Agricultural Systems

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1 Agricultural Systems 110 (2012) 1 9 Contents lists available at SciVerse ScienceDirect Agricultural Systems journal homepage: Public policies for rural poverty alleviation: The case of agricultural households in the Plateau Central area of Burkina Faso Safiétou Sanfo a,, Françoise Gérard b a WASCAL (West Africa Science Service Center on Climate Change and Adapted Land Use), VBA 10, P.O. Box 13621, Ouagadougou 10, Burkina Faso b CIRAD GREEN (Centre de Coopération Internationale pour la Recherche Agronomique et le Développement), 45 bis av de la belle Gabrielle, Nogent sur marne cedex, France article info abstract Article history: Received 20 September 2011 Received in revised form 18 February 2012 Accepted 29 February 2012 Keywords: Rural poverty Burkina Faso Agricultural households Economic policies Model Mathematical programming Are there any economic policies able to break the vicious circle of the poverty trap in rural areas of countries such as Burkina Faso? To help answer this question, a detailed analysis of production systems has been used to elaborate dynamic household models. A mathematical programming model represents the economic decisions of farmers for the three farm types found in the Plateau Central region. Farms are linked together by factor markets. The model calculates a wide range of variables: incomes of agricultural households and their sources, land allocation between the different crops, cropping techniques used, agricultural production, consumption expenditure and labor allocation among the various activities including off-farm activities. These estimates can be obtained by farmer groups and aggregated for the Plateau Central. The model is used to simulate the effects of five economic policies and their combinations: (i) increasing the availability of irrigation water, (ii) lowering marketing costs, (iii) access to animal traction, (iv) access to credit, (v) reducing producer price variability. The analysis reveals different impacts in function of the different farm groups. Public good policies benefit all groups whereas policies aiming at improving capital market access or at diminishing risks on product sales primarily benefit the richest group. Policies focusing on access to equipment are of greater benefit to the est. Given the depth of initial poverty, it is necessary to combine all the measures to allow the incomes of the est group to cross the poverty line. Ó 2012 Elsevier Ltd. All rights reserved. 1. Context and issues Poverty is a polysemic and multidimensional concept. In its broadest sense, it is defined as a situation where the level of individual welfare is inadequate and socially unacceptable. However, its measurement is the subject of considerable debate. The nonmonetarist view of poverty focuses on the deprivation of rights (Sen, 1992). It is the approach adopted by the United Nations in its yearly Human Development Reports (for example, UNDP, 2000, 2011). People are perceived as holders of basic rights such as income level, overall health, education level, etc. These rights are typically met through individual skills, i.e. the ability of each individual to reach a certain level of well-being and increase the scope of his or her opportunities. In this perspective, poverty can be measured directly by estimating the functions of ability of each individual (Ravallion, 1998). The utility approach, developed by the World Bank in its World Development Reports (e.g. World Bank, 1990) considers mostly the monetary aspect. Because of the difficulties of directly assessing agents utility, the level of consumer spending is used. In practice, Corresponding author. Tel.: / addresses: safi.sanfo@laposte.net, ssanfo76@yahoo.fr (S. Sanfo). the minimum threshold above which an individual can be identified as not is calculated from a weighted basket of goods valued by the price system and called the poverty line. This study uses the monetary approach to poverty and places consumption and income at the center of the analysis. However, the use of mathematical models for reproducing farmers opportunities and constraints takes into account the initial endowment in production factors. Indeed, these endowments are a major factor explaining farmers income dynamics and therefore their opportunities to escape poverty. This is directly in line with the ability approach (Ravallion, 1998). Despite political stability and favorable growth (approximately 5%) over the period, Burkina Faso still ranks among the est countries in the world, both in terms of per capita income and human development scale as defined by the United Nations Development Programme (UNDP). Nearly half of the population lives below the poverty line with less than US$1 per day, and a much larger share faces a daily struggle for survival. These households cannot produce or buy enough food to meet a satisfactory intake of kilocalories and proteins: they are food insecure. Moreover, despite considerable attention devoted to poverty alleviation in national development programs, poverty indicators are deteriorating: the incidence of poverty increased from 44.5% X/$ - see front matter Ó 2012 Elsevier Ltd. All rights reserved.

