• TABLE OF CONTENTS
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 Front Cover
 Title Page
 Table of Contents
 Executive summary
 Introduction
 Previous ME classification...
 GIS-based approaches
 Methodology for this study
 Results and discussion
 Conclusion
 Reference
 Appendix A
 Appendix B
 Appendix C
 Appendix D
 Appendix E
 Appendix F
 Appendix G
 Appendix H
 Copyright






Group Title: Maize production environments revisited : a GIS-based approach
Title: Maize production environments revisited
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Title: Maize production environments revisited a GIS-based approach
Physical Description: 33 p. : col. maps ; 28 cm.
Language: English
Creator: Hartkamp, A. D
International Maize and Wheat Improvement Center -- National Resources Group
Publisher: Natural Resources Group, CIMMYT
Place of Publication: Mexico D.F
Publication Date: 2001
 Subjects
Subject: Corn -- Climatic factors -- Latin America   ( lcsh )
Corn -- Climatic factors -- Asia   ( lcsh )
Corn -- Climatic factors -- Africa   ( lcsh )
Genotype-environment interaction -- Latin America   ( lcsh )
Genotype-environment interaction -- Asia   ( lcsh )
Genotype-environment interaction -- Africa   ( lcsh )
Geographic information systems -- Latin America   ( lcsh )
Geographic information systems -- Asia   ( lcsh )
Geographic information systems -- Africa   ( lcsh )
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Bibliography: Includes bibliographical references.
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Table of Contents
    Front Cover
        Front cover
    Title Page
        Page i
    Table of Contents
        Page ii
    Executive summary
        Page 1
        Page 2
    Introduction
        Page 3
    Previous ME classification methods
        Page 4
        Page 5
    GIS-based approaches
        Page 6
    Methodology for this study
        Page 7
    Results and discussion
        Page 8
        Page 9
        Page 10
    Conclusion
        Page 11
    Reference
        Page 12
    Appendix A
        Page 13
    Appendix B
        Page 14
    Appendix C
        Page 15
        Page 16
        Page 17
    Appendix D
        Page 18
    Appendix E
        Page 19
        Page 20
        Page 21
        Page 22
        Page 23
    Appendix F
        Page 24
        Page 25
    Appendix G
        Page 26
        Page 27
        Page 28
        Page 29
        Page 30
        Page 31
    Appendix H
        Page 32
        Page 33
    Copyright
        Page 34
Full Text






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Maize

Production

Environments

Revisited
A GIS-based Approach


A.D. Hartkamp, J.W. White, A. Rodriguez Aguilar,
Natural Resources Group
M. Banziger, G. Srinivasan, G. Granados,
Maize Program
J. Crossa, Biometrics and Statistics Unit


NRG
Natural Resources Group


CIMMYT
The Maize Program


The authors would like to thank Larry Harrington, CIMMYT Natural Resources Group, as well as Greg Edmeades, Shivaji
Pandey, and other members of the CIMMYT Maize Program for their support, ideas and comments. CIMMYT science writer,
Mike Listman, edited and coordinated production of this publication, and designers Wenceslao Almazan and Miguel Mellado
deserve credit for the attractive and readable layout and cover.










Contents


1 Executive summary
3 Introduction
4 Previous ME classification methods
6 GIS-based approaches
7 Methodology for this study
8 Results and discussion
11 Conclusions
12 References
13 Appendices


Tables
2 Table 1. Descriptions, regions, countries, and key sites associated with global maize
mega-environments.
5 Table 2. Agroclimatic criteria used for the maize environment classification by Dowswell
et al. (1996).
5 Table 3. Maize ME Update Committee classification, 1991.
9 Table 4. Cluster mean values for variables: daylength, temperature difference, mean
temperature, precipitation and evapotranspiration.
10 Table 5. Cluster criteria for revised maize mega-environments, including subdivisions
based on precipitation, for a 4-month growing season.


Appendices
13 Appendix A. Estimated area and production percentages for maize mega-environments,
derived from the 1987-1988 mega-environment survey.
14 Appendix B. Maize mega-environment updates using FAO information, 1990 and 1996.
15 Appendix C. Maize mega-environments in South America using the 1991 classification criteria.
18 Appendix D. Latin America maize mega-environment production breakdown
created by overlaying crop distribution (Hyman et al. 1998) and 1991 mega-environment
area maps.
19 Appendix E. Locations selected for GIS-based cluster analysis.
24 Appendix E Clusters for 220 unique maize international testing sites using four
consecutive monthly environmental variables starting from the 'best bet' planting date.
26 Appendix G. Post classification of selected locations according to the new
and 1991 classifications.
32 Appendix H. Zonal maps of maize mega-environments made using trigger season
planting.

(Centerfold: A simplified map of maize mega-environments created by
removing precipitation criteria.)









Executive Summary


To improve the targeting of germplasm by its staff and partners, in the 1980s CIMMYT defined
a set of global maize production environments known as "mega-environments" (MEs). Center
staff interviewed research partners in approximately 70 maize producing countries of the
developing world. Based on the results, the major, non-temperate maize production ecologies
were subdivided into 30 large, not necessarily contiguous areas according to such factors as
maturity, preferred grain color and texture, and important production constraints (drought, low
N conditions, diseases, insect pests). These ME definitions are very useful for targeting and priority
setting, but also have shortcomings. They are based on expert knowledge and are therefore
subjective. Crop losses due to diseases, pests and abiotic stress factors are estimated and not
based on trials. The timing and severity of common stresses are not defined, thus precise
aggregation of areas with similar stress ratings is difficult. Furthermore, definitions focus on regions
where CIMMYT has strong contacts, leaving some areas poorly characterized. In some regions,
different MEs overlap, making the system difficult to use. Finally, maize cropping locations,
circumstances, and systems have changed considerably since the original ME study, but updating
of MEs has been sketchy for lack of resources to do a complete, methodical revision.

This publication presents a revision of the maize MEs that draws on geographic information
systems (GISs). A cluster analysis was performed on climate data, representing a four-month
growing season, for key maize producing locations. The onset of the growing season was
determined based on the month when the ratio of precipitation over potential evapotranspiration
exceeds 0.5, assuming rainfed production conditions. Diagnostic criteria for mapping MEs were
based on cluster analysis results and expert knowledge, and resulted in divisions based on
daylength, mean temperature, and precipitation. The resulting maps and classifications can be
used to select appropriate target environments for maize germplasm and trials at the regional
level, as well as in priority setting and site selection for global maize breeding programs. The full
ME classification and map (Table 5 and Appendix H) is more detailed than many users will want
to deal with, so a simplified map (Centerfold) and table of criteria (Table 1) were prepared that
eliminate the subdivisions based on precipitation. In any case, the creation of surfaces for
precipitation is more problematic than for temperature. Variation in precipitation often shows
no relation to elevation, and precipitation amounts may change rapidly over relatively short
distances (e.g., in rain shadows). We expect that users of the map will usually have a good
understanding of such local variation.

The ME definitions need further refinement using actual maize production' and distribution
data; long-term trial results that represent genotype-by-environment interaction and the incidence
and severity of stresses; improved data on soils; information on consumer preferences; and the
identification of irrigated maize areas in developing countries, to name a few important factors.


1Including planting dates for main and secondary seasons.
1










Table 1. Descriptions, regions, countries, and key sites associated with global maize mega-environments.

ME Name Daylength Mean Description *
(h) temperature
(C)
1 Tropical lowland 11-12.5 >=24 Equatorial Central and South America and Southeast Asia,
as well as coastal regions of Africa. Largely high
humidity, rainfed systems. Includes some winter season
regions at higher latitudes.
Key sites: Suwan (W), Thailand; Bangalore (W), India;
Tarapoto, Peru; Mvuazi, DRC; Kwadoso, Ghana.
2 Tropical midaltitude 11-12.5 >= 18 < 24 Much of inland equatorial sub-Saharan Africa, Central and
South America.
Key sites: Sete Lagos, Brazil; Palmira, Colombia; Turrialba, Costa
Rica; Nazareth, Ethiopia; Embu, Kenya; Poza Rica (W), Mexico.
3 Tropical highland 11-12.5 <18 Equatorial highlands, typically over 2,000 masl.
Key sites: Rio Negro, Colombia; Ambo, Ethiopia; Cajamarca, Peru.
4 Non-equatorial 12.5-13.4 >=24 Major environment of Central and South America,
Tropical Subtropical sub-Saharan and West Africa and Asia.
lowland Key sites: Ludhiana, India; Chiredzi, Zimbabwe; Santa Cruz,
Bolivia; La Ceiba, Honduras; Poza Rica (S), Mexico;
Tlaltizapan (S), Mexico; Suwan (S), Thailand.
5 Non-equatorial 12.5-13.4 >= 18 < 24 Major environment of sub-Saharan Africa and the Mexican
Tropical- Subtropical highlands. Typically less than 1,800 masl. Usually rainfed but
midaltitude with large variation in rainfall.
Key sites: Harare, Zimbabwe; Celaya, Mexico.
6 Non-equatorial 12.5-13.4 < 18 Many scattered highland regions of Central and South
Tropical Subtropical America and Africa. Typically over 1,800 masl.
highland Key sites: El Batan, Mexico; Thaba Seka, Lesotho.
7 Subtropical winter hot <11 >=24 No regions fit these criteria. The category is included only
for completeness.
Key sites: None.
8 Subtropical winter warm <11 >= 18 < 24 Typically irrigated regions at lower elevations.
Key sites: Los Mochis (W), Mexico; Joydebpur (W), Bangladesh.
9 Subtropical winter cold <11 <18 Very limited area with cool, subtropical climate, but no frost
in winter season.
Key sites: Good Hope, Botswana.
10 Subtropical -Temperate hot >= 13.4 >=24 Ranges from very dry irrigated to humid rainfed environments.
Key sites: Sakha, Egypt; Chokwe, Mozambique; Rampur (S),
Nepal; Islamabad, Pakistan; Temple, Texas.
11 Subtropical -Temperate >= 13.4 >=18 <24 Major temperate maize production regions of USA and China.
warm Key sites: Kunming, China; Lumle, Nepal; Potchefstroom, South
Africa; Toulouse, France; Ferrara, Italy; Pyongyang, North
Korea; Ames, Iowa, USA; Davis, California, USA.
12 Subtropical -Temperate cold >= 13.4 <18 Highest latitude regions where maize production is possible.
Key sites: La Platina, Chile; Guelph, Ontario; Orleans, France.

* For key sites, S = summer season and W = winter season.










Maize Production Environments

Revisited: A GIS-based Approach


Introduction

In agricultural research and development,
priorities are often set and promising technologies
targeted within particular crop production
settings, taking into account spatial variation in
biophysical and socioeconomic factors.
Agricultural production environments vary by
climate (a function of latitude, altitude, and other
factors), soil and related aspects, consumer and
producer preferences, accessibility, and input use,
among other things. The effectiveness of
agricultural interventions improved cultivars,
agronomic management practices, decision
support systems depends on these factors. Thus,
researchers often define the limits of an
environment within which a given technology is
applicable.

When the CIMMYT Maize Program began
developing germplasm for maize production
environments in the developing world 35 years
ago, it adopted a strategy for efficiently applying
resources to a range of needs and problems. That
strategy involved grouping the world's maize
production regions into major ecologies: the
lowland tropics, the subtropics, midaltitude
regions, and the highlands. By the late 1980s, the
Program had subdivided these ecologies into 30
areas called mega-environments (MEs) in 70
countries. MEs were defined as the largest
subunits of a crop's growing or target environment
within which a particular variety or related
practice was useful (Pham and Edmeades 1987;
CIMMYT 1989b; Delacy et al. 1994). For the Maize


Program at that time, it involved such factors as
maturity, preferred grain color and texture, and
production constraints (e.g., drought, low N
conditions, diseases, insect pests) to which the
germplasm must be resistant or tolerant. The
typical ME encompassed large (in excess of 1
million ha), not necessarily contiguous areas in
several countries (CIMMYT 1989a).

Use of the ME concept at CIMMYT
Mega-environments were originally intended to
help crop breeders manage genotype-by-
environment interaction and extrapolate
successful varieties and results from one site or
region to other locations where they might have
potential use. The relative size of each ME -
together with considerations such as impact on the
poor, likeliness of success, and the presence of
alternative suppliers was a key criterion in
strategic planning and the subsequent allocation
of resources during the late 1980s and early 1990s
(CIMMYT 1989a). Since then, the concept has also
been applied in designing and testing new crop
management practices (Sayre and Moreno Ramos
1997; CIMMYT Annual Report 1999) and other
products of research by CIMMYT and its partners.
The ME concept has proven useful for setting
priorities, planning strategy, and collaborating
with researchers worldwide (CIMMYT 1990).

Refining ME definitions
CIMMYT initially characterized maize and wheat
production environments through consultation
with regional staff and scientists in national
programs (CIMMYT Maize Program 1988;










CIMMYT 1989b). The criteria used were semi-
quantitative, and there was frequent discussion
about whether the classification of environments
could be revised using more objective and easily
reproducible measures. The development of
geographic information systems (GIS), as well as
improved coverage and reliability in data for
factors such as climate, soils, and topography,
offered a way to achieve this, as demonstrated
in preliminary work by Pollak and Corbett (1993).

This paper describes a GIS-based approach
for refining the definition of maize MEs. It
reviews the origin of the ME concept in more
detail, summarizes various iterations of maize
MEs, and concludes with a version that uses
daylength, temperature, and season rainfall as
classification criteria.


Previous ME Classification
Methods


The first global ME study: 1985-1988
The first initiatives at CIMMYT to assemble ME
information started in 1977 for wheat. Regional
and national program staff were asked to identify
major production regions by country, setting a
lower limit of harvested area of 100,000 ha
annually. These regions were described in terms
of area; crop type; moisture conditions; incidence
of heat, cold, and drought; maturity
requirements; and the average annual loss to
specific diseases and insects. By 1985, after
several iterations of data collection and analysis,
rough maps of national production regions were
available and a computerized database was
produced (CIMMYT 1989b).


Tropical maize is grown over a wider range
of environments than wheat and interacts more
closely with its environment, making the ME
approach potentially more useful for maize.
When CIMMYT began to define global maize
MEs in 1985, it used the same approach as for
wheat, except that losses from pests and diseases
were considered and greater attention paid to
preferences for grain texture and color. The main
criteria were elevation and climate zones
(Appendix A). The information was
computerized and rough maps of 30 MEs, as
drawn by the numerous contributors, were
published in 1988. This reference, often referred
to as "The Yellow Book" (CIMMYT Maize
Program 1988), has since been an important tool
for CIMMYT and its partners. A summary table
was developed (CIMMYT 1989c) in which not
only area but yield level and grain production
were estimated (Appendix A). Production data
could be weighted by factors such as utilization
(food vs feed), per capital income, relative
strength of a national program, or emphasis on
a particular region (e.g., sub-Saharan Africa).


Updating the global ME 1985-88
survey results
The construction of a database from expert
knowledge was very expensive, making it
impracticable to update the ME definitions
comprehensively on a regular basis. Because
biannual updates of maize area, production, and
yield were needed, the 1985-88 estimates were
increased with correction factors derived from
FAO country level statistics. The areas of all MEs
in a given country were automatically increased
at the same ratio as the increase for the country's
entire maize area.










Table 2. Agroclimatic criteria used for the maize
environment classification by Dowswell et al. (1996).

