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PreprintPDF Available IMPACT OF CLIMATE-SMART AGRICULTURE ON THE INCOME AND FOOD SECURITY OF CASSAVA-PRODUCING HOUSEHOLDS IN THE SAVALOU COMMUNE IN BENIN * June 2024 DOI:10.21203/rs.3.rs-4530709/v1 * License * CC BY 4.0 Authors: Lyré Sarah Aymar Midjangninou Lyré Sarah Aymar Midjangninou * This person is not on ResearchGate, or hasn't claimed this research yet. Alice Bonou * Université Nationale d´Agriculture Sylvain Kpenavoun Chogou Sylvain Kpenavoun Chogou * This person is not on ResearchGate, or hasn't claimed this research yet. Download file PDFRead file Preprints and early-stage research may not have been peer reviewed yet. Download file PDF Read file Download citation Copy link Link copied -------------------------------------------------------------------------------- Read file Download citation Copy link Link copied References (47) Figures (8) ABSTRACT AND FIGURES Climate disruption today represents a threat to the environment and sustainable development. Climate-Smart Agriculture (CSA) is an appropriate adaptation approach to climate change. It allows producers to improve their production and act on their food security, despite the conditions of climate change while limiting the causes arising from agriculture. The objective of this study is to evaluate the impact of CSA, through the use of harvest residues in cassava fields, on the income and food security of cassava producing households in Savalou. The three-stage sampling method made it possible to draw a random sample of 360 cassava producers including 180 users of pigeon pea, mucuna and peanut harvest residues. The average income from cassava production during the last 12 months (until July 2023) is 366,400 FCFA with 387,340 FCFA for beneficiaries compared to 322,060 FCFA for non-beneficiaries. The impact analysis was done with the matching method based on propensity scores. The results of this impact study showed that this adoption contributes to improving income from cassava production by 35,135 FCFA and household food security by 0.10 points, on average per household per year. It would therefore be useful to promote and improve this type of agriculture, in order to ensure sustainable development of the agricultural sector and improve food security. Probability of selection of a non-participating producer by village and weighting … Description of the variables involved in the analysis of the determinants of the adoption of climate-smart agriculture by cassava producing households in Savalou … Distribution of SDAM by producer household group … Proportions of study households by food group … +3 Determinants of the use of harvest residues by producers (Probit Model) … Figures - available via license: Creative Commons Attribution 4.0 International Content may be subject to copyright. Discover the world's research * 25+ million members * 160+ million publication pages * 2.3+ billion citations Join for free Powered By 10 a5675e4b4d6b4f64825f501016413bb0 Share Next Stay Public Full-text 1 Available via license: CC BY 4.0 Content may be subject to copyright. Impact of climate-smart agriculture on the income and food security of cassava-producing households in the Savalou commune in Benin Lyré Sarah Aymar Midjangninou Université d'Abomey-Calavi Alice Bonou Université Nationale d'Agriculture Sylvain Kpenavoun Chogou Université d'Abomey-Calavi Research Article Keywords: climate-smart agriculture, food security, income, cassava producers, Savalou Posted Date: July 11th, 2024 DOI: https://doi.org/10.21203/rs.3.rs-4530709/v1 License: This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License Additional Declarations: No competing interests reported. 1 Impact of climate-smart agriculture on the income and food security of cassava-producing households in the Savalou commune in Benin Sarah L. S Midjangninou 1; Alice Bonou 2 and Sylvain Kpenavoun Chogou 1 1 University of Abomey-Calavi, Abomey-Calavi, Benin, kpenavoun@yahoo.fr 2 Unité de Recherche en Analyse de Politiques Agricoles et de Gestion des Exploitations Agricoles et Entreprises Agro-Industrielles, Université Nationale d’Agriculture, Porto-Novo, Benin, phone number: 00229 95019241; alice.bonou@gmail.com Corresponding author: midjangninoulyre@gmail.com ORCID Alice Bonou http://orcid.org/0000-0001-8754-5086 Abstract Climate disruption today represents a threat to the environment and sustainable development. Climate- Smart Agriculture (CSA) is an appropriate adaptation approach to climate change. It allows producers to improve their production and act on their food security, despite the conditions of climate change while limiting the causes arising from agriculture. The objective of this study is to evaluate the impact of CSA, through the use of harvest residues in cassava fields, on the income and food security of cassava producing households in Savalou. The three-stage sampling method made it possible to draw a random sample of 360 cassava producers including 180 users of pigeon pea, mucuna and peanut harvest residues. The average income from cassava production during the last 12 months (until July 2023) is 366,400 FCFA with 387,340 FCFA for beneficiaries compared to 322,060 FCFA for non-beneficiaries. The impact analysis was done with the matching method based on propensity scores. The results of this impact study showed that this adoption contributes to improving income from cassava production by 35,135 FCFA and household food security by 0.10 points, on average per household per year. It would therefore be useful to promote and improve this type of agriculture, in order to ensure sustainable development of the agricultural sector and improve food security. Keywords: climate-smart agriculture, food security, income, cassava producers, Savalou. Introduction The agricultural sector in Benin is the main source of national economic wealth, contributing 27% to the Gross Domestic Product [1]. However, this sector struggles to meet the food security needs of its growing population, particularly in the face of highly variable weather conditions and climate change [2]. Climate change has enormous repercussions on agriculture and slows down its development [3]. According to the Intergovernmental Panel on Climate Change [4], global warming caused by human activities is a reality, making the 2011-2020 decade the hottest in millennia. Indeed, the impacts of climate change on food security and livelihoods affect millions of smallholder farmers in sub-Saharan Africa [4]. According to the World Food Program (WFP), 2.3 million people, or approximately 23% of Beninese households, have limited food security, and an additional 11% suffer from moderate and severe hunger [2]. The effects of climate change vary depending on the region of the world. In sub-Saharan Africa, climate change is increasing the risk of drought and fires [5]. In Europe, they contribute to the increase in maximum and minimum temperatures and to inequalities [6]. According to the French Development Agency [7], climate change has enormous consequences on food security. Climate change has already impacted atoccess to water and food; health and economic activity [4]. Several priority sectors are affected by climate variations. According to the IPCC report, the impacts of climate change will increase as global warming continues: extreme temperatures, the intensity of precipitation, the severity of droughts, etc. In Benin, as in most developing countries, climate change and its effects constitute a major concern given the impacts it has on the population in general and on farmers in particular. Climate change increasingly affects the rural environment and the agrarian 2 balance, thus causing a decline in the productivity and efficiency of producers [8]. The Collines department where the commune of Savalou is located is one of the four agro-climatic zones (Zone V: Cotton zone of Central Benin) identified as the most exposed to climatic variations [9]. For [10],based on exposure, sensitivity and adaptation capacity indices, the municipalities of Savalou, Tchaourou, Dassa-Zoumé, Glazoué and Copargo are the most vulnerable to climate change. This is one of the reasons which justifies the choice of the municipality of Savalou, which was covered by this study. In order to cope with these effects of climate change, it is therefore necessary to promote an adaptation system that is resilient to the effects of climate change. Climate-smart agriculture or Climate-Smart Agriculture (CSA) is an approach to adequate adaptation