Directory UMM :Data Elmu:jurnal:A:Agricultural Systems:Vol66.Issue1.Oct2000:

Agricultural Systems 66 (2000) 17±32
www.elsevier.com/locate/agsy

Risk and agricultural systems in northern
CoÃte d'Ivoire
A.A. Adesina a,*, A.D. Ouattara b
a

The Rockefeller Foundation, Agricultural Sciences Division, Food Security Program, 420 Fifth Avenue,
New York, NY 10018-2702, USA
b
Centre Ivorien des Recherches Economies et Sociales (CIRES), Abidjan, CoÃte d'Ivoire
Received 1 May 2000; received in revised form 21 May 2000; accepted 30 June 2000

Abstract
In the Savannah region of West Africa, the highly variable rainfall and poor soils have been
shown to di€erentially a€ect the yield potential of various crops. The paper applies a simple risk
programming model to assess the e€ects of price and yield risk on the incomes of smallholder
farmers in northern region of CoÃte d'Ivoire. The analysis showed that by considering price and
yield risks, it would be possible for farmers to improve their incomes. Considerable evidence
has been gathered to show that smallholder allocative ineciency is a common place in CoÃte

d'Ivoire. This study also found that farmers were operating at sub-optimal levels. This could be
due to several factors, including multiple market failures, lack of information on prices, price
and yield risks, labor market search costs or high transaction costs. The results from this paper
suggests that when such price series information on the risks of di€erent crops are considered,
farmers would be better o€ re-allocating their cropping to a more optimal cropping plan. In
evaluating cropping systems in the Savanna zone it is important to consider not only the yield
of alternative crops, but also the yield risk, price risk, and income risk that farmers face in
adjusting their cropping patterns. Second, to reduce production risks faced by farmers,
emphasis should be placed on yield-stability of technology interventions intended for farmers in
this zone. Lastly, policy makers should focus e€orts on achieving farm income stabilization for
farmers in this zone by: (1) developing e€ective market price information transmission system;
(2) providing low-cost but high-resolution climatic information; and (3) developing riskmanagement institutions. Unless policy makers improve the availability of information that
allows farmers to improve their managerial capacity for making more risk-ecient cropping
decisions, it is unlikely that farmers in the zone will be able to cope with the pervasive risks that
a€ect their welfare and livelihoods. # 2000 Elsevier Science Ltd. All rights reserved.
Keywords: Risk; Risk programming; Farmer decision making; CoÃte d'Ivoire

* Corresponding author. Tel.: +1-212-852-8342; fax: +1-212-852-8442.
E-mail address: aadesina@rockfound.org (A.A. Adesina).
0308-521X/00/$ - see front matter # 2000 Elsevier Science Ltd. All rights reserved.

PII: S0308-521X(00)00033-0

18

A.A. Adesina, A.D. Ouattara / Agricultural Systems 66 (2000) 17±32

1. Introduction
Risk and uncertainty are pervasive characteristics of agricultural production.
These could arise due to several biophysical factors such as highly variable weather
events, diseases or pest infestations. Other factors such as changing economic
environment, introduction of new crops or technologies, and uncertainties surrounding the public institutions and their policy implementation, also combine with
these natural factors to create a plethora of yield, price, and income risks for farmers
(Heyers, 1972; Mapp et al., 1979; Anderson et al., 1985; Adesina and Brorsen, 1987).
The risk situation is acute for the majority of agricultural producers in sub-Saharan
Africa. The low and highly erratic rainfall (Sivakumar, 1988), and the absence of
institutional innovations (e.g. crop insurance, disaster payments, emergency loans)
to shift part of the risks from the private sector to the public sector, makes riskmanagement a critical part of farmers' decision making (Matlon, 1990; Adesina and
Sanders, 1991; Shapiro et al., 1993).
Interest in risk by policy makers in Africa has heightened with the recent e€ects of
El Nino on global climate and its consequences on local climate changes and agriculture. Strategies to help smallholder farmers cope with the myriad of risks they

face requires an understanding of how risk a€ects their choice of cropping patterns.
In West Africa, studies of risk that have so far been conducted have focussed on the
drier Sahelian zones (Kristjanson, 1987; Adesina and Sanders, 1991; Shapiro et al.,
1993). No similar risk studies have been done for the Savanna zones where rainfall
risk is also pervasive. In CoÃte d'Ivoire, no study has analyzed the e€ects of risk on
farmers' production decisions and land uses. Moreover, policy makers in CoÃte
d'Ivoire are currently debating the role that risk plays in in¯uencing farmers' cropping decisions, and what types of policy and institutional reforms are needed to
permit farmers to better cope with e€ects of risk on their production, incomes and
welfare. This study contributes information to this policy discussion. The objective
of the paper is to apply a simple risk-programming model (Hazell, 1979; Hazell and
Norton, 1986) to assess how risk a€ects farmers' cropping decisions in the rainfed
agricultural systems of the Savanna agro-ecological zone of CoÃte d'Ivoire. The
information will be useful for assisting policy makers in CoÃte d'Ivoire to evaluate the
role of risk and measures to mitigate risks faced by farmers.

