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 Introduction
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 Conclusion
 Acknowledgement
 Bibliography
 Tables and graphs


PETE



Modeling changes in farming systems with the adoption of improved fallows in southern Mali
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 Material Information
Title: Modeling changes in farming systems with the adoption of improved fallows in southern Mali
Physical Description: 32 leaves : ; 28 cm.
Language: English
Creator: Kaya, B
Hildebrand, Peter E
Nair, P. K. R
University of Florida -- Institute of Food and Agricultural Sciences
Publisher: University of Florida, Institute of Food and Agricultural Sciences
Place of Publication: Gainesville, FL
Publication Date: 200u?
 Subjects
Subjects / Keywords: Fallowing -- Mali   ( lcsh )
Crop rotation -- Mali   ( lcsh )
Agricultural systems -- Linear programming -- Mali   ( lcsh )
Genre: bibliography   ( marcgt )
non-fiction   ( marcgt )
Spatial Coverage: Mali
 Notes
Abstract: Agricultural production in the Koutiala region, southern Mali, is based on cash sources (cotton and groundnut), cereal sources (maize, sorghum, millet), and a store of wealth (livestock). In these low-input farming systems, crop production is seriously constrained by soil fertility decline. Research is being conducted in the region to investigate the potential of improved fallows planted to leguminous agroforestry tree species to improve soil fertility and crop production. This study examines the potential for adoption of this technology.
Bibliography: Includes bibliographical references (leaves 21-22).
Statement of Responsibility: B. Kaya, P.E. Hildebrand, and P.K.R. Nair.
General Note: Cover title.
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Source Institution: University of Florida
Rights Management: All rights reserved by the source institution and holding location.
Resource Identifier: oclc - 610569595
ocn610569595
Classification: lcc - S603 .K39 1999
System ID: AA00008180:00001

Table of Contents
    Title Page
        Page i
    Abstract
        Page 1
    Introduction
        Page 2
        Page 3
    Materials and methods
        Page 4
        Page 5
        Page 6
        Page 7
        Page 8
        Page 9
        Page 10
    Results
        Page 11
        Page 12
        Page 13
        Page 14
        Page 15
        Page 16
        Page 17
    Conclusion
        Page 18
        Page 19
    Acknowledgement
        Page 20
    Bibliography
        Page 21
        Page 22
        Page 23
    Tables and graphs
        Page 24
        Page 25
        Page 26
        Page 27
        Page 28
        Page 29
        Page 30
        Page 31
        Page 32
Full Text







Modeling Changes in Farming Systems With the Adoption of Improved

Fallows in Southern Mali









































B. Kayal, P.E. Hildebrand2'*, and P.K.R. Nair'



SUniversity of Florida, 118 Newins-Ziegler Hall P.O. Box 110420, Gainesville,

FL 32611-0420. 2 University of Florida, Food and Resource Economics, Institute

of Food and Agricultural Sciences, 2126 McCarty Hall, P.O. Box 110240,

Gainesville, FL 32611-0420. Author for Correspondence Tel: (352) 392-5830

ext. 436 Fax: (352) 392-8634 E-mail:peh@mail.ifas.ufl.edu










Abstract

Agricultural production in the Koutiala region, southern

Mali, is based on cash sources (cotton and groundnut), cereal

sources (maize, sorghum, millet), and a store of wealth

(livestock). In these low-input farming systems, crop production

is seriously constrained by soil fertility decline. Research is

being conducted in the region to investigate the potential of

improved fallows planted to leguminous agroforestry tree species

to improve soil fertility and crop production. This paper

examines the potential for the adoption of this technology on

different household groups using linear programming-based

modeling. The model revealed that an improved fallow would be an

interesting venture only if fodder has a market value and if

maize yields equal to or higher than the regional average yield

of 2500 kg ha-1 can be achieved. Improved fallows are not

financially attractive to farmers if they do not produce benefits

other than soil fertility improvement measured in terms of crop

yield. Any subsidy program which would prevent farmers from

cutting the fodder, as secondary output before the end of the

planned fallow length, would not have adoption potentials. A

special fallow installation loan program, similar to the one that

cotton enjoys, would make the venture viable.



Key words: Linear programming, resource allocation, household

characteristics, simulation, adoption.

1










Introduction

Land degradation and soil fertility decline lead to

decreasing yields and low food production in low-input tropical

farming systems such as in Mali. The input of nutrients as

fertilizers is far less than the quantity exported through

erosion, leaching, and crop harvest. Lack of cash and adequate

infrastructure such as roads, transportation and markets, limit

the use of chemical sources of nutrients. Traditionally, farmers

have relied on long fallow periods to replenish the soil

fertility depleted through cropping. However, due to increases of

human and animal populations and land-use pressure, fallows have

been reduced both in length and in area or even abandoned in many

farming systems.

Low-input, family-based farming systems are characterized by

limited resources, technology, cash, and information, and their

subsistence nature rather than producing surplus for the market.

They operate in fragile environments where their integrated

activities are strongly constrained by socioeconomic,

biophysical, and institutional constraints.