to climate change, which aims to transform agricultural systems in order to respond to the problems of food security in the face of the realities of change. climate, while limiting greenhouse gas emissions [11]. This concept was developed in 2010 by the FAO to meet the challenges of climate change [12]. Indeed, AIC is implemented in several African countries, including Benin. According to [13], “climate-smart agriculture presents several advantages which go in the direction of rationalizing the use of industrial resources, orientation towards natural resources and preservation of land quality in a manner to comply with the guiding principles of reducing greenhouse gases while ensuring good productivity”. Several projects and studies have been carried out in relation to the AIC, whether on the national, regional and international level. The Soil Protection and Rehabilitation Project to improve food security in Benin (ProSOL) is one of the projects that has been promoting CSA practices since 2015 in Benin. CSA practices vary depending on sub-sectors of the agricultural sector [14].Among the practices promoted by ProSOL, the management of crop residues, specifically those of pigeon pea, peanut and mucuna, as taught by ProSOL, is adopted by 90% of ProSOL beneficiaries, in the Hills of Benin [2].This high percentage of adoption of this practice made it possible to choose it among the AIC practices, in order to examine whether the use of harvest residues as practiced in Savalou by cassava producers contributes to improving household income and food security. Several adaptation measures have been studied and developed to deal with the effects of climate change on agricultural production. The necessity and urgency of this subject has aroused the interest of much scientific work. In Madagascar, [15] studied the impact of AIC on organic carbon stocks in the soil through two approaches, namely: agroforestry and the use of organic fertilizers. [16] studied the impact of using three climate-smart approaches (adoption of climate-smart peanut varieties, cereal- peanut intercropping, and use of organic fertilizers) on yield of peanut production and food security in three West African countries (Ghana, Mali and Nigeria). Climate-smart agriculture encompasses many more practices than those investigated in these studies. Results of the study by [17] in Senegal on the management of soil organic matter, it appears that harvest residues are generally used much more as food by animals or burned. According to these authors, the incorporation of harvest residues directly into fields is less developed in the literature. This study therefore aims to fill this void. This involves analyzing the AIC approach through the use of pigeon pea, peanut and mucuna residues in cassava fields, harvested during the last twelve (12) months (until July 2023), in order to judge whether this practice in Central Benin, particularly in Savalou, is an effective approach to improve income and guarantee sustainable household food security. To address this topic, the propensity score-based matching method was used. It is a question of testing whether the following hypotheses are valid or not: Hypothesis 1: The use of harvest residues in cassava production positively influences household income from cassava production. Hypothesis 2: The use of harvest residues positively influences the dietary diversity of producing households. Materials and methods Sampling and data collected The data collection phase was preceded by an exploratory field phase. It consisted of exploring the study environment. It was facilitated by qualitative research tools. It began with an interview with the ProSOL monitoring and evaluation manager, in order to assess the feasibility of this study. Using an interview guide, general information on the ProSOL was collected, in order to take into account the 3 needs of the ProSOL and to be fixed on the climate-smart practice to be studied as well as on the population of the study. In order to be able to conduct this impact study, it was decided to consider the beneficiaries of ProSOL in the municipality of Savalou since 2018 (i.e. for 5 years). Consequently, the population of this study consists of all cassava producers who have benefited from ProSOL in the municipality of Savalou since 2018 and all non-beneficiaries of ProSOL. For this study, the beneficiaries of ProSOL are cassava producers who were trained by ProSOL on good practices for sustainable management of degraded lands through the use of harvest residues from pigeon peas, peanuts and mucuna, as taught by ProSOL. Beneficiaries are therefore differentiated from non-beneficiaries by the use of pigeon pea, mucuna, peanut and oil palm residues (low proportion: 2.01%). On the other hand, most non-beneficiaries use corn as the main crop from which harvest residues are used (76%). They also use cassava, soya and cowpea residues in small proportions (Figure 1). Figure 1: Proportion of ProSOL beneficiaries by type of harvest residue used Source: Survey (2023) Indeed, ProSOL covers the Center, North and South of Benin. In Central Benin, the municipalities of ProSOL intervention are those of Savalou and Bantè, in the Department of Collines. Following the interviews carried out with the monitoring and evaluation manager, the sampling frame of cassava producers benefiting from ProSOL was obtained. Sampling was then done after processing the sampling frame. Filtering this sampling frame showed greater coverage in Savalou, with 1,253 ProSOL beneficiaries compared to 587 in Bantè. This is how the commune of Savalou was chosen for this study. An interview guide was developed to hold group interviews with 10 producers per village, to better guide the in-depth survey questionnaire. The survey questionnaire was then developed with CS Pro 7.7 software and tested in the field with 5 producers, before the actual collection. The size of the population of the commune of Savalou which has been using harvest residues in their cassava field since 2018 is 1253 beneficiaries. This population is distributed in six (6) of the fourteen (14) districts of this municipality, and in sixteen (16) villages of these six districts. A three - degrees sampling was carried out in order to select a total of four (04) villages from two (02) districts of the municipality of Savalou where ProSOL intervened in 2018: selection of two districts in all the six districts of the study population; choice of two villages per selected district and finally sampling of cassava producers in the four selected villages. - 1st degree: the probability proportional to size (PPS) random sampling method, measured by the number of cassava producers who used pigeon pea, peanut and mucuna harvest residues in their field cassava during the last 12 months, in each district, was used to select a district i, , in all the districts where ProSOL intervened in 2018. Thus, the districts of Savalou-Attakè and Gobada were selected. The probability of selecting a district i (Table 1) is equal to with the number of districts to select and the number of beneficiaries of district i. Here then . - 2nd degree: the random sampling technique by probability proportional to size, measured by the number of cassava producers who used pigeon pea, peanut and mucuna harvest residues in their cassava field during the last 12 months, in each village, was used to select the villages N'gbèhan and Sokpa, in the district of Savalou -Attakè and the villages Lama and Zadowin, in 0 20 40 60 80 100 Pois d'angole Arachide Mucuna Maïs Manioc Palmier Soja Niebe 100 100 100 68.76 3.57 2.01 0 00 0 0 75.76 8.94 04.11 3.38 Bénéficiaire (%) Non Bénéficiaires (%) 4 the district of Gobada. The size of ProSOL beneficiaries in these 4 villages is 180. The probability of selection of a village j in a district i (table 1) is given by the following formula: with the number of villages to be selected in district i , the number of beneficiaries of village j, for the district of Savalou-Attakè and for the district of Gobada, is the number of beneficiaries of district i. Here, The formula will be applied at the level of each district, as follows: for the district of Savalou-Attakè, and for the district of Gobada, . - 3rd degree: The size of the population being small for the calculation of the size of this sample, the work was done with this population of = 180 beneficiaries. The probability of selecting a beneficiary within a previously selected village k is therefore 1, all the beneficiaries of the selected villages