2 2 S. Sanfo, F. Gérard / Agricultural Systems 110 (2012) 1 9 in 1994 to 45.3% in 1998 and to 46.5% in 2003 (INSD, 2003). The same trend is seen for malnutrition (measured as the percentage of population under the age of 5 that is more than two standard deviations from the median of the international population for weight/age): from 29.6% in 1993 to 33.7% in 1999, 35.2% in 2003, and 37.4% in 2006 (WDI, 2010). According to the Human Development Index for 2010, Burkina Faso ranks 181st out of the 187 countries ranked (UNDP, 2011). The population of Burkina Faso is mainly rural (80%) and primarily employed in agriculture (86% of the workforce). Due to weather conditions and available technologies, yields are highly variable. Agricultural production is indeed a risky economic activity. Production levels are random, and the geographic correlation of risks and moral hazards makes insurance difficult. The simultaneity of borrowing and deposits leads to difficulties for the banking system. Farmers are therefore very exposed to financial risk. The high variability of agricultural product prices increases the risks associated with agricultural activity. Yields are also very low compared to international standards, due to low input levels and the lack of adequate farm equipment in most cases. Cultivable land per capita is decreasing because of population growth and a lack of economic opportunities outside of agriculture. Low and uncertain incomes do not allow farmers to invest. As a result, they cultivate with limited equipment which explains their low labor productivity. It is a well known vicious circle explaining the persistence of poverty in farming communities. Farmers tend to minimize their relationship with the market, producing for subsistence and selling only to meet their cash needs. In these circumstances, there is no drive to increase yields. On the contrary, farmers tend to minimize cash needs by using as few inputs as possible (Dorward et al., 2004). All experts agree on the complexity of the problem, explaining why it is so difficult to find appropriated solutions. The need for government intervention in this context is the subject of wide controversy among economists. Some argue that, due to the importance of small farms in development and the difficulty of market operations, it may be necessary to intervene on some lever able to help farmers break this vicious circle (Hazell et al., 2010). A vast body of literature examines a wide range of instruments (Poulton et al., 2006). Others argue, on the contrary, that the cost of government intervention exceeds its benefits, and that government should only provide public goods such as roads, health care and education, and let private activities develop. This study aims to contribute to the discussions on this topic. This complexity is partly due to the diversity of agro-climatic and socio-economic production conditions. They are great differences between regions within the country and great diversity of farm households within any given region. This study has therefore opted for a detailed analysis of an important Burkina Faso agricultural region, the Plateau Central. This complexity is also related to the wide range of parameters at work, from economic relationships to natural conditions in the environment, explaining farmers strategies. Modeling provides an efficient tool to deal with such complexity, allowing the impact of various environmental or economic changes on incomes to be assessed (e.g. Boussard and Daudin, 1988; Dijksterhuis and Neeteson, 1995; Pandey and Hardaker, 1995; Gerard et al., 1999; Hazell and Norton, 1986). Models allow one to handle a large number of parameters and variables simultaneously and test the changes associated with variations in some of them. We assume that it is necessary to understand farmers strategies in order to define appropriate measures capable of helping farmers break the vicious circle of poverty. This is why this study proposes to use a model reproducing farmers behaviors to contribute to this debate. After an in-depth analysis of the production conditions faced by agricultural households in the Plateau Central region, measures able to lift the main constraints faced by farmers are defined (Section 2). Then, the model built to test these measures is presented (Section 3), and the results discussed (Section 4). 2. A case study from Burkina Faso Several things explain the choice of the Plateau Central region in Burkina Faso. The Plateau Central is one of the est regions of the country despite its proximity to the capital and related market access. Its agriculture is typical of the constraints on agriculture in the country. Located in the driest part of the country, climatic risk is very high in agricultural activity. Poverty is widespread. Access to safe water, health services and transportation is. However, strong diversity characterizes agricultural households Agricultural households from the Plateau Central Agriculture is the main economic activity in the region and employs nearly 98% of the workforce. Agricultural production is organized in two seasons. In the rainy season, grains such as millet (Pennisetum glaucum), sorghum (sorghum bicolor), maize (Zea mays), rice (oryza) and legumes such as cowpea (Vigna unguiculata), voandzou (Voandzeia subterranea), and groundnut (Arachis hypogea) are grown. Some small dams were built after the great droughts in the 1970s and 1980s by the government and NGOs. Some farmers have settled near the reservoirs, both downstream and upstream of the dams in order to produce dry-season irrigated rice and winter crops such as vegetables. Farm households practice subsistence agriculture. Thus, sales are made only on crop surpluses over family needs, a situation that occurs when the season is favorable. A wide range of data has been collected at different levels (national, regional, village and family levels) to feed a detailed analysis of farm households constraints and opportunities. Both qualitative and quantitative approaches have been used. The objective was to build a typology or in other words, identify the main farm types located in similar environments and operating with equivalent production factor endowments. Agro-climatic and socio-economic data such as population, yields, rainfall, production costs and the prices of agricultural products and animals were collected from national institutes. 1 Complementary surveys were carried out in three villages (Mogtédo ( W, N), Talembika ( W, N), and Sologo ( W, )) in the Plateau Central. 2 A total of 75 randomly selected farmers, equally distributed in the three sites, were interviewed. Surveys were conducted on farmers fields when possible. Data collected at the farm level include agro-climatic and socio-economic variables (yields, water, land, family labor, animal availability, machine endowment, cash savings and borrowing, consumption, financial constraints, activity management, animal feed, food prices, goods and animal values, risks associated with economic activities, etc.). Statistical analysis 3 was combined with expert interviews, and contextualized based on a bibliographical review. This was used to verify that similar natural resources and economic factor endowments lead to similar land and labor allocations among activities. The objective was not only to identify relatively homogeneous subgroups within the farming community, but also to understand their behaviors. Three groups of farmers were identified (Table 1). The very make up 55% of the agricultural population. Their incomes, including self-consumption, are approximately 1 Institut National des Statistiques et de la Démographie, Institut National de l Environnement et de Recherches Agricoles, and Direction Générale des Prévisions et des Statistiques Agricoles. 2 They took place in January May 2008, and in January April Principal component analysis using the SPAD Version 3.