Mean growing
season
temperature (OC)
Environment Min. Max. Mean Altitude Latitude
(masl)
Tropical 22 32 28 <1,000 330 or lower
Subtropical 17 32 25 <1,600 23-330
17 32 25 1,000-1,800 230orlower
Temperate 14 24 20 <500 340 or higher
Highland 7 24 16 >1,800 230 or lower
9 25 18 >1,600 23-340


In publications on the impact of international
maize breeding (Lopez-Pereira and Morris 1994;
Morris 1998), the 1985-1988 ME classification was
updated with information from the FAO
AGROSTAT (FAO 1990) database (see Appendix
B; Tables B.1 and B.2). Dowswell et al. (1996)
suggested a more expanded table with
agroclimatic criteria (Table 2). Using information
from the 1985-1988 study, they produced an area
table by region (Appendix B; Table B.3).


Classification of testing sites with point
based meteorological data
In 1989, an attempt was made to classify maize
testing sites in sub-Saharan Africa (Pollak and
Pham 1989). Forty-two sites from sub-Saharan
Africa were used to create seven clusters. Fifty-
two monthly agroclimatic variables from ten
years of meteorological data from those sites were
used. The classification used Ward's method of
cluster analysis and canonical discriminant
analysis. They identified four lowland regions,
two midaltitude regions, and one highland
region. Lowland regions were separated into
1) high rainfall, 2) warm temperatures and high
rainfall, 3) low minimum temperature, and
4) very high temperature.


The 1991 classification attempt
In 1991, an interdisciplinary group was formed at
CIMMYT to redefine maize MEs using more
objective criteria. An initial effort resulted in the
criteria shown in Table 3. These classification
criteria were to be sent to CIMMYT outreach staff
and collaborating national programs, as was done
in 1985, but with the hope of obtaining more
objective and reproducible area estimates. The
effort never went further, due to funding
constraints.


Limitations to previous classification
approaches
The 1985-88 study is still widely used in
international maize research, including the private
sector (McCarter 1998, personal communication).
This testifies to the overall utility of the ME
concept and the robustness of the maize MEs
defined largely on expert knowledge. However,
several limitations of this approach are apparent.


The definition of environments is subjective.
For example, terms such as "midaltitude" and
"subtropical" are sometimes used interchangeably
where germplasm requirements are similar, so

Table 3. Maize Mega-Environment Update Committee
classification, 1991.

Mean
growing
season
Ecology temperature Elevation Latitude
(C) (masl)

Lowland tropics >24 0-1,000 30ON-300S
Midaltitude tropical 20-24 800-1,800 30ON-300S
Subtropical 20-24 >20oN & >200S
Tropical highland
transition 17-20 1,500-1,800 300N-300S
Tropical highland 12.5-17 >2,000 300N-300S
Temperate 20-22 >30N & >300S
Highland temperate 15-20 >30N & >300S










results are not easily reproducible, and the two
types of ME are not separated well enough. (The
1985-88 classification created many overlapping
subdivisions in certain regions.) Crop losses due
to diseases, pests and abiotic stress factors are
estimated, rather than being based on trial results.
The timing and severity of common stresses are
not defined, even though they may strongly affect
the extent of crop losses. As a result, an assessment
of one stress factor in a given region may not really
be comparable to the same level of stress
identified in another region. This makes the
aggregation of areas with similar ratings for
various stress factors imprecise. The same applies
for the use of elevation: perceptions of "lowland,"
"midaltitude," and "highland" can vary among
maize researches, especially across regions and
where countries have established their own
classifications. Moreover, the ME definitions
described above focus on regions where CIMMYT
has strong contacts, leaving certain areas in Asia,
for example, poorly characterized. Finally, maize
cropping locations, circumstances, and systems
have changed considerably since the original ME
study, but updating of the definitions has been
sketchy for lack of resources to do a more
complete, methodical revision.


GIS-based Approaches


To avoid confusion in terminology, update area
and production data, identify non-overlapping
MEs, and introduce more useful, diagnostic
variables, CIMMYT proposed in 1996 to redefine
global maize MEs using geographic information
systems (GIS). Use of a GIS can ensure that criteria
are applied consistently across regions.
Furthermore, a GIS can combine or link many
types of data (climate and soils, pests and
diseases, socioeconomic factors) by overlaying
and merging them.


The availability of environmental data has
improved greatly with the development of GIS.
Elevation data on a 1-km grid are available for the
entire globe (USGS 1997). Climate data, including
long-term monthly means for maximum and
minimum temperature and totals for precipitation
and potential evapotranspiration, are available as
interpolated surfaces with a grid cell size typically
of 5 to 10 km2 (Corbett and O'Brien 1997). The
interpolation procedures used can allow for
elevation effects and normally offer more accurate
results than simple estimations of climate based
on reference to the nearest station (Hartkamp et
al. 1999). Obtaining detailed and reliable soil and
crop distribution data is still problematic. The best
global soil database is the FAO digital soil map of
the world (FAO 1996) at a 1:5,000,000 scale. Crop
distribution data are available for Latin America
(Hyman et al. 1998) and efforts to obtain similar
data for Africa are under way (P. Thornton,
personal communication).2


A first attempt to define maize ecologies using
GIS-based approaches was made by Pollak and
Corbett (1993) for Central America and Mexico.
They clustered grid cells based on elevation and
mean monthly precipitation and temperature data
during the growing season (April through
October). Ten ecologies were identified: three
lowland, three highland, two subtropical, and two
transitional from subtropical to highland.


Gebrekidan et al. (1992) and Corbett (1998)
proposed a geographic approach to define maize
production environments for Kenya. The ecologies
(lowland, midaltitude, transitional, and highland)
are based on altitude and a cutoff between moist
(> 550 mm) and dry (< 550 mm) for the growing


2 Crop distribution data are critical sources of
information on area and production, essential to the
definition of maize production environments.










season (March to August). The moist transitional
zone (1,200 to 2,000 masl, > 550 mm) accounts for
the largest portion (41%) of the total maize area.


Criteria from the aborted 1991 study were later
used at CIMMYT to create global maps
representing maize production zones. An example
is given for South America in Appendix C. The
growing season was determined using the Spatial
Characterization Tool (SCT) "optimal season"
climate model (Corbett and O'Brien 1997). The
optimal season is defined as the five-month period
with the largest ratio of precipitation over potential
evapotranspiration. Mean growing season
temperatures were replaced by minimum and
maximum temperature ranges (-6C and +6C
from the mean temperature). This approach is only
approximate, because diurnal temperature ranges
in lowland tropical areas are typically smaller than
in the highlands. Data on the start and length of
the onset of the growing season were estimated
by assuming that the ratio of precipitation to
potential evapotranspiration exceeds 0.5 during
the season ("trigger season" climate model). This
approach has proven useful in targeting different
maturity classes of maize in sub-Saharan Africa,
although the 0.5 limit appears to be too high for
most drier areas where maize is sown at low
densities (Hodson et al. 1999).

In a regional refinement to this global
classification, maize areas were assigned to ME
climatology zones in Latin America, based on
maize production data. A crop distribution
database for Latin America was developed by the
GIS group at the Centro Internacional de
Agriculture Tropical (CIAT; Hyman et al. 1998).
Disaggregated, municipality-level maize
production information was reclassified to identify
municipalities where at least 10,000 ha of maize
was grown, applying a probability model that
included infrastructure, transportation access, and


location of populated areas. The crop distribution
information for maize was then overlaid on the
ME zonal maps for Latin America by country
(Appendix D).


Methodology for this Study


About 150 representative sites were selected from
records for international maize trials, and
approximately 70 sites were added to cover
regions where trials had not been conducted
(Appendix E).3 Information on planting dates was
compiled from international trial reports and in
consultation with maize researchers. A separate
set of planting dates was obtained using the
climate models for the trigger and optimal seasons,
as defined in the SCT environmental database
(Corbett and O'Brien 1997). Monthly climate data
for the selected sites were obtained using the
climate surfaces. For Africa and Latin America,
gridded climate surfaces were derived from
Corbett and O'Brien (1997). For Asia, gridded
climate surfaces were derived from Jones (1998).
Starting with the reported month of planting,
variables from the first four consecutive months
were taken. This interval was chosen to represent
a 120-day maize crop cycle.


Daylength (d) was estimated using the
algorithm of Goudriaan and Van Laar, (1994):

d=12 [1+ (2/pi) asin(a/b)]
a = sin(lat)*sin(om) b = cos(lat)*cos(om)
sin(om) = sin (pi 23.45/180) cos
(2 pi (td + 10)/365),


where:
lat =
td =


latitude
day of year.


3 The geographical coordinates of these sites were
carefully checked.










To represent the daylength that affects floral
initiation, three values for td were used: 15 days,
30 days, and 45 days after planting date.4


For the hierarchical cluster analysis, Ward's
clustering procedure (Ward 1963) within SAS 6.12
(SAS Institute 1998) was used. All variables
(daylength, temperature difference, mean
temperature, precipitation and evapotranspiration)
were standardized by subtracting the mean and
dividing by the standard deviation. Cluster
distance was not stable enough to determine an
optimal number of clusters, so a maximum of 15
clusters was allowed, since this was considered to
provide a manageable number of MEs. Clusters
were estimated based on daylength and the first
four monthly mean climate variables derived from:

* Planting dates as reported in the CIMMYT Maize
International Testing Unit database or compiled
through consultation with maize scientists.
* Planting dates as estimated based on start of the
trigger season.
* Planting dates as estimated based on the start of
the optimal season.

Criteria were derived from evaluating the
values of the environmental variables of members
within each cluster. Maize production
environments were mapped and feedback from
experts was sought. After several iterations this
resulted in a revised set of criteria for defining
maize MEs.











4 The 15th day of the planting month was considered
the planting date.


Results and Discussion


Planting date and clusters
Each approach to define planting date and
growing season (reported, trigger season model,
optimum season model) resulted in different
cluster compositions. Site clusters using planting
date from trigger season resembled site clusters
from reported planting date more than site
clusters from optimal season (data not shown).
A set of "best bet" planting dates was compiled
from the reported planting dates, trigger planting
dates, and expert knowledge. These climate data
were clustered again, giving the clusters shown
in Appendix F. Mean cluster values are shown
in Table 4.


Determining criteria
Classification criteria were determined from the
range of values for environmental variables of
the individual sites within the clusters. This was
done iteratively; e.g., assuming a daylength of
13.5 h for high latitude to temperate areas and
checking the sites that segregate at this criterion.
This proved to segregate better at a daylength of
13.4 h. This process resulted in the use of three
classification criteria: daylength, mean
temperature, and seasonal rainfall over the four-
month growing season (Table 5). After this
process of determining the criteria based on
membership of the selected sites to specific
clusters, another application of the criteria
involving maize scientists took place. For
instance, analysis of the clusters suggested a
different temperature criterion for distinguishing
non-equatorial subtropical lowlands from non-
equatorial subtropical midaltitude environments
(22C) than for distinguishing equatorial tropical
lowlands from equatorial tropical midaltitude
environments (24C). Upon mapping, however
(see next paragraph), this did not provide a










Table 4. Cluster mean values for variables: daylength, temperature difference, mean temperature, precipitation
and evapotranspiration. M1, M2...refers to month of growing season.


Fre-
Cluster quency
1 5
2 28
3 7
4 19
5 25
6 15
7 9
8 23
9 39
10 12
11 17
12 11
13 2
14 5
15 4


Daylength (h)
M1 M2 M3
12.2 12.0 11.8
12.5 12.6 12.7
10.7 10.5 10.5
12.1 12.2 12.1
12.0 12.0 12.0
12.6 12.6 12.6
12.1 12.1 12.1
12.7 12.8 12.9
13.0 13.0 13.0
13.3 13.1 12.9
12.7 12.9 13.1
13.7 13.7 13.5
13.5 13.7 13.8
13.5 13.5 13.4
14.1 13.9 13.7


Difference (oC) Tmean (oC) Precipitation (mm) Evapotranspiration (mm)
M1 M2 M3 M4 M1 M2 M3 M4 M1 M2 M3 M4 M1 M2 M3 M4


7.3 7.7
10.0 9.1
14.5 15.4
7.8 7.6
11.8 10.8
9.9 9.3
9.4 8.8
11.2 9.6
12.5 11.6
14.3 13.6
14.0 13.0
9.1 7.2
9.1 6.9
14.1 13.3
13.9 10.4


27.4 27.0 25.6 23.9 243 125 66 41 170 172
28.0 27.7 27.4 27.1 116 161 196 233 190 181
21.1 18.1 17.7 20.0 16 10 12 13 142 126
25.7 25.7 25.8 26.0 182 196 177 166 109 105
21.8 21.2 20.9 20.9 95 135 119 89 117 108
26.8 26.3 26.1 26.3 279 352 364 322 131 122
15.3 15.2 15.2 15.1 172 233 190 124 89 86
22.7 22.0 21.9 21.9 145 251 244 210 137 121
24.1 23.9 24.0 24.3 129 171 163 138 144 140
21.3 20.6 20.4 20.6 97 113 97 73 149 139
14.3 14.9 15.5 16.0 81 110 119 115 112 114
26.8 26.4 26.2 25.2 218 359 348 240 228 209
23.1 24.0 24.0 23.9 327 735 1133 1061 215 200
25.4 26.8 27.2 25.8 1 3 10 22 196 204
32.5 31.0 29.8 27.8 74 139 144 53 355 295


satisfactory distinction between lowland and
midaltitude environments, so we reverted to a
uniform temperature criterion of 24C to
distinguish between lowland and midaltitude
environments in both the equatorial tropics and
non-equatorial subtropics.


Finally, locations were classified according to
the new criteria. To compare classification
methods, the sites were also classified using the
previous classification criteria. This is depicted in
the first columns of Appendix G.


Mapping maize MEs
Because the actual planting date for each map cell
was not available, we used the planting date
surface as defined by the trigger season model of
the SCT (Corbett and O'Brien 1997) for mapping
maize MEs. Moreover, planting dates as reported
by collaborators conducting international maize
trials may not necessarily follow the actual
growing season, because they may plant late due
to late arrival of seed shipments or use irrigation
(which allows them to plant outside the normal
season). Farmers in contrast are more dependent


on the actual start of the growing season.5
Daylength maps were calculated from latitude
grids and the trigger season planting date
(averaging values for +15, +30 and +45 days after
trigger season planting date). Zonal maps were
made using criteria from Table 5 (Appendix H). A
simplified map (centerfold) was created by
eliminating the precipitation criteria.


Use of maize mega-environment maps
and information
The maize MEs provide a global characterization
of the target environments for tropical maize
germplasm. Potential users and applications
include:


* For research managers to set priorities and allocate
resources.
* For scientists to focus efforts on relevant products;
i.e., those most urgently needed for the most
important MEs. (Thus, if early-maturing, drought



5 The start of the trigger growing season commonly
coincides with the main (summer-autumn) growing
season. The secondary season is thus not represented
here.











Table 5. Cluster criteria for revised maize mega-environments, including subdivisions based on precipitation,
for a 4-month growing season.