have been surveyed. The overall probability of selection of a beneficiary producer k in a village j of a district i, is the product of the probabilities at each degree of selection, with being the total number of ProSOL beneficiary producers in a village j. We then have If it had been possible to select the same number of ProSOL beneficiaries in each of the 4 villages using the simple random method, then we would have . Under these conditions, we would have: Or and are constants. Consequently, the probability of selecting a producer is a constant. We say that the sample is self-weighting. But, we did not have this possibility to exploit this opportunity offered by this selection process. The probabilities are presented in the following table 1: 5 Table 1: Overall probability of selection of a producer k in a village i of a district j and weighting. Borough Village Weighting Savalou-Attakè N'gbèhan 0.18994 1.02521 1 0.194732642 5.1352459 Savalou-Attakè Sokpa 0.18994 0.50420 1 0.095770152 10.4416667 Gobada Lama 0.57302 0.30083 1 0.172386273 5.80092593 Gobada Zadowin 0.57302 0.19499 1 0.111731844 8.95 Source: Survey (2023) The probabilities obtained are not equal, the sample is therefore not self-weighting. According to [18], weighting is a method of correcting or compensating for the unequal probabilities of selection of each unit in the sample. The parameters to be calculated should therefore be multiplied by the inverse of the overall probability at the level of each unit of the sample in order to extrapolate the results found in the commune of Savalou. The control or comparison group is made up of all cassava producers in the commune of Savalou who do not use harvest residues from pigeon peas, peanuts and mucuna, but who nevertheless resemble the beneficiary group from the point of view of location, type of culture practiced, eating habits, etc. In fact, these producers were selected in the same villages as the ProSOL beneficiaries. Regarding the sample size of our control group ( ), [19] suggest that the sample size of the control group should be equal to that of the comparison group to obtain maximum statistical power. So . In total, our sample size for this study is cassava producers in the commune of Savalou. For the comparison group, a census of cassava producers not beneficiaries of ProSOL who do not use revolt residues directly in cassava fields was carried out in the 4 selected villages, in order to have the list of non-beneficiaries of the ProSOL ProSOL in the villages selected above. The same number of non- beneficiaries as that of beneficiaries was then selected by simple random drawing in each of the 4 villages. The probability of selection of a non-beneficiary producer z in a village i of a district j ( is , with being the total number of producers not benefiting from ProSOL in a village j and the number of beneficiaries of village j. Given that it was decided to select the same number of non-participating producers as participating producers in the same village, the probability of selecting a non-participating producer is different from a participating producer in the same village. The probability of selection of a non-participating producer per village and the weighting is presented in the following table 2. Table 2: Probability of selection of a non-participating producer by village and weighting Borough Village Size Pz Weighting Savalou-Attakè N'gbèhan 108 0.564814815 1.7704918 Savalou-Attakè Sokpa 53 0.566037736 1.76666667 Gobada Lama 72 0.75 1.33333333 Gobada Zadowin 47 0.744680851 1.34285714 Source: Survey (2023) The sum of the weights determined above for each respondent corresponds to the total number of the study population . For normalization of weights, the weights were multiplied by the ratio with n the sample size and N the population size, to obtain the normalized weights. Thus, the weighted averages of the variables of interest were determined. The estimates of the determined means and frequencies were therefore weighted. That is, for example, estimating the weighted mean of any variable Y is determined by the following formula: With the normalized weight of a beneficiary producer k over all respondents, the normalized weight of a non-beneficiary producer z over all respondents, the value of the variable y of a beneficiary k and the value of the variable y of a non-beneficiary producer z. 6 The qualitative and quantitative data collected made it possible to: characterize producers according to their socio-economic and demographic status; to estimate household production and income, to assess the level of knowledge, perception and use of harvest residues for household cassava production as well as the factors determining this adoption and to estimate the level of household food security. Measuring household food access and determining the Household Dietary Diversity Score (SDAM) To measure the level of food access of Savalou cassava producing households, the Household Food Diversity Score (SDAM) method was used. According to the [20], the SDAM is a sufficient indicator to measure food access. Studies have shown that increased dietary diversity goes hand in hand with a better socioeconomic situation and a better level of household food security ( [21]; [22] ). Furthermore, according to the [23] ,dietary diversity scores make it possible to evaluate changes in diet following an intervention (expected improvement). However, dietary diversity represents the number of foods or food groups consumed during a given reference period. It was used over the last 24 hours, as recommended by [23] ,for the collection of data relating to dietary diversity. Indeed, longer reference periods lead to less accurate information because recall is no longer perfect ( [24] ). Thus, the respondent (the person who is responsible for preparing the food or, if this person is not available, another adult who is present and who ate in the household the previous day) described the food consumption of the housework, of the day before. These foods were then classified into the 12 food groups recommended by [24], as presented in Table 3 below. The respondent was told to include food groups consumed by members of the household at home, prepared at home to be consumed by members of the household outside the home (lunch in the fields for example), and purchased in catering outside the home but consumed by the household. On the other hand, foods consumed outside the home and which were not prepared at home were excluded, to avoid overestimating the SDAM. Data for the SDAM indicator were collected by asking the respondent a series of “yes or no” questions. These questions concern the household in general and not a single member of the household. For each food group, a dichotomous variable was created with the modalities: 1=yes: if the household consumed a food from this group and 0=no: if the household did not consume this food. All the binomial variables were then added to create a SDAM whose values are between 0 and 12. The average score was determined in order to carry out the analyses. A categorization of households was then made using household dietary diversity scores. The sample was divided into three income groups (income terciles). Thus, the average SDAM of the richest households served as a guide to set the target level of SDAM. Furthermore, the proportions of households consuming food groups constituting good sources of certain micronutrients (vitamin A or iron, for example) were calculated at the sample level ( [23] ). Alongside the calculation of the average dietary diversity scores, the most consumed food groups according to the SDAM value were determined. This made it possible to know what households with the lowest dietary diversity score eat, and what households with a higher score consume more. These various data were analyzed in STATA software. 