3 S. Sanfo, F. Gérard / Agricultural Systems 110 (2012) Table 1 Farm characteristics by typology. Households variables Very (55%) Poor (31%) Less (14%) Size (number of people) Men Women Children (15 years) Credits Total Surface Area Surface Area/Worker (ha) Irrigated Surface Area (ha) Cattle (head) Sheep (head) Goats (head) Poultry (head) Health Expenditures (US$) < and + School expenditures (US$) < and + Able to eat to one s fill No Yes Yes Income from off-farm jobs (% of total income) 33.7% 17.9% 7.3% US$100 per person per year. They live well below the poverty line (US$190). They control very small areas, about 2 ha for every four active family members and support large households (about 9 people on average). Their land is almost entirely devoted to traditional grains (millet, sorghum), which take up 82% of the farm land. Grain production is mainly for self-consumption (85%). They must often sell their labor to meet consumption needs and are highly constrained by cash availability. Given the lack of resources, animal traction is absent from their cropping practices, and use of improved seeds and fertilizers is essentially nonexistent. The households make up 31% of the agricultural population and their incomes are about US$120. They have more land under their control (about 3 ha for every five active family members) and the liquidity constraint is less severe than for the very. They use more intensive techniques, renting animals and hiring labor during critical periods, and purchasing inputs. However, they have to support around 12 people and their living conditions are still difficult. As with the very households, 85% of the grain they produce is destined for self-consumption. They use fertilizer, but below recommended levels, and the use of improved seeds is extremely rare. The less households make up 14% of the agricultural population. With US$160 per person per year, they are still below the poverty line. They cultivate larger areas, with more than 4 ha for seven active family members, and have more irrigated land. These households have an average of 15 people, and 75% of their millet, maize, and sorghum production is devoted to self-consumption. They are less constrained by cash, use animal traction, and hire farm workers during peak labor periods. Given the existence of guarantees (like farm equipment, animals etc.), they have access to credit, although credit is very expensive, with yearly rates of between 12% and 17%. They use more inputs and improved seeds, especially for irrigated crops, than the other two farmer types. In irrigation systems, these farmers know and apply the correct dose of fertilizer, but this is not the case in the rainfed area where the risks greatly limit the use of inputs. The majority of agricultural households in the Plateau Central are below the poverty line, but a vast diversity of situation exists among them. Access to land and irrigation makes the difference between a majority of very households, earning hardly more than half the poverty line, and the top 15%, who are still below the poverty line but economically a little better off. Farmers incomes depend on their ability to produce and sell, and on the productivity of the factors they own. As is widely admitted, labor productivity depends on the capital used in production. If incomes are too low, saving is rare and insufficient for investment; in this case, the level of capital used is low, and so is labor productivity. This vicious circle is well known (Dorward et al., 2004) and explains why farmers are stuck in the poverty trap. The analysis above allows one to define measures expected to improve households economic production conditions. Five measures have been chosen because each acts on a specific variable representing a constraint identified as being especially important: Improve irrigation, since access to water is a major natural resource challenge and plays a key role in household productivity. Improving irrigation may indeed lessen the influence of climate variability, significantly and sustainably increase production and agricultural productivity (Fox et al., 2005), and thus significantly reduce poverty. Let us remember that irrigation strongly contributed to maintaining incomes despite population growth. 4 Lower marketing costs, because roads are a major public good and remain very in Burkina Faso and marketing costs are often mentioned as a major obstacle to development (Jayne et al., 2003). Ensure access to animal traction for each farmer, as the low level of equipment used is a main factor explaining the low labor productivity (Aune and Bationo, 2008), which therefore plays a key role in the poverty trap mechanism. Better access to credit, because of the importance of the liquidity constraint in farmers strategies. Price stabilization, as risk is also a key factor explaining farmers reluctance to produce for the markets Methodology Experts agree on the complexity of the poverty phenomenon due to the multitude of variables involved, their relationships, and the associated difficulty finding remedies (Sawadogo et al., 1995; Wetta, 2002). The question is difficult because the socioeconomic mechanisms at work are complex, highly dependent on the agro-climatic environment and on access to natural resources. For this reason, to participate in this debate, this study made the assumption that it is important to consider the diversity of opportunities and constraints faced by farmers and try to understand and replicate them through modeling. A wide variety of models exists representing economic behaviors at various scales (from plot level to worldwide). General equilibrium models allow one to represent the entire economy and are recommend for studying macro-economic policy and factors markets, but are not able to cover farming diversity in a detailed manner. Partial equilibrium models are used to study the impact of price changes on supply but are not designed for a detailed analysis of farmers behaviors. Mathematical programming models are able to do so. They provide a convenient way to considerer as many constraints as necessary: nutritional, economic and agronomic constraints. It then becomes possible to represent farmers strategies and decisions in a stylized but relatively detailed way, and verify that the model is able to reproduce current trends in farming activities and incomes. Economic measures drawn from the analysis of the constraints and opportunities faced by agricultural households in the Plateau Central and expected to have a favorable impact on poverty alleviation can then be tested and compared with this kind of model. A non-linear mathematical programming model based on a detailed analysis of the production systems found in the Plateau Central area was therefore built to represent the three major farm 4 Sanfo (2010) shows that observed income is halved without recourse to irrigation.

