Risk and agricultural systems in northern
CoÃte d'Ivoire
A.A. Adesina
a,*, A.D. Ouattara
baThe Rockefeller Foundation, Agricultural Sciences Division, Food Security Program, 420 Fifth Avenue,
New York, NY 10018-2702, USA
bCentre 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 dierentially aect the yield potential of various crops. The paper applies a simple risk programming model to assess the eects 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 ineciency 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 dierent 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 eorts on achieving farm income stabilization for farmers in this zone by: (1) developing eective 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-ecient cropping decisions, it is unlikely that farmers in the zone will be able to cope with the pervasive risks that aect their welfare and livelihoods.#2000 Elsevier Science Ltd. All rights reserved.
Keywords:Risk; Risk programming; Farmer decision making; CoÃte d'Ivoire
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* Corresponding author. Tel.: +1-212-852-8342; fax: +1-212-852-8442.
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 sur-rounding 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 risk-management 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 eects of El Nino on global climate and its consequences on local climate changes and agri-culture. Strategies to help smallholder farmers cope with the myriad of risks they face requires an understanding of how risk aects 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 eects 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' crop-ping decisions, and what types of policy and institutional reforms are needed to permit farmers to better cope with eects 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 aects 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 agro-ecological 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 Ð with 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.
to the existence of neighboring dams that supply water for irrigated rice in the vil-lages, although the water reserve capacity of the dam in the Sirasso area is the lar-gest. 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 (man-ual, 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 coecients 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 avail-ability. These range from: the generalized expected utility framework (Anderson et al., 1985), farm-programming models (Wicks, 1978) including mean±variance ana-lysis using quadratic programming (QP); game-theoretical approaches (Heyers, 1972; Low, 1974); stochastic programming (Adesina and Sanders, 1991); or a lin-earized 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
Table 1
Characteristics of the study villages in the Savanna zone of CoÃte d'Ivoire Village
Mbengue Napie Sirasso Agro-ecological zone Sudanian Guinea Savanna Guinea Savanna Total area (km2) 2364 288 1822
Total population 28 039 14 756 25 266 Rural population 22 912 14 756 20 860 Total population density (persons/km2) 12 51 14
Rural population density (persons/km2) 10 51 11
(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 ana-lysis in this paper. The model and its variants have been used in risk anaana-lysis of farmer decision in various parts of the world (Hardaker and Troncoso, 1979; Ade-sina 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 diculties 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 eects 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:
MaxL E; E ÿ F
ST
SjaijXj4bi; for alli
Sj cjtÿEcjXjdt50; for allt; t1;2;. . .; T
YSt dtÿ 0
Xj50;E 50; 50;
whereXjis the area in cropj(ha);aijare the respective input±output coecients that capture the level of use of resourcei in the production of cropj; bi is the available resource endowment for factori;cjtis the revenue of cropjin yeart(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
risk;() is the MAD estimate of the variance of pro®ts from crop production over theTyears 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-os between own-liquidity and credit use. Expenditures for purchased inputs (e.g. ferti-lizers, herbicides, insecticides) could be met from farmer's own-liquidity, with or without complementary credit. Only ocial 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 pre-dominantly 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 pro-vision 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 input-type based on the averages from the ®eld data.
determine the MAD of income returns over all crops in each of the T years con-sidered. 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 reports1for the period 1986±1991, complemented by the ®eld data
col-lected 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 dierent 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 ris-kiness 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 esti-mated price, yield and income risks for dierent crops in the three study regions (Table 3).
For Mbengue zone, the majority of the crops have high coecient 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 yield-risk group consists of cotton (CV=9%). Taking yield and price yield-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
irrigated rice: CV=21%; peanuts: CV=31%). Classi®cation by level of gross-income risk produces the following groupings: low-gross-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
Table 2
Linear programming results for dierent farm types in study villages using prices observed in the survey yeara
Manual farms Oxen farms Motorized farms F.Avgb Base
Upland rice 0.93 0.93 0.8 0.8 3.7 3.7 Lowland rice ± ± 0.6 0.6 0.6 0.6
Cotton 1.70 1.80 3.5 3.4 10 10
Peanuts ± ± 0.47 0.47 0.8 0.8
Expected pro®t (`000 CFA) NAc 156 NA 457 NA 1585
Standard deviation of pro®t (`000 CFA) NA 138 NA 836 NA 3644 Coecient of variation of pro®t (%) ± 89 ± 180 NA 230
Napie village: Guinea Savanna zone
Irrigated rice (®rst season) 0.43 0.43 0.45 0.45 Irrigated rice (second season) 0.43 0.43 0.45 0.45 Upland rice 0.25 0.25 0.40 0.40 Lowland rice 0.12 0.12 0.18 0.18 Peanuts 0.35 0.35 0.28 0.63 Cotton 1.10 1.10 2.50 2.20 Expected pro®t (`000 CFA) NA 193 NA 423 Standard deviation of pro®t (`000 CFA) NA 288 NA 510 Coecient of variation of pro®t (%) NA 150 NA 120
Sirasso village: Guinea Savanna zone
Irrigated rice (®rst season) 0.31 0.31 0.62 0.62 Irrigated rice (second season) 0.31 0.31 0.62 0.62 Upland rice 0.55 0.55 1.03 1.03 Peanuts 0.51 0.51 0.86 0.86 Cotton 0.98 0.98 2.50 2.50 Expected pro®t (`000 CFA) NA NA 1106 Standard deviation of pro®t (`000 CFA)
Coecient of variation of pro®ts (%) NA NA 54
a Savanna zone of CoÃte d'Ivoire. b Averages from ®eld data. c Not available.
