 ) in the tota l data set it became more pronounced when the data set was divided by depth class. R 2 values between s ilt + clay and S fraction C were 0.53 at 0 10 cm 0.44 at 10 40 cm and 0.67 at 40 100 cm ( Figure 5 9 ). Soil pH or bul k density d id not show any strong relationship with C in whole soil or in each of the three fraction s in any depth or for all depth s combined. Percentages of C 3 origin C or C4 origin C d id not have a significant relationship with whole soil C con tent The re was a strong relationship between C 3 origin C con tent and L fraction C con tent (R 2 =0.67) throughout the data set. The relationship was stronger at 0 10 cm soil (R 2 =0.72) when data sets were divided by depth ( Figure 5 10 ), but were not significant in deeper depths The relationship between C3 origin C and S fraction C content was also observed in the 40 100 cm depth (R 2 =0.4 5 ). Discussion Contrary to expectations, C content in all soil depth s abandoned land than in any of the four agroforestry systems ( Figure 5 4 ) although the significance varied depending on the depth class Judging from the observation that the abandoned land soil ha d significantly more silt and clay fracti ons than those of soils under the agroforestry systems ( Table 5 1 ) it seems that the whole soil C content was directly related to the silt and clay contents of the soil In general, soil organic C con tent is known to correlat e positively with the

PAGE 96

96 amount of non crystalline clays (Powers and Schlesinger 2002). Indeed, the whole soil C content was related to silt + clay content in all three depth classes (strongest in 10 40 cm) ( Figure 5 8 ) Silt + clay content also had strong relationship with S fraction C (<53 m ), the strongest being in 40 100 cm ( Figure 5 9 ) but not as well as with whole soil C This suggests that silt and clay are mainly associated with soil C in <53 m size, especially in deep soil, but they are also associated with larger fraction size form of soil C. Based on the data points on Figure 5 9 abandoned land data do not seem to follow this relationship well, while they follow the relationship better in Figure 5 8 This could be because silt and clay are more involved in forming larger than 53 m fraction size C in abandoned land compared with other systems. After being abandoned for a few years (less than 10: see Chapter 4 ) the land probably was subjected to extensive erosion that took away aggregates and sandy particles from the surface lay er. The r emain ing soil was higher in silt and clay and formed a hard surface pan, which prevent ed further disturbance or leaching. The m ajority of soil C in the abandoned land was of C 4 plant origin ( Figure 5 6 ) suggesting tha t the stored C in abandoned land was mainly from the previous land use ( land cultivated with C4 plants) and was well protected. The o ther four land use systems were not significantly different in terms of the soil characteristics (pH, bulk density, parti cle size ). However, as seen in the soil pit photo ( Figure 5 2 ) the color was quite different in each soil pit, suggesting soil variations among plots of same land use systems in the same village. Still, s oil C content variatio ns among these soils can be explained more as a consequence of the influence of trees and land management than caused by soil characteristics as in the case of abandoned land Among the two parklands and live fence systems, C con tent was expected to be: n ear tree > under the crown > outside the crown, as reported in a similar study in the parkland system in South Mali, which showed the significant

PAGE 97

97 difference between near tree and outside the crown in 0 20 cm (although not significant in 20 40 cm) (Kate r et al. 1992). However, in this study, th e tree effect (distance factor) was only significant in the live fence but not in the two parkland systems (the trends of near tree > outside crown were observed in both parklands) ( F igure 5 4 ). From the field observation, this seems to be because of the frequent tillage ( a couple of times during the growing season) in the parklands Many studies reported that tillage cause d significant loss of soil organic C especially in the surfa ce (Gebhart et al. 1994; Campbell et al. 1996; Six et al. 1998) T illage using animal traction is done very close to the trunks of parkland trees where tree density is low and this accelerates the decomposition of organic matter in the top soil, and thus reduces soil C accumulation. Live fence trees are planted in high density and their thorny branches spread, making it almost impossible to do tillage near the tree lines. C con tent a round the live fence tree lines is higher due to the higher litter inpu ts from trees ( Figure 5 6 ) The positive correlation of C 3 (tree) derived C and L fraction (contains new SOM) C in the 0 10 cm soil ( Figure 5 10 ) also suggests that the recent planting of live fence trees and the input of litter have already contribute d to the accumulation of C in the topsoil The reason why fodder bank system s did not have much topsoil C ( Figure 5 4 ) could also be explained by the management style. Firs t, the land is tilled before planting the trees, which cause s the loss of aggregated C (L and M fraction C). Then crops are not grown inside the fodder bank after tree planting, and most of the tree leaves and branches are taken away as fodder. The low litter input after establishment also causes the low density of L and M fraction C ( Figure 5 5 ) Since S fraction C contains well protected C its con tent in fodder bank s was not affected much by the tillage, and stayed similar with other systems in all depths

PAGE 98

98 Long term influence of tree presence was observed in parklands data. S fraction C content (involving well protected C) was higher in deeper soil in parklands compared with live fence s or fodder bank s ( Figure 5 5 ). This is probably because of the long term inputs of litter and tree root biomass in the parkland system, compared to the other two systems (live fence and fodder bank) that represent land that was treeless (only crops) until six to nin e years ago. Also, the distance factor is significant among two parklands and live fence in M fraction C. M fraction C contains various forms of SOM including microaggregate associated C, which means trees contribute to increasing the protected form of S OC. This suggests that tree s help increase not only litter input and the content of unprotected C, but also facilitate a variety of protection to soil C from unprotected to protected state. T he content of C 4 origin C was higher than that of C 3 origin C when the whole data (whole treatment, whole depth) was tested. The tendency of higher amount of C 4 origin C in deeper soil layer and/or in S fraction was also observed ( Figure 5 6 5 7 ) In the studie d land use systems trees and bushes in abandoned land are C3 plant s whereas crops grown underneath the parklands and around the live fence as well as the presumed previous vegetation (crops) of fodder bank and abandoned land are C4 plants (sorghum and mi llet). Moreover, there are other isotopic variation/bias that could be considered in the use of the mass balance equation (Eq.5 2). The 13 C of plant (the main source of SOM) is known to var y depending on species and environmental factors or CO 2 concentration in the atmosphere (Tieszen 1991; Marino et al. 1991) These differentiations are relatively small compared with the large differenc e caused by the different photosynthetic pathway. Another considerable isotopic composition change is related to SOM decay. Over the decomposition process, 13 C value of soil organic C tend s to increase (less negative) (Balesdent and Mariotti 1996). The study using 13 C value for tracing

PAGE 99

99 the vegetation change in East African savannah suggests that using the mass balance approach to interpret soil profiles might lead to an under estimation of past C3 plant abundance (Gillson et al. 2004). Thus, the C3 or igin C, especially in deeper soil tend s to be underestimated, and this might be one of the reasons that C3 origin C in the deeper soil (tree root contribution) was not observed as much as expected. It is also probably due to the higher inputs of belowgrou nd biomass from crops in the sampling depth. A boveground crop residue is usually taken away as fodder in fodder bank but crop roots are annually left in the soil and become the source of soil organic C. Crop roots are also expected to have a faster deco mposition rate than tree roots that contain higher proportion of lignin and other substances that slow down the decomposition process. In addition, sampling depth (1 m) might not be enough to see more tree roots influence than crop roots influence. Most of Mali s soil research sets 40 cm as the crop root influence limit, but a study found a significant amount of sorghum roots close to or deeper than 60cm in a similar climatic condition (Jones et al. 1998). Tree roots, especially in semiarid area s are ex pected to go even deeper than 1 m (Jelt sch et al 1 99 6). Overall, soil organic C content in the stud ied systems w ere of relatively lower magnitude ( 1 to 6 g C kg 1 soil ) than in agriculture or agroforestry systems of other ecoregions This is possibly due, at least partly, to the rapid decomposition of organic C, which is known to be facilitated by the high temperature and low silt and clay contents (Schimel et al. 1994; Hassink 1997). The lower amount of M fraction C than S and L fraction C (when test ed with all treatments data combined) also suggests that most of the litter inputs are decomposed rapidly so that little is going to the process of becoming a more protected form of C. In many situations it is probably best for farmers to allow the major ity of the residues to be eaten by cattle in these systems rather than attempt to build soil organic matter.

PAGE 100

100 A conservative estimate of soil C sequestration potential through addition of organic matter such as plant litter and animal waste to these agric ultural soils is in the range of 0.1 0.3 Mg C ha 1 yr 1 (Batjes 2004). ICRAF is trying to promote live fence s and fodder bank s for land amelioration and count s C sequestration potential as one of the potential benefits. However, it is important to addr ess the possibility of causing net loss of soil C while converting abandoned land into live fence s or fodder bank s in this study region at the initial stage, because of the tillage factor at establishment. And because the subsequent land use practice s pr ovide low levels of litter input especially in fodder bank s, it may take a long time to regain the initial loss of soil C.

PAGE 101

101 Table 5 1. Soil profile characteristics for plots of the five land use systems used in the study at Sgou Region, Mali. Depth ( cm) Sand (g kg 1 soil) Silt (g kg 1 soil) Clay (g kg 1 soil) pH Bulk density (g cm 3 ) Faidherbia albida 0 10 865 69 66 6.7 1.42 Parkland 10 40 805 81 114 6.3 1.50 40 100 715 86 199 5.9 1.50 Vitellaria paradoxa 0 10 857 86 57 6.6 1.48 Parkland 10 40 809 96 95 5.9 1.38 40 100 798 82 120 5.6 1.47 Live fence 0 10 935 22 43 5.8 1.44 10 40 900 22 78 5.3 1.51 40 100 846 31 123 5.0 1.39 Fodder bank 0 10 830 72 98 5.2 1.42 10 40 797 91 112 5.3 1 .46 40 100 752 95 153 5.5 1.55 Abandoned land 0 10 694 129 177 5.4 1.36 10 40 576 164 260 5.3 1.21 40 100 530 164 306 4.9 1.43

PAGE 102

102 Table 5 2. 13 C value s of whole soil and three fraction sizes from five studied land use systems, at Sgou Region, Mali. (all values are average of three replicates)

PAGE 103

103 Figure 5 1. S oil sampling, S gou, Mali The samples were drawn with an auger fro m the defined soil depths and horizontal distances from trees ; each sample was a composite of four sub samples drawn from different points within a plot. (Photographed by author)

PAGE 104

104 A B C D E Figure 5 2. S oil pits dug in plots of the five land use systems studied in Sgou region of Mali. The red stick is marked (in black) at 10 cm intervals. A) F aidherbia albida parkland. B) Vitellaria paradoxa parkland. C) Live fence. D) Fodder bank. E) Abandoned land. (Photographed by author)

PAGE 105

105 Figure 5 3. Model of soil organic matter dynamics Source: Six et al. 2002 Figure 3 in page 163.

PAGE 106

106 Figure 5 4 Whole soil C content of three depth classes (0 10 cm, 10 40 cm, and 40 100 cm) in different land use systems in Sgou, Mali: A) Faidherbia albida parkland B ) Vitellaria paradoxa parkland C ) Live fence D ) Fodder bank and E) Abandoned land. Rang e of the each depth value is 95 % confidence level. Depth indicated is the mid point of sampled depth.

PAGE 107

107 Figure 5 5. S oil C content of three particle size fractions in three depth classes (0 10 cm, 10 40 cm, and 40 100 cm) under f ive l and use systems in S gou, Mali. A) Faidherbia albida parkland B ) Vitellaria paradoxa parkland C) Live fence D ) Fodder bank and E) Abandoned land. Range of the each depth value is 95% confidence level. Depth indicated is the mid point of sampled depth D E A B C

PAGE 108

1 08 Figure 5 6. Whole s oil C, d ivided into C 3 plants (trees) origin and C 4 plants (crops) origin in different soil layers up to 100 cm depth, in five land use systems in S gou, Mali. A) Faidherbia albida parkland near tree trunk B ) Faidherb ia albida parkland outside crown, C) Vitellaria paradoxa parkland near tree trunk D) Vitellaria paradoxa parkland, outside crown, E) Live fence near trees, F) Live fence, 3m away from tree lines, G ) Fodder bank and H) Abandoned land. A B C D D D E D D F D D G D D H D D

PAGE 109

109 Figure 5 7. So il C in three fraction sizes divided into C3 plants origin and C4 plants origin in different soil particle size fractions under different land use systems in Sgou, Mali. A) Faidherbia albida parkland near tree trunk B ) Faidherbia albida parkland outsid e crown, C) Vitellaria paradoxa parkland near tree trunk D) Vitellaria paradoxa parkland, outside crown, E) Live fence near trees, F) Live fence, 3m away from tree lines, G ) Fodder bank and H) Abandoned land.

PAGE 110

110

PAGE 111

111

PAGE 112

112 F igure 5 8. Linear regression betw een silt + clay content of soil and whole soil C content in three depth class es across five land use systems in Sgou region of Mali The three data points, one each in each depth class, marked by circles around them, refer to one of the fodder bank plots the data from which were quite inconsistent with those from the other fodder bank plots as well as all the other treatments; these points were therefore considered as outliers and excluded from the regression.

PAGE 113

113

PAGE 114

114 Figure 5 9. Linear regression between sil t and clay content of soil and C in soil particles of <53 m in three soil depth classes across five land use systems in Sgou, Mali. The three data points, one each in each depth class, marked by circles around them, refer to one of the fodder bank plots the data from which were quite inconsistent with those from the other fodder bank plots as well as all the other treatments; these points were therefore considered as outliers and excluded from the regression.