No. Name Daylength (h) Mean temperature ( C) Precipitation (mm)


Too dry lowland tropical
Lowland tropical mesic
Lowland tropical wet
Lowland tropical excess
Too dry tropical midaltitude
Tropical midaltitude mesic
Tropical midaltitude wet
Tropical midaltitude excess
Too dry tropical highland
Tropical highland mesic
Tropical highland wet
Tropical highland excess
Too dry non-equatorial tropicallsubtropical lowland
Non-equatorial tropicallsubtropical lowland mesic
Non-equatorial tropicallsubtropical lowland wet
Non-equatorial tropicallsubtropical lowland excess
Too dry non-equatorial tropicallsubtropical midaltitude
Non-equatorial tropicallsubtropical midaltitude mesic
Non-equatorial tropicallsubtropical midaltitude wet
Non-equatorial tropicallsubtropical midaltitude excess
Too dry non-equatorial tropicallsubtropical highland
Non-equatorial tropicallsubtropical highland mesic
Non-equatorial tropicallsubtropical highland wet
Non-equatorial tropicallsubtropical highland excess
Subtropical winter hot dry
Subtropical winter hot mesic
Subtropical winter hot wet
Subtropical winter hot excess
Too dry subtropical winter warm
Subtropical winter warm mesic
Subtropical winter warm wet
Subtropical winter warm excess
Too dry subtropical winter cold
Subtropical winter cold mesic
Subtropical winter cold wet
Subtropical winter cold excess
Too dry temperatelsubtropical lowland dry
Temperatelsubtropical hot mesic
Temperatelsubtropical hot wet
Temperatelsubtropical hot excess
Too dry temperatelsubtropical warm dry
Temperatelsubtropical warm mesic
Temperatelsubtropical warm wet
Temperatelsubtropical warm excess
Too dry temperatelsubtropical cold dry
Temperatelsubtropical cold mesic
Temperatelsubtropical cold wet
Temperatelsubtropical cold excess


11 to 12.5
11 to 12.5
11 to 12.5
11 to 12.5
11 to 12.5
11 to 12.5
11 to 12.5
11 to 12.5
11 to 12.5
11 to 12.5
11 to 12.5
11 to 12.5
12.5 to 13.4
12.5 to 13.4
12.5 to 13.4
12.5 to 13.4
12.5 to 13.4
12.5 to 13.4
12.5 to 13.4
12.5 to 13.4
12.5 to 13.4
12.5 to 13.4
12.5 to 13.4
12.5 to 13.4
<11
<11
<11
<11
<11
<11
<11
<11
<11
<11
<11
<11
>13.4
>13.4
>13.4
>13.4
>13.4
>13.4
>13.4
>13.4
>13.4
>13.4
>13.4
>13.4


> 24
> 24
> 24
> 24
> 18 and < 24
> 18 and < 24
> 18 and < 24
> 18 and < 24
<18
<18
<18
<18
> 24
> 24
> 24
> 24
> 18 and < 24
> 18 and < 24
> 18 and < 24
> 18 and < 24
<18
<18
<18
<18
> 24
> 24
> 24
> 24
> 18 and < 24
> 18 and < 24
> 18 and < 24
> 18 and < 24
<18
<18
<18
<18
> 24
> 24
> 24
> 24
> 18 and < 24
> 18 and < 24
> 18 and < 24
> 18 and < 24
<18
<18
<18
<18


<200
> 200 and <600
> 600 and <2,000
> 2,000
<200
> 200 and < 600
> 600 and < 2,000
> 2,000
<200
> 200 and < 600
> 600 and < 2,000
> 2,000
<200
> 200 and < 600
> 600 and < 2,000
> 2000
<200
> 200 and < 600
> 600 and < 2000
2 2,000
<200
> 200 and < 600
> 600 and < 2,000
> 2,000
<200
> 200 and < 600
> 600 and < 2,000
> 2,000
<200
> 200 and < 600
> 600 and < 2,000
> 2,000
<200
> 200 and < 600
> 600 and < 2,000
> 2,000
<200
> 200 and < 600
> 600 and < 2,000
> 2,000
<200
> 200 and < 600
> 600 and < 2,000
> 2,000
<200
> 200 and < 600
> 600 and < 2,000
> 2,000










stressed maize turns out to occupy substantially
more area than currently estimated, it may deserve
increased attention.)
* For scientists to test the right type of germplasm
in the appropriate environments. This should also
allow a reduction in the number of testing sites.
* For national program researchers and other
partners to decide which type of germplasm and
trials most suit their needs.


Challenges
The ME definitions provided here need to be
refined through one or several of the following:

* Development of a global database of actual maize
planting dates for main and secondary seasons.
* Identification of irrigated maize production areas.
* Integration of improved data on soils.
* Integration of data on consumer preferences.
* Integration of data on the incidence and severity
of diseases and insect pests.
* Linkage to crop distribution data to obtain maize
production information.

For validation purposes there is a need to link
the proposed maize environment definitions to
data on genotype-by-environment interactions
from trials across MEs; this could also improve the
efficiency of international testing. The work of
Crossa et al. (1993) exemplifies an effective study
of genotype-by-environment interaction. They
analyzed eight years of historical maize data from
multi-environment trials using pattern analysis on
performance data. In this way, they were able to
1) tease out long-term relationships among
international maize testing environments for
which breeding strategies ought to be defined and
2) assess the long-term precision of trials at the
specific testing locations.


Conclusions


The ME concept has proven a useful tool for setting
priorities, allocating resources, and fostering
international collaboration in agricultural research
and development. Use of a GIS can allow scientists
to 1) define environments according to more
quantitative criteria and 2) visualize how these
criteria affect the location of the environments. To
be fully useful, the ME definitions provided here
need to be refined using actual maize distribution
and production data, as well as information on
major production constraints, by country. A GIS-
based approach can help researchers establish a
framework within which other spatial data can be
consulted. The ultimate goal is for researchers and
other users to be able to formulate task-specific
versions of the MEs or "query" a ME definition
for information of interest.











References

CIMMYT. 1989a. Towardthe 21st Century: CIMMYT's Strategy Mexico
D.F.: CIMMYT.
CIMMYT. 1989b. An account of how priorities are set among mega-
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Number 17. Mexico D.F.: CIMMYT.
CIMMYT. 1989c. CIMMYT's Five Year Budget: 1990-1994. Mexico D.F.:
CIMMYT.
CIMMYT. 1990. The CIMMYT Maize Program Information Packet.
Mexico D.F.: CIMMYT.
CIMMYT Maize Program. 1988. Maize Production Regions in
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Corbett, J.D. 1998. Classifying maize production zones. In: Hassan,
R.M.(ed.), 1998, Maize Technology Development and Transfer.
A GIS application for Research Planning in Kenya. CIMMYT-
KARI-CAB International. New York: CABI.
Corbett, J.D., and R.F. O'Brien. 1997. The Spatial Characterization Tool.
Texas Agricultural Experimental Station, Texas A&M University
System, Blackland Research Center. CD-ROM. College Station:
Texas A&M University.
Crossa, J., S. Chapman, and H. Barreto. 1993. Pattern analysis and
parameters of experimental precision of historical maize data.
Technical Report. Mexico D.F.: CIMMYT.
DeLacy, I.H., PN. Fox, J.D. Corbett, J. Crossa, S. Rajaram, R.A. Fischer,
M. van Ginkel. 1994. Long-term association of locations for
testing spring bread wheat. Euphytica 72: 95-106.
Dowswell, C.R., R.L. Paliwal, and R.P. Cantrell. Maize in the Third World.
Winrock Development Orientated Literature Studies. Boulder,
Colorado: Westview Press.
FAO. 1990. AGROSTAT Database. Rome: FAO.
FAO. 1996. Digital Soil Map of the World and Derived Soil Properties.
Version 3.5. FAO, Rome, Italy.
Gebrekidan, B., B. Wafula, and K. Njoroge. 1992. Agroecological zoning
in relation to maize research priorities in Kenya. In: KARI.
Review of the National Maize Research Program. Proceedings
of a Workshop. November 19-23, Kakamega, Kenya. Nairobi:
Kenya Agricultural Research Institute (KARl).
Goudriaan, J. and H.H. van Laar. 1994. Modelling Potential Crop Growth
Processes. Textbook with Exercises. Current issues in
Production Ecology. Dordrecht: Kluwer Academic Publishers.
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Interpolation Techniques for Climate Variables. NRG-GIS Series
99-01. Mexico, D.F.; CIMMYT.
Hodson, D.P., A. Rodriguez, J.W. White, J.D. Corbett, R.F. O'Brien, and
M. Banziger. 1999. Africa Maize Research Atlas (v. 2.0). CD-
ROM. Mexico D.F.: CIMMYT.
Hutchinson, M.F. 1989. A new procedure for gridding elevation and
streamline data with automatic removal of spurious pits.
Journal of Hydrology106: 211-232.


Hutchinson, M.F. 1995. Interpolating mean rainfall using thin plate
smoothing splines. International Journal of GIS 106: 211-232.
Hyman, G., P. Jones, and G. Lema. 1998. Latin America Crop Distribution
Database. CD-ROM. Cali Colombia: CIAT.
Jones, P. 1998. Climate Surfaces for Southeast Asia. CD-ROM. Cali
Colombia: CIAT.
Lopez-Pereira, M.A., and M.L. Morris. 1994. Impacts of International
Maize Research in the Developing World, 1966-1990. Mexico
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MacMillan, J.A., and P. Aquino. 1997. Impacts of the Mexican hybrid
maize seed industry. CIMMYT Economics Program. Internal
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Morris, M.L.(ed.) 1998. Maize Seed Industries in Developing Countries.
Boulder, Colorado: Lynne Rienner.
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growing regions in Mexico and Central America. Agronomy
Journal85: 1133-1139.
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236-244.










Appendix A. Estimated area and production percentages for maize mega-environments, derived from the 1987-
1988 mega-environment survey. (Classification criteria used in the 1987-1988 maize MEs are listed below.)


Mega-environment


Estimated area (%)


Estimated production (%)


Weighted production (%)


Highland
Tropical highlands 6.0 4.4 1.0
Tropical transitional 4.1 6.4 12.0
Temperate highlands 1.0 0.9 0.7
Subtotal 11.0 11.7 13.7

Subtropical
Early white flint/dent 1.2 0.9 1.6
Early yellow flint 1.8 1.1 1.2
Intermediate white dent 7.5 9.5 8.6
Intermediate yellow flint 1.9 1.3 1.5
Late white flint 3.9 3.3 11.2
Late white dent 5.1 6.1 12.4
Late yellow dent/flint 7.8 9.5 2.6
Other 0.8 1.0 1.3
Subtotal 30.3 32.6 40.4

Lowland tropical
Early white flint 4.0 2.2 3.7
Early white dent 1.2 0.7 1.0
Early yellow flint 7.1 4.3 3.5
Early yellow dent 2.5 1.3 0.5
Intermediate white flint 2.1 1.5 3.2
Intermediate white dent 5.3 6.2 3.6
Intermediate yellow flint 9.6 9.7 6.1
Late white flint 4.4 3.6 4.4
Late white dent 6.9 7.3 5.0
Late yellow flint 8.4 9.7 9.4
Late yellow dent 1.7 2.3 2.6
Others 5.8 7.0 2.9
Subtotal 59.0 55.7 45.9

Total 100.0 100.0 100.0


Classification criteria used in the 1987-1988 maize mega-environments study.


Lowland tropical
Temperate
Highland tropical
Subtropical
Transitional zone
Temperate highland


Grain type:
WD: White dent
WF: White flint
WFD: White (flint or dent)
YD: Yellow dent
YF: Yellow flint
YFD: Yellow (flint or dent)
MD: Mixed colors dent
MD: Mixed colors flint
MIX: Mixed colors (flint or dent)
GF: Gray flint
BF: Black flint


WO:
YO:
GO:
BO:
WM:
YM:
BM:
M:


White floury
Yellow floury
Gray floury
Black floury
White morocho
Yellow morocho
Black morocho
Morocho (yellow and white)


Growing season:
MA: Major season
MI: Minor season


Maturity:
XE: Extra early
E: Early
I: Intermediate
L: Late
XL: Extra late


Moisture:
A: Rarely stressed
B: Sometimes stressed
C: Frequently stressed
D: Usually stressed


Disease and insects: A relative importance rating of 0-5, where 0 means no presence, and 5 means that maize cannot be
grown unless a resistant variety is grown or chemical control is applied.


Ecology:
LT:
TE:
HT:
ST:
TZ:
SH:


Soil type:
1: Normal
2: Acid










Appendix B. Maize mega-environment updates using FAO information, 1990 and 1996.

Table B.1. Maize production ecologies in developing and industrialized countries in 1990 (Lopez-Pereira
and Morris 1994).

Developing countries Industrialized countries
Total Total
maize area Percent Percent of maize area Percent of
Ecology (million ha) of total non-temperate (million ha) total

Lowland tropical 34.5 43 57 0
Subtropical 13 16 21 42 9
Tropical midaltitude 7.6 9 12 0
Tropical highland 6 8 10 0
Temperate regions 19.6 24 44.3 91

Total 80.7 100 100 86.3 100


Table B.2Distribution of non-temperate maize in 1990, from Lopez-Pereira and Morris (1994).

Total
Lowland Tropical Tropical maize area
Region tropics Subtropics midaltitude highland All (million ha)

Sub-Saharan Africa 49 2 38 11 100 14.4
West Asia & North Africa 0 99 0 1 100 1.2
Asia 71 21 3 5 100 19
Latin America 59 23 3 14 100 23.1

Total 59 19 12 10 100 57.7


Table B.3 Maize environments in the developing world (million ha), from Dowswell et al. (1996).

Region Tropical Subtropical Temperate Highland

Southern Cone, South America 8.7 4 1.8 0
Andean Region, South America 1.6 0.3 0 0.5
Mexico and Central America 4.3 1.6 0 3
West and Central Africa 5.2 0.3 0 0.05
West and Southern Africa 2.1 7 0 1.5
North Africa and the Mideast 0 1 1.1 0
South Asia 5.6 1.4 0 0.6
Southeast Asia 8.7 0.2 0 0
East Asia 0.5 1.3 19.3 0.55

Total 36.7 17.1 22.2 6.2













Appendix C. Maize mega-environments in South America using the 1991 classification criteria. (Crop
distribution from Hyman et al., 1998, is overlayed as dots.)


447
'-\










.... 0..0.-
,--























1 Dot-1 .O hia

SighIland temperate

Lowland Irgccs

Mideltitude

Sublopicel

STumparhan

Tropict highland


K.


Lr r. k



-? -
I *II^

-,4,




1 -;.. .

" V.' .. A- .;


S" ,'-/" f
.. ", ,. .,r

i r -"--

; _* -;-. .".


a







i. ;, 1.
.-








.- r

iVp^-.


., 1
L. _


.
T"


N

O o~KIn~r


ffghland IJop!cal8 rarc!lonBI


i






A simplified map of maize mega-environments
created by removing precipitation criteria
(see Table 1 for descriptions, regions, countries, and key sites associated with global maize mega-environments).
Source of climate data: Latin America, Africa: Corbett and O'Brien (1997); Asia: Jones (1998).


no dala


.1 1
-.'



47'
'- A
:


I


ir"~ .


* /L

..
\ *-'"
% i '


)iL


t --


'I


000 4000 KilameIrse




















no data



no data _


r -"..,


'II
i '" ". .- ,
I.~

.-' L_ /,'
n" 4. *." n
-t


rI


~fI


K>1 r
k~ 4 I -

'~br,, IU
~M -c


ii,
9* -i '4


o data


Mq!a-aiMMnmrn DL
.... .......... .... .......... .