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 dierent 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 sur-vey 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 eects across dierent til-lage 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 correla-tion between farm incomes and methods of tillage. Risk aversion is a funccorrela-tion 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 Fwas parameterized to simulate the eects of risk aversion on cropping choices. The use of time series data allows us then to compare
Table 3
Classi®cation of crops by level of yield and income risk in each village zonea
Village Price risk Yield risk Income risk
Mbengue High ± Maize Maize
Medium Upland rice Lowland rice Lowland rice Peanuts Peanuts Peanuts Lowland rice Upland rice
Low Maize Cotton Cotton
Cotton Upland rice
Napie High ± Maize Maize
Medium Upland rice Upland rice Irrigated rice Irrigated rice Irrigated rice Peanuts Peanuts Peanuts
Low Cotton Cotton
Maize Upland rice
Cotton
Sirasso High ± Maize Maize
Medium Peanuts Upland rice Upland rice Irrigated rice Peanuts Irrigated rice Upland rice Irrigated rice Peanuts Low Maize
Cotton Cotton Cotton
what the eects 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 solu-tions 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 pea-nuts. 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
area of maize in the risk model solution declines from 6.3 ha forF=0 to 0.19 ha for allF>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 0.35 0.35 0.35 0.35 0.35
Upland rice 2.80 2.80 2.36 1.78 1.73
Lowland rice ± ± ± ± ±
Cotton 0.14 0.14 0.58 1.16 1.22
Peanuts ± ± ± ± ±
Volume of maize sold (kg) ± ± ± ± ± Volume of rice sold (kg) 2172 2172 1757 1199 1149 Volume of cotton sold (kg) 196 196 786 1576 1648 Expected pro®t (`000 CFA) 192 192 183 183 182 Standard deviation of pro®ts (`000 CFA) 150 150 109 109 108 Coecient of variation (%) of pro®ts 78 78 60 60 59
Oxen farms
Maize 0.52 0.52 0.52 0.52 0.52
Upland rice 4.50 4.50 4.50 4.58 3.60
Lowland rice ± ± ± ± ±
Cotton 1.64 1.64 1.64 1.64 2.56 Peanuts 0.21 0.21 0.21 0.21 0.21 Volume of maize sold (kg) ± ± ± ± ± Volume of rice sold (kg) 8227 8227 8227 8227 6342 Volume of cotton sold (kg) 2214 2214 2214 2214 3463 Expected pro®t (`000 CFA) 647 647 647 647 634 Standard deviation of pro®ts (`000 CFA) 190 190 190 190 174 Coecient of variation (%) of pro®ts 29 29 29 29 27
Tractor farms
Maize 6.32 0.19 0.19 0.19 0.19
Upland rice 3.66 3.66 3.66 3.66 3.66 Lowland rice 0.83 0.83 0.83 0.83 ±
Cotton 12 13 13 13 13
Peanuts 0.24 0.24 0.24 0.24 0.24 Volume of maize sold (kg) 8713 ± ± ± ± Volume of rice sold (kg) 6029 6029 6029 6029 4689 Volume of cotton sold (kg) 13 584 14 660 14 660 14 660 14 660 Expected pro®t (`000 CFA) 1670 1608 1608 1608 1535 Standard deviation of pro®ts (`000 CFA) 2458 486 486 486 416 Coecient of variation (%) of pro®ts 150 30 30 30 27
a Mbengue village, Savanna zone of CoÃte d'Ivoire.