7 Table 3: Food groups used in determining SDAM Food group Examples Yes=1 No=0 A = Cereals Corn, rice, wheat, sorghum, millet and any other cereals or foods made from cereals (bread, noodles, porridge or others) + add local foods, such as ugali, nshima, porridge or dough B = White roots and tubers White potatoes, white yams, white cassava or other root foods C = Vegetables Pumpkin, carrot, squash or sweet potato (orange flesh) + other rich vegetables in vitamin A available locally (red pepper, for example) dark green leafy vegetables, including wild varieties + rich leaves in vitamin A available locally, like amaranth leaves and cassava, green cabbage, spinach other vegetables (such as tomato, onion, eggplant) + other vegetables available locally D = Fruit Ripe mango, melon, apricot (fresh or dried), ripe papaya, dried peach and pure juice obtained from these same fruits + other fruits rich in vitamin A available locally E = Meat Beef, pork, lamb, goat, rabbit, game, chicken, duck, other poultry or birds, insects F = Eggs Chicken, duck, guinea fowl or any other eggs G = Fish and seafood Fresh or dried fish, shellfish or crustaceans H = Legumes, nuts and seeds Dried beans, dried peas, lentils, nuts, seeds or foods made from them (hummus or peanut butter, for example) I = Milks and dairy products Milk, cheese, yogurt or other dairy products J = Oils and fats Oils, fats or butter added to food or used for cooking K = Sweets Sugar, honey, soda or fruit juice with added sugar, sugary foods such as chocolate, candy, cookies and cakes L = Spices, condiments and drinks Spices (black pepper, salt), condiments (soy sauce, hot sauce), coffee, tea, alcoholic beverages (beer, wine, spirits) Source: [23] Determination of income Agricultural income is “the difference between the amount of the value of production realized in the exercise of the gross product and what this production overall cost, designated by real farm costs”( [25]). It corresponds to the difference in sales compared to expenses, and excludes the value of consumption ( [26] ) and is generated by agricultural activities during a given accounting period ( [27] ). According to [28], agricultural income is what remains when the farmer has paid all his actual charges. There are two types of income: Gross Income (RB) and Net Income (RN). Gross income is obtained by reducing the added value of wages paid to employees, interest and taxes. On the other hand, net income is determined by subtracting depreciation costs from gross income ( [29] ). The income from cassava that was estimated as part of this study is the gross income. In this case of income, the estimation of family labor and depreciation are not taken into account in the estimation of production costs because it is not a question of measuring profit or net income. As part of this study, the income from cassava production per respondent was determined by subtracting the actual costs from the gross product. The gross product requires the average price and quantities of different transactions. The average price was obtained as follows. The questions were asked about the quantities self- consumed, given, stored, transformed and sold (if sold). The quantities sold were collected by sales transactions carried out as well as the respective sales amounts. The average price was then determined using the following formula: 8 This average price obtained was used to estimate the values of the quantities self-consumed, donated, stored and transformed. Thus, the gross product was obtained by adding the total sales amounts and the estimated amounts of the products donated, stored, self-consumed and transformed. The actual costs include: the total cost of the plants used, the land rent, the total cost of mineral fertilizers, the total cost of the herbicide, the total cost of occasional labor, the cost of transport, the cost communication, by surveyed on the plots which grew cassava over the last 12 months. Method for estimating the impact of harvest residues on household income and food security Impact evaluation is one of a wide range of complementary methods contributing to evidence-based policymaking. The works of ( [30]; [31]; [32]; [33] and [34] ) on the different methods of evaluating the impact of a project or program, made it possible to identify four (04) groups of methods: qualitative methods, quantitative methods (grouping together those experimental, quasi-experimental and non- experimental), mixed methods and economic modeling methods. In the context of our study, a quasi- experimental method is more appropriate. These non-random methods reconstitute, most often after the intervention, a control group as comparable as possible to the group concerned by the intervention ( [32]; [34] ), which is the case in our present study. These evaluations bring together four (04) methods ( [32] and [31] ): the reflexive comparison method or simple before-after comparison, regression on discontinuities established on a continuous eligibility index and an eligibility threshold, the double difference method or the difference in differences and the matching methods. To assess the impact of climate-smart agriculture on the income and food security of cassava-producing households in Savalou, the matching method based on a propensity score was used on each of the factors to be studied. It has been used in several project/program impact evaluation studies; an intervention; of an innovation, on a given factor ( [35] ; [36] and [37] ). This method consists of using statistical techniques to constitute an artificial comparison group by seeking, for each participant, an observation (or a series of observations) from the non-beneficiary group, which presents the most similar observable characteristics possible. A function for calculating the probability of participation in the program is established on the basis of these variables characterizing the individuals. For each individual who participated in the program, we identify the few individuals who did not participate who are comparable to them in terms of their characteristics and their probability of participating in the program: the difference between the results of an individual and the average of its comparison group constitutes the impact of the program on that individual. The impact of the intervention is therefore constituted by the average of all these differentials individually considered ( [38] ). The propensity score is based on two (02) fundamental hypotheses, namely: the hypothesis of independence conditional on observable characteristics (Conditional Independence Assumption: CIA) and the hypothesis of the common support condition (Overlap). The CIA hypothesis means that “selection bias can be controlled if there exists a set of observable variables for which independence of treatment assignment can be verified” ( [39] ). The second hypothesis ensures that the individuals in each analysis group are sufficiently similar for the comparison to be meaningful. Like any other method, the propensity score matching method is not without limitations. [40] highlighted three (03) limitations. First, the method is sensitive to the choice of variables included in the propensity score estimation. Second, propensity scoring focuses on selection by observable characteristics. Thus, unobservable characteristics, such as individuals' preferences or motivations, can still introduce bias into the results. Finally, for the causal inferences made to be robust, the common support must be significant. That is to say, the method is of no interest if all the individuals with a high propensity score are in the treated group and if the individuals with a low propensity score are in the control group. Despite its limitations, the propensity score is an innovative method for better evaluating the causal effects of an intervention ( [40] ). According to [36] the approach includes 5 main steps, namely: Estimate the propensity scores: this propensity score is the probability that a cassava producing household (beneficiary or not) uses harvest residues. It was estimated with a Probit-type regression model based on observable factors. The specification of the Probit model retained is as follows: 9 - , Or is the constancy, the error terms and the respective coefficients of the explanatory variables of the model in the order of their arrangement. A description of the model variables is presented in Table 4 below. is a continuous index (latent variable). It was assumed that for each household there is a limit value above which the use of harvest residues for cassava occurs. Specifically, if , the producer uses harvest residues for cassava if , the producer does not use harvest residues for cassava - represents the probability of use of harvest residues for cassava by the household , with . - is a random variable distributed according to the normal law with zero mean and unit variance. Verification of the distributions of propensity scores: this involved constructing a counterfactual for each participating household from non-participating households. Thus, it must be possible to associate each participating household with at least one non-participating household with similar characteristics (on average) and which would have had the same chance of practicing climate-smart agriculture. Carrying out the actual matching: in order to ensure the robustness of the results, the so-called nearest neighbor method neighbor, the Radius method and the matching method with kernel function kernel matching were used. Indeed, these three methods are the most used in the literature. Verification of the balance of the distribution of the explanatory variables of the Probit model between the population of participants and non-participants (balancing test). This consisted of verifying that the matching using the propensity score resulted in the constitution of a comparison group similar to that of the participants from the point of view of the distribution of the explanatory variables retained to estimate the propensity scores. It was a question of a test of equality of means which consists of comparing the means of the explanatory variables in the two samples (participants and non- participants). Estimation of the impact: this involved estimating the effect of climate-smart agriculture (the use of harvest residues for cassava) on the income and food security of cassava-producing households, in calculating the empirical average of the differences between each participating household and the constructed counterfactual. 10 Table 4: Description of the variables involved in the analysis of the determinants of the adoption of climate-smart agriculture by cassava producing households in Savalou Variable Nature of the variable Description Modality Hoped sign Reference (author and year) Variable explained Residues Dichotomous qualitative variable Do you use harvest residues for cassava production? 0=No, 1=Yes Explanatory variable Sex Dichotomous qualitative variable What is the gender of the head of household? 1=Male 0=Feminine + [41] Objective_Prod Polytomous qualitative variable Main objective of cassava production? 1=Sale 2=Self-consumption 3=Transformation AnneeExpe Discontinuous quantitative variable Number of years of experience (cassava production) + [41] Agri_Advice Dichotomous qualitative variable Have you benefited from agricultural advice over the last 5 years? 0=No, 1=Yes + [36] Organis_Prod Dichotomous qualitative variable Do you belong to a group? 0=No, 1=Yes + [42] Sum_Sup_Parcel Continuous quantitative variable Area sown for the production of cassava harvested over the last 12 months + [35] Access_Land Continuous quantitative variable Mode of access to the land of the exploited land 1=Heirloom, 2=Purchase 3=Rental, 4=Donation, 5=Other + [41] Asso_Cassava Dichotomous qualitative variable Do you combine crops in cassava fields? 0=No, 1=Yes Forma_Itiner Dichotomous qualitative variable Have you received technical training for cassava production? 0=No, 1=Yes + [35] Other_AIC Dichotomous qualitative variable Are you using other climate-smart practices for cassava? 0=No, 1=Yes - [43] Other_Residue Dichotomous qualitative variable Do you use any other crop residues other than pigeon pea, mucuna and peanut in cassava fields? 0=No, 1=Yes - [43] Source: Survey (2023) 11 Results Household Dietary Diversity Score and food consumption The Household Dietary Diversity Score (SDAM) of cassava producers in this study gave an average of 7.47 with an average of 7.51% among beneficiaries and 7.50 among non-beneficiaries (Table 5). Cassava-producing households therefore consumed on average 7 food groups out of the 12 foods in the SDAM, with a minimum of 5 groups and a maximum of 10 food groups. We can already deduce from these results that beneficiary households have better access to food than non-beneficiaries. There is a significant difference in SDAM between the two groups (p=0.00). This study also categorized cassava producer households based on dietary diversity scores (Table 5). Thus, the sample was divided into three groups according to the distribution of income: the first group is made up of all the producers surveyed having obtained a SDAM less than or equal to 6, the second is that of all the producers surveyed having an SDAM equal to 7, group 3 is characterized by all of the producers who have an SDAM strictly greater than 7. These three groups were respectively qualified as all of the households with low, medium and high diversity. Indeed, 41.39% of respondents are found in group 2 with a workforce of 149 producers. We note that the SDAM of group 3 is higher than the average SDAM of the entire sample. Table 5: Distribution of SDAM by producer household group Setting Group 1 ]4.6] Group 2 ]6.7] Group 3 ]7.10] Together P-value Effective 114 149 97 360 - Proportion 31.67 41.39 26.94 100 - SDAM 5.83 (0.04) 7(0) 8.57 (0.67) 7.47 (1.14) 0.00*** General data Beneficiaries Non-beneficiaries Together SDAM Mean (Standard deviation) 7.51 (0.98) 7.50 (0.74) 7.47 (1.14) Minimum 6 5 5 Maximum 10 9 10 *** significant at the 1% level NB: Parentheses represent standard deviations in the case of these quantitative variables Source: Survey (2023) Furthermore, the data in Table 6 show that cereals and spices are the two groups consumed by all respondents (100%). Then follow vegetables (99.12%), roots and tubers (97.96%), oils and fats (93.58%), legumes, nuts and seeds (85.93%). However, food groups of animal origin are consumed at low proportions: fish and seafood (43.39%), milk and dairy products (43.07), meat (5.77%) and egg. (3.40%). Note that fish and seafood and dairy products are mainly consumed by all the beneficiaries of our study. Meat, on the other hand, is only consumed by the beneficiaries. Beneficiaries differ significantly from non-beneficiaries at the 1% threshold in terms of consumption of meat, fruit, fish and seafood, legumes, nuts and seeds and milk and dairy products (p=0.00), at the threshold of 5% by egg consumption (p=0.03). Furthermore, producers eat on average 2.48 meals per day: beneficiaries eat on average 2.48 meals per day and non-beneficiaries 2.35. Most of the time, households consume corn paste with peanut sauce. 12 Table 6: Proportions of study households by food group Food group Frequency (%) P-value Beneficiary No Beneficiary Together Cereals 100 (180) 100 (180) 100 (360) - Roots and tubers 97.85 (177) 100 (180) 97.96 (357) 0.08* Vegetables 99.07 (179) 99.74 (178) 99.12 (357) 0.56 Fruits 37.69 (79) 39.17 (19) 36.61 (98) 0.00*** Meat 6.06 (10) 0 5.77 (10) 0.00*** Egg 3.57 (7) 0.14 (1) 3.40 (8) 0.03** Fish and seafood 44.71 (82) 33.99 (42) 43.39 (124) 0.00*** Legumes, nuts and seeds 86.88 (164) 87.45 (120) 85.93 (284) 0.00*** Milks and dairy products 44.46 (92) 43 (27) 43.07 (119) 0.00*** Oils and fats 93.63 (172) 97.07 (165) 93.58 (337) 0.13 Sweets and honey 36.92 (81) 45.92 (44) 38.58 (125) 0.00*** Spices, condiments and drinks 100 (180) 100 (180) 100 (360) - * Significant at the 10% level, ** significant at the 5% level, *** significant at the 1% level NB: The parentheses represent the numbers in the case of these qualitative variables Source: Survey (2023) Determination of producers’ income from cassava production The results in Table 7 show that the average income per ha from cassava production in Savalou over the last 12 months is 366,400 FCFA with 387,340 FCFA on average for beneficiaries and 322,060 FCFA for non-beneficiaries. There is a significant difference between the two groups for the quantity harvested and the income from cassava respectively at the 1% and 5% thresholds. Indeed, the average quantity of cassava harvested over the last 12 months is 14789.18 kg. The average price of a kg of cassava is 143.94 FCFA. Table 7: Income from cassava production of study producers Features Terms Beneficiaries Non- beneficiaries Together P- value Quantity harvested from cassava (kg) Mean & standard deviation 14917.50 (8934.96) 13172.74 (7032.33) 14789.18 (8166.49) 0.00 *** Minimum 5000 5000 5000 Maximum 64000 51000 64000 Price per kg of cassava (FCFA) Mean & standard deviation 144.32 (29.81) 143.39 (28.28) 143.94 (29.44) 0.00 *** Minimum 51.55 72.17 51.55 Maximum 180.42 180.42 180.42 Cassava income per ha over the last 12 months (FCFA) Mean & standard deviation 387340 (937208.34) 322060 (747771.59) 366400 (881603.5) 0.02 ** Minimum 103466 98872.4 98872.4 Maximum 696547 524262 696547 ** significant at the 5% level; *** significant at the 1% level NB: Parentheses represent the numbers in the case of qualitative variables and the standard deviations in the case of quantitative variables. Source: Survey (2023) 13 Impact of climate-smart agriculture on the income and food security of cassava producing households in