2. The study villages
The study was conducted in three villages located in the moist-Savanna agroecological zone of CoÃte d'Ivoire (Table 1). One of the villages (MbengueÂ) is located
in the Sudanian zone, while the other two (Napie and Sirasso) are located in the
higher potential Guinea-Savanna zone. Mbengue village Рwith its low rural
population density (10 persons/km2) Ð is a relatively land-abundant area. Sirasso

also has low population pressure with a rural population density of 11 persons/km2.
By contrast, Napie is a land-scarce village, with a very high population density (51
persons/km2). Both Napie and Sirasso have better endowments of water supply due

19

A.A. Adesina, A.D. Ouattara / Agricultural Systems 66 (2000) 17±32
Table 1
Characteristics of the study villages in the Savanna zone of CoÃte d'Ivoire
Village

Agro-ecological zone
Total area (km2)
Total population
Rural population
Total population density (persons/km2)
Rural population density (persons/km2)
Land supply situation
Presence of irrigation

MbengueÂ

Napie

Sirasso

Sudanian
2364
28 039
22 912
12
10
Surplus
None

Guinea Savanna
288
14 756
14 756
51

51
Limiting
Yes

Guinea Savanna
1822
25 266
20 860
14
11
Surplus
Yes

to the existence of neighboring dams that supply water for irrigated rice in the villages, although the water reserve capacity of the dam in the Sirasso area is the largest. Crops grown vary by village, but in the entire zone the major crops are cotton,
upland rice, lowland rice, maize, and peanuts. For most of the farmers, cotton is the
predominant cash crop. Three types of farms are found in the zone: manual, oxen
and tractor farms. Field data for the study were collected over two crop seasons
(1991/1992±1992/1993) from a representative sample of 85 farm-households (manual, 39; oxen, 41; tractor, ®ve) in the three villages. Data collection was intensive and
resource consuming. Field enumerators were stationed in the three villages for 2
years to monitor activities on all plots of the farmers in the sample. This allowed the

collection of data for the second cycle irrigated rice crop in Napie and Sirasso.
Detailed data were collected on cropping systems, family and non-family labor use,
use of oxen and small tractors, use of purchased inputs such as chemical fertilizers,
improved seeds and herbicides, and all input±output coecients for all the crops on
the manual tillage, oxen tillage and motorized farms. Crop yields were carefully
measured on all the surveyed plots of the farms using yield-cut estimates, and, where
appropriate, were converted into dry weight equivalents.

3. Empirical model
Several approaches have been used for incorporating risks on the farm with
varying degrees of sophistication depending on the issue of interest and data availability. These range from: the generalized expected utility framework (Anderson et
al., 1985), farm-programming models (Wicks, 1978) including mean±variance analysis using quadratic programming (QP); game-theoretical approaches (Heyers,
1972; Low, 1974); stochastic programming (Adesina and Sanders, 1991); or a linearized variation of the mean±variance approach (MOTAD) (Hazell, 1979). The use
of QP requires the assumptions that the decision maker has a quadratic utility
function and the activity net returns follow a multivariate normal distribution

20

A.A. Adesina, A.D. Ouattara / Agricultural Systems 66 (2000) 17±32

(Mapp et al., 1979). Studies have found that these two assumptions may be rejected
in empirical studies of farmers' behavior (Roumasset, 1976).
The nonlinear nature of the objective function in the QP formulation arises from
the use of the variance±covariance matrix of enterprise returns. The advantage of the
MOTAD approach is the linearization of the objective function via the use of mean
absolute deviation (MAD) (Hazell and Norton, 1986). The use of MAD has been
found in Monte Carlo studies to rank farm plans as well as sample variance when
there is normally distributed outcomes, and most especially when the sample size is
small (Thomson and Hazell, 1972). In particular situations where the enterprise
income distributions are skewed, MAD may outperform sample variance from the
QP formulation (Hazell and Norton). The MOTAD approach is used for the analysis in this paper. The model and its variants have been used in risk analysis of
farmer decision in various parts of the world (Hardaker and Troncoso, 1979; Adesina et al., 1988; Maleka, 1993). The variant of the MOTAD model applied in this
study allows the integration of farmers' risk attitudes. Several studies have shown
that farmers are generally risk averse, most especially in rainfed agricultural systems,
and their risk attitudes in¯uence cropping decisions (Adesina et al., 1988; Shapiro et
al., 1993). The enormous time, data requirements, and diculties associated with
measuring farmers' utility functions (Dillon and Scandizzo, 1978; Halter and
Mason, 1978; Binswanger, 1980) preclude us from using the generalized expected
utility maximization framework. Given its low computational costs compared to
other non-linear optimization algorithms and our interest in examining the e€ects of

temporal variation in yield, price and income risks on the whole farm cropping
patterns, we selected to use a simple MOTAD model. The empirical model used in
the analysis is simpli®ed below:
Max L…E; † ˆ E…† ÿ F…†
ST
Sj aij Xj 4bi ; for all i
Sj …cjt ÿ E‰cj ŠXj ‡ dt 50; for all t; t ˆ 1; 2; . . . ; T
YSt dt ÿ …† ˆ 0
Xj 50; E…†50; …†50;
where Xj is the area in cropj (ha); aij are the respective input±output coecients that
capture the level of use of resourcei in the production of cropj; bi is the available
resource endowment for factor i; cjt is the revenue of cropj in year t (t=1, 2,. . ., T);
E(cj) is the sample mean revenue for the cropj across all of the T years; dt is
the measure for the absolute value of negative deviations in total revenue; Y=2s/T
is a constant, and s=(T/2(Tÿ1))1/2 is the square root of the Fisher's constant
(Hazell and Norton, 1986); E() is the expected pro®t from the crop production
plans; F (the risk aversion parameter) measures the attitude of the farmer towards

A.A. Adesina, A.D. Ouattara / Agricultural Systems 66 (2000) 17±32

21

risk; () is the MAD estimate of the variance of pro®ts from crop production over
the T years in the analysis.
The objective function is the maximization of a utility function that depends on
the expected pro®t discounted by the weighted-standard deviation of pro®ts, the
weight being the risk aversion parameter for the decision maker. The second sets of
equations, which are highly disaggregated, model the farm resource use patterns.
Land type or ecosystem constraints were speci®ed for each crop. For rice, this
includes upland, lowland and irrigated ecosystems. Irrigated land constraints were
further subdivided, based on the crop season, into ®rst (March/April±August) and
second season crop (August±December). For the other crops (i.e. maize, cotton,
peanuts), crop-speci®c upland ecosystem constraints were speci®ed for each crop
based on the observed cropping patterns in the ®eld data. Labor use patterns (in
man-days per ha) were modeled using monthly (intra-seasonal) labor constraints for
crop cultivation. Both household and external labor were considered. Due to the
existence of active labor markets during certain periods in the season, ¯exibility for
monthly labor hiring activities was allowed (using transfer rows) for complementing
available household labor in each period. The wage rate in each of these periods was
set equal to the ongoing wage rate paid for hired agricultural labor in each village.