Short-duration fallows are now being tried as an alternative

to the traditional fallows in these farming systems. These

improved fallows are planted to leguminous, fast-growing tree

species for shorter periods (3 to 4 years) than traditional

fallows. Several studies of a biophysical nature have ascertained

the potentials of such managed fallows to soil fertility

2










replenishment in a number of sub-Saharan countries (Batiano and

Mokwunye, 1991; Palm and Sanchez, 1991; Mittal et al., 1992;

Kwesiga and Coe, 1994; Mafongoya and Nair, 1997; Mugendi and

Nair, 1997; Buresh and Cooper, 1999). However, numerous authors

have reported that adoption rates associated with such fallows

are low even in places where they are supposed to have greater

potential (Hoefsloot et al., 1993; Franzel, 1999; Place and

Dewees, 1999; Tarawali et al., 1999). Most of these reports

suggest socioeconomic and policy issues as the main reasons for

these low adoption rates. It can be hypothesized that (i) low

adoption is a result of discrepancies between scientifically

sound biophysical findings and political/socioeconomic realities

and (ii) providing social and economic information along with the

biophysical information related to new technologies can increase

adoption rates. The objectives of this research are (i) to assess

the potential for adoption of improved fallows by two types of

households, (ii) to describe the likely changes in resource

allocation and income generation of different types of households

with the adoption of an improved fallow in the Koutiala region of

southern Mali; and (iii) to analyze the identified changes under

different policy scenarios and help policy formulation which may

increase the adoption rates of improved fallows in the region.

Although the specific results of the study may be limited in

scope to the study region, we expect that the study procedure










would be applicable to similar resource-limited farming systems

that are abundant elsewhere in the tropics.



Materials and Methods

Farming Systems in Southern Mali (Koutiala)

The region is mainly populated with the Minianka, Senoufo,

Bobo, and Bamanan ethnic groups. The total population in 1997 was

568,212 inhabitants living in 454 villages (CMDT, 1998). Almost

all the population is rural with an annual growth rate of 2%. The

population is young, with 44% aged less than 15 and only 3.8%

over 65. The working population is 43%.

Farmers have access to local weekly markets to sell products

and buy manufactured and other goods. There are no adequate

banking facilities and farmers are not very keen on keeping their

money in banks. They invest their earnings in cattle that also

represent a social wealth. Cattle are a key component of the

farming systems. They help in nutrient recycling through the

production of farm-yard manure. They also provide power in

animal-traction-dominated farm operations, fulfill social and

religious needs, and can be sold to meet some cash requirements

for investment. This has resulted in a transfer of livestock

ownership from traditional herders to farmers, traders, and urban

bureaucrats (World Bank, 1994) and has caused an increase in

stocking rate up to 0.3 tropical livestock units (TLU) ha-1, far

beyond the carrying capacity of 0.13 to 0.15 TLU ha-I (Bosma et

4










al., 1993). This situation brought about severe damage to natural

resources, pastures in particular.

There is a large variation among farmers according to

household composition, land holding, wealth, farm equipment, and

their risk-bearing capacities. In the early 1980s, the Sikasso

Farming Systems Research team (DRSPR, now ESPGRN), in

collaboration with Compagnie Malienne pour le Developpement de

Textiles (CMDT, the Malian cotton company), identified four major

household groups (or recommendation domains). This

functional/operational classification is summarized as follows

(Kleene et al., 1989):

* Type A has at least 15 ha of land, all the required

equipment (plow (s), drill(s), cultivator(s), and two pairs

of draft oxen), a herd of at least ten cattle, is well

trained to properly use the equipment, and is self

sufficient in staple food (cereal).

* Type B has at least 10 ha of land, all the required

equipment (plow, drill, cultivator) and one pair of draft

oxen, a herd of less than ten cattle, is well trained to

properly use the equipment, and is self sufficient in food.

Type C may have as much as 10 ha of land which is "poorly

managed" due to a lack of (or insufficient) agricultural

equipment, no more than five cattle and is not self

sufficient in food. A household in this group could have










been in type B but due to special social and/or economic

situations lost most of its wealth and is downgraded. In

most of the cases these farmers have an adequate technical

knowledge and are aware of the economic situation.

Type D, unlike the other types, has difficult access to

land. The farmers have no training, no cattle and no

experience in growing a cash crop. The household can hardly

provide food to feed the members for more than five months

per year.

Types A and B, while admittedly the better-off farmers of

the region, comprise 74% of all farms. They are the focus of

this study.



The Model

Many sophisticated mathematical analysis tools have been

developed to comprehensively analyze household economics and

farming systems at multiple scales and serve as decision making

tools. Though most of these tools operate on similar basic

principles, each has specific components and is unique in the way

it is handled. For this research, linear programming (LP) is

selected to assess the potential adoption of improved fallows by

type A and type B households in the Koutiala region. These

households are the kind thought most likely to adopt promising

new technology.










The LP models are ethnographic' in nature (ELPs). The ELPs

are a means of quantifying ethnographic data (mostly qualitative)

and are both descriptive and analytic. The descriptive models

are developed 1) to help researchers understand the complexity

and diversity of these systems and ultimately 2) to simulate the

systems. Once the simulations are validated, the models are used

analytically to assess differential adoption or rejection of

potential technologies or the potential differential impacts on

households of proposed infrastructure or policy changes. The

technology under consideration here is an improved fallow planted

to Gliricidia sepium + Stylosanthes hamata or Stylosanthes hamata

alone.