PAGE 115

115

PAGE 116

116 Figure 5 10. Linear regression between C derived from C3 plant s and C in the large soil particles ( 250 2000 m ) at 0 10 cm soil depth across five land use systems of Sgou region, Mali.

PAGE 117

117 CHAPTER 6 SOCIOECONOMIC ANALYSIS OF THE CARBON SEQUESTRATION POTENTIAL OF IMPROVED AGROFORESTRY SYSTEMS IN MALI, WEST AFRICA Introduction T he success in the implementation of any project for greenhouse gas (GHG) mitigation This is particularly so in a region such as this study site where the vast majority of inhabitants rely o n the outputs from their crop fields and animals for subsistence and cash income rarely exists in their household budgets. These farmers might be encouraged to plant trees in their croplands for potential carbon (C) benefits (cash payments) considering t hat agroforestry is a recognized GHG mitigation strategy according to the Kyoto Protocol. Several major reasons have been recognized as favoring introduction of C sequestration benefits into smallholders agroforestry practices in developing countries O ne is that this sequestration service does not need to be transported, thus, it can benefit people in remote areas, many of whom are poor. Secondly, there are no quality differences: a molecule of C is the same where ver it is located; so the problem often faced by smallholders in not being able to achieve the quality required by international markets in agricultural commodities does not apply here (Cacho et al. 2003a) Furthermore, even small amount s of additional income would make a great difference for t hese subsistence farmers who have practically no opportunity to make such additional cash income The political environment involvement in GHG mitigation projects. The U nited N ations F ramework C onvention on C limate C hange (UNFCCC) included development, equity and sustainability as conditions to be met when setting its principles for stabilizing GHG concentrations through mitigation policy (UNFCCC 2007) Large sca l e adoption of C sequestration activities by agroforestry in African countries could contribute to these objectives through biodiversity conservation, rural employment, and

PAGE 118

118 soil amelioration (Breman 1997; Tschakert 2007) The World Agroforestry Centre (ICRAF) announced that they were confident that establishment of agroforestry, especially in degraded land, would qualify and play an important role under the Clean Development Mechanism (CDM) of Kyoto Protocol (ICRAF 2007) A few major problems exist, however, in the context of promoting agroforestry p ractices by smallholders for enter ing CDM market F or example we need to examine cost per unit of C sequestration since there are many other options such as emission reduction or sequestration by large scale monoculture plantation (De Jong et al. 2004) Furthermore based on the feasibility studies, appropriate technical and political assistance should be provided so that smallholder of agroforestry owners will not always be less competitive than other mitigation projects options. Thus, socioeconomic fe asibility of the improved systems is an important consideration in the context of agroforestry implementation for GHG mitigation, and that is the scope of this chapter. Under the Kyoto Protocol, only newly sequestered C as a result of the project is reco gnized as tradable C ; t he traditional agroforestry systems such as parklands are, thus, not likely to be counted as C sequestration projects. The improved agroforestry systems that are currently being introduced in the study region can be qualified for tradable C credits The refore, the target agroforestry systems for this study are live fence and fodder bank in S gou region, Mali (detail ed system description in Chapter 4) Research Question s 1. W hat is the relative attractiveness of the two improved agr oforestry systems (live fence and fodder bank) considering their C sequestration potential, economic profitability, and social acceptability? 2. If C credit market s were introduced under the CDM of Kyoto Protocol would adoption of agroforestry provide more profits to land owners? If yes, how much?

PAGE 119

119 Materials and Methods The World Agroforesty Centre (ICRAF) conducted monitoring surveys for farmers implementing live fences and fodder banks after introduction of the systems (Hamer et al. 2005, van Duijl 2000). Data from these studies as well as databases from ICRAF research station were used for this study. Furthermore, field surveys were conducted during February March 2006 (the dry season after the harvests when farmers were less busy with agricultural act ivities) to collect additional data necessary for th e analysis. The target population was composed of farmers living in the S gou region who had adopted live fences and/or fodder banks with assistance of ICRAF. A comprehensive cost benefit study of live f ences had already been conducted for ICRAF by van Dorp et al (2005) A lso, the need and social acceptability of live fences and fodder banks had been discussed in several previous studies ( van Duijl 1999; Levasseur 2003; Yossi et al 2005). The informati on about the fodder bank implementation was much more scarce than that of live fence Thus, t he survey focus ed on collecting more data for fodder banks, specifically data to conduct the cost benefit analysis (CBA) equivalent to the existing live fence stu dy, as well as data such as the price of timber and non timber products from both live fences and fodder banks to conduct risk simulation analysis Social Survey of Fodder Bank Farmers The questionnaire was designed based on that of the live fence survey (Annex A) following the protocol of the Institutional Review Board of University of Florida (Protocol # 2005 U 1023). The structured questionnaire consisted of open ended and/ or close ended questions of 14 sections, asking for information about material s and labor used for managing and harvesting fodder banks as well as related benefits from the implementation.

PAGE 120

120 Twenty two farmers from 13 different villages in the S gou region were interviewed (the live fence survey was conducted on 18 owners from 15 di fferent villages). The language used was Bambara, the most common local language in Mali, although the questionnaire was made in French. A translator (French Bambara) an agronomist who had conducted social survey for ICRAF was hired to communicate wi th interviewees ( Figure 6 1 ). T o ensure his survey/interviewing skill, an experienced ICRAF officer went through the survey questionnaire with him before the real survey started and made him practice the follow up explanations in case farmers did not understand the questions The majority of fodder banks were 0.25 ha (50 m 50 m) in area since that was the default recommendation of ICRAF. Some interviewed farmers turned out to have larger or smaller sizes by the time of the s urvey due to the success or failure of the management. All the labor data and other costs were converted to per 0.25 ha basis before taking the mean. The live fence study was based on the average live fence row length, 291 m (van Dorp et al. 2005). Be cause the live fence and/or fodder bank installation was at least a couple of years ago, farmers seemed to have difficulties recalling the installation costs, especially labor (days and people) needed for planting and management. Also, it was very difficu lt to estimate the amount s of products harvested such as f odder and fruits. The sizes of the bags farmers were using to collect the harvests varied. Direct measurements of the bag size and the fodder weight (air dried) were conducted at several villages to reduce the estimation variability. Local Market Survey There are three local markets inside the city of S gou where most of the farmers in the villages go to buy/sell their products and equipment. Price data were collected from all markets although some products such as fodder were not sold in all the markets. T he average price of each item w as used for the analysis.

PAGE 121

121 Types of Analysis Cost benefit analysis (CBA) CBA involves weighing the total expected costs against the total expected benefits of one or more actions in order to choose the best or most profitable project, taking time into consideration (Campbell and Brown 2003) In this case, the it is profitable or not for farmers to start a live fence and/ or a fodder b ank. To appraise these projects, three decision rules were used: net present value (NPV) ; benefit cost ratio (BCR) and internal rate of return (IRR). The NPV of a project simply expresses the difference between the discounted present value (PV) of futu re benefits and the discounted present value of future costs In other words, that NPV is the sum of revenues in each year, y, discounted to year 0 minus the sum of costs in each year discounted to year 0. NPV = PV(Benefits) PV(Costs) = (Eq. 6 1) B y : Project benefits (revenues) of a given year y C y : Project costs of a given year y r: Discount rate/interest rate n: Project life, years According to the NPV guideline, a project is acceptable (profitable) if NPV is zero or greater. Projects with a negative NPV are unacceptable (Klemperer 1996). BCR is another way of determining whether the project should be accepted or rejected as an inve st ment. It is the present value of benefits divided by the present value of costs. BCR = = (Eq. 6 2)

PAGE 122

122 When PV benefits equals to PV costs, the BCR is 1, and NPV is 0. Also, if PV benefits exceed PV costs, BCR must be greater than 1 and if PV costs exceed PV revenues, BCR<1 Thus, according to the BCR decision rule, projects are acceptable when the BCR is 1 or greater, and unacceptable if BCR<1 (Klemperer 1996). The IRR of the project is the discount rate at which the NPV becomes 0 in the NPV formula (Eq. 6 1) (Eq. 6 3) The IRR is the rate of return earned on funds invested in a project. The equation 6 3 also says that the IRR is the interest rate at which PV benefits equals PV costs. A project is acceptable if its IRR is equal to or greater than the minimum acceptable rate of return (Klemperer 1996). In this study s case, however, the farmers will not have a specific acceptable rate of return. So, the rate can be recognized as acceptable if it is greater than the interest rate (when farmers t ake a loan from a local bank or financial institution ) Several basic budgets were available for calculating the above three decision rules such as whole farm budget, enterprise budget, partial budget and cash flow budget. Each budget is specific in its application, and the partial budgets were used in this study. Partial budget is used to evaluate the economic effect of minor adjustments in some portion of the business. Many changes that do not require a complete reorganization are pos sible in a farming business. Given a fixed set of resources, a farmer can employ these resources in more than one way in response to changes in product price levels, cropping patterns or carrying capacity. Partial budgets are useful to evaluate changes such as expanding an enterprise ( e.g. a crop), alternative enterprise, and different production practices (Dalsted and Gutierrez 2007) B ecause introducing a live fence and/or a fodder bank has limited impacts on the costs and returns of a farmer s budget due

PAGE 123

123 to their small size s, the partial budget was appropriate to use. Partial budgeting is based on the princip le that a small change in a farming business will have one or more of the following effects: 1 Eliminate or reduce some costs, 2 Eliminate or reduce some benefits, 3 Cause additional costs to be incurred, and 4 Cause additional returns to be received (Dalsted and Gutierrez 2007) The net effects, i.e. NPV, BCR, and IRR, can be calculated from those four components. Only the changes in costs and benefits that resulted directly from starting a live fence and/or a fodder bank were collected/ extracted from the survey data and taken to account in the analysis. The project cycle was set to 25 years. This is the expected rotation time for both li ve fence and fodder bank tree species (personal communication with Dr. Bocary Kaya, 2006 ). The growth curve was estimated based on the available data from biomass measurement. A discount rate of 15 % was used, drawing on the information about standard in terest rates applied by the several local institutions for micro credits (available to local farmers) in the study region (van Dorp et al. 2005). Data collection and analysis were done with local currency, FCFA. It is called CFA (Communaut Financiaire Af ricaine) franc, which is fixed against the euro at 1 = 655.96 FCFA in 2006. When the results are shown in US dollar for the reference, exchange rate of US $ 1 = 550 FCFA (average exchange rate during the field survey) was used Cost structure : Cost of seedlings w as calculated following the method of Tr aor et al. (2003): an aggregation of the cost of seeds and the labor cost needed to grow seedlings in the nursery. The average size of the fodder bank was 0.25 ha (50 m*50 m ) the length of live fence rows was 200 m (50 m*4 with 800 trees ) around to prote ct the fodder trees. Thus, planting and maintaining these trees were included in the costs of fodder bank

PAGE 124

124 To protect the newly planted trees, dead fences (made from dead bush brunches) were needed for the initial three years. Branches were obtained fr ee of charge in the wild; no cash costs were involved. To estimate the material cost of the dead fence, the volume of branches and the price farmers would be willing to pay on the market were asked, and recalculated to the standard size. The total averag e tool cost per farmer per year for the average size of live fence was estimated 1,000 FCFA ($1.82) (van Dorp et al 2005). Since the fodder bank requires more use of tools based on the data for required labor, the cost was set at 1,500 FCFA ( $ 2.73 ) per y ear To estimate the labor costs, farmers were asked for the average wage rate they pay for a hired labor. The most common daily labor wage (7 hours work) was 750 FCFA ( $ 1.36), ranging between 500 and 1 000 FCFA ($ 0.91 $ 1.82) depending on the seaso n. The respondents of the survey were also asked if they actually hired labor to install their fodder banks. The large majority of them did not; instead they used their family members including children, or exchanged the labor with neighbors. Labor tas ks are divided into: 1. Obtaining seedlings (or seeds) 2. Planting 3. Watering 4. Collecting materials for the dead fence 5. Constructing the dead fence around the live fence for protecting the seedlings (first three years) 6. Maintenance of the live fence/fodder bank ( weeding, replanting, pruning etc.) 7. Collecting products from the live fence and the fodder bank 8. Marketing live fence products (bringing to local market and selling) 9. Harvesting the timber/fuelwood at the end of the rotation

PAGE 125

125 From the survey, the time farmers spent on each of the above tasks were estimated for every year, and converted to the standard size and an average or median value was taken for the CBA analysis. Median values were used when the sample population had extreme outliers. In the cash flow ch art, the labor time was calculated to monetary value, using the average labor wage (Appendix B, C) Benefit structure : Yields from live fence trees are: Acacia nilotica : Fruits for tanning agent for the treatment of leather and traditional medicines are s old in local markets. Branches are used as material for dead fences. Acacia senegal : Bark is used as a traditional medicine, although it is not easily harvested, and not sold in market. Bauhinia rufescens : Leaves can be used as medicine, but not sold in the market. Lawsonia inermis : Leaves are transformed into powder to be used for the dying of hands and feet of women (cosmetic use), and highly valued in local markets. Ziziphus mauritiana : Fruits are edible, but mostly for home consumption. Benefits fro m these products were calculated in monetary value with local market price in van Dorp study (2005). That data were used in this study s cash flow. From the third year, farmers started harvesting the fodder (branches and leaves). Since the fodder tree, Gl ir icidia sepium is an exotic species and had not been commonly used in the study region no market value was available for its fodder; none of the farmers interviewed had sold its fodder in the market. T hus, the expected price of G. sepium fodder, if i t is sold in the market, was asked for in the survey, and the average of the answers w as used to estimate the fodder value. In addition fodder bank provides another source of revenue i.e. the saved time The owners were asked how many people and days t hey used to spend looking for fodder in the wild before the fodder bank installation, as well as after they started harvesting from their fodder banks. The difference is the saved time/labor, which they can use for other activities. The saved