4 4 S. Sanfo, F. Gérard / Agricultural Systems 110 (2012) 1 9 types described above. 5 This model uses static optimization combined with a recursive dynamic where some parameters like equipment or available liquidity are updated every year based on the results of the previous growing period. In line with the situation of agricultural households in the Plateau Central, it takes into account the risks associated with production activities. This makes it possible to reproduce the dynamic paths of each farm type, including trends in assets, income sources and the evolution of factor allocations. Models were calibrated to reproduce past trends. They are used to test whether some measures are likely to generate a cumulative process able to allow households to escape poverty over the course of 10 years. A lot of attention was devoted to representing market imperfections: The market information imperfection is included through price risk. Risk is indeed an important factor in farmers decisionmaking processes. Farmers decisions are made on the basis of expected gross margins and potential deviations for each activity (Hazell and Scandizzo, 1977). Transactions costs, which lower the prices of products sold and increase the prices of products bought are also included. They are especially important when infrastructures are and markets ly developed. They include the time and energy necessary to find a partner or a product, negotiate the terms of the transaction and supervise the transaction, and transportation costs, which can be very high when no roads exist to link farmers to markets. The labor market imperfection is represented with the possibility of labor shortages that prevent farmers from finding enough labor to hire. The capital market imperfection is represented through credit rationing, and liquidity and equipment constraints. To reproduce the situation of the agricultural households of the Plateau Central, the mean-variance utility function (Markowitz, 1959), slightly modified to introduce endogenous risk aversion, is used as the objective function. It assumes that utility increases with expected wealth and decreases with the expected associated risk. MaxUðW F Þ¼EðW F Þ 1=2r 2 WF AV F where E (W F ) represents the expected wealth for the farm F, r 2 WF the associated expected possible deviation, and AV F, the risk aversion coefficient, which is endogenous and inversely proportional to wealth. EðW F Þ¼ P A F;a E ðpaþ a A F represents the volume of assets a owned by the farm F, and E (Pa) represents the expected price associated with it. Thus, wealth (Eq. (2)) is defined as the sum of the value of assets (equipment, livestock, cash and savings). The risk associated with a given wealth level depends on the portfolio of activities and assets for the period. r 2 WF ¼ P r ae ðpaþa 2 F;a þ P 2 act EðMB act ÞÞ ð3þ a actðr Act includes all crop and animal activities and off-farm jobs. E (MB act ) designates the expected gross margin for each activity, and r act designates the associated expected deviation. The covariances between activities are assumed to be zero. 5 The presentation of the model in this paper is synthetic, it is based on the agricultural production module of the MATA model (Gerard et al., 1999). Additional information can be obtained by contacting the authors. ð1þ ð2þ Opportunities and constraints are determined by the agroclimatic and socio-economic conditions for each type of farming system. Usual constraints are considered at the farm level. They are related to factor endowments (land, labor, and capital), natural resource use, and economic variables (cash flow, consumption, etc.). For example, agricultural activities need labor, and labor factor balance states that available family labor (after subtracting the labor rented out by the household) and hired labor are sufficient to meet the labor requirements associated with in-farm activities. The family workforce is allocated between agricultural activities and off-farm activities, which include not risky and risky jobs. The same kind of equations hold for water availability, animal traction, and machine allocation. Nutritional constraints are also included: family food consumption of agricultural products must meet minimum calorie and protein needs. They are either produced on the farm (selfconsumption), or purchased at market prices. Consumption is defined as minimum consumption, representing expenditures that cannot be reduced, such as school costs, minimum clothing and food expenditures, plus one part of the expected profit, defined by a consumption propensity parameter. Investment and savings can be negative if some selling of assets is necessary to meet current household needs. Input and factor prices, possibilities of access to credit, interest, and wage rate levels, as well as off-farm activity opportunities and population growth are exogenous. If there is no shortage in the input market, its use is constrained by cash availability. For each period, expenditures associated with production activities and expenditures related to family consumption, investment or savings should be covered by the available cash flow from the previous period, and current earning activities or borrowing. If some surplus cash remains, it is carried over to the next period. The periods of time when farmers pay production costs and when they earn money have to be carefully determined in order to take into account production lags, and properly represent farmers liquidity constraints. The optimization is calculated on a yearly basis according to the expected results of the seasonal activities in order to consider existing links between seasons. The representation of the main stylized facts of farmers situations are of key importance: On the agronomic side, special attention is paid to the technical factors and techniques included in the model, representing not only current techniques but also all the crops it would be possible to grow, and innovations or