Source and type of data

The data for this research originated primarily from an

existing long-term data base of the Sikasso Farming Systems

Research Team (ESPGRN), from farm surveys conducted in the

Koutiala region in 1996 (ESPGRN and Projet Jachere), and at the

N'Goukan village during 1997 and 1998 cropping seasons. Data

related to crop yield and dry matter production in the research

area come mainly from the experiments conducted on farmers'

fields at N'Goukan (1996 to 1998) and at the N'Tarla research



Ethnographic linear programming is an adaptation that has been
developed at the University of Florida by Hildebrand and his associates. This
present study uses a simplified version.

7










station in 1997 and 1998. Secondary sources such as other

Institute d'Economie Rurale (IER) programs, CMDT annual reports,

and review of literature were used to complement and refine the

collected data.



Production activities

Though we are aware of the fact that farmers in this region

undertake many activities in their production systems, for

simplicity, activities in the models are limited to crop (cereals

and cash crops) and fodder production. Cotton is solely for sale

while the cereals (maize and sorghum) can be sold and/or used for

consumption as staple food. The increasingly important

integration of livestock and agriculture in the region and the

acute shortages of feed during the six-to-seven-month dry season

have made fodder production a necessity for households with

cattle. Thus, besides the soil fertility improvement aspects, an

improved fallow will directly benefit the system through

production of high quality fodder.



Constraints

The Malian Cotton Company (CMDT) provides all the required

inputs for cotton on loan to the farmers at the beginning of the

rainy season. The other crops do not benefit from this pre-

financing. Cereal consumption in the region is estimated to be









350 kg person-1 year-1 with equal preference for maize and

sorghum. The constraints imposed on the models are self

sufficiency in maize and sorghum and cash availability on a

seasonal basis for other necessities (both from ethnographic

data). To reduce weight loss of cattle during the dry season,

their feed should be supplemented by 2 kg of fodder head-1 day-'

from March to June. An additional constraint in the analytic

models is fodder from improved fallows and/or fodder banks for

animal consumption.



Scenarios

Scenario 1. The farmer pays all expenses for an improved fallow,

except for protection against cattle during the dry season. An

estimated 825 gliricidia seedlings at 25 CFA/seedling and 10 kg

of stylosanthes seeds at 1000 CFA kg-1 are required for one ha

of a gliricidia + stylosanthes-improved fallow. The total amount

is 30625 CFA ($61.25) ha-1. After meeting the annual fodder

requirement of the farm animals, the opportunity to sell fodder

is included at an estimated price of 10 CFA kg-1 (based on ESPGRN

Sikasso figures for a similar or lower-quality fodder made of

Dolichos purpureum + maize stalks). Dry-matter yield of the

improved fallow is 4000, 5000, and 7000 kg ha-1 at ages 2, 3, and

4 years, respectively.









Scenario 2. This scenario introduces a 50% subsidy on the

installation cost of the improved fallow but, at the same time,

reduces by half the monetary value of fodder. Such reduction is

in line with the anticipated high adoption of the technology

making a higher supply of fodder on the market.



Scenario 3. Total subsidy of the installation cost and no

monetary value for fodder. With the anticipation that all type A

and B households in the region would adopt the technology, there

will be no market for fodder.



Objective function

Households in these systems have several goals. First among

these is food security closely followed by seasonal cash for

other necessities. These goals are considered as constraints

that must be met. In some cases minimizing male labor on the

farm (when men migrate to mines, for example) or maximizing

production of a food crop (where women are responsible for basic

food production) are appropriate objective functions. However,

in most cases the objective function that has provided best

results in simulating these systems is to maximize the amount of

cash available for discretionary spending at the end of the year

after meeting other constraints such as food and cash needs. This

is the objective used in the present case.








Results

Validation

The results of the simulation compared with known data are

presented in Table 1. The Sikasso ESPGRN reports average farm

sizes of 15 ha and 10 ha, respectively, for type A and type B

households. On average, a type A household allocates 9 ha to

cotton, 2 ha to maize, 3.5 ha to sorghum, and variable acreage

(ranging from 0.5 to 1 ha) to produce fodder. The results of the

simulation were 7.5, 2.03, 3.31, and 0.67 ha respectively for

cotton, maize, sorghum, and fodder. Type B households allocate

4.36 ha to cotton, 0.78 ha to maize, 2.30 ha to sorghum and

variable acreage to fodder. The simulation resulted in 4.39,

1.27, 2.07, and 0.42 ha respectively for cotton, maize, sorghum,

and fodder. The households are very dynamic in their evolution

and decision making. Thus, these combinations may vary from one

year to another depending on the objectives of the household and

the resources at its disposal which would result in allocating

different hectareage. However, based on the 1998 ESPGRN figures,

it can be concluded that the model simulates well the situation

for both household types in the Koutiala region and can be used

to predict whether improved fallow would likely be adopted and

changes with the adoption should it occur.



Scenario 1










Tables 2 and 3 summarize four-year results on land use and

end-of-year cash derived from selling activities for type A and B

households, respectively. It is interesting to notice that while

fodder bank land area was reduced by half after the first year

(from 0.67 ha to 0.35 ha) by type A households, there was a

slight increase in fodder bank land area by B type households.