PAGE 126

126 time was th en converted to monetary value using the labor wage, and counted as benefits of the fodder bank in the cash flow. Timber harvests at the end of rotation period (25 years) were also estimated by the projected growth lines. The local market prices of timbe r and fuelwood were used to estimate the monetary values. E ighty percent of trees planted in a live fence and a fodder bank is assumed to produce a small log per tree which can be sold in the local market at 700 1,500 FCFA ($ 1.27 $ 2.73) per one car t (about 40 logs). The rest of trees and all b ranches foliages etc. (about 40% of the total expected biomass) were assumed to be sold as fuelwood which is 4,000 6,000 FCFA ($ 7.27 $ 9.10) per one cart (250 300 kg) (personal communication with ICRAF o fficers and local merchants 2006 ). C sale : Price of C varies quite largely at the international market. This study used the average price for C emission trading in 2006, $42 per Mg C (World Bank 2006). Transaction costs [which are the costs of arranging a contract ( i.e. C sequestration project and consequent C credit sale ) and monitoring and enforcing the contract, as opposed to production costs (implementation costs of the project) ] were considered to be 0 in this cash flow. This is because the transac tion costs of agroforestry projects for C sequestration are usually covered by the third party such as the project s trust fund (Scolel T 2007). The trust fund deals with C buyers (companies or individuals in developed countries) for the trade, monitors the project performance, and provides the C payment and technical assistance to farmers. The payment method of C credit is also a long debated issue in the negotiation of the Kyoto Protocol and related meetings as discussed in Chapter 3 Two major paymen t methods (Cacho et al. 2003 b ) were tried in this study for comparison.

PAGE 127

127 Ideal accounting method : In this method, payments for C sequestration occur as the service is provided and a debit occurs when C is released (i.e. by fire or harvest). Farmers annuall y receive the payment according to the amount of C sequestered in their project s fields (live fence/fodder bank) The full debit at harvest means that the total amount of C credits sale received during the life of the project ( live fence and/or fodder ba nk ) are paid back to the investor by farmers. Tonne year accounting method : Although the ideal accounting system is ideal for the land owners (i.e. farmers), it is risky for investors because they are not sure the project will last until the end of the rotational age. The tonne year method does not require redemption of C credits upon harvest, because the payment occurs based only on the equivalent amount of permanently avoided emissions during a given year (Moura Costa and Wilson 2000). This method has the advantage that no guarantee is needed if the project will last a required number of years, as the annual payments are adjusted by the equivalent factor. This is a more favorable method for the investors, and politically popular (Hardner et al 2000 ). In this study, the equivalent factor of 0.0215 (Cacho et al. 2003a) was used. Farmers annually receive C credit payment only equivalent to the amount of C sequestered in each year 0.0215 but there is no payment back to the investors at the end of the project. These two methods were separately incorporated into the cash flow of both a live fence project and a fodder bank project (see Appendix B, C). The decision rules (NPV, BCR, and IRR) were calculated in three different cash flows: 1) Cash flo w without C sale, 2) Cash flow with C sale (ideal accounting), and 3) Cash flow with C sale (tonne year accounting). Sensitivity analysis The calculation of NPV, BCR, or IRR in the CBA described above is based on the best guess scenario where all variabl es of costs and benefits included in a cash flow are most likely

PAGE 128

128 values such as an average of a data set. However, the future is uncertain: we do not know with certainty what the future values of a project s costs and benefits will be. Sensitivity anal ysis is the simple process of establishing the extent to which the outcome of the benefit cost analysis is sensitive to the assumed values of the inputs used in the analysis (Campbell and Brown 2003). In this study, sensitivity of NPV to the change of maj or five input variable were tested and presented in the results section. The tested variables were : 1) discount rate, 2) seedling cost, 3) labor price (wage), 4) yield of harvests, and 5) C credit price. All variables except the discount rate were change d +/ 50 % from the best guess scenario to compare which variable would affect the NPV most. Discount rate was changed only +/ 5% because it was unreasonable to assume the local discount rate to change largely ( such as 50 % ) Risk modeling Risk analysi s refers to the identification and description of the nature of uncertainty surrounding the project variables using probability distributions. When there is no appropriate information on the expected range of values of input variable (= risks), only sensi tivity analysis can be done to observe the uncertainty of output projection. However, if some expectations are available for the occurrence of the variability of the input variable, it is possible to conduct the risk analysis. Each input variable has a r ange of possible values; high, medium, low; or, maximum, mean, minimum. Risk modeling describes the likelihood of occurrence of these input variables within the given range (probability distribution). The probability distribution for the out put (NPV) wil l then depend on the aggregation of probability distributions for these individual input variables into a joint probability distribution. The NPV probability distribution was simulated by a computer program called @RISK (Palisade Corporation). The prog ram performs a simulation known as Monte Carlo analysis, whereby the NPV of the projects is recalculated over and over again, each time using a different,

PAGE 129

129 randomly chosen, set of values of input variables The random selection of values is based on the ch aracteristics of each input variable s probability distribution. In this study, there was insufficient information for the distribution of each four input variable (labor wage, seedling cost, yield, and C credit price) Campbell and Brown (2003) recommen d ed the use of a triangular or three point distribution for this kind of analysis. This is the distribution described by a high, low and best guess estimate, which provide the maximum, minimum, and modal values of the distribution respectively. Each in put variable s distribution was set based on the surveys data, observation, and personal communication with ICRAF field officers. Labor wage distribution was likely to be between 500 and 1,000 FCFA (0.91 1.82 US$), while 750 FCFA ($ 1.36)/man/day was the most prevalent labor wage. Seedling cost distribution was assumed to be 50 % to +50 % from the best guess scenario. Harvests or yield distribution was assumed to be 0 % to +50 % from the best guess scenario. C price was assumed to have a range of $ 3. 7 to $ 99 per Mg C which was the minimum and maximum price traded in international C market in 2006 (World Bank 2006). Mean price was $ 42/Mg C as used in the best guess scenario. Results Demographic Characteristics of Target Population Demographic chara cteristics ( Table 6 1 ) were not based on data collected in the social survey conducted in this study However, the information on live fence farmer s was already available in the previous studies of ICRAF (Levasseur 2003; van D orp et al. 2005). T he fodder bank owners who were interviewed in this study were mostly in the same village or neighboring villages with similar condition s, and therefore their demographic characteristics were considered to be similar. Average household size is 27.7 persons, consisting o f 6.8 male members, 6.7 female members and 14.2 children. An active household member is a person actively

PAGE 130

130 contributing to agricultural activities (male 4.9, female 5.2, children 5.7 on average). Non active members are ge nerally sick or elderly or children under 10 years old (9.2 on average). Also, about 10 % of the household members have migrated from the village, usually for temporary labor in the town of S gou or other urban areas, to work or study. The average area of cultivated land owned by a household is 15.3 ha. The main enterprise is millet, occupying more than 50 % of total cultivating area. Other major crops include sorghum (2.3 ha), rice (2.1 ha) and groundnut (0.9 ha). On average, each household possesses 3.6 work oxen (for agricultural labor such as tillage), 5.8 cows (for breeding purposes), 2.1 donkeys, and other small livestock including sheep, goats, and poultry. Cost Benefit Analysis: Best Guess Scenario of the Live Fence and the Fodder Bank Based on the collected data, all costs and revenues (benefits) consisting of cash flow (year 0 to 25) were put into the spreadsheet. The cash flows of the live fence project and the fodder bank project in the best guess scenario are shown in Appendix B, C. The net benefit (total revenue s total costs) of each year was negative at the beginning and turned positive from the second year for both projects. Among components of costs, labor cost was the largest. Total labor cost throughout the project year conv erted to present value was 60,738 FCFA ($ 110.4) for the 291 m live fence project and 94,589 FCFA ($ 172.0) for the 0.25 ha fodder bank project Labor cost was high in the first three years for the live fence project compared with the rest of the project term, because of the initial management such as construction of dead fence for protecting seedlings. T he fodder bank project needed more consistent management practices (labor) than for the live fence project due to the fodder tree management such as weed ing and pruning. Seedling cost was a relatively large component of the costs on net cash flow of both projects, since it was initial investment (at year 0) and was not discounted to calculate the present value.

PAGE 131

131 When the components of revenues were exami ned, a big difference between the two projects cash flows was the revenue from the saved time of the fodder bank. Before the fodder bank installation, farmers had to graze animals almost everyday for quite a long time during the dry season. Since the su rvey data showed that this grazing time/labor a farmer could save by the fodder bank was con siderably high, the revenue of this component became the significant difference between the live fence and the fodder bank. Other revenue components, timber and fu elwood sale occurred at the end of the project year, thus, the revenues from them were discounted largely when converted to the present values Another revenue component, C sale was added to the cash flow with the ideal accounting method and the tonne ye ar method separately. The amount paid in US$ was converted to FCFA, and put into the cash flows. Three decision rules (NPV, BCR, and IRR ) for three different conditions ( No C sale, C sale with the ideal accounting method, and C sale with the tonne year accounting method ) were calculated ( Table 6 2 ). C sale by the ideal accounting method significantly changed all three decision rules. NPV of the live fence was 52 802 FCFA ($ 96.0) without C sale, and it increased to 60 465 F CFA ($ 109.9) with C sale by the ideal accounting method. NPV of the fodder bank was 87 319 FCFA ($ 158.8) without C sale, and 96 394 FCFA ($179.3) with C sale by the ideal accounting method BCR and IRR also increased (economically more profitable) with C sale. However C sale by the tonne year method did not increase the three decision rules much from those without C sale In NPV, only 172 FCFA ($0.3) increase for the live fence and 204 FCFA ($0.3) increase for the fodder bank were observed compared with NPV without C sale. The C sale profits by the tonne year method were too small to make a change of BCR and IRR values for both live fence and fodder bank projects.

PAGE 132

132 Sensitivity Analysis Sensitivity analysis was conducted, changing five major input v ariables separately : when one variable is changed, others are not changed ( Table 6 3 ) Two scenarios, with or without C sale were tested to see the NPV sensitivity. The ideal accounting method was used for calculating the NP V of with C sale since the CBA results ( Table 6 2 ) showed that the tonne year accounting method hardly changed the NPV or other two values from No C sale scenario. When the discount rate was changed from 15 %, the NPV cha nged greatly in both live fence and fodder bank projects Seedling costs change (+/ 50 %), on the other hand, did not fluctuate the NPV much compared with other variables in both projects Labor price changes (+/ 50%) affect ed the NPV of the live fence project and the fodder bank project differently, causing a large change in the NPV of live fence while causing very little in the NPV of fodder bank. This is because the labor wage variable was used for calculating both costs (labor) and benefits (revenu es from the saved time) of the fodder bank project cash flow. The variable, which has the largest impact on NPV values, was yield (harvest of fodder, live fence products and timber). When the yield was tested with +/ 50 % the range of NPV was largest in both live fence and fodder bank projects. T h e NPV values became even negative ( meaning: the project is economically unacceptable) for the live fence project when yield is 50 % from the best guess scenario C price change (+/ 50 %) did not change the N PV of both projects largely, suggesting C price is not an influential factor to change the projects profitability Risk Modeling and Simulation The risk simulation program @RISK was run based on the best guess scenario cash flow ( shown in Appendix B, C) w ith the major four variable s range described in the Materials and Methods section. The mean of the NPV distribution of the live fence project without C sale was 28,730 FCFA ($ 52.2), and the 90 % confidence range was from 50,178 FCFA ($ 91.1) to

PAGE 133

133 96,546 FCFA ($ 175.7) ( Figure 6 2 ) The chance of the NPV being negative (meaning: the project is economically not acceptable ) was 26.38 %. The net cash flow, each year s total revenue minus total costs, of the project was also simul ated ( Figure 6 3 ) This shows the probability distribution of the net benefit in each year from year 0 to 25. According to this simulation, the net benefit will be positive with 95 % likelihood from the third year of the projec t Then the net benefit is likely to be stable, and will increase significantly because of the harvests of timber and firewood at the end of the rotation. C sale was added to the live fence project cash flow, and the risk simulation was run again. I t w as conducted only with the ideal accounting method, because C sale by tonne year accounting method change d NPV very little from that of No C sale scenario Adding C sale changed the NPV probability distribution T he mean NPV of the distribution increas ed to 36,058 FCFA ($ 65.6), and 90 % confidence range was from 52,713 FCFA ($ 95.8) to 110,069 FCFA ($ 200.1) ( Fig ure 6 4 ). The chance of the NPV becoming negative is 24.47%, slightly less than that without C sale. The two pr obability distributions (with or without C sale) were found to be significantly different, when compared using t test ( p <0.01). The @RISK program also conduct ed a regression sensitivity analysis, which is able to show how each input variable is influentia l for the NPV simulation ( Figure 6 5 ). Yield has a positive as well as the largest impact among the input variables. Both labor wage and seedling cost had negative coefficients (when the variable increase s the NPV would decrea se s ), but relatively small extent. C price although positive, had the smallest influence on the NPV projection. Mean of the NPV distribution of the fodder bank project without C sale was 63,153 FCFA ($ 114.8), and its 90 % confidence range was from 53, 386 FCFA ($ 97.1) to 161,313 FCFA ($ 293.3) ( Figure 6 6 ) The chance that the NPV becomes negative (economically not acceptable)