techniques used in other regions that could be used if the socio-economic environment changes. On the economic side, main market imperfections are represented. A liquidity constraint and maximum borrowing level for the whole region are included to represent the capital market s imperfection. Imperfect information on agricultural product markets is represented through the use of two variables for prices: expected (with several expectation processes) and real prices received by farmers. Decisions are based on expected prices and yields, accounting for time lags between decisions and agricultural results. Real economic performances, as cash available and income, are then calculated with real yields and prices, computed by applying a random coefficient on an average value. Effective results are used to determine savings and consumption. Exchanges between farms on labor markets are included in the model and limit the possibility of buying or selling labor. The possibility of off-farm activities is also introduced and rationed. The objective is to represent the main elements of agricultural supply dynamics as outlined in Nerlove (1979). As with any styl-

5 S. Sanfo, F. Gérard / Agricultural Systems 110 (2012) ized representation of reality, limits exist. The complexity of farmers decisions and the diversity of situations found in the Plateau Central have been simplified considerably. Another limit comes from the household model approach followed in this study: output and factor prices are exogenous. In reality, prices may change in response to variations in supply or demand. 3. Results and discussions Five measures aiming at poverty alleviation were tested with the model described above. They focus on the provision of public goods (irrigation and roads), capital access improvement (equipment and credit), decreasing risks (price regulation), and combinations of these measures. The relative impacts on farmers strategies, income levels and dynamic trends are analyzed in each case. Before presenting how these options are incorporated into the model and their results, the baseline is discussed Baseline and scenarios 1000 FCFA S0 Poverty line Y1 Y2 Y3 Y4 Y5 Y6 Y7 Y8 Y9 Y10 Years Fig. 1. Very households incomes trajectory FCFA S0 Poverty line Y1 Y2 Y3 Y4 Y5 Y6 Y7 Y8 Y9 Y10 Years Fig. 3. Less households incomes trajectory. The model calculates a wide range of variables: incomes of agricultural households and their sources, land allocation between the various crops, techniques used, agricultural production, consumption expenditure, labor allocation among the various activities including off-farm activities, etc. These estimates can be differentiated by farmer group and aggregated for the Plateau Central. The baseline simulation (S0) reproduces observed producers crop and technique choices, as well as the dynamic path of stagnant incomes, which fluctuate strongly according to weather conditions and price levels which simultaneously determine good and bad years (Figs. 1 3). The most promising technologies that are able to increase yields and incomes were not adopted in the baseline simulation, as is the case in reality. Several reasons explain this: (i) high fertilizer prices compared to grain prices, (ii) severe liquidity constraints, and (iii) risks associated with improved technologies due to the inputs used and yield variability. Under these conditions, subsistence agriculture dominates. For the est, the strategy is based on risk minimization, and the model reflects this. It confirms that the provision of more intensive techniques is not sufficient for their adoption. The main policy options were translated into five scenarios that were tested separately before being combined. The first scenario concentrates on irrigation, with a 50% increase in irrigation water availability (S1). The second scenario (S2) tests a 50% reduction in marketing costs. The third scenario (S3) simulates an increase of available capital, through access to animal traction for each farmer. The fourth scenario (S4) simulates better access to credit, through an increase in the overall level of borrowing permitted by the model. The fifth scenario (S5) assumes more stable prices, as risk is also a key factor explaining poverty traps. Finally, combinations of these measures are tested as more than one constraint may have to be lifted simultaneously to allow the emergence of a virtuous circle in which investment and incomes cumulatively increase Impacts of the five scenarios on farmers strategies and income levels The first scenario (S1) assumes a 50% increase in irrigation water availability, which could be attained by small tanks to store rain water at farm level. Households incomes increase by 17 21%, which is a significant positive impact but does not push them over the poverty line given the initial level of poverty (Table 2). Vegetable and rice production increase (Table 3). As these high added value crops are also very risky, this scenario is less beneficial to the very because of their strong risk aversion. However, because of the strong demand for labor during the dry season, due to the increase in irrigated crops, they benefit from more off-farm job opportunities. The second scenario (S2) assumes that the construction of roads and collection and storage infrastructures for agricultural products lead to a 50% drop in marketing costs, which translates into a 15% decrease in input prices and a similar rise in agricultural product 1000 FCFA S0 Poverty line Y1 Y2 Y3 Y4 Y5 Y6 Y7 Y8 Y9 Y10 Years Fig. 2. Poor households incomes trajectory. Table 2 Various policies impacts on farm households incomes. Households policies Very Less Increase availability of irrigation water by 50% Reduce marketing costs by 50% Access to animal traction Increase credit availability by 50% Market regulation: stabilization of millet, sorghum, maize and rice prices high impact (more than 50%); ++ moderate impact (20% to 50%); + low impact (less than 20%); 0 no impact.