Type A households would have to reduce maize and sorghum areas to

release some labor for the installation of an improved fallow in

year 1. However, these two crops retrieve their initial land area

and labor values in subsequent years. To the contrary, the

introduction of an improved fallow did not affect the initial

allocation of land and labor for the type B households.

The financial situation is presented in Figures 1 and 2,

respectively, for type A and type B households. The

redistribution of land and labor undertaken by type A in year 1

resulted in a 5% decrease in the end-of-year cash as compared to

the initial/basic plan (Figure 1). The family gross income

increased by $68, $92 and $223, respectively, in years 2, 3 and 4

for an initial investment of $61.25 for the installation of the

improved fallow. The technology is even more lucrative for type B

households which did not have to redistribute their resources for

the installation of the improved fallow. Their end of year cash

increased $90, $110, and $273 compared to the basic plan,

respectively, at years 2, 3, and 4 (Figure 2).










When the models were given the choice of a gliricidia +

stylosanthes and a stylosanthes-planted fallow, the preference

was always for stylosanthes under this scenario. Type A

households had 0.67 ha of fodder bank, 0.96 ha of gliricidia +

stylosanthes, and 1.34 ha of stylosanthes. The fodder bank was

dropped out of the rotation after year 1 to make up for the area

under cotton. At the end of the fallow period in year 4, the

model did not chose to put the stylosanthes-planted fallow into

cultivation; rather it included 1.06 ha of fodder bank (Table 4).

Type B households, on the other hand, had 0.42 ha of fodder bank,

0.67 ha of gliricidia + stylosanthes, and 0.93 ha of

stylosanthes. There was a slight increase in the fodder bank area

(0.53) in year 2. These proportions were kept throughout the

fallow period. At the end of the fallow period in year 4, all the

land area under gliricidia + stylosanthes and 65% of that under

stylosanthes was planted to maize in replacement of the

fertilized maize (Table 5).



Scenario 2

Tables 6 and 7 summarize the results for type A and B

households in the Koutiala region. The situation in scenario 2

was identical to the first scenario except that in the latter,

the family income was much reduced because of the low monetary

value attributed to fodder.









In the situation where farmers had the opportunity to choose

between the two types of improved fallows, type B households kept

the same allocation of their resources as previously in scenario

1. Type A households had 0.67 ha of fodder bank in year 1 which

was dropped out of the rotation in years 2 and 3, 2.3 ha of

stylosanthes, and no gliricidia + stylosanthes. However, at the

end of the fallow period in year 4, only 42% of the stylosanthes

was planted to maize (Tables 8 and 9).



Scenario 3

For both household types the fodder bank was dropped after

year 1 when the improved fallow started producing fodder for home

consumption (Tables 10 and 11). Family end-of-year cash increased

to its initial value with the basic plan ($3154.4) after a 5%

decrease in year 1 ($3002.8) and remained constant through year 3

for type A households. Only about a 3% increase ($3237.6) was

observed at the end of the fallow period in year 4 (Figure 1).

Type B households did not suffer any monetary reduction and

achieved a 7.5% increase in gross family income at the end of the

fallow phase (Figure 2).

Tables 12 and 13 summarize the allocation of resources by

farmers in the situation where there were two types of improved

fallows and fodder had no monetary value. Type A households did

not have a stylosanthes-planted fallow in their rotation. The

area under gliricidia + stylosanthes was only 0.11 ha which was

14










all planted to maize in year 4 at the end of the fallow period.

The initial 0.67 ha of fodder bank was progressively reduced to

become only 0.41 in year 4. On the other hand, type B households

had 0.42 ha of fodder bank in year 1, which was eliminated from

the rotation from year 2, 0.67 ha of gliricidia + stylosanthes,

and 0.82 ha of stylosanthes. All the area under gliricidia +

stylosanthes was planted to maize in year 4 while 27 and 73% of

the area under stylosanthes was planted to maize in years 3 and

4, respectively.



Discussion

In the simulation the model underestimated the 1998 area

under cotton in Type A households by 17.31% (7.50 ha vs 9.07 ha).

However, the prediction was very good when compared with a longer

term average (7.50 ha vs 7.36 ha). The other results of the

simulation were in the range of acceptability for both household

types. The minor deviations observed are an indication of the

very nature and the dynamism of these households.

The model indicated that the introduction of improved

fallows of Gliricidia sepium + Stylosanthes hamata and

Stylosanthes hamata would be attractive to type A and B

households in the Koutiala region under both scenarios. The most

direct soil fertility benefit to farmers is the increase in crop

yield. The average maize grain yield in the region is 2350 kg










ha-1 and 1870 kg ha-', respectively, for type A and B households

(ESPGRN, 1998). An improved fallow would be of interest only if

it can increase maize grain yield to 3,000 kg ha-'. The results

from the on-farm experiment indicated that gliricidia +

stylosanthes-planted treatments can achieve similar yields as a

result of soil improvement (Kaya, 2000). Allowing for less than

ideal conditions (climate, soil, protection, etc.), this increase

would be achieved after four years of fallowing.