PAGE 134

134 was 19.15 %. P robability distribution of the net benefit of each project year (0 to 25) was simul ated ( Figure 6 7 ). The net benefit turned positive in 95 % confidence from the fourth year of the project. The distribution is much more largely spread from the mean than the same simulation of the live fence ( Figure 6 3 ). C sale was added to the fodder bank cash flow and the simulation was run again. The NPV probability distribution of the fodder bank project with C sale (by the ideal accounting method) is shown in Figure 6 8 T he mean NPV became 63 289 FCFA ( $115.1 ), slightly more than that of without C sale simulation T he 90 % confidence range was from 54,305 FCFA ($ 98.7) to 159,301 FCFA ($ 289.6), and the chance of NPV being negative was 19.12 %. The distribut ions of with C sale and without C sale were compared; they were not significantly different in t test. The sensitivity regression ( Figure 6 9 ) showed again that the yield was the most influential and positive factor for the fodder bank NPV simulation. C price remained to be relatively small and positive variable. Seedling cost and labor wage were both negative variables, same as the results of the live fence project, but the impact of labor wage was much smaller in the fodd er bank simulation. This is because labor wage variable was used for calculating both labor costs and benefits (the saved time) of the fodder bank project, while it was used only for calculating labor costs in the live fence project. Discussion Overall, C sale seemed to increase the profitability of both live fence and fodder bank systems for farmers. However, if the tonne year accounting method is applied, the amount of C payment per farmer will be too little; it will not be attractive for farmers to p articipate the C sale program. Even with the ideal accounting system, the benefits from C sale will not be the major part of the farmer s income. The live fence or the fodder bank project can provide multiple benefits, and the best guess scenario shows i t is likely to be profitable without C sale. If farmers

PAGE 135

135 can gain the C payment without changing the use of the live fence or the fodder bank and without paying some of the transaction costs, which were the assumption s of this analysis, the payment does in crease expected NPV. And there is no reason for farmers not to participate the C sale program, if it exists As mentioned in the beginning of this chapter, even if the amount of cash income is very small (in the perspective of C credit buyers in develope d countries) it will make a big difference to the economic situation and well being of the farmers in the studied region. For investors, these agroforestry projects might not be very attractive regarding the amount of C credits they can purchase. However the price used in the simulation was the same as in any large scale C mitigation project. In addition, contributing to the socioeconomic development of communities in Africa could provide an environmental friendly image to the companies/corporations who are often blamed as responsible for large amount s of GHG emission; this image value may add more attractiveness for the companies to invest in this type of C sequestration project. The added C sale increased the mean of the simulated NPV significantly for the live fence project, but not for the fodder bank project, which suggests that C sale is likely to have more economic impacts for the live fence owners than for the fodder bank owners. In both the best guess scenario analysis and the risk simulation analysis yield was the most influential and uncertain factor among the input variables. A ccording to the survey data farmers who had larger trees and more harvests tend ed to spend more time for watering and weeding during the initial years of the proje ct Thus, proper training for the initial year treatment could significantly increase the expected yield for both live fence and fodder bank. On the other hand, the annual precipitation, which influence s the tree growth greatly varies largely in the stu dy region and it risks the expected yield Future climate shifts is unknown, but it would change the

PAGE 136

136 amount of labor for the management ( especially watering) required for each project, which will change the project s attractiveness. A nother issue is th at the assumed labor wage (750 FCFA/man/day) is a major factor affecting the project s profitability. From the interviews, it was clear that the rate used in the analysis was prevalent in the region but the occasions when farmers were hired for the manual labor and received this amount of wages were rather very few, for example during busy farming season s such as harvesting. Thus, the real opportunity cost of the labor may be considerably less than the assumption, substantially lower. As shown in the NPV probability distribution of the simulations (Figure 6 2 6 4 6 6 6 8 ), the mean NPV of each distributi on was much smaller than that of the best guess scenario ( Table 6 2 ). The best guess scenario s NPVs were at the highest peak of each probability distribution. I t means that the risk and uncertainty of the project were somewh at taken into account in the simulation s and suggests that evaluating the project s profitability only by the best guess scenario may overestimate the project s expected profitability. Both the best guess scenario and the risk simulation suggest that th e fodder bank project has larger expected profits than the live fence project, although the scale of these two systems are not same and cannot be compared as two options. In reality, much more farmers adopted the live fence than the fodder bank in the reg ion. This is probably because live fences already exist ed somewhat in the form of traditional live fences or dead fences, and farmers do not need large parcels of land or extra labor inputs. The fodder bank is a rather new concept that introduces exotic species and requires larger areas of land and extra labor. These factors seem to be the heavy burden for farmers, especially for relatively poor ones. In addition, the probability distribution of the fodder bank project was more horizontally spread than that of the live fence

PAGE 137

137 project (Figure 6 2 6 6 ) and the net benefit flow of the fodder bank had wider probability range than that of the live fence (Figure 6 3 6 7 ) This suggests that the fodder bank has a higher chance of NPV fluctuation. Overall, the fodder bank seems to be a high risk, high return project than the live fence. The majority of the farmers in the region are subsistence oriented and are expected to be very risk averse. Profitability may not be the first consideration in the ir adoption process. Other factors, such as water or labor availability and the presence of fodder in the open land nearby might be the more important factor s in the adoption of the fodder bank, if they are to be promoted.

PAGE 138

138 Table 6 1. Demographic characteristics of the target population in S gou, Mali. Average household size (Number of people) Active 1 male 4.9 Active female 5.2 Active children 5. 7 Non active male 0.7 Non active female 1.4 Non active children 7.1 Migrated 2 2.7 Total 27.7 Cultivating area (ha) Millet (Pennisetum glaucum) 6.4 Sorghum ( Sorghum bicolor) 2.3 Rice ( Oryza glaberrima and Oryza sativa ) 2.1 Groundnut ( Arachis hypogaea ) 0.9 Chickpea ( Cicer arietinum ) 0.6 Cassava ( Manihot esculenta ) 0.6 Fonio ( Digitaria exilis ) 0.6 Watermelon ( Cucuribitaceae ) and other fruits 0.9 Vegetable and others 0.9 Total 15.3 Cattle possession per household Work oxen 3.6 Cows 5 .8 Donkeys 2.1 Sheep 4.5 Goats 4.7 Poultry 21.7 Horses 0.1 1. Actively working in agricultural activities. 2. Temporarily moving out from the village. Data from van Dorp et al. 2005. Table 6 2 N et P resent V alue (N PV ) B enefit C ost R atio (B CR ) an d Internal R ate of R eturn ( IRR ) of the live fence and the fodder bank projects in the three different scenarios (without C sale, with C sale by the ideal accounting method, and with C sale by the tonne year accounting method) in Sgou, Mali Live Fence Fodder bank No C sale Ideal accounting Tonne year accounting No C sale Ideal accounting Tonne year accounting NPV (FCFA) 52 802 60 465 52,974 87,319 96,394 87,523 BCR 1.53 1.60 1.53 1.67 1.74 1.67 IRR 25.5% 27.3% 25.5% 29.5% 31.4% 29.5%

PAGE 139

139 Table 6 3 NPV sensitivity of the live fence project and the fodder bank project to the change of an input variable in Sgou, Mali Live fence Fodder bank No C sale With C sale No C sale With C sale ----------FCFA ----------Ba se 52 802 60,465 87,319 96,394 Discount rate 5 % 109 367 118 391 166 434 177 304 Discount rate +5 % 20 999 27 478 43 094 50 670 Seedling cost 50 % 68 829 76 492 101 844 110 919 Seedling cost +50 % 36 775 44 438 72 794 81 869 Lab or price 50 % 83 171 87 002 88 750 93 287 Labor price +50 % 22 433 26 264 85 888 90 425 Yield of harvests + 50 % 129 330 140 825 149 624 163 236 Yield of harvests 50 % 23 727 19 894 25 014 19 551 C price +50 % 64 297 100 931 C price 50 % 56 633 91 856 Source: Base values are from the best guess scenario cash flow. NPV values of with C sale are from ideal accounting method scenario.

PAGE 140

140 Figure 6 1. S ocial survey with farmers in S gou, Mali. Based on the ICRAF da tabase, all farmers who have at least once harvested fodder from the fodder bank were interviewed. The survey was conducted in Bambara (local language) and translated to French through the interpreter (man with a jacket in the photos) Figure 6 2. Simu lated NPV probability distribution of the live fence project (without C sale). The distribution is likelihood (y axis) of the project s NPV (x axis): the worst scenario can be less than 100,000 FCFA in NPV, and the best scenario can be close to 150,000 F CFA in NPV. The peak of the distribution is most likely (best guess) scenario of the project.

PAGE 141

141 Figure 6 3. Simulated net benefit (total costs total revenues in each year) of the live fence project (without C sale). Mean value of each year s probabi lity distribution is shown in the yellow line, red range is plus minus 1 standard deviation from the mean, and green range is the 5 to 95 % likelihood of the value.

PAGE 142

142 Figure 6 4. Simulated NPV probability distribution of the live fence project (with C sale by the ideal accounting method). The distribution is likelihood (y axis) of the project s NPV (x axis): the worst scenario can be less than 75,000 FCFA in NPV, and the best scenario can be close to 150,000 FCFA in NPV. The peak of the distribution is most likely (best guess) scenario of the project.

PAGE 143

143 Figure 6 5. Regression sensitivity analysis for NPV of the live fence project (with C sale by the ideal accounting method). Standard b coefficients show how these input variables are related to the results (NPV). Positive (negative) value means the input variable positively (negatively) affect the NPV, and the absolute value represents the extent of the influence.

PAGE 144

144 Figure 6 6. Simulated NPV probability distribution of the fodder bank project (without C sale). The distribution is likelihood (y axis) of the project s NPV (x axis): the worst scenario can be less than 100,000 FCFA in NPV, and the best scenario can be up to 200,000 FCFA in NPV. The peak of the distribution is most likely (best g uess) scenario of the project.

PAGE 145

145 Figure 6 7. Simulated net benefit (total costs total revenues in each year) of the fodder bank project (without C sale). Mean value of each year s probability distribution is shown in the yellow line, red range is pl us minus 1 standard deviation from the mean, and green range is the 5 to 95 % likelihood of the value.

PAGE 146

146 Figure 6 8. Simulated NPV probability distribution of the fodder bank project (with C sale by the ideal accounting method). The distribution is lik elihood (y axis) of the project s NPV (x axis): the worst scenario can be close to 100,000 FCFA in NPV, and the best scenario can be up to 200,000 FCFA in NPV. The peak of the distribution is most likely (best guess) scenario of the project.

PAGE 147

147 Figure 6 9. Regression sensitivity analysis for NPV of the fodder bank project (with C sale by the ideal accounting method). Standard b coefficients show how these input variables are related to the results (NPV). Positive (negative) value means the input vari able positively (negatively) affect the NPV, and the absolute value represents the extent of the influence.

PAGE 148

148 CHAPTER 7 SUMMARY AND CONCLUSION S This dissertation study examine d the carbon (C) sequestration potential of major agroforestry practices in S gou Region, Mali of the West African Sahel (WAS), and analyze d the feasibility and socioeconomic characteristics of the selected agroforestry systems in the context of C sequestration service s The selected systems were two traditional parkland agrofores try systems with Faidherbia albida or Vite ll aria paradoxa as the d ominant tree species two newly introduced (improved) agroforestry systems (live fence and fodder bank), and a so abandoned (degraded) land. The research revolved around four major questions. 1. How much C is stored in different agroforestry systems aboveground and belowground? 2. Do trees contribut e to store C in soil, and how stable is that C ? 3. W hat is the overall relative attractiveness of each of the selected agroforestry systems in te rms of its C sequestration potential ? 4. If C credit market s were available, would adopting agroforestry provide more profits to land owners? C Sequestration P otential Biophysical Potential T he selected agroforestry systems proved to have potential s for sequ estering more C both above and belowground than the tree less cultivated land in the study region However, the estimated amount s of C stored in the se systems and sequestered after the systems establishment are quite vari able depending on the baseline ( without project) status a s well as the accounting method used T he t wo traditional parklands store significant amount s of C in the biomass C. Especially, the large F. albida trees ( average DBH 59.4 cm, height 13 m) store considerable amounts of C. Howe ver, parklands are not likely to be considered for C sequestration projects any time soon

PAGE 149

149 because Kyoto Protocol currently admits only C sequestered as a result of newly im p lemented mitigation project s, and traditional land use systems such as parklands an d f orest conservation sustaining parklands are not recognized as emission reduction activities under the Protocol at least until 2012 On the other hand, i mproved agroforestry systems ( live fence and fodder bank) were found to have a better chance to be recognized as C sequestering activities than parkland systems. Because these systems are newly introduced, most of the biomass stored in the systems can be counted as sequestered C credits although their potential s as C sequestration projects were not as high as expected. C sequestration potential of a land use system has to be expressed on a unit area basis for a given period of time. From that perspective, some i mproved agroforestry systems ( live fence and fodder bank, in this study) do not rank high because of the nature of their planting configurations and/or management requirements. Live fence trees are densely planted rows are on plot boundaries an d therefore the area occupied or influenced by a fence row in relation to the total area of the plot it borders is low. As far as the fodder banks are concerned, the f odder trees that are frequently harvested for their leafy biomass cannot obviously be ex pected to store large quantities of biomass C. Therefore, the absolute amounts of C stored in these systems per unit area would not be as large as that for, say, parklands While the amounts of biomass C stored (calculated from general allometric equatio ns following UNFCC C guidelines) were 54.0 and 22.4 Mg C ha 1 respectively for 40 year or older stands of F. a lbida and V. p aradoxa parkland systems, the amounts were 4.7 Mg C ha 1 for a 8 year old stand of live fence and 2.2 Mg C ha 1 for 6 9 year old s tand of fodder bank.