6 6 S. Sanfo, F. Gérard / Agricultural Systems 110 (2012) 1 9 Table 3 Farm households land allocation to crops by policy. Policies Households Millet Sorghum Maize Cowpea Voandzou Groundnut Rice Total Rice DS Vegetables DS Variation of the surfaces expressed as a percentage of the total surface area of the agricultural household Baseline Very 30% (WOAT) 52% (WOAT) 3% (WOAT) 6% (WOAT) 3% (WOAT) 3% (WOAT) 3% (WOAT) 100% 0% 1% (WOAT) Poor 31% (WOAT) 47% (WOAT) 6% (WOAT) 6% (WOAT) 2% (WOAT) 3% (WOAT) 5% (WOAT) 100% 5% (WOAT) 2% (WOAT) Less 29 (WAT) 43% (WAT) 8% (WAT) 5% (WAT) 3% (WAT) 5% (WAT) 7% (WAT) 100% 7% (WAT) 4% (WAT) Increase availability of irrigation water by 50% Reduce marketing costs by 50% Access to animal traction Increase credit availability by 50% Price stabilization (millet, sorghum, maize and rice) Very 33% (WOAT) 52% (WOAT) 3% (WOAT) 3% (WOAT) 3% (WOAT) 3% (WOAT) 3% (WOAT) 100% 6% (WOAT) 0.5% (WOAT) Poor 31% (WOAT) 47% (WOAT) 6% (WOAT) 6% (WOAT) 2% (WOAT) 3% (WOAT) 5% (WAT) 100% 7% (WAT) 3% (WOAT) Less 29% (WAT) 43% (WAT) 8% (WAT) 5% (WAT) 3% (WAT) 5% (WAT) 7% (WAT) 100% 9% (WAT) 5% (WAT) Very 28% (WOAT) 46% (WOAT) 15% (IC) 2% (IC) 3% (IC) 3% (IC) 3% (IC) 100% 3% (WAT) 1% (WOAT) Poor 27% (WOAT) 33% (WOAT) 27% (IC) 3% (IC) 2% (IC) 3% (IC) 5% (IC) 100% 5% (WAT) 2% (WOAT) Less 23% (WOAT) 24% (WAT) 35% (IC) 3% (IC) 3% (IC) 5% (IC) 7% (IC) 100% 7% (WAT) 4% (WAT) Very 23% (WAT) 44% (WAT) 10% (WAT) 14% (WAT) 3% (WAT) 3% (WAT) 3% (WAT) 100% 3% (WAT) 1% (WAT) Poor 30% (WAT) 32% (WAT) 18% (WAT) 10% (WAT) 2% (WAT) 3% (WAT) 5% (WAT) 100% 5% (WAT) 2% (WAT) Less 23% (WAT) 31% (WAT) 11% (WAT) 19% (WAT) 3% (WAT) 6% (WAT) 7% (WAT) 100% 7% (WAT) 4% (WAT) Very 30% (WOAT 52% (WOAT) 3% (WOAT) 6% (WOAT) 3% (WOAT) 3% (WOAT) 3% (WOAT) 100% 3% (WAT) 1% (WOAT) Poor 31% (WOAT) 31% (WOAT) 22% (WAT) 6% (WOAT) 2% (WOAT) 3% (WOAT) 5% (WAT) 100% 5% (WAT) 2% (WOAT) Less 29% (WAT) 42% (WAT) 8% (IC) 5% (IC) 3% (IC) 6% (IC) 7% (IC) 100% 7% (IC) 4% (WAT) Very 27% (WOAT) 50% (WOAT) 7% (WOAT) 5% (WOAT) 1% (WOAT) 0% 10% (WOAT) 100% 4% (WOAT) 0.5% (WOAT) Poor 19% (WOAT) 43% (IC) 19% (WOAT) 5% (WOAT) 2% (WOAT) 0% 12% (WOAT) 100% 6% (WOAT) 1% (WOAT) Less 14% (WAT) 30% (IC) 22% (WAT) 5% (WAT) 2% (WAT) 0% 27% (WAT) 100% 13% (WOAT) 2% (WAT) DS: dry season; IC: intensive crops; : rainy season; WAT: with animal traction; WOAT: without animal traction. prices. This scenario lifts the liquidity constraint and promotes product sales instead of self-consumption. In this scenario, maize, rice, voandzou, and groundnut production increases while millet, sorghum, and cowpea production decreases (Table 3). Thanks to the lifting of the liquidity constraint, increased use of fertilizers and improved seeds is seen, which leads to higher yields. The very farmers incomes increase by 39%, the farmers incomes increase by 50%, and the less farmers incomes increase by 44% (Table 2). Despite these very favorable results, only the less household group escapes poverty, at least in good years. In the third scenario (S3), the objective is to assess the impact of policies focusing on capital access for farmers. Animal traction is chosen because its impact on labor productivity may be considerable. It could be made available for each farmer through a farmers organization. The very and farmers adopt animal traction and improve their incomes by 34% and 49%, respectively (Table 2). The less farmers (who already had animal Fig. 4. Very households income dynamics. Fig. 6. Less households income dynamics. Table 4 Impact of combined policies on farm households incomes. Households policies Very Poor Less Increasing availability of irrigation water by % + reduce marketing costs by 50% Access to animal traction + increase credit availability by 50% All policies combined Fig. 5. Poor households income dynamics. +++ high impact (more than 50%); ++ moderate impact (20% to 50%); + low impact (less than 20%).