In addition to labor, oxen and tractor availability constraints were speci®ed based
on the number of hours possible to use this equipment in the season. As rental
market for oxen and mechanical services exists at certain periods in the season, hand
tillage or manual farm models permitted the possibility of renting oxen and
mechanical services. An annual cash constraint was used to model the trade-o€s
between own-liquidity and credit use. Expenditures for purchased inputs (e.g. fertilizers, herbicides, insecticides) could be met from farmer's own-liquidity, with or
without complementary credit. Only ocial credit use (from the cotton company,
Compagnie Ivorienne des Textiles [CIDT]) was considered, as it was impossible to
obtain reliable information from farmers on their use of informal credit. The predominantly Islamic society in study zone does not `permit' interest charges.
Several accounting or balance rows were used to ensure internal consistency for
the total production of each crop. For each crop, these accounting rows speci®ed
that the amount of the crop sold plus the on-farm consumption requirements cannot
exceed total production. Crop-speci®c subsistence requirements were speci®ed to
account for economies of scale in farm-household size. Based on the age and sex of
household members, consumer adult-equivalent requirements (Eponou, 1983) were
developed for each crop. These were then aggregated to the household level to
determine the minimum consumption requirements for each crop. Additional provision above this minimum requirement was permitted to allow farm-households to
keep extra grains against other social obligations such as marriages, baptisms and
burials that often constitute a signi®cant share of household expenditures. Other
constraints in the models include use of external inputs, speci®ed by crop and inputtype based on the averages from the ®eld data.
The third sets of equations are the MOTAD constraints, which measure the
deviation (i.e. dt) of incomes in any given year from the mean incomes across all
the years. Together with the fourth set of equations, these constraints are used to

22

A.A. Adesina, A.D. Ouattara / Agricultural Systems 66 (2000) 17±32

determine the MAD of income returns over all crops in each of the T years considered. This involves the transformation of the sum of the income deviations into
an estimate of standard deviation of income (i.e. ) using the Fisher constant
(Hazell and Norton, 1986). The crop prices and yield trends collected from regional
statistical reports1 for the period 1986±1991, complemented by the ®eld data collected in the villages in 1992/1993, were used to generate distribution of crop
incomes for the seven years (i.e. T=7) used in the risk analysis. Using this model
framework, representative farm models for manual, oxen and tractor farms in the
villages were developed. Optimal crop plans were generated for di€erent levels of
the risk aversion parameter, F.

4. Results
First, the results of the linear programming base models are given in Table 2.
These models use the observed market prices for the crops in the survey year and the
predicted cultivated areas for manual tillage, oxen tillage and motorized farms.
Predicted cultivated areas from the models are close to the averages observed from
the ®eld data for each farm type.
But these cropping patterns from the linear programming models were based on
observed average prices in the survey year, and may not necessarily re¯ect the riskiness of cropping when longer time series information on prices and yields are
considered. To get an idea of the riskiness of the various crops, and thus the need to
use a risk programming framework to choose optimal cropping patterns, we estimated price, yield and income risks for di€erent crops in the three study regions
(Table 3).
For Mbengue zone, the majority of the crops have high coecient of variation
(CV) of output prices, with most having indices greater than 0.9, except cotton and
maize. Estimates of CV of yields show that the crops in this zone can be categorized
into high, medium and low yield-risk groups. The high yield-risk group consists
of maize (CV=61%). The medium yield-risk group consists of lowland rice
(CV=22%), peanuts (CV=29%) and upland rice (CV=29%), while the low yieldrisk group consists of cotton (CV=9%). Taking yield and price risks together, we
re-categorized the crops based on the riskiness of their gross returns. In terms of
gross returns, the crop with the highest risk is maize (CV=63%). The medium risk
crops are lowland rice (CV=25%) and peanuts (CV=35%). The crops with the
lowest risk of gross returns are cotton (CV=14%) and upland rice (CV=13%).
For Napie zone, the crops with high yield risk is maize (CV=61%), while those
with medium yield risk are upland rice (CV=28%), irrigated rice (CV=21%), and
peanuts (CV=29%). The low yield-risk group has only cotton (CV=9%). Using
price risks, the riskiness of the crops shows the following groupings: low price risk
(cotton: CV=8%; maize: CV=4%) and medium price risk (upland rice: CV=21%;
1
These data were collected from the statistical reports of the CIDT, the agency responsible for crop
statistical data collection for the Savanna region.

23

A.A. Adesina, A.D. Ouattara / Agricultural Systems 66 (2000) 17±32

Table 2
Linear programming results for di€erent farm types in study villages using prices observed in the survey
yeara
Manual farms

Oxen farms

Motorized farms

F.Avgb

Base
model

F.Avg

Base
model

Fd.Avg.