Under scenarios 1 and 2, this type of improved fallow

represents an interesting venture for types A and B households of

the region mostly because of the additional value-product, i.e.,

fodder, that it provides. Acute fodder shortages during the 6- to

7-month dry season constitute a serious constraint to livestock

productivity in this region where integration of crops and

animals is the basis of the farming systems (Breman and Traor6,

1987; Kleene et al., 1989; Leloup and Traor6, 1989; Bosma et al.,

1996). The model allowed farmers to sell any extra fodder after

having satisfied the family consumption requirements of 2,000 kg

and 1,260 kg dry matter respectively for type A and type B

households. Under these circumstances, type A and type B

households produced respectively 10,000 kg and 11,670 kg of extra

high-quality fodder for sale from year 2 to year 4 of the fallow

phase.

It is interesting to point out that our results indicated

farmer preference for improved fallows planted to stylosanthes

16










over the ones planted to gliricidia + stylosanthes in scenarios

where fodder was valued at market price. This could be explained

by the ease in installation and management of this type of

improved fallow both in terms of initial labor and cash

investments. Also farmers in the region had experience with

planting stylosanthes about ten years ago through previous

programs, though at a very small scale due to seed unavailability

and protection constraints. The worsened fodder shortages they

have been experiencing for the last decades constitute a real

motivation for the technology as evidenced by the number of

farmers asking for stylosanthes seeds.

The fact that stylosanthes-planted fallows remain

uncultivated even after the fallow period could be an indication

that farmers might be more interested in fodder value than the

soil fertility aspects of stylosanthes. However, when both soil

fertility replenishment translated in terms of increased maize

grain yield and fodder production were considered, the farmers'

choice shifted toward gliricidia + stylosanthes-planted fallows.

Selling fodder is not a widespread practice in the region

because of its scarcity, not because of lack of market.

Therefore, this option offers a possibility for farmers to

diversify their income sources. The most likely use of such high-

quality fodder would be to fatten cattle for meat production. The

beef market is very healthy in Mali and in the sub-region,

especially Cote d'Ivoire to where most animals are exported and

17










sold at interestingly high prices. Another diversifying activity

attracting interest in the region is dairy production and

marketing milk through well organized farmer cooperatives. Thus,

these two ventures would be even more financially attractive to

farmers in the region than selling the fodder. Such alternatives,

though not overlooked, were not investigated in the present

research.

Unlike the previous two, scenario 3, despite the fact that

installation costs were entirely subsidized, appeared not to be

financially attractive to farmers in the region. While type B

households doubled the value of their initial investment at year

4 with a 3,000 kg ha-I grain yield type A households gained only

35.5% more on their initial investment.

The model revealed interesting differences between the two

types of households in the region. Though both household types

have serious labor constraints, especially during weeding

periods, type B are more constrained when it comes to fallow

establishment and management. Thus, for type B, labor is more

constraining than availability of land. Nevertheless, the model

indicated that type B households would benefit more from the

value-added fodder than would type A.



Conclusion

The improved fallows of gliricidia + stylosanthes would be

an interesting venture for types A and B households in the

18










Koutiala region only if fodder is valued and if maize yields of

at least equal to or higher than the regional average yield of

2,500 kg ha-1 can be achieved. If improved fodder production is

the only objective of planted fallows, then farmers in the region

would prefer stylosanthes which is easier and cheaper to

establish and manage than gliricidia + stylosanthes. Improved

fallows are not financially attractive to farmers when the only

benefit they produce is higher grain yields. Any subsidy program

that would prevent farmers from cutting the fodder as secondary

output before the end of the planned fallow length would not have

adoption potentials.

Detailed studies would be needed in the future to

comprehensively evaluate the impact of improved fallow-produced

fodder on animal weight gain, milk production, and the resulting

household economy. Similarly the environmental impact of improved

fallows should be assessed and valued to better quantify

ecological benefits. Finally, a special fallow establishment loan

program, similar to that which cotton enjoys, is worth putting in

place to increase adoption rates of the technology in the region.

Although the specific results of the study are limited in

scope to the study region, the study procedure, especially the

ethnographic linear programming approach, is applicable to small-

scale farming systems that are common in sub-Saharan Africa and

elsewhere in the tropics.










Acknowledgments

The authors are grateful to Programme d'Appui A la Recherche

Agronomique (PARA) a project of the national research institute

(IER)sponsored by the United States Aid for International

Development (US-AID). They are also grateful to the Center for

Latin American Studies of the University of Florida, Gainesville;

the International Foundation for Science (IFS), Stockholm; and to

the Rockefeller Foundation, New York for having provided extra

financial support to conduct the research in Mali.









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321.










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Pays-Bas, Collection: Systemes de Production au Mali, Vol.1
155p.

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Sesbania sesban planted fallows on maize yield. For. Ecol.
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Sud-Est du Mali (Regions CMDT de Sikasso et de Koutiala).
IER/Bamako, KIT/Amsterdam, DRSPR/Sikasso.