7 S. Sanfo, F. Gérard / Agricultural Systems 110 (2012) Table 5 Farm households land allocation to crops for various policy combinations. Crops Policies Households Millet Sorghum Maize Cowpea Voandzou Groundnut Rice Total Rice DS Vegetables DS Variation of the surfaces expressed as a percentage of the total surface area of the agricultural household Initial situation Very Poor 30% (WOAT) 52% (WOAT) 3% (WOAT) 6% (WOAT) 3% (WOAT) 3% (WOAT) 3% (WOAT) 100% 0% 1% (WOAT) Poor 31% (WOAT) 47% (WOAT) 6% (WOAT) 6% (WOAT) 2% (WOAT) 3% (WOAT) 5% (WAT) 100% 5% (WAT) 2% (WOAT) Less Poor 29 (WAT) 42% (WAT) 8% (WAT) 5% (WAT) 3% (WAT) 6% (WAT) 7% (WAT) 100% 7% (WAT) 4% (WAT) Increase the availability of Irrigation water by 50% + reduce marketing costs by 50% Access to animal traction + increase credit availability by 50% All policies combined Very Poor 29% (WOAT) 45% (WOAT) 13% (IC) 4% (IC) 3% (IC) 3% (IC) 5% (IC) 100% 5% (WAT) 1% (WOAT) Poor 31% (WOAT) 27% (WOAT) 26% (IC) 3% (IC) 2% (IC) 3% (IC) 7% (IC) 100% 7% (WAT) 3% (WOAT) Less Poor 24% (WAT) 23% (WAT) 5% (IC) 3% (IC) 3% (IC) 5% (IC) 9% (IC) 100% 9% (WAT) 5% (WAT) Very Poor 23% (WAT) 44% (WAT) 10% (WAT) 14% (WAT) 3% (WAT) 3% (WAT) 3% (WAT) 100% 3% (WAT) 1% (WAT) Poor 30% (WAT) 23% (WAT) 26% (IC 11% (IC) 2% (WAT) 3% (WAT) 5% (IC) 100% 5% (WAT) 3% (WAT) Less Poor 23% (WAT) 34% (WAT) 8% (IC) 19% (IC) 3% (WAT) 6% (WAT) 7% (IC) 100% 7% (WAT) 4% (WAT) Very Poor 27% (IC) 42% (IC) 9% (IC) 6% (IC) 3% (IC) 0% 13% (IC) 100% 5% (IC) 2% (IC) Poor 15% (IC 27% (IC) 23% (IC) 6% (IC) 2% (IC) 0% 27% (IC) 100% 8% (IC) 3% (IC) Less Poor 11% (IC) 24% (IC) 27% (IC) 5% (IC) 3% (IC) 0% 30% (IC) 100% 9% (IC) 5% (IC) DS = dry season; IC = intensive crops; = rainy season; WAT = with animal traction; WOAT = without animal traction. 6 In this version of the model, there is no water stress in the winter season, one limitation of the model. Accordingly, supply responses are probably slightly overstated, and a new version is now being developed to reflect this. traction in the baseline scenario and are faced with a drop in income from renting out the animals) increase cowpea production to maintain their income levels. For all household types, amount of land devoted to cowpeas, which require more labor, increases while millet and sorghum decrease (Table 3). Food consumption is then provided by sorghum purchased on the market. This scenario emphasizes that despite the population pressure in rural areas and large under-employment, labor remains a constraint during specific periods (weeding, harvest). Scenario four (S4) improves credit access, increasing the credit available for the region by 50%. The and the less farmers incomes increase by 5% and 16%, respectively, but there is no effect on very farmers who do not borrow because of the risks (Table 2). The simulation results highlight differences in strategies between farmer groups. The less farmers use credit for intensification in maize and rice, while the farmers use animal traction to increase the amount of land devoted to maize with extensive techniques. This scenario emphasizes the role of the liquidity constraint and risk aversion in adopting technologies that can significantly increase labor productivity. Scenario five (S5) assumes a price stabilization policy that is translated in the model by a halving of expected gross margin variation coefficients for grain crops (rice, maize, millet, sorghum) combined with more stable real prices. It reduces risk and is especially beneficial to the less farmers as their incomes increased by 23%. The very and the farmers incomes increased by 6% and 18% respectively (Table 2). This policy targets the grain market, mainly rice and maize, which are favored at the expense of other crops. While groundnut disappears from the households production plans, maize production doubles for the very and less farmer groups (+127% and +117%, respectively) and increases by 204% for households. Rice production increases by 31% for very households and doubles for the and less households (+117% and +156%, respectively 6 ). The shift from millet and/or sorghum to rice and/or maize according to farmer groups is explained by the price stabilization policy. Indeed, price stabilization reduces risks associated with rice and maize production, and promotes these crops thus increasing labor productivity. Millet and sorghum are preferred in the baseline scenario because they have low input requirements and are more rustic, and therefore are much less risky than maize and rice, which are more profitable. Total maize and rice production for the Plateau Central is twice the level in the baseline scenario. The labor constraint takes on the utmost importance, especially for very households. Off-farm jobs become less profitable with lower off-farm incomes as underlined by Dorward (2003). Off-farm incomes decrease respectively by 3% for very households, 7% for less households, and 2% for households. Lower off-farm incomes are explained by the increase in rice production, which required more labor. The opportunity cost of labor strongly increases in this scenario, passing from US$1.50 per day in the baseline to US$2. One main limitation of this model is that product and factor prices are exogenous. Labor prices should increase in this scenario in the absence of underemployment. Moreover, in each scenario where production increases substantially, output prices should decrease unless strong demand and marketing facilities exist. For this reason, the results on incomes are most likely over evaluated Income dynamics Farmer income dynamic trajectories over the 10-year simulation horizon are presented in Figs None of the scenarios tested, even the most favorable (S2), generate a positive income trend: they shift upward but remain stagnant over time. This shows that while the policies tested improve households situations, they are not enough to promote a virtuous circle of growth allowing households to escape the poverty trap. Income growth remains too low to induce a cumulative process of capital accumulation able to progressively increase labor productivity. This is related to the depth of the initial poverty and the associated weakness of resources under farmers control and farmers investment rates. On average, an active adult must feed 4 people while cultivating around 0.5 hectares with low and extremely fluctuating yields. In such conditions, savings rates are extremely low due to the ratio of consumption expenditures to incomes. The level of savings does not allow households to invest and acquire equipment, a possibility shown by the model. This remains true when the simu-