Tropical lowland 1 11-125
Tropical ridaltlLLe 2 11-12.5 ~1 24


Tropica ligriland r
Non-Eq tropical .' Subtropical lo wla n
Nan-Eq tropical I Subtropical midalituald
Non-Eq tropical i Subtropical highland
Subtropical winner hot
Subtrbpicel winter wenar
Suitropical winter cold
STerio Subliopic al Pat
Terip Subuiipic al warm
Tremp 5Sibirc:ii al Cld
-I No growirn season
Too dry


11-12.5


'e r=1J.4
1? >.13.4


TmeaS


cli-~Z


.2
K A


**? )


9-




I. -


k.


.P1

i
r


j-











Appendix D. Latin America maize mega-environment production breakdown created by overlaying crop
distribution (Hyman et al. 1998) and 1991 mega-environment area maps.

Table A assings maize area within a district according to the percentages of environment areas per district.
Table B assigns all maize area within a district to the environment area that is largest in that district.

S MA priority over ST Maize area by ME (000 ha)
Lowland Highland tropical Tropical Highland
Region/country tropics Midaltitude transitional highland Subtropical Temperate temperate Total area
CentralAmerica 6,946 2,529 927 1,061 117 11,580
Belize 11 11
Costa Rica 28 6 3 2 38
El Salvador 387 13 -399
Guatemala 182 37 34 24 300
Honduras 167 125 14 306
Mexico 5,782 2,276 856 1,035 117 10,066
Nicaragua 213 14 227
Panama 177 59 20 256
South America 15,241 1,369 343 496 845 27 178 18,499
Argentina 159 43 13 14 209 27 153 618
Bolivia 252 16 5 15 288
Brazil 10,826 627 2 -588 23 12,067
Chile
Colombia 2,691 410 225 276 3,603
Ecuador 209 99 60 127 495
Guyana 7 0 7-
Paraguay 95 48 143
Peru 426 62 24 56 -568
Surinam 0 0 0
Uruguay 1 1
Venezuela 576 111 14 8 709
Latin America 22,187 3,897 1,270 1,557 845 27 295 30,079
73.8% 13.0% 4.2% 5.2% 2.8% 0.1% 1.0% 100.0%

S MA priority over ST Maize area by ME (000 ha)
Lowland Highland tropical Tropical Highland
Region/country tropics Midaltitude transitional highland Subtropical Temperate temperate Total area
CentralAmerica 7,035 457 1,376 1,947 865 11,680
Belize 11 11
Costa Rica 38 38 0
El Salvador 399 -399
Guatemala 132 101 39 66 338
Honduras 126 167 12 306
Mexico 5,948 48 1,324 1,880 865 10,066
Nicaragua 137 89 227
Panama 243 13 256

South America 8,269 2,608 46 2,287 5,213 0 87 18,511
Argentina 96 12 -422 0 87 618
Bolivia 198 -89 288
Brazil 4,270 1,626 1,523 4,647 12,067
Chile
Colombia 2,705 809 -88 3,603
Ecuador 197 22 2 274 495
Guyana 7 7-
Paraguay 0 143 143
Peru 111 105 44 307 568
Surinam 0 0
Uruguay 1
Venezuela 683 33 -5 -721

Latin America 15,304 3,065 1,422 4,234 6,079 0 87 30,190
50.7% 10.2% 4.7% 14.0% 20.1% 0.0% 0.3% 100.0%











Append ix E. Locations selected for GIS-based cluster analysis.

Number Season* Longitudet Latitudet Planting month Country Location

1 a 13.72 -9.10 3 Angola Catete-Mazozo
2 a 15.12 -11.42 10 Angola Cela
3 a 15.83 -12.73 10 Angola Chianga
4 a 13.43 -15.03 11 Angola Humpata
5 a 14.73 -8.91 10 Angola Kilombo
6 a 16.32 -9.52 10 Angola Poligno-Florestal
7 a 12.20 -5.57 11 Angola St. Vincente
8 a -60.57 -33.93 9 Argentina Pergamino
9 a -65.25 -25.83 12 Argentina Leales
10 a 90.42 24.00 4 Bangladesh Joydebpur
11 b 90.42 24.00 11 Bangladesh Joydebpur
12 a 89.08 24.12 4 Bangladesh Ishurdi
13 b 89.08 24.12 11 Bangladesh Ishurdi
14 a 89.23 25.73 4 Bangladesh Rangpur
15 b 89.23 25.73 11 Bangladesh Rangpur
16 a -89.00 17.00 6 Belize Central Farm
17 a -63.13 -17.70 10 Bolivia Santa Cruz
18 a -66.32 -17.35 12 Bolivia Parirumani
19 a -66.17 -17.43 12 Bolivia Cochabamba
20 a -63.77 -19.87 11 Bolivia Iboperanda
21 a -63.95 -18.12 11 Bolivia Mairana
22 a -63.65 -21.45 11 Bolivia Algarrobal
23 a 25.47 -25.48 6 Botswana Good-Hope
24 a 21.75 -23.97 2 Botswana Hukunsi
25 a 25.65 -18.55 12 Botswana Pandamatenga
26 a 25.95 -24.57 2 Botswana Sebele
27 a -44.25 -19.47 10 Brazil Sete Lagoas
28 a -47.80 -20.98 10 Brazil Jardinopolis
29 a -49.57 -18.68 10 Brazil Capinopolis
30 a -36.00 -6.22 11 Brazil Sta. Cruz Palmeira
31 a -4.33 11.10 5 Burkina Faso Farako-Ba
32 a 105.00 12.00 5 Cambodia Banteay-Dek
33 a -70.63 -33.57 10 Chile La Platina
34 a 102.72 25.12 6 China Kunming
35 a 108.17 22.60 4 China Nanning
36 a 106.65 26.48 6 China Gui-Yang
37 a -75.60 4.93 4 Colombia Chinchina
38 b -75.60 4.93 9 Colombia Chinchina
39 a -75.58 6.26 4 Colombia Medellin
40 b -75.58 6.26 9 Colombia Medellin
41 a -76.60 2.45 9 Colombia Popayan
42 a -76.35 3.55 9 Colombia Palmira
43 a -75.97 8.65 4 Colombia Turipana
44 b -75.97 8.65 9 Colombia Turipana
45 a -75.78 8.83 4 Colombia Monteria
46 b -75.78 8.83 9 Colombia Monteria
47 a -76.37 3.50 9 Colombia Cali
48 a -74.20 4.70 4 Colombia Tibaitata
49 b -74.20 4.70 9 Colombia Tibaitata

* Season a= main season; season b= second season.
t Digital degree format.










Appendix E. Locations selected for GIS-based cluster analysis (cont'd).

Number Season* Longitudet Latitudet Planting month Country Location


-75.43
-75.43
-76.80
-85.13
-83.77
-83.77
-83.65
-83.65
-5.23
-5.03
-3.07
-82.52
-82.52
-70.83
-79.48
-80.43
-78.52
30.98
31.12
30.95
30.50
-89.42
37.82
41.08
38.48
37.08
39.50
-1.58
23.00
-61.67
-90.00
-91.57
-91.52
-90.23
-89.92
-89.75
-89.75
-14.55
-88.20
-88.20
-87.67
-87.67
-85.90
-86.87
-86.87
75.80
78.50
79.45
94.27


6.18
6.18
7.70
10.35
10.22
10.22
9.88
9.88
9.58
7.68
9.62
23.83
23.85
18.38
-1.10
-1.07
-0.38
28.93
30.72
31.12
31.00
13.80
9.05
9.40
7.08
5.85
8.50
6.75
40.55
16.33
14.25
14.30
14.87
15.08
14.25
15.31
15.31
12.35
14.25
14.25
14.40
14.40
14.91
15.75
15.75
30.90
17.33
29.00
26.77


* Season a = main season; season b:
t Digital degree format.


:second season.


4 Colombia
9 Colombia
5 Colombia
7 Costa Rica
5 Costa Rica
10 Costa Rica
5 Costa Rica
10 Costa Rica
4 Cote d'lvoire
7 Cote d'lvoire
4 Cote d'lvoire
6 Cuba
6 Cuba
6 Dominican Republic
6 Ecuador
7 Ecuador
11 Ecuador
6 Egypt
6 Egypt
6 Egypt
6 Egypt
5 El Salvador
5 Ethiopia
7 Ethiopia
5 Ethiopia
5 Ethiopia
6 Ethiopia
6 Ghana
5 Greece
6 Guadeloupe
5 Guatemala
6 Guatemala
5 Guatemala
5 Guatemala
5 Guatemala
5 Guatemala
10 Guatemala
6 Guinea-Bissau
5 Honduras
12 Honduras
5 Honduras
12 Honduras
5 Honduras
6 Honduras
12 Honduras
7 India
7 India
6 India
6 India


Rionegro
Rionegro
Antioquia
Guanacaste
Los Diamantes
Los Diamantes
Turrialba
Turrialba
Ferkessedougou
Bouake
Sinematiali
Alquizar
Tomeguin
Ciaza
Pichilingue
Porto Viejo
Sta.Catalina
Sids
Gemmeiza
Sakha
Nubaria
San Andres
Ambo
Alemaya
Awassa
Bako
Nazareth
Kwadaso
Thessaloniki
Godet
Cuyuta
La Maquina
Quetzaltenango
San Jeronimo
Jutiapa
Polochic
Polochic
Cenmac
La Esperanza
La Esperanza
Omonita
Omonita
Catacamas
La Ceiba
La Ceiba
Ludhiana
Hyderabad
Pantnagar
Jorhat











Appendix E. Locations selected for GIS-based cluster analysis (cont'd).

Number Season* Longitudet Latitudet Planting month Country Location

98 a 86.25 25.98 6 India Dholi
100 a 75.88 19.85 6 India Jalna
101 a 81.60 27.72 6 India Bahraich
102 b 81.60 27.72 12 India Bahraich
103 a 74.90 16.20 6 India Arabhavi
104 a 77.58 12.97 2 India Bangalore
105 a 105.10 -5.30 9 Indonesia Lampung
106 a 106.73 -6.62 9 Indonesia Bogor
107 a 113.28 -8.27 4 Indonesia Muneng
108 b 113.28 -8.27 9 Indonesia Muneng
109 a 119.60 -5.00 4 Indonesia Maros
110 b 119.60 -5.00 9 Indonesia Maros
111 a 50.00 35.78 4 Iran Karaj
112 a 54.00 36.00 4 Iran Gorgan
113 a 35.00 -1.01 4 Kenya Kitale
114 a 37.45 -0.50 3 Kenya Embu
115 a 37.23 -1.58 3 Kenya Katumani
116 a 28.05 -28.88 10 Lesotho Leribe
117 a 27.50 -29.28 11 Lesotho Maseru
118 a 29.08 -29.28 10 Lesotho Mokotlong
119 a 28.62 -29.50 10 Lesotho Thaba-Tseka
120 a 34.43 -14.17 12 Malawi Bembeke
121 a 33.63 -13.98 12 Malawi Chitedze
122 a 34.92 -16.47 12 Malawi Ngabu
123 a 35.07 -15.92 11 Malawi Bvumbwe
124 a 34.07 -13.13 12 Malawi Chitala
125 a -100.82 20.52 6 Mexico Celaya (INIFAP)
126 a -109.00 25.77 11 Mexico Los Mochis
127 a -97.43 20.53 6 Mexico Poza Rica
128 a -109.42 26.67 10 Mexico Cd. Obregon
129 a -99.13 18.68 6 Mexico Tlaltizapan
130 a -98.87 19.52 5 Mexico El Batan
131 a -99.65 19.28 5 Mexico Toluca
132 a -96.12 19.15 6 Mexico Veracruz
133 a -105.20 21.51 6 Mexico Nayarit (INIFAP)
134 a -104.80 21.43 6 Mexico Xalisco
135 a -107.75 29.33 7 Mexico Gomez Farias
136 a -107.40 24.80 7 Mexico Culiacan
137 a -103.77 20.47 6 Mexico Tlajomulco
138 a -103.50 20.50 6 Mexico S.M. Cuyutlan
139 a -103.38 20.70 6 Mexico Zapopan
140 a -100.67 20.52 6 Mexico Queretaro
141 a -101.32 20.73 6 Mexico Irapuato
142 a -99.63 19.30 5 Mexico Metepec
143 a -102.30 22.18 6 Mexico Pabellon
144 a -104.05 20.55 6 Mexico Ameca
145 a -98.77 19.13 5 Mexico Amecameca
146 a -102.65 22.90 7 Mexico Calera
147 a -99.18 19.68 6 Mexico Cuautitlan

* Season a = main season; season b= second season.
t Digital degree format.











Appendix E. Locations selected for GIS-based cluster analysis (cont'd).

Number Season* Longitudet Latitudet Planting month Country Location

148 a 33.22 -19.33 11 Mozambique Sussundenga
149 a 32.38 -26.58 11 Mozambique Umbeluzzi
150 a 35.23 -13.30 11 Mozambique Lichinga
151 a 39.28 -15.10 12 Mozambique Nampula
152 a 33.00 -24.53 12 Mozambique Chokwe
153 a 86.07 27.68 5 Nepal Kabre-Dolakha
154 a 87.33 27.08 6 Nepal Pakhribas
155 a 84.00 28.22 5 Nepal Pokhara
156 a 83.80 28.30 5 Nepal Lumle
157 a 81.72 28.85 6 Nepal Dailekh
158 a 84.42 27.62 6 Nepal Rampur
159 a 81.60 28.60 6 Nepal Surkhet
160 a -86.18 12.13 5 Nicaragua Santa Rosa
161 a 71.58 34.02 6 Pakistan Peshawar
162 a 72.83 34.00 6 Pakistan Pirsabak
163 a 74.00 31.00 6 Pakistan Yousafwala
164 a 72.75 33.50 6 Pakistan Islamabad
165 a -79.37 9.05 5 Panama Tocumen
166 a -80.33 7.83 6 Panama Guarare
167 a -82.33 8.38 5 Panama Chiriqui
168 a -80.38 7.93 6 Panama El Ejido
169 a -80.00 8.00 9 Panama La Honda
170 a -57.10 -25.40 9 Paraguay Caacupe
171 a -55.82 -27.28 9 Paraguay Capitan Miranda
172 a -80.63 -5.18 6 Peru Piura
173 a -76.95 -12.08 6 Peru La Molina
174 a -76.42 -6.52 2 Peru Tarapoto
175 a -78.07 -7.10 10 Peru Cajamarca
176 a -76.35 -6.52 2 Peru El Porvenir
177 a -73.25 -3.75 2 Peru Iquitos
178 a -78.05 -7.62 10 Peru Cajabamba
179 a 124.38 8.29 5 Philipinnes Cagayan
180 a 121.25 14.17 5 Philippines U.P. Los Banos
181 a 125.00 7.00 5 Philippines Karaan
182 a 121.15 16.50 4 Philippines Ilagan
183 b 121.15 16.50 9 Philippines Ilagan
184 a 125.18 6.12 5 Philippines Gral. Santos
185 a 124.82 7.23 6 Philippines Mindanao
186 a 30.60 -29.02 10 RSA Greytown
187 a 27.07 -26.67 12 RSA Potchefstroom
188 a 26.92 -27.17 1 RSA Vijienskroon
189 a 31.15 -26.55 10 Swaziland Malkerns
190 a 31.92 -26.78 11 Swaziland Big-bend
191 a 36.52 33.18 6 Syria Aleppo
192 a 120.23 23.50 8 Taiwan Po-tzu-chia-i
193 a 120.68 24.02 8 Taiwan Taichung
194 a 35.17 -4.50 12 Tanzania Selian
195 a 33.37 -8.92 11 Tanzania Uyole
196 a 37.03 -6.77 12 Tanzania Ilonga

* Season a = main season; season b= second season.
t Digital degree format.