Mafongoya, P.L. and Nair, P.K.R. 1997. Multipurpose tree prunings
as a source of nitrogen to maize under semiarid conditions
in Zimbabwe. Part 1. Nitrogen-recovery rates as influenced
by pruning quality and methods of application. Agrof.
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Mittal, S.P., Grewal, S.S., Agnihotri, Y., and Sud, A.D. 1992.
Substitution of nitrogen requirement of maize through leaf
biomass of Leucaena leucocephala: agronomic and economic
considerations. Agrof. Syst. 19:207-216.

Place, F. and Dewees, P. 1999. Policies and incentives for the
adoption of improved fallows. Agrof. Sys. 47:323-343.

Tarawali, G., Manyong, V.M., Carsky, R.J., Vissoh, P.V., and
Galiba, M. 1999. Adoption of improved fallows in West
Africa: Lessons from mucuna and stylo case studies. Agrof.
Syst. 47:93-122.

World Bank 1994. Mali: Policy framework paper 1994-96.
Washington, DC: World Bank.











Figure Captions


Figure 1. Evolution of a Koutiala type A household end-of-year
income under different scenarios.

Figure 2. Evolution of a Koutiala type B household end-of-year
income under different scenarios.











Table 1. Allocation of land to different crops by type A and B
households (1998 ESPGRN data) in the Koutiala region and
comparison with the LP model results.


Crops LP Simulation ESPGRN, 1998
Type A Type B Type A Type B

Cotton 7.50 4.39 9.07 4.36
Fodder bank 0.67 0.42 0.5 1 0.5 1
Fertilized maize 2.03 1.27 1.97 0.78
Sorghum 3.31 2.07 3.56 2.30










Table 2. Activities and end of year cash for type
the Koutiala region in scenario 1, and comparison
simulation model.


A households in
with the basic


Activities Basic Year 1 Year 2 Year 3 Year 4

Production (ha)
Cotton 7.50 7.50 7.50 7.50 7.50
Fodder bank 0.67 0.67 0.35 0.35 0.35
Improved fallow 0.00 0.80 0.80 0.80 0.00
Maize after IF 0.00 0.00 0.00 0.00 0.80
Fertilized maize 2.03 1.78 2.03 2.03 1.23
Sorghum 3.31 3.09 3.31 3.31 3.31
Total cultivated land 13.51 13.84 13.99 13.99 13.19

Selling (t)
Cotton 7.87 7.87 7.87 7.87 7.87
Maize 2.18 1.58 2.18 2.18 2.70
Sorghum 2.07 1.76 2.07 2.07 2.07
Fodder 0.00 0.00 2.26 3.06 4.66

End-of-year cash (USD) 3154.4 3002.8 3222.2 3246.2 3377.4






Table 3. Activities and end of year cash for type B households in
the Koutiala region in scenario 1, and comparison with the basic
simulation model.


Activities Basic Year 1 Year 2 Year 3 Year 4

Production (ha)
Cotton 4.39 4.39 4.39 4.39 4.39
Fodder bank 0.42 0.42 0.53 0.53 0.53
Improved fallow 0.00 0.67 0.67 0.67 0.00
Maize after IF 0.00 0.00 0.00 0.00 0.67
Fertilized maize 1.27 1.27 1.27 1.27 0.60
Sorghum 2.07 2.07 2.07 2.07 2.07
Total cultivated land 8.15 8.82 8.93 8.93 8.26

Selling (t)
Cotton 3.38 3.38 3.38 3.38 3.38
Maize 0.60 0.60 0.60 0.60 1.37
Sorghum 0.74 0.74 0.74 0.74 0.74
Fodder 0.00 0.00 3.00 3.67 5.00

End-of-year cash (USD) 1653.8 1653.8 1743.8 1763.8 1926.5













Table 4. Activities and end-of-year cash for type A households in
the Koutiala region in scenario 1 with gliricidia + stylosanthes
and stylosanthes improved fallows compared with the basic
simulation model.


Activities Basic Yearl Year2 Year3 Year4

Production (ha)
Cotton 7.50 4.80 5.79 5.79 5.79
Fodder bank 0.67 0.67 0.00 0.00 1.06
Improved fallow 1 0.00 0.96 0.96 0.96 0.00
Improved fallow 2 0.00 1.34 1.34 1.34 1.34
Maize after IF 1 0.00 0.00 0.00 0.00 0.96
Maize after IF 2 0.00 0.00 0.00 0.00 0.00
Fertilized maize 2.03 3.81 3.81 3.81 2.85
Sorghum 3.31 3.09 3.09 3.09 2.81
Total cultivated land 13.51 14.67 15.00 15.00 14.82

Selling (t)
Cotton 7.87 4.99 6.02 6.02 6.02
Maize 2.18 6.36 6.36 6.36 6.99
Sorghum 2.07 0.80 0.80 0.80 0.49
Fodder 0.00 0.00 7.21 10.48 14.63

End-of-year cash (USD) 3154.4 2718.4 3257.8 3355.8 3524.6



Table 5. Activities and end of year cash for type B households in
the Koutiala region in scenario 1 with gliricidia + stylosanthes
and stylosanthes improved fallows compared with the basic
simulation model.