8 8 S. Sanfo, F. Gérard / Agricultural Systems 110 (2012) 1 9 lation horizon is extended to 25 years. Therefore, combinations of several measures are tested to evaluate whether this constitutes a sufficient lever to end the vicious circle of poverty Scenario combinations The first combination focused on public goods, combining scenarios 1 (hydro-agricultural) and 2 (infrastructures: roads, collection centers, and storage). Very household incomes increase by 49%, those of households by 70%, and finally those of less households by 57% (Table 4). The less households passed over the poverty line, a result already achieved with scenario 2 alone. Maize, rice, and vegetable production increases due to the lifting of the liquidity constraint, which enables increasing amounts of land and intensification. The quantity of inputs used increases by 60%. The and the less households benefit more than the very households (Table 5). The second combination combining scenarios 3 (access to animal traction) and 4 (easy access to agricultural credit) focused on access to capital. Incomes increase respectively by 35%, 53% and 17% for very, and less farmers (Table 4). All the farmers adopt animal traction. The farmers expand their maize area, while the less adopt more intensive technology for maize, rice, and cowpea production. Land allocation among crops remains unchanged for the very farmers (Table 5). All the possible combinations were tested seeking those combinations that would lift all three categories of farmers out of poverty. Results were similar for all combinations: various levels of improvements in incomes and production, but insufficient to trigger a virtuous circle pulling all farmers out of poverty. The results show that the combination of all five policies (S6) is necessary to allow an overall break with poverty (Figs. 4 6). Indeed, this requires the very households incomes to be twice what they were in the baseline scenario. The combination of the five measures results in an income growth of 102% for very farmers, 152% for farmers, and 69% for less farmers. This scenario simultaneously lifts the liquidity, labor, and water constraints in a less risky environment. It allows productivity gains and significant income growth. 4. Conclusions Various agricultural policies aiming at fighting poverty were tested and their impacts on households behaviors and incomes analyzed. In contrast to previous studies on this topic, a farm modeling approach was used. The model takes into account the complexity of farmers decisions in an unfavorable agro-climatic environment and the heterogeneity of initial conditions. This study highlights the difficulties involved in breaking the vicious circle of poverty. Given the depth of the initial poverty, the magnitude of the task is obvious: the est incomes have to double but these households are the most difficult to reach through economic policies because they minimize their relations with markets. Thus, it is necessary to simultaneously provide better access to equipment, irrigation and markets, and decrease risks through price stabilization to succeed in lifting the est group out of poverty. Despite the widespread and deep poverty in the area, it is interesting to note that some policies benefit the est more while others benefit the better endowed households more. The differences in initial factor endowments call for different policy responses. Credit and price stabilization policies alone do not benefit the est, but have significant impacts on the wealthiest. In contrast, public goods policies (development of irrigation or marketing cost reduction) benefit all the households, and policies favoring direct access to agricultural equipment benefit mainly the est. Access to animal traction is especially favorable to cowpea production, while lower marketing costs mainly benefit maize and rice. A substantial drop in output prices in response to a significant growth in supply is not included in the model. This may mean overly optimistic results for incomes. Such effects are more suitably studied with a general equilibrium approach, but GE models do not consider in detail the complexity of farmers decisions as represented here. Moreover, demand growth due to improved incomes will play a role in maintaining remunerative prices, especially as production costs decrease with intensification. Therefore, a virtuous circle of growth in accordance with Timmer (2000) can be expected. Finally, a major concern emerges from this analysis. Due to the weakness of the area per capita and the high number of persons that each active household member must support, no scenario is able to generate a cumulative process of investment able to increase labor productivity and generate cumulative income growth. Incomes increased and sometimes doubled compared to the baseline, but they did not grow over time. An increase in off-farm job opportunities seems necessary to access enough production resources per capita for cumulative income growth. This study shows that substantial poverty reduction is possible but difficult. It requires investments in rural areas. Implementation of the measures described would require funds that may be difficult to mobilize in the current global economic crisis. Our results are in line with the scientific literature showing that investments in the agricultural sector need to be a priority for poverty alleviation and food security improvements (World Bank, 2008; Barett et al., 2010; de Janvry, 2010). 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