PAGE 150

150 Another issue is that the initial C loss (both in biomass and soil) resulting from land clearing and tillage for facilitating tree establishment in these improved practices is expected to be significant ; this loss may not be compensat ed by the planted tree s any time soon given their slow growth rates owing to poor soil fertility and adverse climatic conditions. Therefore, introducing the se improved systems in abandoned land for land amelioration as the World Agroforestry Centre (ICR AF) is promoting, m ay not make a significant contribution to net C sequestration in the near term; indeed it is likely to cause net negative C balance in the initial stage of implementation. Currently soil C is not considered to be tradable but the rela tive portion of soil C in the studied systems turned out to be comparatively large For example, the percentages of soil C (0 100 cm) in total C (biomass C + soil C 0 100 cm) stock of the studied agroforestry systems were 38 % in F. albida parkland, 5 5 % in V. paradoxa parkland, 84 % in live fence, and 94 % in fodder bank. This can not be ignor ed when the potential for long term storage is considered. Soil fractionation studies and isotopic ratio measurement s showed that tree litter tend s to increase unprotected, relatively new C on the surface soil. In the deeper soil, t he parklands that have had trees in the system for a long time were likely to h o ld more protected C than the newly introduced live fence or fodder bank systems. Also, management pra ctices such as tillage and litter usage seemed to have a large influence on soil C accumulation. Socioecomic Potential T he cost benefit analysis suggested that live fence and fodder bank were likely to be profitable for farmers, whether with or without C sale. C sale changed the profitability: $ 13.9 more in net present value (NPV) of average size live fence (291 m), and $ 20.5 more in NPV of average size fodder bank (0.25 ha). These estimations are based on the assumptions of 25 year rotation, no trans action costs on the farmers and an accounting method ideal to C sellers

PAGE 151

151 (farmers) ; all of these assumptions are subject to change. With the accounting method that is in favor for the investors, the expected profits from C sale in the same model produced an increase in NPV of only about $ 0.3 in both systems. Even in the local currency with farmer s monetary values $ 0. 3 increase in NPV is almost nothing for a 25 year project. Thus, it is clear that the accounting method is a very strong factor to deter mine whether C sale through agroforestry should be introduced to the region. Also, sensitivity analysis and r isk analysis showed that C price did not have a major influence on changing the cost and benefit flow of both systems. I t would contribute to th e increase of profitability but had relatively small effect compared with other major variables such as yield, labor cost, and seedling cost. On the other hand, t ree growth (yield) ha d a strong influence on the project s profitability. Regression sensiti vity analysis showed that the effect of yield was 10 times or more strong than that of other factors. Since natural environment such as rainfall and pests greatly affects tree growth, the uncertainty (risk) regarding yield ( whether tree would grow expecte dly or not) is quite difficult to control and is a major discouraging factor for applying the improved agroforestry in general, whether with or without C sale. Regarding relative attractiveness of live fence and fodder bank, it was difficult to compare b ecause the scale of the project (the land needed) was different in the simulation of two systems. The f odder bank project that needs about three times more land (and correspondingly higher labor cost) than live fence project showed larger range of expecte d NPV ($ 98.7 to $ 289.6 in fodder bank vs. $ 95.8 to $ 65.6 in live fence ) Considering that improved agroforestry systems require additional work for farmer s cultivati on practices and that their resources (land, money for buying seeds etc.) are ve ry limited live fence would be easier and less risk y project for them to implement.

PAGE 152

152 C onclusion s Regarding the overall attractiveness of the selected land use systems, live fence and fodder bank are more suitable to start as agroforestry C sequestration p rojects than the parkland systems for small scale farmers in the studied region Between the two improved systems, live fence has higher C sequestering potential per unit area and is less risky than fodder bank s. T his situation could change, however, dep ending on tree management and conditions that affect tree growth. Adopting these systems on cultivated land rather than on abandoned land is likely to sequester more C and be more profitable. Since parklands are traditionally practiced they do not quali fy as a new C sequestration project. Nevertheless, F. albida trees are more attractive than V. paradoxa trees in terms of C sequestration potential. Agroforestry Adoption for C sequestration in the Study Region Based on the findings summarized above and information acquired through fieldwork and literature review, some factors that either limit or favor agroforestry adoption in the regions can be identified. Limiting Factors With the current price range (and its large fluctuation s ) for C credit s and the amount of C potentially sequestered the income from C sale is not likely to be a major source of income for farmers in the WAS and therefore is not likely to be a strong incentive to start the live fence or fodder bank. I n addition, farmers are concern e d about other factors as well, such as risks in undertaking a new farming practice. Farmers might need some support such as technical and/or material assistance to cover initial costs, and/or insurance or some kind of safety net in case the trees die due to unexpected causes. As regards parklands increasing the tree density is difficult because parkland trees grow relatively slowly Also, it is technically challenging since parkland tree species rely on natural regeneration.

PAGE 153

153 Soil C estimation is far m ore labor intensive and costly than biomass C estimation, and methods of measurements/accounting are still under discussion. Even in the same land use system, the amount of soil C per unit area can vary depending on the depth of sampling and/or the instru ments used to measure C content. C storage is related to s oil properties which makes it difficult to standardize soil C sequestration potential for any land use. Whether all C or only protected C should be counted is another issue I f only protected C is to be counted its method and ease of determination would become an issue. Based on farmer interviews observations, it appeared that relatively rich farmers were the ones who tried the improved agroforestry systems as ICR AF recommended, and succeed ed which is not different from the experience with many (or most) agricultural development initiatives In order to achieve poverty alleviation through C credit sale, it is important that the poorest poor of the region can ado pt the technology Involving farmers with little resource s needs naturally, extra support. Since C sale is not likely to provide much income under current condition s covering the cost of assistance and transaction costs for C trade would be a large fin ancial burden. Institutions such as international NGOs or national/local governments will have to be encouraged to bear these costs. Favorable Factors Some of the successful live fence and fodder bank owners started their projects by themselves without ICRAF s support. These successful projects had demonstration value too in that farmers of o ther villages were interested in starting live fence and fodder bank by looking at those pilot plots and came to request ICRAF s support in their villages This st rongly indicates the interests in the products and effects of live fence and/or fodder bank. As the land degradation proceeds and more farm fields are abandoned, the ability of live fence and

PAGE 154

154 fodder bank for pr otecting farmlands from free r oaming animals and producing fodder is expected to make them even more attractive for farmers. In the economic analysis, labor cost was converted to a monetary value using the local labor wage. However, since there are very few employment opportunities for farmers most occurring only during the harvesting or planting season, the real opportunity cost (labor cost) might be much lower than the assumed rate. This would lower the cost of live fence and fodder bank project s than the model used in the analys is and consequently, increase their profitability Thus, profitability of the improved system could be larger than the values shown in C hapter 6, although the extent is unknown The situation or understandings of C sequestration project as a mitigation activity is also changing. Climate change is a major global issue these days. Both price and amount of traded C are rising rapidly every year. After the first commitment period (2008 2012) of the Kyoto Protocol soil C m ay be counted for sequestration projects, which would increase the C sale income for the studied agroforestry projects. Also, at the international negotiation regarding rules of the Kyoto Protocol, conservationists such as the Nature Conservancy and researchers point out the importance of the forest conservation effect prevent ing CO 2 emission from deforestation and suggest the conservation cost to be shared internationally T hus, in future, the system steadily storing certain amount of C, such as parkland systems, m ight also be r ecognized as mitigation projects Implications for Agroforestry Economic benefits of establishing the improved agroforestry practice s were clearly found in the studied region. V arious social and environmental benefits were also found such as increasing soil organic matter and preventing soil erosion through introducing trees in the agricultural practices (in both traditional and improved agroforestry). Some of the non

PAGE 155

155 marketable benefits such as C sequestration are not directly perceived by the individ ual farmer. From C credit payment farmer s can at least receive some rewards for the environmental service they provide (internalize the non market values). In this sense, C credit sale is an option to charge for many of the non market services agrofores try systems provide. When the C sale through CDM becomes more popular in the future, agroforestry systems will definitely have potential to promote economic development of subsistence farmers as well as environmental improvement in developing countries. Future Research For the biophysical aspect s of the C sequestration project study, there is a strong need for more studies on soil C dynamic s. One of the reasons why soil C is not recognized as tradable C currently is the lack of information. To start the small scale C sequestration projects such as agroforestry, it is necessary to develop guidelines similar to the biomass C estimation guideline by UNFCCC. Conduct ing direct soil C measurement for each small scale project will be too costly for the projec t to be attractive. E stablish ing guideline s or default values would however, be quite challenging and controversial both academically and politically. In order to administ er agroforestry projects for C sequestration, an organization such as a project trust fund is needed to bear the transaction costs such as costs of monitoring and certifying the C sequestered, providing technical assistance and C payments, and selling accumulated C credits to the buyers at the international C market. It will be worth while to launch such a new pilot project the type of which has never been attempted in the study region or entire Africa, to understand a trust fund s responsibilities including designing the project, explaining to farmers the project objectives and provi ding technical/material assistance conducting inventories related to C, and setting the baseline Indeed, such a research project would be essential for promoting C sequestration through agroforestry in the WAS

PAGE 156

156 APPENDIX A SOCIAL SURVEY QUESTIONNAIRE FOR FODDER BANK OWNERS Questionnaire #: Date: Duree approximative: BANQUES FOURRAGERES 1.1 (Entrer code)_____________________________________ 1: Semi direct 2 : Plants a ra cines nues 3: Plants en pots 4: Autres 9: Pas de rponse 1.2 Comment vous avez obtenu les semences ou les plants? (Entrer code)_____________________________________ 1: Cultivs en ppinire par le paysan tre paysan / au march 5: Autres 9: Pas de rponse 1.3 Si vous avez achet les semences ou les plants, quels taient les cots? Unit Prix par unit Quantit Cot total Semences Plants 1.4 Outils utiliss pour installer et maintenir les banque fo ur rageres: Note es banque fourrageres pas les outils uniquement utiliss pour le travail champtre agricole. (preferer le prix de achete, mais si difficile, not er quel annee). Outils(entrer codes): Nombre (prix de march en F CFA) Valeur total Nombre 1: Houe 2: Hache 3: Pioche 4: Coupe coupe 5: Pelle 6: Piquet/Piquasse 7: Brouett e 8: Arrosoir 9: Charrue 10: Multiculteur 11: Charrette 12: Barre mine 13: Autres 99: Pas de rpons

PAGE 157

157 1.5 est ure? 1.6 personne/jour)? Note: Spcifier si le repas est compris dans le taux. 1.7 Personnes impliqus dans les diffrentes activits pour de l es banques fourrageres (pendant les premires trois annes): Notes: Expliquer comment vous tes arriv au nombre de personne/jours. le An 3: 1999; An 4: 2000 etc.) tape 1: Obtenir les plants ou les semences (PAS la production des plants en ppinire!) Personnes impliques (H/F/E): No. de personne/ jours (heures) An 1:___ Note: tape 2: Transplanter les plants ou semences au champ (inclure la haie vive) Personnes impliques (H/F/E): No. de personne/ jours (heures) An 1:____ Note: tape 3: Arroser le s banque s fourragere s Personnes impliques (H/F/E): No. de personne/ jour s (heures) An 1:____ No. de personne/ jours (heures) An 2:____ No. de personne/ jours (heures) An 3:___ Note :

PAGE 158

158 tape 4: Chercher du matriel pour la haie morte autour de la haie vive Personnes impliques (H/F/E): No. de personne/ jours (heure s) An 1:____ No. de personne/ jours (heures) An 2:____ No. de personne/ jours (heures) An 3:___ Note : tape 5: Construire la haie morte autour de la haie vive Personnes impliques (H/F/E): No. de personne/ jours (heures) An 1:____ No. de p ersonne/ jours (heures) An 2:____ No. de personne/ jours (heures) An 3:___ Note : tape 6: Entretien de s banques fourrageres et la haie vive (suivi, boucher les espaces etc. SANS r colte ) Personnes impliques (H/F/E): No. de personne/ jours (heu res) An 1:____ No. de personne/ jours (heures) An 2:____ No. de personne/ jours (heures) An 3:___ Note : An 4, 5, 6.... (si possible)

PAGE 159

159 1.8 Utilisation des produits des banques fourrageres et la haie vive pendant la dernire anne: Espce Produit (en trer codes) Utilisation de produit (entrer codes) Unit de rcolte (par ex. 1 sac) unit (en kg.) Production annuelle (en units) G.Sepium P. Lucens Produits: 1: Feuilles 2: Branches 3: Bois 4: Fruits 5: Ecorces 6: Racines 7: Fleures 8: Semences 9: Pas de rponse Utilisation: 1: Alimentation 2: Mdicaments 3: Bois de chauffe 4: Bois de service 5: Parure (beaut) 6: Fourrage 7: Tannage 8: Autres 9: Pas de rponse 1.9 Distribution des produits des banque s f orrageres et la haie vive pendant la dernire anne (en Espce Produit (voir ci dessus pour les codes) Auto consommati on (en units) Dons (en units) Vente (en units) Rserv (en units) G. Sepium P. Lucens