Activities Basic Year 1 Year2 Year3 Year4

Production (ha)
Cotton 4.39 4.39 4.39 4.39 4.39
Fodder bank 0.42 0.42 0.53 0.53 0.53
Improved fallow 1 0.00 0.67 0.67 0.67 0.00
Improved fallow 2 0.00 0.93 0.93 0.93 0.33
Maize after IF 1 0.00 0.00 0.00 0.00 0.67
Maize after IF 2 0.00 0.00 0.00 0.00 0.60
Fertilized maize 1.27 1.23 1.27 1.27 0.00
Sorghum 2.07 2.07 2.07 2.07 2.07
Total cultivated land 8.15 9.71 9.86 9.86 8.59

Selling (t)
Cotton 3.38 3.38 3.38 3.38 3.38
Maize 0.60 0.53 0.60 0.60 1.76
Sorghum 0.74 0.74 0.74 0.74 0.74
Fodder 0.00 0.00 6.73 9.00 9.67

End-of-year cash (USD) 1653.8 1642.6 1855.8 1923.8 2129.4










Table 6. Activities and end of year cash for type
the Koutiala region in scenario 2, and comparison
simulation model.


A households in
with the basic


Activities Basic Year 1 Year 2 Year 3 Year 4

Production (ha)
Cotton 7.50 7.50 7.50 7.50 7.50
Fodder bank 0.67 0.67 0.35 0.35 0.35
Improved fallow 0.00 0.80 0.80 0.80 0.00
Maize after IF 0.00 0.00 0.00 0.00 0.80
Fertilized maize 2.03 1.78 2.03 2.03 1.23
Sorghum 3.31 3.09 3.31 3.31 3.31
Total cultivated land 13.51 13.84 13.99 13.99 13.19

Selling (t)
Cotton 7.87 7.87 7.87 7.87 7.87
Maize 2.18 1.58 2.18 2.18 2.70
Sorghum 2.07 1.76 2.07 2.07 2.07
Fodder 0.00 0.00 2.26 3.06 4.66

End-of-year cash (USD) 3154.4 3002.8 3188.3 3200.3 3307.5





Table 7. Activities and end of year cash for type B households in
the Koutiala region in scenario 2, and comparison with the basic
simulation model.


Activities Basic Year 1 Year 2 Year 3 Year 4

Production (ha)
Cotton 4.39 4.39 4.39 4.39 4.39
Fodder bank 0.42 0.42 0.53 0.53 0.53
Improved fallow 0.00 0.67 0.67 0.67 0.00
Maize after IF 0.00 0.00 0.00 0.00 0.67
Fertilized maize 1.27 1.27 1.27 1.27 0.60
Sorghum 2.07 2.07 2.07 2.07 2.07
Total cultivated land 8.15 8.82 8.93 8.93 8.26

Selling (t)
Cotton 3.38 3.38 3.38 3.38 3.38
Maize 0.60 0.60 0.60 0.60 1.37
Sorghum 0.74 0.74 0.74 0.74 0.74
Fodder 0.00 0.00 3.00 3.67 5.00

End-of-year cash (USD) 1653.8 1653.8 1698.8 1708.8 1851.5










Table 8. Activities and end of year cash for type A households in
the Koutiala region in scenario 2 with gliricidia + stylosanthes


and stylosanthes improved fallows
simulation model.


compared with the basic


Activities Basic Year 1 Year2 Year3 Year4

Production (ha)
Cotton 7.50 5.12 5.79 5.79 5.79
Fodder bank 0.67 0.67 0.00 0.00 1.06
Improved fallow 1 0.00 0.00 0.00 0.00 0.00
Improved fallow 2 0.00 2.30 2.30 2.30 1.33
Maize after IF 1 0.00 0.00 0.00 0.00 0.00
Maize after IF 2 0.00 0.00 0.00 0.00 0.97
Fertilized maize 2.03 3.81 3.81 3.81 2.84
Sorghum 3.31 3.09 3.09 3.09 3.00
Total cultivated land 13.51 14.99 14.99 14.99 14.99

Selling (t)
Cotton 7.87 5.32 6.02 6.02 6.02
Maize 2.18 6.36 6.36 6.36 6.51
Sorghum 2.07 0.80 0.80 0.80 0.70
Fodder 0.00 0.00 7.21 9.52 12.70

End-of-year cash (USD) 3154.4 2823.4 3149.6 3184.2 3237.3




Table 9. Activities and end of year cash for type B households in
the Koutiala region in scenario 2 with gliricidia + stylosanthes
and stylosanthes improved fallows compared with the basic
simulation model.


Activities Basic Year 1 Year2 Year3 Year4

Production (ha)
Cotton 4.39 4.39 4.39 4.39 4.39
Fodder bank 0.42 0.42 0.53 0.53 0.53
Improved fallow 1 0.00 0.67 0.67 0.67 0.00
Improved fallow 2 0.00 0.93 0.93 0.93 0.33
Maize after IF 1 0.00 0.00 0.00 0.00 0.67
Maize after IF 2 0.00 0.00 0.00 0.00 0.60
Fertilized maize 1.27 1.23 1.27 1.27 0.00
Sorghum 2.07 2.07 2.07 2.07 2.07
Total cultivated land 8.15 9.71 9.86 9.86 8.59