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 main-season irrigated crop. The yield risk of the second-main-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 km2and 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) Risk aversion levels
F=0 F=0.1 F=0.25 F=0.5 F=0.15
Manual farms
Maize ± ± ± ± ±
Upland rice 0.10 0.10 0.10 ± ± Irrigated rice (®rst season) 0.43 0.43 0.43 0.43 0.43 Irrigated rice (second season) 0.43 0.43 0.43 0.39 0.15
Lowland rice 0.03 0.03 ± ± ±
Cotton 1.25 1.25 1.25 0.05 ±
Peanuts 0.24 0.24 0.24 0.90 0.90 Volume of maize sold (kg) ± ± ± ± ± Volume of rice sold (kg) 1424 1424 1396 1180 746 Volume of cotton sold (kg) 1690 1690 1690 63 ± Expected pro®t (`000 CFA) 215 215 215 109 90 Standard deviation of pro®ts (`000 CFA) 318 318 317 71 41 Coecient of variation (%) of pro®ts 147 147 147 65 46
Oxen farms
Maize ± ± ± ± ±
Upland rice ± ± ± 0.76 1.81
Lowland rice ± ± ± ± 1.18
Irrigated rice (®rst season) 0.45 0.45 0.45 0.45 0.15 Irrigated rice (second season) 0.45 0.45 0.45 0.40 Cotton 2.55 2.55 2.55 1.78 0.27 Peanuts 0.63 0.63 0.63 0.63 0.73 Volume of maize sold (kg) ± ± ± ± ± Volume of rice sold (kg) 1039 1039 1039 2039 2708 Volume of cotton sold (kg) 4730 4730 4730 3308 508 Expected pro®t (`000 CFA) 492 492 492 419 195 Standard deviation of pro®ts (`000 CFA) 585 585 585 436 127 Coecient of variation (%) of pro®ts 119 119 119 104 65
a Napie village, Savanna zone of CoÃte d'Ivoire.
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 expec-ted 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 0.96 0.96 0.92 0.18 0.18
Upland rice 0.53 0.53 0.51 0.20 0.24 Irrigated rice (®rst season) 0.31 0.31 0.31 0.31 0.31 Irrigated rice (second season) 0.31 0.31 0.31 0.31 0.31
Cotton ± ± 0.10 1.42 1.42
Peanuts 0.24 0.24 0.24 0.90 0.90 Volume of maize sold (kg) 3869 3869 3704 375 369 Volume of rice sold (kg) 2749 2749 2700 2232 2287 Volume of cotton sold (kg) ± ± 85 1607 1609 Expected pro®t (`000 CFA) 440 440 436 353 352 Standard deviation of pro®ts (`000 CFA) 384 384 370 88 87 Coecient of variation (%) of pro®ts 87 87 85 25 25
Oxen farms
Maize 5.22 5.22 5.22 5.22 0.34
Upland rice ± ± ± ± 1.85
Irrigated rice (®rst season) 0.62 0.62 0.62 0.62 0.62 Irrigated rice (second season) 0.62 0.62 0.62 0.62 0.62
Cotton ± ± ± ± 2.5
Peanuts 0.20 0.20 0.20 0.20 0.80 Volume of maize sold (kg) 30 486 30 486 30 486 30 486 1557 Volume of rice sold (kg) 6389 6389 6389 6389 10 183 Volume of cotton sold (kg) ± ± ± ± 1593 Expected pro®t (`000 CFA) 2186 2186 2186 2186 955 Standard deviation of pro®ts (`000 CFA) 1693 1693 1693 1693 185 Coecient of variation (%) of pro®ts 77 77 77 77 19
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 ofFrises 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 aected 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 km2and 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 aect cultivated area of the second-season irrigated rice crop.
These results have important implications for eorts 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 sucient 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 season rice crop or totally abandon growing the second-season rice crop.
5. Conclusions
The logical question is why is it that farmers have not been able to achieve a more optimal risk-ecient cropping plan? Considerable evidence has been gathered to show that smallholder allocative ineciency is common place in developing country agriculture (Feder, 1985; Ali and Byerlee, 1991; Barrett, 1997). These ineciencies 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 ineciency 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 varia-bility 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 dierent 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 eects of risk on cropping patterns, the analysis in this paper suers 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 diculty 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 eects 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 dierent if village-level infor-mation 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.
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 eorts on achieving farm-income stabilization for farmers in this zone by: (1) developing eective market price information transmission system; (2) providing low-cost but high-resolution climatic information; and (3) developing risk management institu-tions. Unless policy makers improve the availability of information that allows farmers to improve their managerial capacity for making more risk-ecient crop-ping decisions, it is unlikely that farmers in the zone will be able to cope with the pervasive risks that aect 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 sug-gestions 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).
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