PAGE 160

160 1.10 L es prix et la valeur de la vente au march (en F CFA pour 2006) Note: Si le produit se ne vend pas au march, estimer le prix. Espce Produit (voir ci dessus pour les codes) Prix minimum (par unit) Prix maximum (par unit) Prix moyen Valeur de la vente to tale (en 2006) G. Sepium 1.11 Personnes impliqus dans les diffrentes activits pour la rcolte, la transformation et la vente des produits de les banques fo u r r ageres ou/et la haie vive ( partir de la troisime anne) Notes: Spcifier pour les diffrents espece Expliquer comment vous tes arriv au nombre de personne/jours (heures) ; ta pe 1: Collecte des produits de les banques forrageres Personnes impliques (H/F/E) No. de P/J (H) An 3:_____ No. de P/J (H) An 4: _____ No. de P/J (H) An 5: _____ No. de P/J (H) An 6: _____ No. de P/J (H) An 7: _____ No. de P/J (H) An 8: _____ G. sepium : Note : (An. 9, 10, si possible) tape 2: Transformation des produits de les banques forrageres Personnes impliques (H/F/E) No. de P/J An 3:_____ No. de P/J An 4: _____ No. de P/J An 5: _____ No. de P/J An 6: _____ No. de P/J An 7: ____ No. de P/J An 8: _____ G. Sepium : Note :

PAGE 161

161 (An. 9, 10, si possible) tape 3: Vente des produits de les banques forrageres Personnes impliques (H/F/E) No. de P/J An 3:_____ No. de P/J An 4: _____ No. de P/J An 5: _____ No. de P/J An 6: _____ No. de P/J An 7: _____ No. de P/J An 8: _____ G. Sepium : Note : 1.12 banques forrageres (et la haie vive, si quelquechose) ? Nom d u march Distance (entrer codes) 1: 0 5 km 2: 5 10 km 3: 10 20 km 4: 20 50 km 5: >50 km 9: Pas de rponse 1.13 Quels sont les moyens de transport utiliss pour vendre les produits des banques forrageres au march? Note: Noter tous les moyens de t ransport utiliss pour la vente des produits de les banques forrageres, PAS les moyens de transport uniquement utiliss pour le travail champtre agricole. Moyen de transport (entrer codes): Nombre Prix (en CFA) Montant total Nombre n 1: Charrette 2: Bicyclette/mobylette 3: Vhicule 4: Autres 9: Pas de rponse

PAGE 162

162 1. 14 Personnes impliqus dans le activit pour le rasembler forrage ligneux de champs ou brousse: Notes: Expliquer comment vous tes arriv au nombre de personne/jours; Personnes impliques (H/F/E): No. de personne/ jours (heures) An. 2005 Note : Si possible, avant commencer les banques forrageres Personnes implique s (H/F/E): No. de personne/ jours (heures) An. Note:

PAGE 163

163 APPENDIX B COST BENEFIT ANALYSIS (CASH FLOW) OF LIVE FENCE

PAGE 164

164

PAGE 165

165

PAGE 166

166

PAGE 167

167 APPENDIX C COST BENEFIT ANALYSIS (CASH FLOW) OF FODDER BANK

PAGE 168

168

PAGE 169

169

PAGE 170

170

PAGE 171

171 LIST OF REFERENCES Alavalapati, J.R.R., R.K. Shrestha, G.A. Stainback, and J.R. Matta. 2004. Agroforestry development: An environmental economic perspective. Agrofor. Syst. 61 62: 299 310 Albrecht, A. and S.T. Kandji. 2003. Carbon sequestration in tropical agroforestry systems. Agric. Ecosyst. Envir on. 99:15 27. Alvarez R., R.A. Diaz, N. Barbero, O.J. Santanatoglia, and L. Blotta. 1995. Soil organic carbon, microbial biomass and CO2 C production from three tillage systems. Soil Tillage Res. 33: 17 28. Alvarez, R. and C. R. Alvarez. 2000. Soil organic matter pools and their associations with carbon mineralization kinetics. Soil Sci Soc Am J 64:184 189. Balesdent, J. and A. Mariotti. 1996. Measurement of soil organic matter turnover using 13C natural abundance. p.83 112. In T.W. Boutton and S.I. Yam asaki (ed.) Mass spectrometry of soils. Marcel Dekker, New York. Balesdent, J., E. Besnard, D. Arrouays, and C. Chenu. 1998. The dynamics of carbon in particle size fractions of soil in a forest cultivation sequence. Plant Soil 201:49 57. Baron, R. and A. Lanza. 2000. Kyoto commitments: macro and micro insights on trading and the Clean Development Mechanism. Integrated Assessment 1:137 144. Bationo, A. and B.R. Ntare. 2000. Rotation and nitrogen fertilizer effects on pearl millet, cowpea and groundnut yield and soil chemical properties in a sandy soil in the semi arid tropics, West Africa. J. Agric. Sci. (Cambridge) 134:277 284. Batjes, N.H. 2004. Estimation of soil carbon gains upon improved management within croplands and grasslands of Africa. Environ. Dev Sust. 6:133 143. Batjes, N.H. 2001. Options for increasing carbon sequestration in West African soils: An exploratory study with special focus on Senegal. Land Deg. Dev. 12:131 142. Beare, M. H. M. L. Cabrera, P. F. Hendrix, and D. C. Coleman. 1994. Ag gregate protected and unprotected organic matter pools in conventional and no tillage soils. Soil Sci Soc Am J. 58:787 795. Beer, J., A. Bonnemann, W. Chavez, H.W. Fassbender, A.C. Imbach, and I. Martel. 1990. Modelling agroforestry systems of cacao ( T heobroma cacao ) with laurel ( Cordia alliodora ) or poro ( Erythrina poeppigiana ) in Costa Rica. Agrofor. Syst. 12:229 249. Blair, G.J., R.D.B. Lefroy, and L. Lisle. 1995. Soil carbon fractions based on their degree of oxidation, and the development of a carb on management index for agricultural systems. Aust. J. Agric. Res. 46:1459 1466.

PAGE 172

172 Boffa, J. M. Agroforestry parklands in sub Saharan Africa 1999. FAO Conservation Guides 34. FAO, Rome, Italy. Bouliere, F. 1983. Tropical Savannas. Elsevier Scientific Publi shing Co. New York, NY. Breman, H. and J.J. Kessler. 1997. The potential benefits of agroforestry in the Sahel and other semi arid regions. Euro. J. Agron. 7:25 33. Brown, S. 1997. Estimating Biomass and Biomass Change of Tropical Forests: a Primer. F AO, R ome, Italy. Brown, S. 1999. Guidelines for Inventorying and Monitoring Carbon Offsets in Forest Based Projects Winrock International Arlington, VA Brown, S. 2002. Measuring carbon in forests: current status and future challenges. Environ. Pollut. 116: 363 372. Buresh, R.J. and G. Tian. 1997. Soil improvement by trees in sub Saharan Africa. Agrofor. Syst. 38:51 76. Cacho, O.J., G.R. Marshall, and M. Milne. 2003a. Smallhoder agroforestry projects: potential for carbon sequestration and poverty alleviation ESA Working Paper No.03 06 FAO. www.fao.org/es/esa Cacho, O.J., R.L. Hean, and R.M. Wise. 2003b. Carbon accounting methods and reforestation incentives. Aust. J. Agric. Res. Econ. 47:153 179. Cairns, M.A., S. Brown, E.H. Helmer, and G.A. Baumgardner. 19 97. Root biomass allocation in the world's upland forests. Oecologia (Berlin) 111:1 11. Campbell C.A., B.G. McConkey, R.P. Zentner, F. Selles, and D. Curtin. 1996. Tillage and crop rotation effects on soil organic C and N in a course textured Typic Haplobor oll in southwestern Saskatchewan. Soil Tillage Res. 37: 3 14. Campbell, H.A. and P.C. Brown. (ed.) 2003. Benefit cost analysis: Financial and economic appraisal using spreadsheets. Cambridge University Press, Port Melbourne, Australia. Choudhary, M.A., A. Akramkhanov, and S. Saggar. 2002. Nitrous oxide emissions from a New Zealand cropped soil: tillage effects, spatial and seasonal variability. Agric. Ecosys t Environ. 93:33 43. CIA 2007 The World Factbook: Mali. [Online] Available at https://www.cia.gov/library/publications/the world factbook/geos/ml.html (verified 5 Jul. 2007). CIA, Washington, DC. Cisse, M.I. 1980. Production fourragre de quelques arbres saheliens: relations entre la biomasse foliaire maximale et divers parametresphysiques. p.203 208. In H.N. Le Houerou. (ed.) Les fourrages ligneux en Afrique, L etat actuel des connaissances. C iPEAA, Addis Abeba.

PAGE 173

173 Ciss, M.I. Les parcs agroforestiers au Mali. 1995. E tat des connaissances et perspectives pour leur amlioration Rapport AFRENA. ICRAF, Nairobi, Kenya. Coughenour, M.B., J.E. Ellis, and R.G. Popp. 1990. Morphometric relationships and developmental patterns of Acacia tortilis and Acacia reficiens in Southe rn Turkana, Kenya. Bull. Torrey Bot Club 117:8 17. Dai, A., P.J. Lamb, K.E. Trenberth, M. Hulme, P.D. Jones, and P. Xie. 2004. The recent Sahel drought is real. Int. J. Climat. 24:1323 1331. Dalsted, N.L. and P.H. Gutierrez. 2007. Partial budgeting. [Onli ne]. Available at http://www.ext.colostate.edu/PUBS/farmmgt/03760.html (verified 5 Jul. 2007). Colorado State University Extension Agriculture, Fort Collins, CO. D e Alwis, K.A. Recapital ization of soil productivity in sub Saharan Africa. 1996. FAO Investment Center, Rome, Italy. De Jong, B.H., S.O. Gaona, S.Q. Montalvo, E.E. Bazan, and N.P. Hernandez. 2004. Economics of agroforestry carbon sequestration: A case study from southern Mexico Chap.4. In J.R.R. Alavalapati and D.E. Mercer (ed.) Valuing agroforestry systems. Kluwer Academic Publishers, Netherlands. De Jong, B.H.J. 2001. Uncertainties in estimating the potential for carbon mitigation of forest management. For. Ecol. Manage. 154: 85 104. Del Galdo, I., J. Six, A. Peressotti, and M. F. Cotrufo. 2003. Assessing the impact of land use change on soil C sequestration in agricultural soils by means of organic matter fractionation and stable C isotopes. Glob. Chan. Bio. 9:1204 1213. Delane y, M. and J. Roshetko. 1999. Field test of carbon monitoring methods for home gardens in Indonesia p.45 51. In Field Tests of Carbon Monitoring Methods in Forestry Projects. Forest Carbon Monitoring Program. Winrock International, Arlington, VA. Denton, F ., Y. Sokona, and J.P. Thomas. Climate change and sustainable development strategies in the making: What should West African countries expect? 2001. OECD Climate Change and Development. Environnement et Dveloppement du Tiers Monde (ENDA TM) Dakar, Senega l. Dixon, R.K. 1995. Agroforestry system: sources or sinks of greenhouse gases? Agrofor. Syst. 31:99 116. Dixon, R.K., S Brown, R.A. Houghton, M.C. Solomon, M.C. Trexler, and J. Wisniewski. 1994 a Carbon pools and flux of global forest ecosystems. Science ( Washington, DC) 263:185 190. Dixon, R.K., J.K. Winjum, K.J. Andrasko, J.J. Lee, and P.E. Schroeder. 1994b. Integrated land use systems: assessment of promising agroforest and alternative land use practices to enhance carbon conservation and sequestration. Clim. Change 27:71 92.

PAGE 174

174 SEGOU 2000. Sgou, Mali Droppelmann, K.J., J. Lehmann, J.E. Ephrath, and P.R. Berliner. 2000. Water use efficiency and uptake patterns in a runoff agroforestry system in an arid environment. Agrofor. Syst. 49:223 243. Duguma, B., J. Gockowski, and J. Bakala. 2001. Smallholder cacao ( Theobroma cacao Linn .) cultivation in agroforestry systems of West and Central Africa: challenges and opportuni ties. Agrofor. Syst. 51:177 188. Duiker S.W. and R. Lal. 1999. Crop residue and tillage effects on carbon sequestration in a Luvisol in central Ohio. Soil Tillage Res. 52, 73 81. Dzurec, R.S., T.W. Boutton, M.M. Caldwell, and B.N. Smith. 1985. Carbon isot ope ratios of soil organic matter and their use in assessing community composition changes in Curlew Valley, Utah. Oecologia (Berlin) 66:17 24. Eleki, K., R.M. Cruse, and K.A. Albrecht. 2005. Root segregation of C3 and C4 species using carbon isotope compo sition. Crop Sci 45:879 882. Eswaran, H., R. Almaraz, E. Berg, and P. Reich. 1996. An Assessment of the Soil Resources of Africa in Relation to Productivity. [ Online] Available at htt p://soils.usda.gov/use/worldsoils/papers/africa1.html (verified 5 Jul. 2007). N RCS, Washington, DC. FAO 2000. Global Forest Resources Assessment 2000. FAO Forestry Paper 140. Rome, Italy. FAO 1997. Estimating biomass and biomass change of tropical fore sts. FAO Forestry Paper 134. Rome, Italy. FAO. 1991. Feeding dairy cows in the tropics. FAO Animal Production and Health Paper 86. Rome, Italy. Ferguson, W. 1 983. Integrating crops and livestock in West Africa. FAO Animal Production and Health Paper 41 R ome, Italy. Franklin, J. and P. Hiernaux. 1991. Estimating foliage and woody biomass in Sahelian and Sudanian woodlands using a remote sensing model. Int. J. Remote Sens.12:1387 1404. Franzluebbers, A.J., F.M. Hons, and D.A. Zuberer. 1995. Tillage induce d seasonal changes in soil physical properties affecting soil CO2 evolution under intensive cropping. Soil Tillage Res. 34: 41 60. Garcia Oliva, F. and O.R. Masera. 2004. Assessment and measurement issues related to soil carbon sequestration in Land Use, L and Use Change, and Forestry (LULUCF) projects under the Kyoto Protocol. Clim. Chan ge 65:347 364.