Selling (t)
Cotton 3.38 3.38 3.38 3.38 3.38
Maize 0.60 0.53 0.60 0.60 1.76
Sorghum 0.74 0.74 0.74 0.74 0.74
Fodder 0.00 0.00 6.73 9.00 9.67

End-of-year cash (USD) 1653.8 1642.6 1754.8 1788.8 1984.4










Table 10. Activities and end of year cash
in the Koutiala region in scenario 3, and
basic simulation model.


for type A households
comparison with the


Activities Basic Year 1 Year 2 Year 3 Year 4

Production (ha)
Cotton 7.50 7.50 7.50 7.50 7.50
Fodder bank 0.67 0.67 0.00 0.00 0.00
Improved fallow 0.00 0.80 0.80 0.80 0.00
Maize after IF 0.00 0.00 0.00 0.00 0.80
Fertilized maize 2.03 1.78 2.03 2.03 1.23
Sorghum 3.31 3.09 3.31 3.31 3.31
Total cultivated land 13.51 13.84 13.64 13.64 12.84

Selling (t)
Cotton 7.87 7.87 7.87 7.87 7.87
Maize 2.18 1.58 2.18 2.18 2.70
Sorghum 2.07 1.76 2.07 2.07 2.07
Fodder 0.00 0.00 0.00 0.00 0.00

End-of-year cash (USD) 3154.4 3002.4 3154.4 3154.4 3237.6





Table 11. Activities and end of year cash for type B households
in the Koutiala region in scenario 3, and comparison with the
basic simulation model.


Activities Basic Year 1 Year 2 Year 3 Year 4

Production (ha)
Cotton 4.39 4.39 4.39 4.39 4.39
Fodder bank 0.42 0.42 0.00 0.00 0.00
Improved fallow 0.00 0.67 0.67 0.67 0.00
Maize after IF 0.00 0.00 0.00 0,00 0.67
Fertilized maize 1.27 1.27 1.27 1.27 0.60
Sorghum 2.07 2.07 2.07 2.07 2.07
Total cultivated land 8.15 8.82 8.40 8.40 7.73

Selling (t)
Cotton 3.38 3.38 3.38 3.38 3.38
Maize 0.60 0.60 0.60 0.60 1.37
Sorghum 0.74 0.74 0.74 0.74 0.74
Fodder 0.00 0.00 0.00 0.00 0.00

End-of-year cash (USD) 1653.8 1653.8 1653.8 1653.8 1776.5










Table 12. Activities and end of year cash for type A households
in the Koutiala region in scenario 3 with gliricidia +
stylosanthes and stylosanthes improved fallows compared with the
basic simulation model.


Activities Basic Year 1 Year2 Year3 Year4

Production (ha)
Cotton 7.50 5.79 5.79 5.79 5.79
Fodder bank 0.67 0.67 0.52 0.45 0.41
Improved fallow 1 0.00 0.11 0.11 0.11 0.00
Improved fallow 2 0.00 0.00 0.00 0.00 0.00
Maize after IF 1 0.00 0.00 0.00 0.00 0.11
Maize after IF 2 0.00 0.00 0.00 0.00 0.00
Fertilized maize 2.03 2.60 3.81 3.81 3.71
Sorghum 3.31 3.09 3.09 3.09 3.09
Total cultivated land 13.51 12.26 13.32 13.25 13.11

Selling (t)
Cotton 7.87 6.02 6.02 6.02 6.02
cotton
Maize 2.18 6.36 6.36 6.36 6.44
Sorghum 2.07 0.80 0.80 0.80 0.80
Fodder 0.00 0.00 0.00 0.00 0.00

End-of-year cash (USD) 3154.4 3041.4 3041.4 3041.4 3052.7



Table 13. Activities and end of year cash for type B households
in the Koutiala region in scenario 3 with gliricidia +
stylosanthes and stylosanthes improved fallows compared with the
basic simulation model.


Activities Basic Year 1 Year2 Year3 Year4

Production (ha)
Cotton 4.39 4.39 4.39 4.39 4.39
Fodder bank 0.42 0.42 0.00 0.00 0.00
Improved fallow 1 0.00 0.67 0.67 0.67 0.00
Improved fallow 2 0.00 0.82 0.82 0.60 0.00
Maize after IF 1 0.00 0.00 0.00 0.00 0.67
Maize after IF 2 0.00 0.00 0.00 0.22 0.60
Fertilized maize 1.27 1.27 1.27 1.05 0.00
Sorghum 2.07 2.07 2.07 2.07 2.07
Total cultivated land 8.15 9.64 9.22 9.00 7.73

Selling (t)
Cotton 3.38 3.38 3.38 3.38 3.38
Maize 0.60 0.60 0.60 0.64 1.76
Sorghum 0.74 0.74 0.74 0.74 0.74
Fodder 0.00 0.00 0.00 0.00 0.00

End-of-year cash (USD) 1653.8 1653.8 1653.8 1659.1 1839.4











3400





3300





3200





3100





3000


0 1 2 3 4


Years


---Scenario 1 ---Scenario 2 ----Scenario 3
-- Scnar I












2000





1900


CO
0

1800
>1



S1700
1800~~~~~ ~ -------------------------------- -----







1600
0 1 2 3 4

Years

---Scenario 1 -*-Scenario 2 ---Scenario 3