the study Determinants of the use of crop residues by producers The results of the Probit model used to predict the probability of utilization of pigeon pea, groundnut and mucuna crop residues by cassava producers were presented in the following Table 8. From these results, it appears that there is at least one coefficient statistically different from zero (Prob = 0.00) at the 5% threshold. The model is therefore highly significant. The model fit measure is good ( Pseudo R² =0.65). In reality, only the variables membership of a producer organization and adoption of other climate-smart practices are not significant. The use of these harvest residues is positively influenced by: the production objective, training in the technical process of cassava, access to agricultural advice and the number of years of experience in cassava production. However, the adoption of this practice is negatively influenced by the gender of the head of household. Analysis of the gender of household heads shows that households headed by women use crop residues more than those headed by men. Women are therefore more likely to adopt this climate-smart practice than men. Table 8: Determinants of the use of harvest residues by producers (Probit Model) Explanatory variable Coefficient Error standard z P > |z| Cassava production objective 0.17** 0.14 -2.60 0.01 Training on the cassava technical route 0.75*** 430.39 5.43 0.00 Access to agricultural advice 0.67*** 18.70 5.82 0.00 Member of a producer organization 0.07 0.39 -0.82 0.41 Sex -0.08*** 0.10 -3.13 0.08 Number of years of experience in cassava production 0.09* 0.02 1.73 0.07 Other Smart Strategies -0.02 0.22 -1.54 0.12 Other crop residues used -0.08 0.26 -1.42 0.15 Constant 0.04* 0.10 -1.69 0.09 Nickname R² 0.65 LR chi2 (6) 322.79 Prob > chi2 0.00 Log Likelihood -86.87 Number of observations 360 *Significant at the 10% threshold; ** significant at the 5% level; *** significant at the 1% level Source: Survey (2023) Impact of climate-smart agriculture on the income of cassava producing households Figure 2 below shows that the propensity score distributions overlapped and common support was very broad. This means that each beneficiary has the chance to find at least one counterfactual with the closest possible propensity score. 14 Figure 2: Checking the distribution of propensity scores and common support for household income Source: Survey (2023) From the results of the balancing test in Table 9, we note that the propensity scores balanced the distribution of the explanatory variables retained, which affected the probability of use of harvest residues. Table 9: Result of the balance test for household income Variables Treaty Comparison T-Stat P>T Production target 2.68 2.71 -0.37 0.71 Experience in cassava production 27.91 27.08 0.75 0.45 Gender of COO 0.57 0.52 0.95 0.34 Total assets 2.54 2.34 1.72 0.09 Price per kg of cassava 141.80 142.75 -0.29 0.77 Sum of ha plots used for cassava production 1.27 1.23 0.44 0.66 Age of farm manager 44.53 44.01 0.50 0.62 Herbicide 0.54 0.53 0.32 0.75 Mode of access to land 1.14 1.15 -0.22 0.83 Source: Survey (2023) Table 10 presents the results of the estimation of the effect of the use of harvest residues on the income of cassava producers. These results confirm the impact of the use of harvest residues on the cassava income of households in Savalou. It appears from this table that the annual cassava income of beneficiaries was improved by 35,135 FCFA on average. This effect is significant at the 1% level with the four matching methods, which demonstrates the robustness of these results. Table 10: Impact of the use of harvest residues on the income of cassava producing households Model type Causal effect T-stat P-Value Paired: nearest neighbor 58533.87*** 6.75 0.00 Matching: kernel matching 47601.18*** 8.28 0.00 Matching: Radius matching 35135.30*** 8.04 0.00 *** significant at 1% Source: Survey (2023) 15 Impact of climate-smart agriculture on the food security of cassava producing households Figure 3 below shows that the propensity score distributions overlapped and common support was very broad, such that each beneficiary found at least one counterfactual with a propensity score as close as possible. Figure 3: Verification of the distribution of propensity scores and common support for household dietary diversity Source: Survey (2023) From the results of the balancing test in Table 11, we see that the propensity scores balanced the distribution of the explanatory variables retained, which affected the probability of use of harvest residues. Table 11: Result of the balance test for household food security Variables Treaty Comparison T-Stat P>T Production target 2.68 2.72 -0.52 0.60 Experience in cassava production 27.91 28.76 -0.80 0.42 Gender of COO 0.57 0.52 0.95 0.34 Household size 4.26 4.27 -0.06 0.95 Carrying out an extra-agricultural activity 0.42 0.4 0.53 0.59 Age of farm manager 44.53 46.02 -1.47 0.14 Herbicide 0.54 0.50 0.74 0.46 Mode of access to land 1.14 1.14 0.11 0.91 Source: Survey (2023) Table 12 below presents the results of the estimation of the effect of the use of harvest residues on the dietary diversity of cassava producers. It appears from this table that dietary diversity was improved by 0.10 points on average at the level of beneficiaries. This effect is significant at the 1% level. These results confirm the impact of the use of harvest residues on the cassava income of households in Savalou. Table 12: Impact of the use of harvest residues on the food security of cassava producing households Model type Causal effect T-stat P-value Paired: nearest neighbor 0.12*** 13.68 0.00 Matching: kernel matching 0.10*** 15.45 0.00 Matching: Radius matching 0.10*** 18.96 0.00 ***significant at 1% Source: Survey (2023) 16 Discussion From the results of this study, it appears that the households of cassava producers in the study have an average dietary diversity score of 7.47. This average score is similar to that found by [44] in Burkina Faso (7.3). In a study carried out in Mali on the factors associated with low household consumption and dietary diversity scores, an average score of 7.23 was found ( [45] ). On the other hand, the average score of our study is higher than the minimum standard (4) set by the [46] . In addition, the dietary diversity of beneficiaries is better than that of non-beneficiaries (with an average of 7.51% among beneficiaries compared to 7.50 among non-beneficiaries). This is linked to the fact that beneficiary households consume, in addition to other foods, foods of animal origin rich in Vitamin A and Iron such as meat, eggs and fish and seafood. According to [20] ,the consumption of foods rich in protein is essential and plays an essential role in the development and functioning of the body. However, these food groups are consumed in very small quantities by these households (respectively 6.06% of beneficiaries versus 0% of non-beneficiaries, 3.57 versus 0.14 and 44.71 versus 33.99). This ability to obtain these food groups reflects the purchasing power of users of crop residues. Indeed, food purchased on the market promotes the diversification of the diet of agricultural households, even in contexts of subsistence-oriented production ( [47] ). On the other hand, cereals and spices are naturally the food groups most consumed by all respondents. This result justifies the statistics from the [1] ,which places cereals as the first group consumed the most on the national territory. Rural households actually depend on cereals for their food. Furthermore, the purchasing power of the beneficiaries can be explained by the fact that this group of producers have a better income and have the necessary information in relation to food consumption and the usefulness of inserting these food groups into the consumption. The analysis of the results on income shows that the household dietary diversity score increases with household income from cassava production. Improved income is therefore accompanied by an improvement in the composition of the household diet. The average income from cassava production in Savalou over the last 12 months is 366,400 FCFA with 387,340 FCFA on average for beneficiaries and 322,060 FCFA for non- beneficiaries, with a significant difference of 5% between the two groups. According to the results of the Harmonized Survey of Household Living Conditions (EHCVM), this average income is higher than the global annual poverty line estimated at 287187 FCFA in 2022 ( [48] ).The practice of this climate-smart technology has