Base
model

Mbengue village: Sudanian zone
Maize
Upland rice
Lowland rice
Cotton
Peanuts
Expected pro®t (`000 CFA)
Standard deviation of pro®t (`000 CFA)
Coecient of variation of pro®t (%)

0.49
0.93
±
1.70
±
NAc
NA
±

0.49
0.93
±
1.80
±
156
138
89

2.2
0.8
0.6
3.5
0.47
NA
NA
±

2.1
0.8
0.6
3.4
0.47
457
836
180

11
3.7
0.6
10
0.8
NA
NA
NA

9
3.7
0.6
10
0.8
1585
3644
230

Napie village: Guinea Savanna zone
Irrigated rice (®rst season)
Irrigated rice (second season)
Upland rice
Lowland rice
Peanuts
Cotton
Expected pro®t (`000 CFA)
Standard deviation of pro®t (`000 CFA)
Coecient of variation of pro®t (%)

0.43
0.43
0.25
0.12
0.35
1.10
NA
NA
NA

0.43
0.43
0.25
0.12
0.35
1.10
193
288
150

0.45
0.45
0.40
0.18
0.28
2.50
NA
NA
NA

0.45
0.45
0.40
0.18
0.63
2.20
423
510
120

0.31
0.31
0.55
0.51
0.98
NA

0.31
0.31
0.55
0.51
0.98

0.62
0.62
1.03
0.86
2.50
NA

0.62
0.62
1.03
0.86
2.50
1106

NA

54

Sirasso village: Guinea Savanna zone
Irrigated rice (®rst season)
Irrigated rice (second season)
Upland rice
Peanuts
Cotton
Expected pro®t (`000 CFA)
Standard deviation of pro®t (`000 CFA)
Coecient of variation of pro®ts (%)

NA

a

Savanna zone of CoÃte d'Ivoire.
Averages from ®eld data.
c
Not available.
Note: CFA is the currency used across French-speaking countries in West Africa. At the time of study
250 CFA=US$1. The currency was devalued in 1994 with the exchange rate declining to 500 CFA/US$.
b

irrigated rice: CV=21%; peanuts: CV=31%). Classi®cation by level of grossincome risk produces the following groupings: low-income risk (cotton: CV=15%;
upland rice: CV=13%), medium-income risk (irrigated rice: CV=25%; peanuts:
CV=35%), and high-income risk (maize: CV=64%).
For Sirasso zone, the crops can be divided into high yield risk (maize: CV=62%);
medium yield risk (upland rice: CV=42%; peanuts: CV=29%; irrigated rice:
CV=22%); and low yield risk (cotton: CV=13%). Using price variability, maize

24

A.A. Adesina, A.D. Ouattara / Agricultural Systems 66 (2000) 17±32

Table 3
Classi®cation of crops by level of yield and income risk in each village zonea
Village
MbengueÂ

Napie

Sirasso

a

Price risk

Yield risk

Income risk

High

±

Maize

Maize

Medium

Upland rice
Peanuts
Lowland rice

Lowland rice
Peanuts
Upland rice

Lowland rice
Peanuts

Low

Maize
Cotton

Cotton

Cotton
Upland rice

High

±

Maize

Maize

Medium

Upland rice
Irrigated rice
Peanuts

Upland rice
Irrigated rice
Peanuts

Irrigated rice
Peanuts

Low

Cotton
Maize

Cotton

High

±

Maize

Maize

Medium

Peanuts
Irrigated rice
Upland rice

Upland rice
Peanuts
Irrigated rice

Upland rice
Irrigated rice
Peanuts

Low

Maize
Cotton

Cotton

Cotton

Upland rice
Cotton

Savanna zone, CoÃte d'Ivoire.

has the least variation in prices. When both price and yield risks are considered
together to determine income risk, the crops in the village can be divided into three
major groups. The high-income-risk group is maize (CV=64%), followed by the
medium-income-risk group of irrigated rice (CV=25%), upland rice (CV=31%)
and peanuts CV= 35%); and the low-income-risk group of cotton (CV=19%).
These estimates show that the various crops have di€erent degrees of risk and this
needs to be considered in generating optimal farm plans that minimize farm-income
risks. Using these data on gross returns and their variations (as opposed to the survey year data used in the linear programming model), the risk model was speci®ed
for each of the farm types. The consideration of the risk e€ects across di€erent tillage systems derives from ®eld research evidence (Sargeant et al., 1981; Pingali et al.,
1987). Several studies in West Africa have shown the existence of positive correlation between farm incomes and methods of tillage. Risk aversion is a function of
income. Farmers using oxen tillage and motorized systems have more income
endowments than farmers using hand tillage system. This has been shown in several
studies of agricultural systems in West Africa (Sargeant et al.; Pingali et al.).
The risk aversion parameter F was parameterized to simulate the e€ects of risk
aversion on cropping choices. The use of time series data allows us then to compare

A.A. Adesina, A.D. Ouattara / Agricultural Systems 66 (2000) 17±32

25

what the e€ects of incorporating additional information on the risks of various
enterprises would have on the optimal cropping decisions for each of the zones.
The risk programming results for the manual tillage, oxen tillage and motorized
farms in Mbengue village are given in Table 4. Results for the manual model farms
show that the cultivated area in maize was reduced compared to the averages from
the ®eld data, regardless of the level of risk aversion2. This general reduction in
cultivated area in maize re¯ects the high-returns risk of the crop. As the level of the
risk aversion increases, the risk model cropping plans show a decline in the area of
upland rice. As the area in upland rice is reduced, the cultivated area in cotton is
increased. It is important to note that while upland rice and cotton have low-return
risks, cotton has higher returns per hectare than upland rice. The observed higher
area of cotton cultivated by risk-averse farmers may indicate that this group of
farmers use the high share of total area under cotton as hedging against income
risk. Marketed surplus for rice and cotton follow the pattern for cultivated area. As
was observed for the manual farms, the area cultivated in highly risky maize crop
declines substantially (regardless of the risk aversion level) in the risk model solutions for the oxen farms. Maize area in the risk model solution was reduced to 0.52
ha compared to over 2 ha under farmers' existing crop plan. The area in upland
rice was signi®cantly increased in the risk model solutions compared to the existing
crop plan. Other changes in the risk model solution involve the elimination of
lowland rice out of the optimal farm plan and marginal expansion of area in peanuts. Expected incomes from the risk model crop plans are signi®cantly higher than
in the existing crop plans, indicating that by re-allocating the existing crop plans,
farmers can signi®cantly increase farm incomes and lower risks. In the risk model
solutions for oxen farms, the area in upland rice declines from 4.5 ha for the farm
plan of the risk-neutral farmer, to 3.6 ha for the farm plan of the highly risk-averse
farmer (F=1.5). However, the area in cotton expands with increasing level of F.
Regardless of the level of risk aversion parameter, the volumes of marketed output
for rice and cotton on the oxen farms were substantially higher than for manual
farms.
The risk model crop plan for the tractor farms in Mbengue village shows the
highest degree of reduction in maize area, compared to farmers' existing crop plan.
Apart from the risk-neutral farmers' farm plan (where maize area was reduced to 6.3
ha from 9.3 ha in the existing crop plan), a precipitous decline in maize area occurs
for each of the risk aversion levels. It is important to note that the income risk
associated with this farm plan of the risk-neutral farmer is also substantially higher
than for the risk-averse farmers. As the level of farmers' risk aversion increases, the
2
The divergence between the risk model results and farmers actual farm plans may be due to several
factors. One of such factors could be the nature of the land constraint in the models. In the farmers' actual
farm plans, upper bound constraints were used to ensure that the area of the crops does not exceed the
observed cultivated areas. Under the risk model, we relaxed this assumption for each of the crops, while
ensuring that the total cultivated area in all crops does not exceed available arable land. This means that
the model allows farmers to alter individual cropping choices based on the risk of the crops. Overall land
available for all crops, however, cannot exceed the available arable land limit. See the concluding section
of the paper for explanations of why farmers may not be able to achieve a more risk-ecient crop plan.