Appendix E. Locations selected for GIS-based cluster analysis (cont'd).

Number Season* Longitudet Latitudet Planting month Country Location

197 a 33.02 -2.70 11 Tanzania Ukiriguru
198 a 37.23 -3.28 3 Tanzania Lambo
199 a 101.00 14.08 6 Thailand Suwan
200 a 100.50 15.35 6 Thailand Nakhon Sawan R.
201 a 100.60 14.73 6 Thailand Phraphutthabat
202 a 102.83 16.43 6 Thailand Khonkaen
203 a 26.75 36.70 5 Turkey Adapazari
204 a 30.83 37.00 5 Turkey Antalya
205 a 32.58 0.53 3 Uganda Namulonge
206 a -57.68 -34.33 9 Uruguay La Estanzuela
207 a 168.30 -17.75 9 Vanuatu Tagabe
208 a -77.05 12.10 9 Venezuela Maracay
209 a -67.78 10.26 6 Venezuela San Joaquin
210 a -68.65 10.35 7 Venezuela San Javier
211 a 105.78 20.48 6 Vietnam Song Boi
212 a 107.23 10.95 4 Vietnam Hung Loc
213 a 108.38 11.13 5 Vietnam Doc Trong Farm
214 a 105.80 21.03 2 Vietnam Dan Phuong
215 a 23.95 -6.75 9 Zaire Gandajika
216 a 27.42 -11.73 11 Zaire Kaniamesh
217 a 14.90 -5.45 10 Zaire Mvuazi
218 a 28.47 -14.45 11 Zambia Kabwe
219 a 31.13 -10.22 11 Zambia Kasama
220 a 27.75 -15.85 11 Zambia Masabuka
221 a 28.25 -15.53 11 Zambia Mount Makulu
222 a 28.85 -11.10 11 Zambia Mansa
223 a 28.37 -14.17 11 Zambia Golden Valley
224 a 27.92 -15.77 12 Zambia Kaoma
225 a 32.62 -20.20 11 Zimbabwe Chipinge
226 a 31.58 -21.02 12 Zimbabwe Chiredzi
227 a 30.78 -19.83 11 Zimbabwe Makoholi
228 a 28.50 -20.38 11 Zimbabwe Matopos
229 a 32.33 -20.35 12 Zimbabwe Save-Valley
230 a 31.17 -17.67 11 Zimbabwe Rattray-Arnold
231 a 31.05 -17.80 11 Zimbabwe Harare
232 a 30.90 -18.32 11 Zimbabwe Kadoma
233 a 31.02 -16.37 12 Zimbabwe Mzarabani
234 a 32.23 -20.80 12 Zimbabwe Chisumbanji
235 a 31.03 -17.08 11 Zimbabwe Glendale

Extra: for post-classification only

240 b -44.25 -19.47 10 Brazil Sete Lagoas
242 b 108.17 22.60 10 China Nanning
243 b -90.00 14.25 10 Guatemala Cuyuta
238 b -97.43 20.53 10 Mexico Poza Rica
237 b 100.50 15.35 10 Thailand Nakhon Sawan R.
236 b 101.00 14.08 10 Thailand Suwan
241 b 105.78 20.48 10 Vietnam Song Boi
239 b 28.37 -14.17 4 Zambia Golden Valley

* Season a = main season; season b= second season.
t Digital degree format.












































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Appendix G. Post classification of selected locations according to the new and 1991 classifications.

Classification following 1991

Newnr Meclas MEname latitude altitude tmean Cluster Diagnostic Cluster Characteristic


la True Tropical lowland dry
True Tropical lowland dry
True Tropical lowland dry
True Tropical lowland dry
True Tropical lowland dry
lb True Tropical lowland mesic
True Tropical lowland mesic
True Tropical lowland mesic
True Tropical lowland mesic
True Tropical lowland mesic
True Tropical lowland mesic
True Tropical lowland mesic
True Tropical lowland mesic
True Tropical lowland mesic
True Tropical lowland mesic
True Tropical lowland mesic
True Tropical lowland mesic
True Tropical lowland mesic
True Tropical lowland mesic
True Tropical lowland mesic
True Tropical lowland mesic
True Tropical lowland mesic
True Tropical lowland mesic
1c True Tropical lowland wet
True Tropical lowland wet
True Tropical lowland wet
True Tropical lowland wet
True Tropical lowland wet
True Tropical lowland wet
True Tropical lowland wet
True Tropical lowland wet
True Tropical lowland wet
True Tropical lowland wet
True Tropical lowland wet
True Tropical lowland wet
True Tropical lowland wet
True Tropical lowland wet
True Tropical lowland wet
True Tropical lowland wet
True Tropical lowland wet
True Tropical lowland wet
True Tropical lowland wet
True Tropical lowland wet
True Tropical lowland wet
True Tropical lowland wet
True Tropical lowland wet
2a Tropical midaltitude dry
Tropical midaltitude dry
Tropical midaltitude dry
Tropical midaltitude dry
Tropical midaltitude dry
Tropical midaltitude dry
2b Tropical midaltitude mesic
Tropical midaltitude mesic
Tropical midaltitude mesic
Tropical midaltitude mesic
Tropical midaltitude mesic
Tropical midaltitude mesic
Tropical midaltitude mesic
Tropical midaltitude mesic
Tropical midaltitude mesic
Tropical midaltitude mesic
Tropical midaltitude mesic
Tropical midaltitude mesic
Tropical midaltitude mesic
Tropical midaltitude mesic
Tropical midaltitude mesic
Tropical midaltitude mesic
Tropical midaltitude mesic
Tropical midaltitude mesic
Tropical midaltitude mesic
Tropical midaltitude mesic
2c Tropical midaltitude wet
Tropical midaltitude wet
Tropical midaltitude wet
Tropical midaltitude wet
Tropical midaltitude wet


tropical lowland tropical
tropical lowland tropical
tropical lowland tropical
tropical lowland tropical
tropical lowland tropical
tropical midaltitude tropical
tropical lowland tropical
tropical lowland tropical
tropical lowland tropical
tropical lowland tropical
tropical lowland tropical
tropical lowland tropical
tropical lowland tropical
tropical lowland tropical
subtropical lowland tropical
tropical lowland tropical
tropical lowland tropical
subtropical lowland tropical
tropical lowland tropical
tropical lowland tropical
tropical lowland tropical
tropical lowland tropical
tropical lowland tropical


tropical
tropical
tropical
tropical
tropical
tropical
tropical
tropical
tropical
tropical
tropical
tropical
tropical
tropical
tropical
tropical
tropical
tropical
tropical
tropical
tropical
tropical
tropical


lowland
lowland
lowland
lowland
lowland
lowland
lowland
lowland
lowland
lowland
lowland
lowland
lowland
lowland
lowland
lowland
lowland
lowland
lowland
lowland
lowland
lowland
lowland


tropical lowland
subtropical lowland


tropical
tropical
tropical
tropical
tropical
tropical
tropical
tropical
tropical
tropical
tropical
tropical
tropical
tropical
tropical
tropical
tropical
tropical
tropical
tropical
tropical
tropical
tropical
htroptrans
st/ma


subtropical midaltitude st/ma
subtropical midaltitude st/ma
tropical lowland st/ma
tropical lowland st/ma
subtropical lowland htroptrans
tropical troptrans htroptrans
tropical troptrans htroptrans
tropical troptrans htroptrans
tropical midaltitude st/ma
tropical midaltitude st/ma
tropical midaltitude st/ma
tropical troptrans st/ma
tropical midaltitude st/ma
subtropical lowland st/ma
tropical troptrans st/ma
tropical midaltitude st/ma
tropical midaltitude st/ma
tropical midaltitude st/ma
subtropical lowland st/ma
tropical midaltitude st/ma
tropical midaltitude st/ma
subtropical lowland st/ma
tropical lowland st/ma
subtropical lowland st/ma
tropical troptrans htroptrans
tropical midaltitude st/ma
tropical troptrans st/ma
tropical midaltitude st/ma
tropical lowland st/ma


alTmean-alTdif-alP-IDI
na
vhTmean-ITdif-aP-aDI
hTmean-ITdif-vlP-hDL


NONEQ Topics midaltitude mesic
na
Tropical Lowland very hot
Dry subtropical-temperate


variousTmean-ahTdif-ahP-ahDI Subtropical, long day
hTmean-vlTdif-aP-IDI Tropical Lowland
alTmean-alTdif-alP-IDI NONEQ Topics midaltitude mesic
hTmean-vlTdif-aP-IDI Tropical Lowland
variousTmean-ahTdif-ahP-ahDI Subtropical, long day
alTmean-alTdif-alP-IDI NONEQ Topics midaltitude mesic
hTmean-vlTdif-aP-IDI Tropical Lowland
hTmean-vlTdif-aP-IDI Tropical Lowland
hTmean-vlTdif-aP-IDI Tropical Lowland
hTmean-vlTdif-aP-alDI Tropical Lowland
hTmean-vlTdif-aP-alDI Tropical Lowland
vhTmean-ITdif-aP-aDI Tropical Lowland very hot
hTmean-vlTdif-aP-alDI Tropical Lowland
vhTmean-ITdif-aP-aDI Tropical Lowland very hot
hTmean-vlTdif-aP-IDI Tropical Lowland
hTmean-vlTdif-aP-alDI Tropical Lowland
vhTmean-ITdif-aP-aDI Tropical Lowland very hot
vhTmean-ITdif-aP-aDI Tropical Lowland very hot


hTmean-vlTdif-aP-IDI
hTmean-vlTdif-aP-IDI
alTmean-alTdif-alP-IDI
hTmean-altdif-vhP-aDI
hTmean-vlTdif-aP-alDI
hTmean-altdif-vhP-aDI
hTmean-vlTdif-aP-IDI
hTmean-vlTdif-aP-IDI
vhTmean-ITdif-aP-aDI
hTmean-vlTdif-aP-IDI
hTmean-vlTdif-aP-IDI
hTmean-vlTdif-aP-IDI
hTmean-vlTdif-aP-IDI
hTmean-vlTdif-aP-IDI
vhTmean-ITdif-aP-aDI
hTmean-altdif-vhP-aDI
hTmean-altdif-vhP-aDI
hTmean-vlTdif-aP-IDI
hTmean-vlTdif-aP-IDI
vhTmean-ITdif-aP-aDI
vhTmean-ITdif-aP-aDI
vhTmean-ITdif-aP-aDI
vhTmean-ITdif-aP-aDI
na
na
aTmean-htdif-alP-hDI
aTmean-htdif-alP-hDI
alTmean-alTdif-alP-IDI
alTmean-alTdif-alP-IDI
na
alTmean-alTdif-alP-IDI
alTmean-alTdif-alP-IDI
alTmean-alTdif-alP-IDI
alTmean-alTdif-alP-IDI
alTmean-alTdif-alP-IDI
alTmean-alTdif-alP-IDI
alTmean-alTdif-alP-IDI
alTmean-alTdif-alP-IDI


Tropical Lowland
Tropical Lowland
NONEQ Topics midaltitude mesic
Tropical lowland wet
Tropical Lowland
Tropical lowland wet
Tropical Lowland
Tropical Lowland
Tropical Lowland very hot
Tropical Lowland
Tropical Lowland
Tropical Lowland
Tropical Lowland
Tropical Lowland
Tropical Lowland very hot
Tropical lowland wet
Tropical lowland wet
Tropical Lowland
Tropical Lowland
Tropical Lowland very hot
Tropical Lowland very hot
Tropical Lowland very hot
Tropical Lowland very hot
na
na
Subtropical highlands, long day, warm dry
Subtropical highlands, long day, warm dry
NONEQ Topics midaltitude mesic
NONEQ Topics midaltitude mesic
na
NONEQ Topics midaltitude mesic
NONEQ Topics midaltitude mesic
NONEQ Topics midaltitude mesic
NONEQ Topics midaltitude mesic
NONEQ Topics midaltitude mesic
NONEQ Topics midaltitude mesic
NONEQ Topics midaltitude mesic
NONEQ Topics midaltitude mesic


na na
variousTmean-ahTdif-ahP-ahDI Subtropical, long day
alTmean-alTdif-alP-IDI NONEQ Topics midaltitude mesic
alTmean-alTdif-alP-IDI NONEQ Topics midaltitude mesic
alTmean-alTdif-alP-IDI NONEQ Topics midaltitude mesic
variousTmean-ahTdif-ahP-ahDI Subtropical, long day
alTmean-alTdif-alP-IDI NONEQ Topics midaltitude mesic
na na
hTmean-vlTdif-aP-IDI Tropical Lowland
alTmean-alTdif-alP-IDI NONEQ Topics midaltitude mesic
variousTmean-ahTdif-ahP-ahDI Subtropical, long day


alTmean-alTdif-alP-IDI
aTmean-aTdif-hP-haDI
alTmean-alTdif-alP-IDI
alTmean-alTdif-alP-IDI
hTmean-altdif-vhP-aDI


NONEQ Topics midaltitude mesic
NONEQ Tropics wet
NONEQ Topics midaltitude mesic
NONEQ Topics midaltitude mesic
Tropical lowland wet












TreeCLUSTERCLUSNAME CID newid Country Location PLANT LAT LONG Z DL TMEAN TOTP TDIF ETP