thus contributed to improving the income of beneficiary producers. Part of this income would therefore have been used to diversify their household food consumption. The results of the matching method based on propensity scores confirmed that climate-smart agriculture made it possible to significantly improve (at the 1% threshold) household dietary diversity by 0.10 points and the cassava income of 35,135 FCFA, on average. Just like [41] thinks CSA practices help reduce the risk of poor harvests and thus contribute to increasing productivity and agricultural income. Climate- smart agriculture thus contributes to improving the income of producers. This result is related to the first objective of promoting this agriculture, identified by the [49] : increasing agricultural productivity and income. It is therefore up to agricultural policies to ensure its sustainability over time, because the phenomenon of climate change is dynamic. Furthermore, the improvement in dietary diversity by 0.10 points corroborates Martin 's results [16] who, through the Food Consumption Score, found that the adoption of CSA is beneficial for household food security. In short, the results of this study confirm those of [50] according to which there is a positive relationship between CSA, income and food security. Agricultural policies should then focus on promoting and improving climate-smart agriculture, because, as [51] , it is a unique opportunity to simultaneously achieve the objectives of food security, adaptation to climate change and greenhouse gas mitigation. 17 Conclusion This study aimed to assess the impact of climate-smart agriculture on the income and food security of cassava-producing households in the commune of Savalou. This impact was measured through the use of harvest residues from pigeon peas, peanuts and mucuna, in the production of cassava in the households surveyed. By adopting climate-smart agriculture through the use of these harvest residues, cassava producers improved their income by 35,135 FCFA and their food security by 0.10 points on average. Although this impact is low, climate-smart agriculture is one of the viable and sustainable solutions to improve the income and food security of rural households. It would therefore be useful to raise awareness among producers and train them on the adoption of this innovative agriculture, in order to improve the agricultural sector, despite the realities of climate change. Also, raising awareness about the importance of having a balanced diet is also useful. They would aim to raise awareness among rural households about the inclusion of foods rich in proteins and fruits and vegetables, important sources of vitamins and micronutrients essential for the body, in their daily diet. However, this study only looked at climate-smart practice. It would therefore be preferable for future studies to also analyze the impact of other practices, or study the prospects of cumulating certain climate-smart practices in order to maximize its impact, because climate change is a reality and constitutes a real obstacle. agricultural development in particular and food security in general. Also, one of the limitations of this study is that we were not able to counter the contamination effect of the use of the residues concerned, on the non-beneficiaries. Statement for "Consent to participate" Verbal informed consent was obtained prior to the interview. Ethics approval Not applicable Data availability statement Data will be available upon request Disclosure statement No potential conflict of interest was reported by the author(s) Author contributions statement Conception and design S.M., A.B. and S.K.; Collection, analysis and interpretation of the data S.M.; Drafting of the paper S.M. Revising critically for intellectual content S.K and A.B. All authors agree to be accountable for all aspects of the work. Funding S.M. 18 Bibliographic references [1] MAEP/DSA, (2022). Evolution of agricultural production in Benin. [2] FAO, (2022). Figures and facts about food insecurity and malnutrition around the world [3] Agossou. S. M. D. 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[51] Ouédraogo, M., Houessionon , P., Zougmoréet , R.B. and Tetteh Partey , S. (2019). «Adoption of climate-smart agricultural technologies and practices: actual -19,». 21 CITATIONS (0) REFERENCES (47) ResearchGate has not been able to resolve any citations for this publication. “Climate-smart agriculture and food security: Cross-country evidence from West Africa” Article Full-text available * Jul 2023 * GLOBAL ENVIRON CHANG * Martin Paul Jr Tabe-Ojong * Ghislain Aihounton * Jourdain Lokossou View Uptake of Climate-Smart Agricultural Technologies and Practices: Actual and Potential Adoption Rates in the Climate-Smart Village Site of Mali Article Full-text available * Aug 2019 * Mathieu Ouedraogo * Prosper Houessionon * Robert Zougmore * Samuel Tetteh Partey Understanding the level of adoption of Climate-Smart Agriculture (CSA) technologies and practices and its drivers is needed to spur large-scale uptake of CSA in West Africa. This paper used the Average Treatment Effect framework to derive consistent parametric estimators of the potential adoption rates of eight CSA technologies and practices in the Climate-Smart Village (CSV) site of Mali. A total of 300 household heads were randomly selected within the CSV site for data collection. Results showed significant differences in the observed and potential adoption rates of the CSA technologies and practices (drought tolerant crop varieties, micro-dosing, organic manure, intercropping, contour farming, farmer managed natural regeneration, agroforestry and climate information service). The most adopted technology was the organic manure (89%) while the least adopted was the intercropping (21%). The observed adoption rate varied from 39% to 77% according to the CSA options while the potential adoption rates of the technologies and practices ranged from 55% to 81%. This implies an adoption gap of 2% to 16% due to the incomplete diffusion (lack of awareness) of CSA technologies and practices which must be addressed by carrying out more actions to disseminate these technologies in the CSV. Results showed that education, number of workers in the household, access to subsidies, and training have a positive effect on the adoption of most of the CSA technologies and practices. The adoption of drought tolerant varieties and micro-dosing are positively correlated with access to subsidies and training. The study suggests that efforts should be focused concomitantly on the diffusion of CSA options as well as the lifting of their adoption barriers. View Show abstract Farm production, market access and dietary diversity in Malawi Article Full-text available * Sep 2016 * PUBLIC HEALTH NUTR * Stefan Lévy * Menale Kassie Berresaw * Matin Qaim Objective: The association between farm production diversity and dietary diversity in rural smallholder households was recently analysed. Most existing studies build on household-level dietary diversity indicators calculated from 7d food consumption recalls. Herein, this association is revisited with individual-level 24 h recall data. The robustness of the results is tested by comparing household- and individual-level estimates. The role of other factors that may influence dietary diversity, such as market access and agricultural technology, is also analysed. Design: A survey of smallholder farm households was carried out in Malawi in 2014. Dietary diversity scores are calculated from 24 h recall data. Production diversity scores are calculated from farm production data covering a period of 12 months. Individual- and household-level regression models are developed and estimated. Setting: Data were collected in sixteen districts of central and southern Malawi. Subjects: Smallholder farm households (n 408), young children (n 519) and mothers (n 408). Results: Farm production diversity is positively associated with dietary diversity. However, the estimated effects are small. Access to markets for buying food and selling farm produce and use of chemical fertilizers are shown to be more important for dietary diversity than diverse farm production. Results with household- and individual-level dietary data are very similar. Conclusions: Further increasing production diversity may not be the most effective strategy to improve diets in smallholder farm households. Improving access to markets, productivity-enhancing inputs and technologies seems to be more promising. View Show