26

A.A. Adesina, A.D. Ouattara / Agricultural Systems 66 (2000) 17±32

area of maize in the risk model solution declines from 6.3 ha for F=0 to 0.19 ha for
all F>0. This indicates that risk-averse farmers can reduce their risks by reducing
maize-cropped area. Compared to the existing farm plan, the risk model crop mix
gives farmers higher levels of expected incomes.
Table 4
MOTAD results for crop portfolio for manual, oxen tillage and tractor farms under alternative levels of
risk aversiona, Mbengue village
Crops (ha)

Risk aversion levels
F=0

F=0.1

F=0.25

F=0.5

F=0.15

Manual farms
Maize
Upland rice
Lowland rice
Cotton
Peanuts
Volume of maize sold (kg)
Volume of rice sold (kg)
Volume of cotton sold (kg)
Expected pro®t (`000 CFA)
Standard deviation of pro®ts (`000 CFA)
Coecient of variation (%) of pro®ts

0.35
2.80
±
0.14
±
±
2172
196
192
150
78

0.35
2.80
±
0.14
±
±
2172
196
192
150
78

0.35
2.36
±
0.58
±
±
1757
786
183
109
60

0.35
1.78
±
1.16
±
±
1199
1576
183
109
60

0.35
1.73
±
1.22
±
±
1149
1648
182
108
59

Oxen farms
Maize
Upland rice
Lowland rice
Cotton
Peanuts
Volume of maize sold (kg)
Volume of rice sold (kg)
Volume of cotton sold (kg)
Expected pro®t (`000 CFA)
Standard deviation of pro®ts (`000 CFA)
Coecient of variation (%) of pro®ts

0.52
4.50
±
1.64
0.21
±
8227
2214
647
190
29

0.52
4.50
±
1.64
0.21
±
8227
2214
647
190
29

0.52
4.50
±
1.64
0.21
±
8227
2214
647
190
29

0.52
4.58
±
1.64
0.21
±
8227
2214
647
190
29

0.52
3.60
±
2.56
0.21
±
6342
3463
634
174
27

Tractor farms
Maize
Upland rice
Lowland rice
Cotton
Peanuts
Volume of maize sold (kg)
Volume of rice sold (kg)
Volume of cotton sold (kg)
Expected pro®t (`000 CFA)
Standard deviation of pro®ts (`000 CFA)
Coecient of variation (%) of pro®ts

6.32
3.66
0.83
12
0.24
8713
6029
13 584
1670
2458
150

0.19
3.66
0.83
13
0.24
±
6029
14 660
1608
486
30

0.19
3.66
0.83
13
0.24
±
6029
14 660
1608
486
30

0.19
3.66
0.83
13
0.24
±
6029
14 660
1608
486
30

0.19
3.66
±
13
0.24
±
4689
14 660
1535
416
27

a
Mbengue village, Savanna zone of CoÃte d'Ivoire.
Note: CFA is the currency used across French-speaking countries in West Africa. At the time of study
250 CFA=US$1. The currency was devalued in 1994 with the exchange rate declining to 500 CFA/US$.

27

A.A. Adesina, A.D. Ouattara / Agricultural Systems 66 (2000) 17±32

The risk model crop plans for farms in Napie village (Table 5) show that the
manual farms did not include the production of maize, the crop with the highest
income variability. The results show that at high levels of risk aversion (F>0.5) the
area in the second-season irrigated rice crop declines sharply. Although irrigated rice
has a medium yield risk, this risk level mainly re¯ects the situation for the mainseason irrigated crop. The yield risk of the second-season irrigated rice crop is much
higher. As indicated earlier, rainfall in the zone is mono-modal and the cultivation
of the second-season irrigated rice crop is done in the dry season. The dam used by
farmers in Napie village is the smallest of the dams in the Savanna area, with a
watershed area of 5.4 km2 and a reservoir capacity of only 1.7 million m3. Thus,
Table 5
MOTAD results of cropping patterns for manual and oxen farms under alternative levels of risk aversiona,
Napie village
Crops (ha)

Manual farms
Maize
Upland rice
Irrigated rice (®rst season)
Irrigated rice (second season)
Lowland rice
Cotton
Peanuts
Volume of maize sold (kg)
Volume of rice sold (kg)
Volume of cotton sold (kg)
Expected pro®t (`000 CFA)
Standard deviation of pro®ts (`000 CFA)
Coecient of variation (%) of pro®ts
Oxen farms
Maize
Upland rice
Lowland rice
Irrigated rice (®rst season)
Irrigated rice (second season)
Cotton
Peanuts
Volume of maize sold (kg)
Volume of rice sold (kg)
Volume of cotton sold (kg)
Expected pro®t (`000 CFA)
Standard deviation of pro®ts (`000 CFA)
Coecient of variation (%) of pro®ts