2 5 CL17 10602 10602 Ecuador Porto Viejo 7 -1.07 -80.43 213 11 1:1 10.2 93
na na na -21 Thailand Nakhon-S 10 15.35 100.5 83 11 11 1':. 10.6 183
6 2 CL15 10342 10342 Brazil S. Cruz Palmelra 11 -6.22 -36.00 182 1_ 34 1- 9.4 146
9 14 CL16 80427 80427 India Bangalore 2 12.97 77.58 909 11 e2 178 13.8 243
na na na -20 Thailand Suwan 10 14.08 101 13 11 16 1 12.3 205
5 9 CL22 72101 72101 Tanzania Ilonga 12 -6.77 37.03 914 1-'7 549 11.2 132
2 4 CL28 8 8 Angola Kilombo 10 -8.91 14.73 432 1 34 493 8.7 120
12 5 CL17 11018 11018 Peru El Porvenir 2 -6.52 -76.35 457 2lr1 524 10.7 99
2 4 CL28 61103 61103 Ghana Kwadaso 6 6.75 -1.58 287 12.7 592 6.8 96
5 9 CL22 11312 11312 Venezuela San Javier 7 10.35 -68.65 557 1 4I 551 10.5 130
12 5 CL17 11008 11008 Peru Tarapoto 2 -6.52 -76.42 412 1_ 0- 522 10.7 100
2 4 CL28 60702 60702 Cote D'lvoire Bouake 7 7.68 -5.03 360 1 .,1 530 9.0 104
2 4 CL28 20518 -9 Guatemala Polochic 10 15.31 -89.75 40 11 41 549 8.5 102
2 4 CL28 4 4 Angola Catete-Mazozo 3 -9.10 13.72 50 11 P', 341 7.0 116
1 1 CL47 91302 91302 Taiwan Taichung 8 24.02 120.68 82 12 .8 483 8.1 175
1 1 CL47 90507 90507 Indonesia Muneng 4 -8.27 113.28 13 11 338 7.6 145
6 2 CL15 90507 -17 Indonesia Muneng 9 -8.27 113.28 13 1_11 548 8.0 153
1 1 CL47 91301 91301 Talwan Po-Tzu-Chla-I 8 23.50 120.23 4 12.7 510 8.6 175
6 2 CL15 91616 91616 Philippines Gral. Santos 5 6.12 125.18 12 1 3: 396 8.1 168
2 4 CL28 10 10 Angola St. Vincente 11 -5.57 12.20 0 1_ 3' 422 7.2 112
1 1 CL47 90512 90512 Indonesia Maros 4 -5.00 119.60 52 11 e1 348 8.1 160
6 2 CL15 60706 60706 Cote D'lvoire Sinematiall 4 9.62 -3.07 250 1 3'. 509 9.6 143
6 2 CL15 60701 60701 Cote D'lvoire Ferkessedougou 4 9.58 -5.23 283 1_ 3 567 9.9 150
2 4 CL28 20615 -12 Honduras La Ceiba 12 15.75 -86.87 22 11 1 7.4 106
2 4 CL28 72408 72408 Zaire Mvuazi 10 -5.45 14.90 538 1 70 S 8.9 108
12 5 CL17 72401 72401 Zaire Gandajika 9 -6.75 23.95 800 1_0' 12.7 121
3 6 CL23 20304 -7 Costa Rica Los Diamantes 10 10.22 -83.77 245 11 61 10.0 103
1 1 CL47 91609 -19 Philippines Ilagan 9 16.50 121.15 278 11 -. 69 7.5 175
3 6 CL23 90503 90503 Indonesia Bogor 9 -6.62 106.73 341 1_ 0-, 7.7 150
2 4 CL28 10516 10516 Colombia Antioquia 5 7.70 -76.80 168 1 41 1 8.0 111
2 4 CL28 20907 20907 Panama Guarare 6 7.83 -80.33 33 12 1. 7.6 103
6 2 CL15 91502 91502 Vietnam Hung Loc 4 10.95 107.23 193 1 41 8.2 160
2 4 CL28 20911 20911 Panama El Ejido 6 7.93 -80.38 22 1 44 69 7.6 103
2 4 CL28 11029 11029 Peru Iquitos 2 -3.75 -73.25 91 1 i. 183 9.4 96
2 4 CL28 10502 -3 Colombia Turipana 9 8.65 -75.97 50 11 P', 3 8.0 105
2 4 CL28 20902 20902 Panama Tocumen 5 9.05 -79.37 6 1 4'4 6.1 103
2 4 CL28 10504 -4 Colombia Monteria 9 8.83 -75.78 70 11.'? X, 8.2 106
6 2 CL15 3 3 Philipinnes Cagayan 5 8.29 124.38 290 1_ 4. 3 9.0 178
3 6 CL23 20909 20909 Panama Chiriqul 5 8.38 -82.33 16 1 4r. 9.9 107
3 6 CL23 20302 20302 Costa Rica Guanacaste 7 10.35 -85.13 25 12 1 10.4 119
2 4 CL28 10502 10502 Colombia Turipana 4 8.65 -75.97 50 1-_ 8.2 117
2 4 CL28 10504 10504 Colombia Monteria 4 8.83 -75.78 70 1 3: 8.3 118
6 2 CL15 91606 91606 Philippines Karaan 5 7.00 125.00 102 12 J 9.0 168
6 2 CL15 2 2 Indonesia Lampung 9 -5.30 105.10 118 1 0' 9.1 168
6 2 CL15 90512 -18 Indonesia Maros 9 -5.00 119.60 52 1 l,. 3 8.3 163
6 2 CL15 91620 91620 Philippines Mindanao 6 7.23 124.82 24 12 1i:1 7 9.2 175
na na na -24 Brazil Sete Lagoas 4 -19.47 -44.25 823 11 ":. 18.9 1 13.5 79
na na na -25 Vietnam Song-Boin 10 20.48 105.78 55 11 20.9 1': 6.8 120
14 10 CL25 14 14 Botswana Sebele 2 -24.57 25.95 972 1_ 3 21.1 1'- 15.2 124
14 10 CL25 12 12 Botswana Hukunsi 2 -23.97 21.75 1064 12,':. 21.5 1:. 15.1 122
12 5 CL17 11001 11001 Peru Plura 6 -5.18 -80.63 137 11 ;- 22.8 ? 11.0 97
12 5 CL17 20606 -11 Honduras Omonlta 12 14.40 -87.67 560 11 _' 22.8 1 11.1 121
na na na -26 China Nanning 10 22.6 108.17 115 11 11 18.1 206 7.9 115
12 5 CL17 70702 70702 Kenya Kitale 4 -1.01 35.00 1694 11 46 18.5 488 12.8 105
12 5 CL17 70602 70602 Ethiopia Awassa 5 7.08 38.48 1784 1_ 18.7 478 12.6 110
12 5 CL17 23 23 Tanzania Selian 12 -4.50 35.17 1829 1 19.3 516 10.9 116
12 5 CL17 70703 70703 Kenya Embu 3 -0.50 37.45 1380 11 ? 20.1 575 11.3 135
12 5 CL17 70709 70709 Kenya Katumani 3 -1.58 37.23 1477 11 20.2 314 11.2 123
12 5 CL17 70603 70603 Ethiopia Bako 5 5.85 37.08 1257 1 31 21.1 344 10.8 115
12 5 CL17 70607 70607 Ethiopia Nazareth 6 8.50 39.50 1631 1 17 21.1 567 12.0 119
12 5 CL17 9 9 Angola Poligno-Florestal 10 -9.52 16.32 1178 1_ 3 21.3 545 11.7 113
na na na -22 Mexico Poza Rica 10 20.53 -97.43 87 11 21.5 534 9.3 86
5 9 CL22 70601 70601 Ethiopia Alemaya 7 9.40 41.08 1524 12 J 22.3 415 12.6 139
12 5 CL17 72302 72302 Uganda Namulonge 3 0.53 32.58 1112 1_01 22.4 477 10.3 130
12 5 CL17 10501 10501 Colombia Palmlra 9 3.55 -76.35 1189 11 Y. 22.4 555 10.3 101
12 5 CL17 10506 10506 Colombia Call 9 3.50 -76.37 1189 11 46 22.4 555 10.3 101
5 9 CL22 10902 10902 Paraguay Capitan Miranda 9 -27.28 -55.82 91 1_ 22.4 576 12.9 135
12 5 CL17 72102 72102 Tanzania Ukiriguru 11 -2.70 33.02 1239 1 1. 22.6 468 9.8 127
na na na -27 Guatemal Cuyuta 10 14.25 -90 950 11 16 22.7 280 10.4 107
2 4 CL28 91517 91517 Vietnam Dan Phuong 2 21.03 105.80 16 11 :, 22.8 274 6.1 123
12 5 CL17 10601 10601 Ecuador Pichllingue 6 -1.10 -79.48 320 11 '-1 23.3 272 9.1 87
5 9 CL22 10901 10901 Paraguay Caacupe 9 -25.40 -57.10 214 12 J. 23.6 497 11.0 145
12 5 CL17 6 6 Angola Chlanga 10 -12.73 15.83 1736 1- 4J 19.8 11.3 122
4 8 CL42 5 5 Angola Cela 10 -11.42 15.12 1328 1 4: 20.5 9.8 116
12 5 CL17 24 24 Tanzania Uyole 11 -8.92 33.37 1524 1_ 4' 21.0 10.7 113
12 5 CL17 72104 72104 Tanzania Lambo 3 -3.28 37.23 1072 11 "' 21.4 10.1 108
3 6 CL23 20311 -8 Costa Rica Turrialba 10 9.88 -83.65 595 11 : 23.9 10.8 99










Classification following 1991

Newnr Meclas MEname latitude altitude tmean Cluster Diagnostic Cluster Characteristic


3a Tropical Highlands dry
Tropical Highlands dry
Tropical Highlands dry
3b Tropical Highlands mesic
Tropical Highlands mesic
Tropical Highlands mesic
Tropical Highlands mesic
Tropical Highlands mesic
Tropical Highlands mesic
Tropical Highlands mesic
3c Tropical Highlands wet
Tropical Highlands wet
Tropical Highlands wet
Tropical Highlands wet
Tropical Highlands wet
Tropical Highlands wet
Tropical Highlands wet
Tropical Highlands wet
4b True Subtropical lowland mesic
True Subtropical lowland mesic
True Subtropical lowland mesic
True Subtropical lowland mesic
True Subtropical lowland mesic
True Subtropical lowland mesic
True Subtropical lowland mesic
True Subtropical lowland mesic
True Subtropical lowland mesic
True Subtropical lowland mesic
True Subtropical lowland mesic
True Subtropical lowland mesic
True Subtropical lowland mesic
4c True Subtropical lowland wet
True Subtropical lowland wet
True Subtropical lowland wet
True Subtropical lowland wet
True Subtropical lowland wet
True Subtropical lowland wet
True Subtropical lowland wet
True Subtropical lowland wet
True Subtropical lowland wet
True Subtropical lowland wet
True Subtropical lowland wet
True Subtropical lowland wet
True Subtropical lowland wet
True Subtropical lowland wet
True Subtropical lowland wet
True Subtropical lowland wet
True Subtropical lowland wet
True Subtropical lowland wet
True Subtropical lowland wet
True Subtropical lowland wet
True Subtropical lowland wet
True Subtropical lowland wet
True Subtropical lowland wet
True Subtropical lowland wet
True Subtropical lowland wet
True Subtropical lowland wet
True Subtropical lowland wet
True Subtropical lowland wet
True Subtropical lowland wet
True Subtropical lowland wet
True Subtropical lowland wet
True Subtropical lowland wet
True Subtropical lowland wet
True Subtropical lowland wet
True Subtropical lowland wet
True Subtropical lowland wet
True Subtropical lowland wet
True Subtropical lowland wet
True Subtropical lowland wet
True Subtropical lowland wet
True Subtropical lowland wet
True Subtropical lowland wet
True Subtropical lowland wet
True Subtropical lowland wet
True Subtropical lowland wet
True Subtropical lowland wet
True Subtropical lowland wet
True Subtropical lowland wet


tropical lowland trophighland alTmean-alTdif-alP-IDI
tropical troptrans trophighland alTmean-alTdif-alP-IDI
tropical midaltitude htroptrans na
tropical highland trophighland vlTmean-htdif-alP-ahDI
tropical highland trophighland vlTmean-htdif-alP-ahDI
tropical highland trophighland vlTmean-htdif-alP-ahDI
tropical highland trophighland ITmean-ITdif-aP-aDI
tropical highland trophighland ITmean-ITdif-aP-aDI
temperate lowland highlandtemp vlTmean-htdif-alP-ahDI
temperate lowland highlandtemp vlTmean-htdif-alP-ahDI


tropical
tropical
tropical
tropical
tropical
tropical
tropical
tropical


highland trophighland
highland trophighland
highland trophighland
highland trophighland
highland trophighland
highland trophighland
highland htroptrans
highland htroptrans


tropical midaltitude st/ma
tropical midaltitude st/ma
tropical midaltitude tropical
subtropical lowland tropical
subtropical lowland tropical
subtropical lowland tropical
subtropical lowland tropical
tropical lowland tropical
tropical lowland tropical
tropical lowland tropical
tropical lowland tropical
subtropical lowland tropical
temperate lowland tropical
tropical midaltitude st/ma
tropical midaltitude st/ma
tropical midaltitude st/ma
subtropical troptrans st/ma
tropical midaltitude st/ma
tropical midaltitude st/ma
tropical lowland st/ma
tropical midaltitude st/ma
tropical lowland st/ma
subtropical troptrans st/ma
subtropical troptrans st/ma
tropical lowland st/ma
subtropical lowland st/ma
tropical lowland st/ma
tropical midaltitude st/ma
tropical midaltitude st/ma
tropical midaltitude st/ma
tropical lowland tropical
tropical lowland tropical
subtropical midaltitude tropical
tropical lowland tropical
tropical lowland tropical
tropical lowland tropical
tropical lowland tropical
subtropical midaltitude tropical
tropical lowland tropical
tropical lowland tropical
tropical lowland tropical
tropical lowland tropical
tropical lowland tropical
tropical lowland tropical
subtropical lowland tropical
tropical midaltitude tropical
tropical lowland tropical
tropical lowland tropical
tropical lowland tropical
tropical lowland tropical
tropical lowland tropical
tropical lowland tropical
tropical lowland tropical
tropical lowland tropical
tropical lowland tropical
subtropical lowland tropical
tropical lowland tropical
tropical lowland tropical
subtropical lowland tropical
tropical lowland tropical
subtropical lowland tropical


ITmean-ITdif-aP-aDI
ITmean-ITdif-aP-aDI
ITmean-ITdif-aP-aDI
ITmean-ITdif-aP-aDI
ITmean-ITdif-aP-aDI
ITmean-ITdif-aP-aDI
alTmean-alTdif-alP-IDI
ITmean-ITdif-aP-aDI