PAGE 175

175 Gebhart, D.L., H.B. Johnson, H.S. Mayeux, and H.W. Polley. 1994. The CRP increases soil organic carbon. 49: 488 492. J. Soil Water Conserv. Ghani, A., M. Dext er, and K.W. Perrott. 2003. Hot water extractable carbon in soils: a sensitive measurement for determining impacts of fertilisation, grazing and cultivation. Soil Biol. Biochem. 35:1231 1243. Godal, O., Y. Ermoliev, G. Klaassen, and M. Obersteiner. 2003. C arbon trading with imperfectly observable emissions. Environ. Res. Econ. 25:151 169. Gonzalez, P. 2001. Desertification and a shift of forest species in the West African Sahel Clim. Res. 17:217 228. Gordon, J.E., W.D. Hawthorne, G. Sandoval, and A.J. Barr ance. 2003. Trees and farming in the dry zone of southern Honduras II: the potential for tree diversity conservation. Agrofor. Syst. 59:107 117. Gritzner, J.A. 1988. The West African Sahel. University of Chicago, Committee on Geographical Studies Chicago, IL. Hamer, A., S. Franzel, B. Mounkoro, A. Niang, and C.O. Traore. 2005. Fodder banks in Mali. ICRAF, Bamako, Mali. Hardner, J.J., P.C. Frumhoff, and D.C. Goetze. 2000. Prospects for mitigating carbon, conserving biodiversity, and promoting socioeconomi c development objectives through the clean development mechanism. Mitig. Adapt. Strat. Glob. Change 5:61 80. Harris, D., R.H. William, and C. van Kessel. 2001. Acid fumigation of soils to remove carbonates prior to total organic carbon or CARBON 13 isotop ic analysis. Soil Sci Soc Am J 65:1853 1856. Hassink, J. 1997. The capacity of soils to preserve organic C and N by their association with clay and silt particles. Plant Soil 191:77 87. ICRAF. 2007. ICRAF Position on the Clean Development Mechanism in the Land Use, Land Use Change and Forestry Sector [Online] Available at http://www.worldagroforestry.org/climatechange/documents/ICRAFPositiononCDM.pdf (verif ied 5 Jul. 2007 ) Nairobi, Kenya. ICRISAT Crops. 2007 [Online] Available at http://www.icrisat.org/ (verified 5 Jul. 2007). Ingram, J.S.I. and E.C.M. Fernandes. 2001. Managing carbon sequestration in soils: conce pts and terminology. Agric. Ecosys. Environ. 87:111 117. IPCC 2007. Climate Change 2007: Mitigation of Climate Change. Working Group III contribution to the Intergovernmental Panel on Climate Change Fourth Assessment Report Bangkok, Thailand.

PAGE 176

176 IPCC 200 0. Land use, Land use Change, and Forestry. A Special Report of the IPCC Cambridge University Press Cambridge, UK. Jeltsch, F., S.J. Milton, W.R.J. Dean, and N. van Rooyen. 1996. Tree spacing and coexistence in semiarid savannas. J. Ecol. 84:583 595. Job bagy, E.G. and R.B. Jackson. 2000. The vertical distribution of soil organic carbon and its relation to climate and vegetation. Ecol. Applic. 10:423 436. Johnsen, K.H., D. Wear, R. Oren, R.O. Teskey, F. Sanchez, R. Will, J. Butnor, D. Markewitz, D. Richter T. Rials, H.L. Allen, J. Seiler, D. Ellsworth, C. Maier, G. Katul, and P.M. Dougherty. 2001. Meeting global policy commitments: Carbon sequestration and southern pine forests. J. For. 99:14 21. Jones, M., M.L. Sinclair, and V.L. Grime. 1998. Effect of tr ee species and crown pruning on root length and soil water content in semi arid agroforestry. Plant Soil. 201:197 207. Jonsson, K., C.K. Ong, and J.C.W. Odongo. 1999. Influence of scattered Nere and Karite trees on microclimate, soil fertility and millet y ield in Burkina Faso. Exp. Agric. 35:39 53. Kang, B.T., F.E. Caveness, G. Tian, and G.O. Kolawole. 1999. Longterm alley cropping with four hedgerow species on an Alfisol in southwestern Nigeria effect on crop performance, soil chemical properties and nem atode population. Nutr. Cycl. Agroecosyst. 54:145 155. Kater, L.J.M., S. Kante, and A Budelman. 1992. Karite ( Vitellaria paradoxa ) and nere( Parkia biglobosa ) associated with crops in South Mali. Agrofor. Syst. 18:89 105. Kaya, B. 2000. Soil fertility regen eration through improved fallow systems in southern Mali. Ph.D. diss. Univ. of Florida Gainesville, FL Kaya, B. and P.K.R. Nair. 2004. Dynamics of Particulate Organic Matter following biomass addition from fallow improvement species in southern Mali. Agro for. Syst. 60:267 276. Kaya, B. and P.K.R. Nair. 2001. Soil fertility and crop yields under improved fallow systems in southern Mali. Agrofor. Syst. 52:1 11. Klemperer, W.D. (ed.) 1996. Forest resource economics and finance. McGraw Hill, Inc USA Kursten, E. and P. Burschel. 1993. CO2 mitigation by agroforestry. Water Air Soil Pollut. 70:533 544. Lal, R. 2004a. Carbon sequestration in dryland ecosystems. Environ. Manag. 33:528 544 Lal, R. 2004b. Soil carbon sequestration impacts on global climate change and food security. Science (Washington, DC) 304:1623 1627.

PAGE 177

177 Lal, R. 1999. Global carbon pools and fluxes and the impact of agricultural intensification and judicious land use. p. 44 52. In Prevention of Land Degradation, Enhancement of Carbon Sequestration and Conservation of Biodiversity through Land Use Change and Sustainable Land Management with a Focus on Latin America and the Caribbean. FAO, Rome, Italy. Levasseur, V., M. Djimde, and A. Olivier. 2004. Live fences in Sgou, Mali: an evaluation by their early users. Agrofor. Syst. 60:131 136. Littmann, T. 1991. Rainfall, temperature, and dust storm anomalities in the African Sahel. Geogr. J 157:136 160. MacDicken, K.G. (ed.) 1997. A guide to monitoring carbon storage in forestry and agroforestry projects. Winrock International Institute for Agricultural Development, Forest Carbon Monitoring Program. Arlington, VA. Makumba, W., B. Janssen, O. Oenema, F.K. Akinnifesi, D. Mweta, and F. Kwesiga. 2006. The long term effects of a gliricidia maize intercropping s ystem in Southern Malawi, on gliricidia and maize yields, and soil properties: Nutrient management in tropical agroecosystems. Agric. Ecosyst. Environ. 116:85 92. Makundi, W.R. and J.A. Sathaye. 2004. GHG Mitigation potential and cost in tropical forestry relative role for agroforestry. Environ. Dev. Sust. 6:235 260. Manlay, R.J., J.L. Chotte, D. Masse, J.Y. Laurent, and C. Feller. 2002. Carbon, nitrogen and phosphorus allocation in agro ecosystems of a West African savanna: III. Plant and soil components under continuous cultivation. Agric. Ecosyst. Environ. 88:249 269. Marino, B.D. and M.B. McElroy. 1991. Isotopic composition of atmospheric CO2 inferred from carbon in C4 plant cellulose. Nature 349:127. Masera, O.R., J.F. Garza Caligaris, M. Kanninen, T. Karjalainen, J. Liski, G.J. Nabuurs, A. Pussinen, B.H.J. de Jong, and G.M.J. Mohren. 2003. Modeling carbon sequestration in afforestation, agroforestry and forest management projects: the CO2FIX V.2 approach. Ecol. Modell 164:177 199. McDonagh, J.F., T.B .Thomsen, and J. Magid. 2001. Soil organic matter decline and compositional change associated with cereal cropping in southern Tanzania. Land Deg. Dev. 12:13 26. McLauchlan, K.K. and S.E. Hobbie. 2004. Comparison of labile soil organic matter fractionation techniques. Soil Sci Soc Am J 68:1616 1625. Mitchell, T. 2005. Sahel rainfall index (20 10N, 20W 10E), 1898 2004. [Online] Avaliable at http://jisao.washington.edu/data_s ets/sahel/index2.html#values (verified 5 Jul. 2007) University of Washington, Seattle, WA. Montagnini, F. and P.K.R. Nair. 2004. Carbon sequestration: An underexploited environmental benefit of agroforestry systems. Agrofor. Syst. 61 62:281 295.

PAGE 178

178 Moody, P.W., S.A. Yo, and R.L. Aitken. 1997. Soil organic carbon, permanganate fractions, and the chemical properties of acidic soils. Aust. J. Soil Res. 35:1301 1308. Mosier, A., R. Wassmann, L. Verchot, J. King, and C. Palm. 2004. Methane and nitrogen oxide flu xes in tropical agricultural soils: Sources, sinks and mechanisms. Environ. Dev. Sust. 6:11 49. Moura Costa, P. and C. Wilson. 2000. An equivalence factor between CO2 avoidedemissions and sequestration description and applications in forestry. Mitig. Ada pt. Strat. Glob. Change 5:51 60. Nair, P. K. R. and V.D. Nair. 2003. Carbon storage in North American agroforestry systems. In: Kimble, J., Heath, L.S., Birdsey, R.A., and Lal, R. (eds). The Potential of U.S. Forest Soils to Sequester Carbon and Mitigate t he Greenhouse Effect, pp. 333 346. CRC Press LLC, Boca Raton, FL. Nair, P.K.R. (ed.) 1993. An Introduction to Agroforestry. Kluwer Academic Publishers, Dordrecht, Netherlands Niang, A., M. Djimde, B. Kaya, E.G. Bonkoungou, and M. Macalou. 2002. Paper pre sented at Improving the quantity and quality of dry season fodder av a ilability in the Sahel. Presented at the Regional Agroforestry Conference. Pretoria, South Africa. May 20 24, 2002. Nierop, K.G.J., F.H. Tonneijck, B. Jansen, and J.M. Verstraten. 2007. Organic matter in volcanic ash soils under forest and paramo along an Ecuadorian altitudinal transect. Soil Sci Soc Am J 71:1119 1127. Oba, N., N.C. Stenseth, and W.J. Lusigi. 2000. New perspectives on sustainable grazing management in arid zones of su b Saharan Africa. Bioscience 50:35 51. Onim, J.F.M., M. Mathuva, K. Otieno, and H.A. Fitzhugh. 1990. Soil fertility changes and response of maize and beans to green manures of leucaena, sesbania and pigeonpea. Agrofor. Syst. 12:197 215. Oren, R., D.S. Ells worth, K.H. Johnsen, N. Phillips, B.E. Ewers, C. Maier, K.V.R Schafer, H. McCarthy, G. Hendrey, S.G. McNulty, and G.G Katul. 2001. Soil fertility limits carbon sequestration by forest ecosystems in a CO2 enriched atmosphere. Nature (London) 411:469 472. Pa lm, C. A T. Tomich, M. Van Noordwijk, S. Vosti, J. Alegre, J. Gockowski, and L. Verchot. 2004. Mitigating GHG emissions in the humid tropics: Case studies from the Alternatives to Slash and Burn Program (ASB). Environ. Dev. Sust. 6:145 162.