Risk aversion levels
F=0

F=0.1

F=0.25

F=0.5

F=0.15

±
0.10
0.43
0.43
0.03
1.25
0.24
±
1424
1690
215
318
147

±
0.10
0.43
0.43
0.03
1.25
0.24
±
1424
1690
215
318
147

±
0.10
0.43
0.43
±
1.25
0.24
±
1396
1690
215
317
147

±
±
0.43
0.39
±
0.05
0.90
±
1180
63
109
71
65

±
±
0.43
0.15
±
±
0.90
±
746
±
90
41
46

±
±
±
0.45

±
±
±
0.45
0.45
2.55
0.63
±
1039
4730
492
585
119

±
±
±
0.45
0.45
2.55
0.63
±
1039
4730
492
585
119

±
0.76
±
0.45
0.45
1.78
0.63
±
2039
3308
419
436
104

±
1.81
1.18
0.15
0.40
0.27
0.73
±
2708
508
195
127
65

2.55
0.63
±
1039
4730
492
585
119

a
Napie village, Savanna zone of CoÃte d'Ivoire.
Note: CFA is the currency used across French-speaking countries in West Africa. At the time of study
250 CFA=US$1. The currency was devalued in 1994 with the exchange rate declining to 500 CFA/US$.

28

A.A. Adesina, A.D. Ouattara / Agricultural Systems 66 (2000) 17±32

water level is generally low during the dry season posing signi®cant problems of
water distribution to paddy ®elds in the dry season. Yield of the second-season rice
crop is relatively lower than the main crop and is more highly variable. The sharp
reduction in the area of the second-season rice crop by the highly risk-averse farmers
appears to re¯ect this relatively higher risk. In general, the results show that expected incomes and income risks follow an inverse pattern as the level of risk aversion
increases. This indicates that risk-averse farmers can select enterprise combinations
that provide lower income risks by trading o€ higher expected pro®ts.
The risk model crop portfolio for the oxen farms in Napie village show that the
area in main-season irrigated rice is largely stable across the various levels of risk
aversion. However, the area in the second-season irrigated rice crop decline sharply
at higher levels of risk aversion, and drops out of the optimal solution for the highly
risk-averse farmer. This result, when taken together with that of the manual farms,
Table 6
MOTAD results of cropping patterns for manual and oxen farms under alternative levels of risk aversiona,
Sirasso village
Crops (ha)

Risk aversion levels
F=0

F=0.1

F=0.25

F=0.5

F=0.15

Manual farms
Maize
Upland rice
Irrigated rice (®rst season)
Irrigated rice (second season)
Cotton
Peanuts
Volume of maize sold (kg)
Volume of rice sold (kg)
Volume of cotton sold (kg)
Expected pro®t (`000 CFA)
Standard deviation of pro®ts (`000 CFA)
Coecient of variation (%) of pro®ts

0.96
0.53
0.31
0.31
±
0.24
3869
2749
±
440
384
87

0.96
0.53
0.31
0.31
±
0.24
3869
2749
±
440
384
87

0.92
0.51
0.31
0.31
0.10
0.24
3704
2700
85
436
370
85

0.18
0.20
0.31
0.31
1.42
0.90
375
2232
1607
353
88
25

0.18
0.24
0.31
0.31
1.42
0.90
369
2287
1609
352
87
25

Oxen farms
Maize
Upland rice
Irrigated rice (®rst season)
Irrigated rice (second season)
Cotton
Peanuts
Volume of maize sold (kg)
Volume of rice sold (kg)
Volume of cotton sold (kg)
Expected pro®t (`000 CFA)
Standard deviation of pro®ts (`000 CFA)
Coecient of variation (%) of pro®ts

5.22
±
0.62
0.62
±
0.20
30 486
6389
±
2186
1693
77

5.22
±
0.62
0.62
±
0.20
30 486
6389
±
2186
1693
77

5.22
±
0.62
0.62
±
0.20
30 486
6389
±
2186
1693
77

5.22
±
0.62
0.62
±
0.20
30 486
6389
±
2186
1693
77

0.34
1.85
0.62
0.62
2.5
0.80
1557
10 183
1593
955
185
19

a

Sirasso village, Savanna zone of CoÃte d'Ivoire.

A.A. Adesina, A.D. Ouattara / Agricultural Systems 66 (2000) 17±32

29

suggests that farmers currently cultivating the second-season irrigated rice crop in
the zone are likely to be either risk neutral or moderately risk averse.
The risk model results for farms in Sirasso village (Table 6) indicates that the crop
mix selected on manual farms at various levels of risk aversion closely mirrors the
risk patterns of the crops. For maize, the cultivated area declines substantially for
highly risk-averse farmers: declining from 0.96 for the risk-neutral and moderately
risk-averse farmers, to 0.18 for the highly risk-averse farmer. Cultivated area in
peanuts and upland rice (crops with medium-income risks) declines with increases
in the level of the risk-aversion index. However, area in cotton (with low-income
risk) increases as the level of F rises from 0.5 to 1.5. It is important to note that Ð in
contrast to the situation at Napie village Ð the cultivated area in irrigated rice for both
the ®rst and second seasons remained constant, regardless of the level of risk aversion.
The explanation for this is given later, after discussing the results for the oxen farms.
The risk model crop mix for the oxen farms in Sirasso village shows that at low to
moderate levels of risk aversion, maize is the predominant crop. However, at high
levels of risk aversion (F=1.5), the area in maize is substantially reduced (i.e. from
5.2 to 0.34 ha). As was observed for the manual farms, the areas of the main-season
and second-season irrigated rice crop were not a€ected by the level of risk aversion.
This result provides an important contrast when compared with the situation in
Napie village. The dam that supplies the irrigation water to the Sirasso farms is the
largest in the entire Savanna area, with a watershed area of 144 km2 and a reservoir
capacity of 60 million m3. Water reserve from the dam is adequate for a successful
second-season irrigated rice crop. This may explain why the risk attitude of the
farmer does not a€ect cultivated area of the second-season irrigated rice crop.
These results have important implications for e€orts to increase rice production
via double-cropping in the Savanna region. Given the mono-modal pattern in the
region, it can be expected that in areas where there exists dams with sucient water
reserve capacity for a second-season rice crop farmers Ð regardless of their risk
attitudes Ð will attempt double-cropping of irrigated rice. By contrast, where water
sources are irregular Ð due either to low water reserve capacity of dams or highly
variable river ¯ows Ð double cropping of the second-season rice becomes a more
risky decision. Under such situations, risk-averse farmers may either reduce area
cultivated in the second-season rice crop or totally abandon growing the secondseason rice crop.