NONEQ Topics midaltitude mesic
NONEQ Topics midaltitude mesic


Subtropical, cold highlands
Subtropical, cold highlands
Subtropical, cold highlands
Tropical Highlands but low temperature difference
Tropical Highlands but low temperature difference
Subtropical, cold highlands
Subtropical, cold highlands
Tropical Highlands but low temperature difference
Tropical Highlands but low temperature difference
Tropical Highlands but low temperature difference
Tropical Highlands but low temperature difference
Tropical Highlands but low temperature difference
Tropical Highlands but low temperature difference
NONEQ Topics midaltitude mesic
Tropical Highlands but low temperature difference


variousTmean-ahTdif-ahP-ahDI Subtropical, long day
variousTmean-ahTdif-ahP-ahDI Subtropical, long day
variousTmean-ahTdif-ahP-ahDI Subtropical, long day
variousTmean-ahTdif-ahP-ahDI Subtropical, long day
variousTmean-ahTdif-ahP-ahDI Subtropical, long day
variousTmean-ahTdif-ahP-ahDI Subtropical, long day
variousTmean-ahTdif-ahP-ahDI Subtropical, long day
vhTmean-ITdif-aP-aDI Tropical Lowland very hot
vhTmean-ITdif-aP-aDI Tropical Lowland very hot
variousTmean-ahTdif-ahP-ahDI Subtropical, long day
variousTmean-ahTdif-ahP-ahDI Subtropical, long day
variousTmean-ahTdif-ahP-ahDI Subtropical, long day
vhTmean-aTdif-IP-hDL Dry temperate lowlands
aTmean-aTdif-hP-haDI NONEQ Tropics wet
aTmean-aTdif-hP-haDI NONEQ Tropics wet
aTmean-aTdif-hP-haDI NONEQ Tropics wet
variousTmean-ahTdif-ahP-ahDI Subtropical, long day
aTmean-aTdif-hP-haDI NONEQ Tropics wet
aTmean-aTdif-hP-haDI NONEQ Tropics wet
aTmean-aTdif-hP-haDI NONEQ Tropics wet
aTmean-aTdif-hP-haDI NONEQ Tropics wet
aTmean-aTdif-hP-haDI NONEQ Tropics wet
variousTmean-ahTdif-ahP-ahDI Subtropical, long day
variousTmean-ahTdif-ahP-ahDI Subtropical, long day
aTmean-aTdif-hP-haDI NONEQ Tropics wet
variousTmean-ahTdif-ahP-ahDI Subtropical, long day
variousTmean-ahTdif-ahP-ahDI Subtropical, long day
aTmean-aTdif-hP-haDI NONEQ Tropics wet
variousTmean-ahTdif-ahP-ahDI Subtropical, long day
aTmean-aTdif-hP-haDI NONEQ Tropics wet
aTmean-aTdif-hP-haDI NONEQ Tropics wet
variousTmean-ahTdif-ahP-ahDI Subtropical, long day
variousTmean-ahTdif-ahP-ahDI Subtropical, long day
hTmean-altdif-vhP-aDI Tropical lowland wet
variousTmean-ahTdif-ahP-ahDI Subtropical, long day
hTmean-altdif-vhP-aDI Tropical lowland wet
variousTmean-ahTdif-ahP-ahDI Subtropical, long day
variousTmean-ahTdif-ahP-ahDI Subtropical, long day
variousTmean-ahTdif-ahP-ahDI Subtropical, long day
aTmean-aTdif-hP-haDI NONEQ Tropics wet
variousTmean-ahTdif-ahP-ahDI Subtropical, long day
hTmean-altdif-vhP-aDI Tropical lowland wet
hTmean-altdif-vhP-aDI Tropical lowland wet
variousTmean-ahTdif-ahP-ahDI Subtropical, long day
vhTmean-ITdif-aP-aDI Tropical Lowland very hot
variousTmean-ahTdif-ahP-ahDI Subtropical, long day
vhTmean-ITdif-aP-aDI Tropical Lowland very hot
hTmean-altdif-vhP-aDI Tropical lowland wet
vhTmean-ITdif-aP-aDI Tropical Lowland very hot
vhTmean-ITdif-aP-aDI Tropical Lowland very hot
hTmean-altdif-vhP-aDI Tropical lowland wet
hTmean-altdif-vhP-aDI Tropical lowland wet
vhTmean-ITdif-aP-aDI Tropical Lowland very hot
vhTmean-ITdif-aP-aDI Tropical Lowland very hot
hTmean-vlTdif-aP-IDI Tropical Lowland
hTmean-altdif-vhP-aDI Tropical lowland wet
hTmean-altdif-vhP-aDI Tropical lowland wet
vhTmean-ITdif-aP-aDI Tropical Lowland very hot
vhTmean-ITdif-aP-aDI Tropical Lowland very hot
vhTmean-ITdif-aP-aDI Tropical Lowland very hot
hTmean-altdif-vhP-aDI Tropical lowland wet












TreeCLUSTERCLUSNAME CID newid Country Location PLANT LAT LONG Z DL TMEAN TOTP TDIF ETP


CL17 11005 11005 Peru
CL17 20604 -10 Honduras
na -23 Zambia
CL19 10603 10603 Ecuador
CL19 11034 11034 Peru
CL19 11013 11013 Peru
CL51 10507 -5 Colombia
CL51 10507 10507 Colombia
CL19 11201 11201 Uruguay
CL19 10101 10101 Argentina
CL51 15 -1 Colombia
CL51 15 15 Colombia
CL51 17 17 Colombia
CL51 16 -2 Colombia
CL51 16 16 Colombia
CL51 10510 -6 Colombia
CL17 33 33 Ethiopia
CL51 10510 10510 Colombia
CL22 30 30 Zimbabwe
CL22 13 13 Botswana
CL22 10207 10207 Bolivia
CL22 10224 10224 Bolivia
CL22 72608 72608 Zimbabwe
CL22 32 32 Zimbabwe
CL22 29 29 Zimbabwe
CL15 10203 10203 Bolivia
CL15 80404 80404 India
CL22 72607 72607 Zimbabwe
CL22 71004 71004 Malawi
CL22 20764 20764 Mexico
CL31 80403 80403 India


La Molina
La Esperanza
Golden-V
Sta. Catalina
Cajabamba
Cajamarca
Tibaltata
Tibaltata
La Estanzuela
Pergamino
Chinchina
Chinchina
Popayan
Medellin
Medellin
Rionegro
Ambo
Rionegro
Makoholl
Pandamatenga
Iboperanda
Algarrobal
Chisumbanji
Save-Valley
Chiredzi
Santa Cruz
Hyderabad
Mzarabanl
Ngabu
Culiacan, Sin.
Ludhlana


CL42 72501 72501 Zambia Mount-Makulu
CL42 71001 71001 Malawl Chitedze
CL42 72406 72406 Zaire Kanlamesh
CL22 20798 20798 Mexico S. M. Cuyutlan,
CL42 25 25 Zambia Kabwe
CL42 20506 20506 Guatemala San Jeronlmo
CL42 10302 10302 Brazil Sete Lagoas
CL42 72503 72503 Zambia Golden Valley
CL42 20612 20612 Honduras Catacamas
CL22 20794 20794 Mexico Tlajomulco, Jal
CL22 21101 21101 Mexico Zapopan, Jal.
CL42 71005 71005 Malawl Bvumbwe
CL22 10317 10317 Brazil Jardinopolis
CL22 71201 71201 Mozambique Sussundenga
CL42 20507 20507 Guatemala Jutlapa
CL22 27 27 Zambia Masabuka
CL42 20501 20501 Guatemala Cuyuta
CL42 71006 71006 Malawl Chitala
CL22 11303 11303 Venezuela San Joaquin
CL22 21143 21143 Mexico AmecaJal.
CL23 80422 80422 India Arabhavl
CL22 10322 10322 Brazil Capinopolis
CL23 20311 20311 Costa Rica Turrialba
CL22 91602 91602 Philippines U.P Los Banos
CL22 20725 20725 Mexico Xallsco, Nay
CL22 72504 72504 Zambia Kaoma
CL42 20606 20606 Honduras Omonlta
CL22 20401 20401 El Salvador San Andres
CL23 20304 20304 Costa Rica Los Diamantes
CL23 20102 20102 Belize Central Farm
CL22 71208 71208 Mozambique Nampula
CL15 90403 90403 China Nanning
CL22 20712 20712 Mexico Tlaltizapan
CL15 61401 61401 Burkina Faso Farako-Ba
CL23 61302 61302 Guinea-Bissau Cenmac
CL15 91609 91609 Philippines Ilagan
CL15 80417 80417 India Jalna
CL23 20716 20716 Mexico Veracruz
CL23 20502 20502 Guatemala La Maquina
CL15 20805 20805 Nicaragua Santa Rosa
CL15 91513 91513 Vietnam Doc Trong Farm
CL28 20615 20615 Honduras La Ceiba
CL23 20710 20710 Mexico Poza Rica
CL23 20518 20518 Guatemala Polochic
CL15 90302 90302 Cambodia Banteay-dek
CL15 80209 80209 Bangladesh Rangpur
CL15 91406 91406 Thailand Nakhon Sawan R.
CL23 20717 20717 Mexico Nayarit (INIFAP)


-12.08 -76.95 762 11 ;
14.25 -88.20 1821 11 1
-14.17 28.37 1170 11 4'
-0.38 -78.52 3353 1 0
-7.62 -78.05 3353 1 -
-7.10 -78.07 2896 1:
4.70 -74.20 2545 11 '?
4.70 -74.20 2545 11 1
-34.33 -57.68 30 1 50
-33.93 -60.57 61 1 4.
4.93 -75.60 2500 11 i4
4.93 -75.60 2500 1 1.
2.45 -76.60 2362 11 I
6.26 -75.58 2286 11 'i
6.26 -75.58 2286 1-
6.18 -75.43 2156 11 *^
9.05 37.82 2083 1 '
6.18 -75.43 2156 1-
-19.83 30.78 1111 13.12
-18.55 25.65 1097 13.05
-19.87 -63.77 1066 13.12
-21.45 -63.65 777 13.22
-20.80 32.23 412 13.19
-20.35 32.33 446 13.16
-21.02 31.58 433 13.20
-17.70 -63.13 350 12.69
17.33 78.50 521 12.71
-16.37 31.02 429 12.92
-16.47 34.92 108 12.92
24.80 -107.40 48 13.06
30.90 75.80 241 13.38
-15.53 28.25 1281 12.86
-13.98 33.63 1097 12.78
-11.73 27.42 1295 12.64
20.50 -103.50 1616 13.17
-14.45 28.47 1200 12.80
15.08 -90.23 1420 12.83
-19.47 -44.25 823 12.76
-14.17 28.37 1170 12.78
14.91 -85.90 819 12.82
20.47 -103.77 1513 13.17
20.70 -103.38 1566 13.19
-15.92 35.07 889 12.88
-20.98 -47.80 709 12.82
-19.33 33.22 787 13.09
14.25 -89.92 948 12.78
-15.85 27.75 1000 12.88
14.25 -90.00 950 12.78
-13.13 34.07 733 12.73
10.26 -67.78 580 12.57
20.55 -104.05 1244 13.18
16.20 74.90 531 12.91
-18.68 -49.57 579 12.73
9.88 -83.65 595 12.53
14.17 121.25 52 12.77
21.43 -104.80 987 13.23
-15.77 27.92 668 12.88
14.40 -87.67 560 12.79
13.80 -89.42 452 12.75
10.22 -83.77 245 12.55
17.00 -89.00 353 12.96
-15.10 39.28 329 12.84
22.60 108.17 115 12.89
18.68 -99.13 945 13.06
11.10 -4.33 466 12.60
12.35 -14.55 23 12.69
16.50 121.15 278 12.63
19.85 75.88 485 13.13
19.15 -96.12 3 13.09
14.30 -91.57 55 12.80
12.13 -86.18 77 12.66
11.13 108.38 173 12.60
15.75 -86.87 22 12.88
20.53 -97.43 87 13.18
15.31 -89.75 40 12.84
12.00 105.00 15 12.65
25.73 89.23 32 13.03
15.35 100.50 83 12.86
21.51 -105.20 7 13.24











Classification following 1991

Newnr Meclas MEname latitude altitude tmean Cluster Diagnostic Cluster Characteristic


157 True Subtropical lowland wet
158 True Subtropical lowland wet
159 True Subtropical lowland wet
160 True Subtropical lowland wet
161 True Subtropical lowland wet
162 True Subtropical lowland wet
163 5b Subtropical midaltitude mesic
164 Subtropical midaltitude mesic
165 Subtropical midaltitude mesic
166 Subtropical midaltitude mesic
167 Subtropical midaltitude mesic
168 Subtropical midaltitude mesic
169 Subtropical midaltitude mesic
170 Subtropical midaltitude mesic
171 Subtropical midaltitude mesic
172 Subtropical midaltitude mesic
173 Subtropical midaltitude mesic
174 Subtropical midaltitude mesic
175 Subtropical midaltitude mesic
176 Subtropical midaltitude mesic
177 5c Subtropical midaltitude wet
178 Subtropical midaltitude wet
179 Subtropical midaltitude wet
180 Subtropical midaltitude wet
181 Subtropical midaltitude wet
182 Subtropical midaltitude wet
183 Subtropical midaltitude wet
184 Subtropical midaltitude wet
185 Subtropical midaltitude wet
186 6b Subtropical highland mesic
187 Subtropical highland mesic
188 Subtropical highland mesic
189 Subtropical highland mesic
190 Subtropical highland mesic
191 Subtropical highland mesic
192 Subtropical highland mesic
193 Subtropical highland mesic
194 Subtropical highland mesic
195 6c Subtropical highland wet
196 8a Subtropical winter warm dry
197 Subtropical winter warm dry
198 Subtropical winter warm dry
199 Subtropical winter warm dry
200 Subtropical winter warm dry
201 Subtropical winter warm dry
202 9a Subtropical winter cold dry
203 10a Temperate-HighlatST hot dry
204 Temperate-HighlatST hot dry
205 Temperate-HighlatST hot dry
206 Temperate-HighlatST hot dry
207 10b Temperate-HighlatST hot mesic
208 Temperate-HighlatST hot mesic
209 Temperate-HighlatST hot mesic
210 Temperate-HighlatST hot mesic
211 Temperate-HighlatST hot mesic
212 Temperate-HighlatST hot mesic
213 10c Temperate-HighlatST hot wet
214 Temperate-HighlatST hot wet
215 Temperate-HighlatST hot wet
216 Temperate-HighlatST hot wet
217 Temperate-HighlatST hot wet
218 Temperate-HighlatST hot wet
219 Temperate-HighlatST hot wet
220 Temperate-HighlatST hot wet
221 10d Temperate-HighlatST hot extreme wet
222 11b Temperate-HighlatST warm mesic
223 Temperate-HighlatST warm mesic
224 Temperate-HighlatST warm mesic
225 11c Temperate-HighlatST warm wet
226 Temperate-HighlatST warm wet
227 Temperate-HighlatST warm wet
228 11d Temperate-HighlatST warm extreme wet
229 12a Temperate-HighlatST cold dry


tropical
tropical
subtropical
subtropical
subtropical
tropical
subtropical
tropical
subtropical
tropical
subtropical
subtropical
subtropical
subtropical
tropical
subtropical
subtropical
subtropical
subtropical
tropical
tropical
tropical
tropical
subtropical
tropical
tropical
tropical
tropical
tropical
subtropical
tropical
tropical
subtropical
tropical
tropical
tropical
tropical
subtropical
tropical
subtropical
subtropical
subtropical
subtropical
subtropical
subtropical
subtropical
temperate
temperate
temperate
subtropical
subtropical
subtropical
subtropical
temperate
temperate
temperate
subtropical
subtropical
subtropical
subtropical
subtropical
subtropical
subtropical
subtropical


lowland
lowland
lowland
lowland
lowland
lowland
troptrans
highland
highland
midaltitude
highland
midaltitude
highland
midaltitude
troptrans
lowland
troptrans
troptrans
troptrans
midaltitude
troptrans
midaltitude
midaltitude
midaltitude
midaltitude
midaltitude
midaltitude
midaltitude
midaltitude
highland
highland
highland
highland
highland
highland
highland
highland
midaltitude
highland
lowland
lowland
lowland
lowland
lowland
lowland
midaltitude
lowland
lowland
lowland
lowland
lowland
lowland
lowland
lowland
lowland
lowland
midaltitude
midaltitude
lowland
lowland
lowland
lowland
lowland
lowland


tropical
tropical
tropical
tropical
tropical
tropical
htroptrans
htroptrans
htroptrans
htroptrans
htroptrans
st/ma
st/ma
st/ma
st/ma
st/ma
st/ma
st/ma
st/ma
st/ma
htroptrans
st/ma
st/ma
st/ma
st/ma
st/ma
st/ma
st/ma
st/ma
trophighland
trophighland
trophighland
trophighland
trophighland
trophighland
trophighland
htroptrans
htroptrans
trophighland
htroptrans
htroptrans
htroptrans
st/ma
st/ma
st/ma
trophighland
tropical
tropical
tropical
tropical
tropical
tropical
tropical
tropical
tropical
tropical
tropical
tropical
tropical
tropical
tropical
tropical
tropical
tropical


subtropical midaltitude tropical


subtropical
subtropical
subtropical


troptrans htroptrans
midaltitude st/ma
midaltitude st/ma


subtropical highland htroptrans
subtropical troptrans st/ma
subtropical midaltitude st/ma
subtropical midaltitude st/ma
temperate midaltitude highlandtemp