PAGE 179

179 Palm, C.A., P.L Woomer, J. Alegre, L. Arevalo, C. Castilla, D.G. Cordeiro, B. Feigl, K. Hairiah, J. Kotto Same, A. Mendes, A. Moukam, D. Murdiyarso, R. Njomgang, W.J. Parton, A. Ricse, V. Rodrigues, S.M. Sitompul, and M van Noordwijk. 1999. Carbon sequestration and trac e gas emissions in slash and burn and alternative land use in the humid tropics. ASB Climate Change Working Group Final Report, Phase II. ASB Coordination Office, ICRAF Nairobi, Kenya. Paustian, K., O. Andren, H.H. Janzen, R. Lal, P. Smith, G. Tian, H. T iessen, M. van Noordwijk, and P. L. Woomer. 1997. Agricultural soils as a sink to mitigate CO2 emissions. Soil Use and Manage 13: 230 244. Pfaff, A.S.P., S. Kerr, R.F. Hughes, S. Liu, G.A. Sanchez Azofeifa, D. Schimel, J. Tosi, and V. Watson. 2000. The Ky oto protocol and payments for tropical forest: An interdisciplinary method for estimating carbon offset supply and increasing the feasibility of a carbon market under the CDM. Ecol. Econ. 35:203 221. Phillips, D L ., P.D. Hardin, V. W. Benson, J.V. Baglio. 1 993. Nonpoint source pollution impacts of alternative agricultural management practices in Illinois: A simulation study. J. Soil Water Consev. 48: 449 457. Pikul, J.L., S. Osborne, M. Ellsbury, and W. Riedell. 2007. Particulate organic matter and water st able aggregation of soil under contrasting management. Soil Sci Soc Am J 71:766 776. Post, W. M. and K.C. Kwon. 2000. Soil carbon sequestration and land use change: processes and potential. Glob. Change Biol. 6:317 327. Potvin, C., E. Whidden, and T. M oore. 2004. A case study of carbon pools under three different land uses in Panama. Clim. Change 67:291 307. Powers, J.S. and E. Veldkamp. 2005. Regional variation in soil carbon and 13C in forests and pastures of northeastern Costa Rica. Biogeochem. 72:31 5 336. Powers, J.S. and W.H. Schlesinger. 2002. Relationships among soil carbon distributions and biophysical factors at nested spatial scales in rain forests of northeastern Costa Rica. Geoderma 109:165 190. ProKarite. Projet d Appui Technique la Filli re Karit 2007. [Online] Available at http://www.prokarite.org/index eng.html (verified 5 Jul.2007). Bamako, Mali. Reich, P.F., S.T. Numbrem, R.A. Almaraz, and H. Eswaran. 2001. Land resource stress es and desertification in Africa. In E.M. Bridges, I.D. Hannam, L.R. Oldeman, F.W.T. Pening de Vries, S.J. Scherr, and S. Sompatpanit (eds.) Responses to Land Degradation. Proc. 2nd. International Conference on Land Degradation and Desertification, Khon K aen, Thailand. Oxford Press, New Delhi, India. Rpublique du Mali. 2005. Minitere du Plan et de L amenagement du Territoire and Ministere de L agriculture de L elevage et de la Peche. Enquete Agricole de Conjoncture Campagne 2003/2004. Bamako, Mali.

PAGE 180

180 Rhoto n, F.E. 2000. Influence of time on soil response to no till practices. Soil Sci Soc Am J 64: 700 709. Richter, D.D., D. Markewitz, S.E. Trumbore, and C.G. Wells. 1999. Rapid accumulation and turnover of soil carbon in a re establishing forest. Nature ( London) 400:56 58. Ringius, L. 2002. Soil carbon sequestration and the CDM: Opportunities and challenges for Africa. Clim. Change 54:471 495. Roose, E., V. Kabore, and C. Guenat. 1999. Zai Practice: A w est African traditional rehabilitation system for semi arid degraded lands, a case study in Burkina Faso Arid Soil Res. Rehabil. 13: 343 355. Rosalina, U., Setiabudhi, and A.E. Putra. Vegetation analysis and database management system in Lampung and Jambi. 1997. ICRAF Southeast Asia Regional Office, Bogor, Indonesia. Rose, S., H. Ahammad, B. Eickhout, B. Fisher, A. Kurosawa, S. Rao, K. Riahi, and D. van Vuuren. 2007. Land in climate stabilization modeling: Initial observations. [Online] Available at http://www.stanford.edu/group/EMF/projects/group21/Landuse.pdf (verified 5 Jul. 2007). Stanford Energy Modeling Forum (EMF), Stanford University, Stanford, CA. Roshetko, J.M., M. Delaney, K. Hairiah, and P Purnomosidhi. 2002. Carbon stocks in Indonesian homegarden systems: Can smallholder systems be targeted for increased carbon storage? Am. J. Alt. Agric. 17:138 148. Sathaye, J.A., W.R. Makundi, K. Andrasko, R. Boer, N.H. Ravindranath, P. Sudha, S. Rao, R. Lasco, F. Pulhin, O Masera, A. Ceron, J. Ordonez, X. Deying, X. Zhang, and S. Zuomin. 2001. Carbon mitigation potential and costs of forestry options in Brazil, China, India, Indonesia, Mexico, the Philippines and Tanzania. Mitig. Adapt. Strat. Glob. Change 6:185 211. Schi mel, D.S., B.H. Braswell, E.A. Holland, R. McKeown, D.S. Ojima, T.H. Painter, W.J. Parton, and A.R. Townsend. 1994. Climatic, edaphic, and biotic controls over storage and turnover of carbon in soils. Glob. Biogeochem. Cycl. 8:279 293. Schroeder, P. 1994. Carbon storage benefits of agroforestry systems. Agrofor Syst. 27:89 97. Schroth, G., S.A. D'Angelo, W.G. Teixeira, D. Haag, and R. Lieberei. 2002. Conversion of secondary forest into agroforestry and monoculture plantations in Amazonia: consequences for biomass, litter and soil carbon stocks after 7 years. For. Ecol. Manag. 163:131 150. Schuur, E.A.G. 2001. The effect of water on decomposition dynamics in mesic to wet Hawaiian montane forests. Ecosystems 4:259 273. Scolel Te. 2007. [Online] Available at http://www.eccm.uk.com/scolelte/ (verified 5 Jul. 2007). Edinburgh, UK.

PAGE 181

181 Seghieri, J., M. Simier, A. Mahamane, P. Hiernaux, and S. Rambal. 2005. Adaptative above ground biomass, stand density and leaf water potential to droughts and clearing in Guiera senegalensis a dominant shrub in Sahelian fallows (Niger). J. Trop. Ecol. 21:203 213. Sharrow, S.H. and S. Ismail. 2004. Carbon and nitrogen storage in agroforests, tree plantations, and pastures in western Ore gon, USA. Agrofor. Syst. 60:123 130. Shepherd, D. and F. Montagnini. 2001. Above ground carbon sequestration potential in mixed and pure tree plantation in the humid tropics. J Trop. For. Sci. 13:450 459. Six, J., R.T. Conant, E.A. Paul, and K. Paustian. 2002a. Stabilization mechanisms of soil organic matter: Implications for C saturation of soils. Plant Soil 241:155 176. Six, J., P. Callewaert, S. Lenders, S. De Gryze, S.J. Morris, E.G. Gregorich, E.A. Paul, and K. Paustian. 2002b. Measuring and understan ding carbon storage in afforested soils by physical fractionation. Soil Sci Soc Am J 66:1981 1987. Six, J., R. Merckx, K. Kimpe, K. Paustian, and E.T. Elliott. 2000c. A re evaluation of the enriched labile soil organic matter fraction. Eur. J. Soil Sci 51: 283 293. Six, J., E.T. Elliott, K. Paustian, and J.W. Doran. 1998. Aggregation and soil organic matter accumulation in cultivated and native grassland soils. Soil Sci Soc Am J 62:1367 1377. Sm ith P., D.S. P owlson M J. G lending and J.U. S mith 1998. Preliminary estimates of the potential for carbon mitigation in European soils through no till farming. Glob. Change Bio. 4: 679 685. Stainback, A.G. and J.R.R. Alavalapati. 2002. Economic analysis of slash pine forest carbon sequestration in the so uthern U.S. J. For. Econ. 8:105 117. Sun, O.J., J. Campbell, B.E. Law, and V. Wolf. 2004. Dynamics of carbon stocks in soils and detritus across chronosequences of different forest types in the Pacific Northwest, USA. Glob. Chan. Bio l .10:1470 1481. Tan, Z. X., R. Lal, N.E. Smeck, and F.G. Calhoun. 2004. Relationships between surface soil organic carbon pool and site variables. Geoderma 121: 187 195. Tiepolo, G., M. Calmon, and A.R. Feretti. 2002. Paper presented at measuring and monitoring carbon stocks at t he Guaraquecaba Climate Action Project, Parana, Brazil Proceedings of the International Symposium on Forest Carbon Sequestration and Monitoring. 11 15 November 2002, Taipei, Taiwan. Taiwan Forestry Research Institute Winrock International. Tiessen, H., H. K. Hauffe, and A.R. Mermut. 1991. Deposition of Harmattan dust and its influence on base saturation of soils in northern Ghana. Geoderma 49:285 299. Tieszen, L.L. 1991. Natural variations in the carbon isotope values of plants: implicaations for archaeolog y, ecology and paleoecology. J. Archaeol. Sci. 18: 227.

PAGE 182

182 Tipper, R. and B.H. De Jong. 1998. Quantification and regulation of carbon offsets from forestry: comparison of alternative methodologies, with special reference to Chiapas, Mexico. Commonwealth For. Rev. 77:219 228. Tomich, T.P., H. de Foresta, R. Dennis, Q. Ketterings D. Murdiyarso, C. Palm, F. Stolle, Suyanto, and M. van Noordwijk. 2002. Carbon offsets for conservation and development in Indonesia? Am. J. Alt. Agric. 17:125 137. Traore, C.O., F. P lace, and A. Niang. 2002. Evaluation economique des couts de production des plants d'especes haies vives a la pepiniere de Sgou. Mali. ICRAF, Bamako, Mali. Tschakert, P. 2004. Carbon for farmers: Assessing the potential for soil carbon sequestration in th e Old Peanut Basin of Senegal. Clim. Change 67:273 290. Tschakert, P. 2007. Environmental services and poverty reduction: Options for smallholders in the Sahel: Making carbon sequestration work for Africa's rural poor Opportunities and constraints. Agric Syst. 94:75 86. UNFCCC. 2006. Revised simplified baseline and monitoring methodologies for selected small scale afforestation and reforestation project activities under the clean development mechanism. [Online] Available at http://cdm.unfccc.int/UserManagement/FileStorage/CDMWF_AM_A3II6AX6KGW5GBB 7M6AI98UD3W59X4 (verified 5 Jul.2007). Bonn, Germany. UNFCCC 2007. [Online] Available at http://unfccc.int/2860.php (verified 5 Jul.2007). Bonn, Germany. USGS. 2007. International Program Sahel Land Use. [Online] Available at http://edcintl.cr.usgs.gov/sahel.html (verified 5 Jul. 2007). Reston, VA. V an Dorp, M., B. Mounkoro, S. Soumana, C.O. Traore, S. Franzel, F. Place, and A. Niang. 2005. Economic analysis of improved live fences as an agroforestry technology as compared to traditional live fences and dead fen ces in the Sgou Region, Mali. ICRAF, Bamako, Mali. V an Duijl, E. 2000. Feeding livestock in the dry season: towards identifying potential adopters for fodderbanks in Sgou, Mali. Draft Report. ICRAF, Bamako, Mali. Van Duijl, E.C. 1999. Characterisation of potential adopters for live fences in Sgou, Mali. ICRAF, Bamako, Mali. Van Noordwijk, M., S. Rahayu, K. Hairiah, Y.C. Wulan, A. Farida, and B. Verbist. 2002. Carbon stock assessment for a forest to coffee conversion landscape in Sumber Jaya (Lampung, I ndonesia): from allometric equations to land use change analysis. Science China 45:75 86. Walker, S.M. and P.V. Desanker. 2004. The impact of land use on soil carbon in Miombo Woodlands of Malawi. For. Ecol. Manag. 203:345 360.

PAGE 183

183 Wise, R. and O. Cacho. 2005. Tree crop interactions and their environmental and economic implications in the presence of carbon sequestration payments. Environ Modell. Software 20:1139 1148. Woomer, P.L., L.L. Tieszen, G. Tappan, A. Toure, and M. Sall. 2004a. Land use change and ter restrial carbon stocks in Senegal. J. Arid Environ. 59:625 642. Woomer, P.L., A. Toure, and M. Sall. 2004b. Carbon stocks in Senegal's Sahel transition zone. J. Arid Environ. 59:499 510. World Bank. 2007. Mali Country Brief. [Online] Available at www.worldbank.org/mali (verified 5 Jul. 2007). Washington, DC. World Bank. 2006. State and trends of the carbon market 2006. W orld Bank, Washington DC. Yossi, H., B. Kaya, C.O. Traore, A. Niang, I. Butare, V. Levass eur, and D. Sanogo. 2005. Les haies vives au Sahel: Etat des connaissances et reccommandations pour la recherche et le developpement. ICRAF Programme Regional Sahel, Bamako, Mali. Zibilske, L.M. and J.M. Bradford. 2007. Soil aggregation, aggregate carbon and nitrogen, and moisture retention induced by conservation tillage. Soil Sci Soc Am J 71:793 802. Zinn, Y L., R. Lal, J.M. Bigham, and D.V.S. Resck. 2007. Edaphic controls on soil organic carbon retention in the Brazilian Cerrado: Texture and mineral ogy. Soil Sci Soc Am J 71: 1204 1214.

PAGE 184

184 BIOGRAPHICAL SKETCH Asako Takimoto was born in Ashiya, Hyogo, Japan in 1975. She graduated from the Kyoto University in Kyoto, Japan, in 1998 where she earned her B.Sc. in Agricultural Science and Forestry. In 1997, she received the Rotary International Scholarship and entered the graduate program of the Nicholas School of the Environment, Duke University, where she finished her Master of Forestry. After graduation in 1999, she returned to Japan and worked for the Japan International Cooperation Agency. In 2003, she received the Fulbright Scholarship to start her PhD study in agroforestry at University of Florida.