5. Conclusions
This paper applied a simple risk programming model to analyze the role of risk in
the cropping systems under rainfed agriculture in the Savanna zone of CoÃte d'Ivoire.
The results showed that signi®cant reduction in income risks (and increased income
gains) can be made by re-allocating the existing crop mix. In particular, the results
show that maize is the most risky crop in the two zones and risk-averse farmers
would be able to increase incomes while reducing risks by decreasing the area cultivated in maize.

30

A.A. Adesina, A.D. Ouattara / Agricultural Systems 66 (2000) 17±32

The logical question is why is it that farmers have not been able to achieve a more
optimal risk-ecient cropping plan? Considerable evidence has been gathered to
show that smallholder allocative ineciency is common place in developing country
agriculture (Feder, 1985; Ali and Byerlee, 1991; Barrett, 1997). These ineciencies
occur in a structurally predictable manner in several cases (Feder, 1985; Barrett,
1997) due to multiple market failures (e.g. in land and insurance markets). Others
occur due to lack of access to market information on prices, labor market search
costs or high transaction costs (Binswanger and Rosenzweig, 1986), in addition to
price risk (Barrett, 1996) and yield risk. Although self-learning and experimentation
is one way that farmers may be able to adjust their decisions (Sumberg and Okali,
1997), such learning has clearly not been able to explain nor compensate for the
observed ineciency in farmers' decisions. In the farming systems of CoÃte d'Ivoire,
other studies have shown that these smallholder farmers often have structurally
predictable mis-allocation of resources. Using plot-level data across the country of
CoÃte d'Ivoire, Barrett et al. (2000) found non-trivial resource mis-allocation in the
cropping decisions of farm households. This evidence supports the result from this
present study. A major problem for farmers across the study zone is that of lack of
access to market price information that would allow them to appreciate the variability of crop prices and risk levels of various crops over time. The results from this
paper suggests that when such price series information on the risks of di€erent crops
are considered, farmers would be better o€ with re-allocating their cropping to a
more optimal cropping plan.
The relatively high risk of maize in the Sudanian zone is due largely to its high
yield variability. Technology development strategies to expand maize area in the
zone may need to focus more on yield stability in order to lower the risks that
farmers face. The relatively higher success of maize in the Guinea Savanna zone may
be due to the higher rainfall and lower yield risks in this zone compared to the
Sudanian zone (Smith et al., 1993).
Although we evaluated the e€ects of risk on cropping patterns, the analysis in this
paper su€ers from one limitation. It was impossible for us to obtain information on
the time series of yield and prices from the actual surveyed farms. The alternative
was to base the analysis on farmers' recall of information on prices, yields and
incomes. We did not judge this appropriate since farmers often had diculty even
recalling within-year information on resource use when the operations have been
conducted for several months preceding the date of interview. Thus, we had to use
time series from the regional data to model the e€ects of incorporating price, yield
and income risks. However, because aggregation problems over villages and farms
often arise in such regional data, the results might be di€erent if village-level information from the individual farms had been available. The use of aggregate data to
proxy farm data may have led to possible miss-speci®cation errors. Thus, we wish
to interpret the results of this analysis cautiously to avoid over-generalizations, given
the data limitations.
Three conclusions follow from the analysis. First, in evaluating cropping systems
in the Savanna zone it is important to consider not only the yield of alternative
crops, but also the yield risk, price risk, and income risk that farmers face in

A.A. Adesina, A.D. Ouattara / Agricultural Systems 66 (2000) 17±32

31

adjusting their cropping patterns. Second, to reduce production risks faced by
farmers, emphasis should be placed on yield stability of technology interventions
intended for farmers in this zone. Lastly, policy makers should focus e€orts on
achieving farm-income stabilization for farmers in this zone by: (1) developing
e€ective market price information transmission system; (2) providing low-cost but
high-resolution climatic information; and (3) developing risk management institutions. Unless policy makers improve the availability of information that allows
farmers to improve their managerial capacity for making more risk-ecient cropping decisions, it is unlikely that farmers in the zone will be able to cope with the
pervasive risks that a€ect their welfare and livelihoods.

Acknowledgments
We are grateful to the Editor-in-Chief, Professor Barry Dent, Associate Editor,
Dr. Scott Andrews, and two anonymous reviewers for critical comments and suggestions that substantially helped us in the revision of this paper. The comments
provided by Peter Matlon, Kama Berthe, Kouadio Yao, Jacques Pegatienan and
Louise Haly-Djoussou are gratefully acknowledged. All usual disclaimers apply,
and we are responsible for any errors. The work on which this paper is based was
funded jointly by the African Development Bank (AfDB), Centre Ivoriene de
Recherche Economies et Sociales (CIRES) and the West Africa Rice Development
Association (WARDA).