vhTmean-ITdif-aP-aDI
vhTmean-ITdif-aP-aDI
vhTmean-ITdif-aP-aDI
vhTmean-ITdif-aP-aDI
vhTmean-ITdif-aP-aDI
vhTmean-ITdif-aP-aDI
vlTmean-htdif-alP-ahDI
aTmean-htdif-alP-hDI
aTmean-htdif-alP-hDI
variousTmean-ahTdif-ahP-ahDI
aTmean-htdif-alP-hDI
aTmean-htdif-alP-hDI
aTmean-htdif-alP-hDI
variousTmean-ahTdif-ahP-ahDI
variousTmean-ahTdif-ahP-ahDI
variousTmean-ahTdif-ahP-ahDI
aTmean-htdif-alP-hDI
aTmean-htdif-alP-hDI
aTmean-htdif-alP-hDI
variousTmean-ahTdif-ahP-ahDI
aTmean-aTdif-hP-haDI
aTmean-aTdif-hP-haDI
aTmean-aTdif-hP-haDI
aTmean-aTdif-hP-haDI
aTmean-aTdif-hP-haDI
aTmean-aTdif-hP-haDI
aTmean-aTdif-hP-haDI
aTmean-aTdif-hP-haDI
aTmean-aTdif-hP-haDI
vlTmean-htdif-alP-ahDI
vlTmean-htdif-alP-ahDI
vlTmean-htdif-alP-ahDI
vlTmean-htdif-alP-ahDI
vlTmean-htdif-alP-ahDI
vlTmean-htdif-alP-ahDI
vlTmean-htdif-alP-ahDI
vlTmean-htdif-alP-ahDI
vlTmean-htdif-alP-ahDI
vlTmean-htdif-alP-ahDI
alTmean-htdif-vlP-vlDI
alTmean-htdif-vlP-vlDI
alTmean-htdif-vlP-vlDI
alTmean-htdif-vlP-vlDI
alTmean-htdif-vlP-vlDI
alTmean-htdif-vlP-vlDI
alTmean-htdif-vlP-vlDI
hTmean-ITdif-vlP-hDL
hTmean-ITdif-vlP-hDL
hTmean-ITdif-vlP-hDL
hTmean-ITdif-vlP-hDL
variousTmean-ahTdif-ahP-ahDI
variousTmean-ahTdif-ahP-ahDI
variousTmean-ahTdif-ahP-ahDI
vhTmean-aTdif-IP-hDL
vhTmean-aTdif-IP-hDL
vhTmean-aTdif-IP-hDL
ahTmean-ITdfl-hP-vhDL
ahTmean-ITdfl-hP-vhDL
ahTmean-ITdfl-hP-vhDL
ahTmean-ITdfl-hP-vhDL
ahTmean-ITdfl-hP-vhDL
ahTmean-ITdfl-hP-vhDL
ahTmean-ITdfl-hP-vhDL
ahTmean-ITdfl-hP-vhDL
hTmean-ITdif-vvhP-hDL


Tropical Lowland very hot
Tropical Lowland very hot
Tropical Lowland very hot
Tropical Lowland very hot
Tropical Lowland very hot
Tropical Lowland very hot
Subtropical, cold highlands
Subtropical highlands, long day, warm dry
Subtropical highlands, long day, warm dry
Subtropical, long day
Subtropical highlands, long day, warm dry
Subtropical highlands, long day, warm dry
Subtropical highlands, long day, warm dry
Subtropical, long day
Subtropical, long day
Subtropical, long day
Subtropical highlands, long day, warm dry
Subtropical highlands, long day, warm dry
Subtropical highlands, long day, warm dry
Subtropical, long day
NONEQ Tropics wet
NONEQ Tropics wet
NONEQ Tropics wet
NONEQ Tropics wet
NONEQ Tropics wet
NONEQ Tropics wet
NONEQ Tropics wet
NONEQ Tropics wet
NONEQ Tropics wet
Subtropical, cold highlands
Subtropical, cold highlands
Subtropical, cold highlands
Subtropical, cold highlands
Subtropical, cold highlands
Subtropical, cold highlands
Subtropical, cold highlands
Subtropical, cold highlands
Subtropical, cold highlands
Subtropical, cold highlands
Subtropical winter, short Day, rainfall Ilmited
Subtropical winter, short Day, rainfall Ilmited
Subtropical winter, short Day, rainfall Ilmited
Subtropical winter, short Day, rainfall Ilmited
Subtropical winter, short Day, rainfall Ilmited
Subtropical winter, short Day, rainfall Ilmited
Subtropical winter, short Day, rainfall limited
Dry subtropical-temperate
Dry subtropical-temperate
Dry subtropical-temperate
Dry subtropical-temperate
Subtropical, long day
Subtropical, long day
Subtropical, long day
Dry temperate lowlands
Dry temperate lowlands
Dry temperate lowlands
Subtropical wet
Subtropical wet
Subtropical wet
Subtropical wet
Subtropical wet
Subtropical wet
Subtropical wet
Subtropical wet
Subtropical extreme wet


aTmean-htdif-alP-hDI Subtropical highlands, long day, warm dry
variousTmean-ahTdif-ahP-ahDI Subtropical, long day
aTmean-htdif-alP-hDI Subtropical highlands, long day, warm dry


ahTmean-ITdfl-hP-vhDL
ahTmean-ITdfl-hP-vhDL
ahTmean-ITdfl-hP-vhDL
hTmean-ITdif-vvhP-hDL
vlTmean-htdif-alP-ahDI


Subtropical wet
Subtropical wet
Subtropical wet
Subtropical extreme wet
Subtropical, cold highlands













TreeCLUSTERCLUSNAME CID newid Country

6 2 CL15 91407 91407 Thailand
6 2 CL15 91414 91414 Thailand
6 2 CL15 91501 91501 Vietnam
6 2 CL15 80201 80201 Bangladesh
6 2 CL15 80202 80202 Bangladesh
6 2 CL15 91401 91401 Thailand
15 11 CL19 18 18 Lesotho
14 10 CL25 21162 21162 Mexico
14 10 CL25 21150 21150 Mexico
5 9 CL22 7 7 Angola
14 10 CL25 20763 20763 Mexico
14 10 CL25 71404 71404 RSA
14 10 CL25 21138 21138 Mexico
5 9 CL22 31 31 Zimbabwe
5 9 CL22 10208 10208 Bolivia
5 9 CL22 72001 72001 Swaziland
14 10 CL25 20708 20708 Mexico
14 10 CL25 21117 21117 Mexico
14 10 CL25 21124 21124 Mexico
5 9 CL22 72604 72604 Zimbabwe
4 8 CL42 20604 20604 Honduras
4 8 CL42 72602 72602 Zimbabwe
4 8 CL42 72601 72601 Zimbabwe
4 8 CL42 28 28 Zimbabwe
4 8 CL42 71203 71203 Mozambique
4 8 CL42 21 21 Malawi
4 8 CL42 26 26 Zambia
4 8 CL42 72609 72609 Zimbabwe
4 8 CL42 72502 72502 Zambia
15 11 CL19 20 20 Lesotho
15 11 CL19 21130 21130 Mexico
15 11 CL19 20715 20715 Mexico
15 11 CL19 70801 70801 Lesotho
15 11 CL19 10206 10206 Bolivia
15 11 CL19 10205 10205 Bolivia
15 11 CL19 21144 21144 Mexico
15 11 CL19 20713 20713 Mexico
15 11 CL19 22 22 RSA
15 11 CL19 20504 20504 Guatemala
11 3 CL20 80420 -16 India
11 3 CL20 80209 -15 Bangladesh
11 3 CL20 20709 20709 Mexico
11 3 CL20 20711 20711 Mexico
11 3 CL20 80202 -14 Bangladesh
11 3 CL20 80201 -13 Bangladesh
11 3 CL20 11 11 Botswana
9 14 CL16 50403 50403 Egypt
9 14 CL16 50404 50404 Egypt
9 14 CL16 50402 50402 Egypt
9 14 CL16 50401 50401 Egypt
5 9 CL22 71202 71202 Mozambique
5 9 CL22 72005 72005 Swaziland
5 9 CL22 71209 71209 Mozambique
10 15 CL31 80705 80705 Pakistan
10 15 CL31 80701 80701 Pakistan
10 15 CL31 80702 80702 Pakistan
7 12 CL18 35 35 Nepal
7 12 CL18 38 38 Nepal
7 12 CL18 80413 80413 India
7 12 CL18 80608 80608 Nepal
7 12 CL18 80601 80601 Nepal
7 12 CL18 80405 80405 India
7 12 CL18 80407 80407 India
7 12 CL18 80420 80420 India
8 13 CL35 36 36 Nepal
14 10 CL25 19 19 Lesotho
5 9 CL22 10102 10102 Argentina
14 10 CL25 71401 71401 RSA
7 12 CL18 90402 90402 China
7 12 CL18 34 34 Nepal
7 12 CL18 90406 90406 China
8 13 CL35 37 37 Nepal
15 11 CL19 10402 10402 Chile


Location PLANT LAT LONG Z


Phraphutthabat 6
Khonkaen 6
Song Boi 6
Joydebpur 4
Ishurdi 4
Suwan 6
Leribe 10
Cuautitlan 6
Calera, Zac 7
Humpata 11
Gomez Fanas, C 7
Viljienskroon 1
Pabellon Ags 6
Matopos 11
Mairana 11
Malkerns 10
Celaya (INIFAP) 6
Queretaro 6
Irapuato 6
Kadoma 11
La Esperanza 5
Harare 11
Rattray-Arnold 11
Chipinge 11
Lichinga 11
Bembeke 12
Kasama 11
Glendale 11
Mansa 11
Mokotlong 10
Metepec 5
Toluca, Mex. 5
Thaba-Tseka 10
Cochabamba 12
Parlrumani 12
Amecameca, Mex. 5
El Batan 5
Greytown 10
Quetzaltenango 5
Bahraich 12
Rangpur 11
Los Mochis, Sin 11
Cd. Obregon 10
Ishurdl 11
Joydebpur 11
Good-Hope 6
Sakha 6
Nubarla 6
Gemmeiza 6
Sids 6
Umbeluzzi 11
Big-Bend 11
Chokwe 12
Islamabad 6
Pirsabak 6
Yousafwala 6
Pakhribas 6
Dailekh 6
Jorhat 6
Surkhet 6
Rampur 6
Pantnagar 6
Dholi 6
Bahraich 6
Pokhara 5
Maseru 11
Leales 12
Potchefstroom 12
Kunming 6
Kabre-Dolakha 5
Gui-Yang 6
Lumle Reg. Agric. Res. C5
La Platina 10


100.60 6
102.83 162
105.78 55
90.42 8
89.08 31
101.00 13
28.05 1699
-99.18 2246
-102.65 2213
13.43 1468
-107.75 2136
26.92 1347
-102.30 1952
28.50 1457
-63.95 1600
31.15 763
-100.82 1765
-100.67 1777
-101.32 1785
30.90 1309
-88.20 1821
31.05 1489
31.17 1452
32.62 1102
35.23 1305
34.43 1170
31.13 1363
31.03 1250
28.85 1267
29.08 2359
-99.63 2650
-99.65 2657
28.62 2218
-66.17 2895
-66.32 2819
-98.77 2491
-98.87 2267
30.60 1314
-91.52 2388
81.60 130
89.23 32
-109.00 10
-109.42 2
89.08 31
90.42 8
25.47 1231
30.95 7
30.50 8
31.12 9
30.98 29
32.38 23
31.92 109
33.00 33
72.75 521
72.83 474
74.00 176
87.33 1148
81.72 1237
94.27 123
81.60 617
84.42 185
79.45 204
86.25 34
81.60 130
84.00 944
27.50 1635
-65.25 1219
27.07 1354
102.72 1957
86.07 1733
106.65 1153
83.80 1492
-70.63 1037


DL TMEAN TOTP TDIF ETP


9.1
8.5
6.9
7.9
9.9
8.8
411 13.5
552 12.9
276 13.3
448 12.1
358 15.4
317 14.8
387 14.2
458 11.6
417 11.3
490 11.6
501 13.8
465 13.7
570 13.8
585 11.3
5 11.0
2 10.5
6 10.5
93 .6
9.9
8 28.8
95 .9
10.5
10.3
395 13.9
572 13.3
576 13.2
363 13.6
485 12.8
500 12.9

492 14.3
473 12.1
11.8
1..6 15.8
14 14.3
43 17.3
S 16.5
S 14.1
Y' 12.6
_L. 18.1
'- 13.3
S 11.9
S 13.6
'- 14.2
371 10.7
351 11.6
411 12.5
414 13.1
446 12.1

S 6.8
6.5
S 7.1
7.6
8.6
8.8
7.6
8.5
8.8
384 13.7
475 11.9
385 14.0
8.8
6.8
9.0
5.1
14 15.9






Appendix H. Zonal maps of maize mega-environments made using trigger season planting.
Source of climate data: Latin America, Africa: Corbett and O'Brien, 1997; Asia: Jones 1998.


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CIMMYT (www.cimmyt.org) is an internationally funded, nonprofit, food and environmental research center. Headquartered
in Mexico, the Center works with agricultural research institutions worldwide to improve the productivity, profitability, and
sustainability of maize and wheat systems for poor farmers in developing countries. CIMMYT is a Future Harvest
(www.futureharvest.org) center and receives its principal funding from 58 governments, private foundations, and international
and regional organizations known as the Consultative Group on International Agricultural Research. Future Harvest builds
awareness and support for food and environmental research for a world with less poverty, a healthier human family, well-
nourished children, and a better environment. Future Harvest supports research, promotes partnerships, and sponsors projects
that bring the results of research to rural communities, farmers, and families in Africa, Latin America, and Asia.

International Maize and Wheat Improvement Center (CIMMYT) 2000. All rights reserved. Responsibility for this publication
rests solely with CIMMYT. The designations employed in the presentation of material in this publication do not imply the
expressions of any opinion whatsoever on the part of CIMMYT or contributory organizations concerning the legal status of any
country, territory, city, or area, or of its authorities, or concerning the delimitation of its frontiers or boundaries. CIMMYT
encourages fair use of this material. Proper citation is requested.

Printed in Mexico.

Correct citation: Hartkamp, A.D., J.W. White, A. Rodriguez Aguilar, M. Banziger, G. Srinivasan, G. Granados, and J. Crossa.
2000. Maize Production Environments Revisited: A GIS-based Approach. Mexico, D.E: CIMMYT.

Abstract: This publication presents a GIS-based approach for revising the definitions of global maize production environments,
called "mega-environments" (MEs), used by CIMMYT and its partners. A cluster analysis was performed on climate data,
representing a four-month growing season, for key maize producing locations. Assuming rainfed production, the onset of the
growing season was determined based on the month when the ratio of precipitation over potential evapotranspiration exceeds
0.5. Diagnostic criteria for mapping MEs were based on cluster analysis results and expert knowledge. The resulting maps can
be used to select appropriate target environments for maize germplasm and trials, as well as in priority setting and site selection
for global maize breeding programs.

ISBN: 970-648-050-1.
AGROVOC descriptors: Zea mays; Maize; Plant production; Production factors; Climatic factors; Environmental factors; Climate;
Rain; Genotype environment interaction; Crop management; Production possibilities; Geographical information systems;
Cartography; Sampling.
Additional keywords: CIMMYT
AGRIS category codes: F01 Crop Husbandry; B10 Geography.
Dewey decimal classification: 633.1523.




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