References
Adesina, A.A., Brorsen, B.W., 1987. A risk responsive acreage response function for millet in Niger.
Agric. Econ. 1, 229±239.
Adesina, A.A., Sanders, J.H., 1991. Peasant farmer behavior and cereal technologies stochastic programming analysis in Niger. Agric. Econ. 5, 21±38.
Adesina, A.A., Sanders, J.H., Abbott, P.C., 1988. Ex-ante risk programming analysis of new agricultural
technology: experiment station fertilizer recommendations in Southern Niger. Agricultural Systems 27,
22±34.
Ali, M., Byerlee, D., 1991. Economic eciency of small farmers in a changing world: a survey of recent
evidence. J. Int. Dev. 3 (1), 21±37.
Anderson, J.R., Dillon, J.L., Hardaker, J.B., 1985. Farmers and risk. Invited paper presented for the
theme: `Theoretical developments: farm management and organization' at the XIX International Conference of Agricultural Economists, 26 August±4 September, Malago, Spain.
Barrett, C., 1996. On price risk and inverse farm size productivity relationship. J. Dev. Econ. 51 (2), 193±
215.
Barrett, C., 1997. How credible are estimates of peasant allocative, scale or scope eciency? A commentary. J. Int. Dev. 9 (2), 221±229.
Barrett, C.B., Sherlund, S.M., Adesina, A.A., 2000. Shadow wages, allocative eciency, and labor supply
in smallholder agriculture. Paper presented at the Winter meetings of the Econometric Society, USA.
Binswanger, H.P., 1980. Attitudes towards risk: experimental measurements in rural India. American J.
Agric. Econ. 62, 395±407.

32

A.A. Adesina, A.D. Ouattara / Agricultural Systems 66 (2000) 17±32

Binswanger, H.P., Rosenzweig (Eds.), 1986. Contractual Arrangements, Employment and Wages in Rural
Labor Markets in Asia. Yale University Press, New Haven.
Dillon, J.L., Scandizzo, P., 1978. Risk attitudes of subsistence farmers in Northeast Brazil: a sampling
approach. American J. Agric. Econ. 60, 425±435.
Eponou, T, 1983 Farm level analysis of rice production systems in North-Western Ivory Coast. PhD
thesis, Michigan State University, East Lansing, MI, USA.
Feder, G., 1985. The relationship between farm size and farm productivity: the role of family labor,
supervision and credit constraints. J. Dev. Econ. 18 (2), 297±313.
Halter, A.N., Mason, R., 1978. Utility measurement for those who need to know. Western J. Agric. Econ.
3 (2), December, 99±109.
Hardaker, J.B., Troncoso, J.L., 1979. The formulation of MOTAD programming models for farm
planning using subjectively elicited net revenue distributions. European Rev. Agric. Econ. 6 (1),
47±60.
Hazell, P.B.R., 1979. A linear alternative to quadratic and semivariance programming for farm planning
under uncertainty. AJAE 53, 53±62.
Hazell, P.B.R., Norton, R.D., 1986. Mathematical Programming for Economic Analysis in Agriculture.
Macmillan, London.
Heyers, J., 1972. An analysis of peasant farm production under conditions of uncertainty. J. Agric. Econ.
23 (2), 135±145.
Kristjanson, P.M., 1987. The role of information and ¯exibility in small farm decision making and risk
management: evidence from the West African Semi-Arid Tropics. PhD thesis, University of Wisconsin,
Madison, USA.
Low, A.R.C., 1974. Decision taking under uncertainty a linear programming model of peasant farmer
behavior. J. Agric. Econ. 25 (3), 311±322.
Maleka, P., 1993. An application of Target MOTAD model to crop production in Zambia: Gwembe
valley as a case study. Agric. Econ. 9, 15±35.
Mapp, H.P., Hardin, M.L., Walker, O.L., Persaud, T., 1979. Analysis of risk management strategies for
agricultural producers. American. J. Agric. Econ. 61 (5), 107l±1077.
Matlon, P.J., 1990. Farmer risk management strategies: the case of the West African Semi-Arid Tropics
Paper presented at the 10th Agricultural Symposium: Risk in agriculture, 9±10 January. World Bank,
Washington, DC.
Pingali, P., Bigot, Y., Binswanger, H.P., 1987. Agricultural Mechanization and the Evolution of Farming
Systems in Sub-Saharan Africa. Johns Hopkins University Press, Baltimore.
Roumasset, J.A., 1976. Rice and Risk. Decision Making Among Low Income Farmers. North Holland
Press, Amsterdam.
Sargeant, M., Litche, J., Matlon, P., Bloom, R., 1981. An assessment of animal traction in Francophone
West Africa (Dept. Agr. Econ. African Rural Economy Working paper, No. 34). Michigan State University, MI, USA.
Shapiro, B.I., Sanders, J.H., Reddy, K.C., Baker, T.G., 1993. Evaluating and adapting new technologies
in a high-risk agricultural system Ð Niger. Agricultural Systems 42, 153±171.
Sivakumar, M.V.K., 1988. Predicting rainy season potential from the onset of rains in the Southern
Sahelian and Sudanian climatic zones of West Africa. Agric. For. Meteorol. 42, 295±305.
Smith, J., Barau, A., Goldman, A., Mareck, J., 1993. The role of technology in agricultural intensi®cation
the evolution of maize production in the northern Guinea Savannah of Nigeria. In: Dvorak, K. (Ed.),
Social Science Research for Agricultural Technology Development. Spatial and Temporal Dimensions.
CABI, UK, pp. 144±166.
Sumberg, J., Okali, C., 1997. Farmers' Experiments Creating Local Knowledge. Lynne Reinner Publishers, Boulder.
Thomson, K.J., Hazell, P.B.R., 1972. Reliability of using mean absolute deviation to derive ecient E,V
farm plans. American J. Agric. Econ. 54, 503±506.