abstract Managing the agricultural calendar as coping mechanism to climate variability: A case study of maize farming in northern Benin, West Africa Article Full-text available * Dec 2014 * Rosaine N. Yegbemey * Kabir Dr. Humayun * Oyémonbadé Hervé Rodrigue Awoye * Armand A. Paraïso Nowadays climate variability and change are amongst the most important threats to sustainable development, with potentially severe consequences on agriculture in developing countries. Among many available coping mechanisms, farmers adjust some of their farming practices. This article aims at exploring observed changes in the agricultural calendar as a response to climate variability in northern Benin. Interviews with local experts (agricultural extension officers and local leaders such as heads of farmer and village organisations) and group discussions with farmers were organised. A household survey was also conducted on 336 maize producers to highlight the factors affecting decisions to adjust the agricultural calendar as a coping mechanism against climate variability. As a general trend, the duration of the cropping season in northern Benin is getting longer with slight differences among and within agro-ecological zones, implying a higher risk of operating under time-inefficient conditions. Farmers receive very limited support from agricultural extension services and therefore design their agricultural calendar on the basis of personal experience. Socio-economic characteristics, maize farming characteristics as well as farm location determine the decision to adjust the agricultural calendar. Consequently, providing farmers with climate related information could ensure a rational and time-efficient management of the agricultural calendar. Moreover, research and extension institutions should help in establishing and popularising clear agricultural calendars while taking into account the driving forces of behaviours towards the adjustment of farming practices as a climate variability response. View Show abstract Does Adaptation to Climate Change Provide Food Security? A Micro-Perspective from Ethiopia Article Full-text available * Jul 2011 * AM J AGR ECON * Salvatore Di Falco * Marcella Veronesi * Mahmud Mohammed Yesuf We examine the driving forces behind farm households’ decisions to adapt to climate change, and the impact of adaptation on farm households’ food productivity. We estimate a simultaneous equations model with endogenous switching to account for the heterogeneity in the decision to adapt or not, and for unobservable characteristics of farmers and their farm. Access to credit, extension and information are found to be the main drivers behind adaptation. We find that adaptation increases food productivity, that the farm households that did not adapt would benefit the most from adaptation. View Show abstract The Economics and Econometrics of Active Labor Market Programs Article Full-text available * Dec 1999 * Handbook Labor Econ * James J Heckman * Robert J. Lalonde * Jeffrey A. Smith Policy makers view public sector-sponsored employment and training programs and other active labor market policies as tools for integrating the unemployed and economically disadvantaged into the work force. Few public sector programs have received such intensive scrutiny, and been subjected to so many different evaluation strategies. This chapter examines the impacts of active labor market policies, such as job training, job search assistance, and job subsidies, and the methods used to evaluate their effectiveness. Previous evaluations of policies in OECD countries indicate that these programs usually have at best a modest impact on participants' labor market prospects. But at the same time, they also indicate that there is considerable heterogeneity in the impact of these programs. For some groups, a compelling case can be made that these policies generate high rates of return, while for other groups these policies have had no impact and may have been harmful. Our discussion of the methods used to evaluate these policies has more general interest. We believe that the same issues arise generally in the social sciences and are no easier to address elsewhere. As a result, a major focus of this chapter is on the methodological lessons learned from evaluating these programs. One of the most important of these lessons is that there is no inherent method of choice for conducting program evaluations. The choice between experimental and non-experimental methods or among alternative econometric estimators should be guided by the underlying economic models, the available data, and the questions being addressed. Too much emphasis has been placed on formulating alternative econometric methods for correcting for selection bias and too little given to the quality of the underlying data. Although it is expensive, obtaining better data is the only way to solve the evaluation problem in a convincing way. However, better data are not synonymous with social experiments. View Show abstract Food variety, socioeconomic status and nutritional status in urban and rural areas in Koutiala (Mali) Article Full-text available * Apr 2000 * Anne Hatløy * Jesper Hallund * Modibo Diarra * Arne Oshaug The purpose of this study was to analyse the associations between the food variety score (FVS), dietary diversity score (DDS) and nutritional status of children, and to assess the associations between FVS, DDS and socioeconomic status (SES) on a household level. The study also assessed urban and rural differences in FVS and DDS. Cross-sectional studies in 1994/95, including a simplified food frequency questionnaire on food items used in the household the previous day. A socioeconomic score was generated, based on possessions in the households. Weight and height were measured for all children aged 6-59 months in the households, and anthropometric indices were generated. Three hundred and twenty-nine urban and 488 rural households with 526 urban and 1789 rural children aged 6-59 months in Koutiala County, Sikasso Region, Mali. Children from urban households with a low FVS or DDS had a doubled risk (OR>2) for being stunted and underweight. Those relations were not found in the rural area. There was an association between SES and both FVS and DDS on the household level in both areas. The FVS and DDS in urban households with the lowest SES were higher than the FVS and DDS among the rural households with the highest SES. Food variety and dietary diversity seem to be associated with nutritional status (weight/age and height/age) of children in heterogeneous communities, as our data from urban areas showed. In rural areas, however, this association could not be shown. Socioeconomic factors seem to be important determinants for FVS and DDS both in urban and rural areas. FVS and DDS are useful variables in assessing the nutritional situation of households, particular in urban areas. View Show abstract Impact Evaluation in Practice Book * Sep 2016 * Paul Gertler * Sebastian Martinez * Laura B. Rawlings * Christel M.J. Vermeersch The second edition of the Impact Evaluation in Practice handbook is a comprehensive and accessible introduction to impact evaluation for policy makers and development practitioners. First published in 2011, it has been used widely across the development and academic communities. The book incorporates real-world examples to present practical guidelines for designing and implementing impact evaluations. Readers will gain an understanding of impact evaluations and the best ways to use them to design evidence-based policies and programs. The updated version covers the newest techniques for evaluating programs and includes state-of-the-art implementation advice, as well as an expanded set of examples and case studies that draw on recent development challenges. It also includes new material on research ethics and partnerships to conduct impact evaluation. The handbook is divided into four sections: Part One discusses what to evaluate and why; Part Two presents the main impact evaluation methods; Part Three addresses how to manage impact evaluations; Part Four reviews impact evaluation sampling and data collection. Case studies illustrate different applications of impact evaluations. The book links to complementary instructional material available online, including an applied case as well as questions and answers. The updated second edition will be a valuable resource for the international development community, universities, and policy makers looking to build better evidence around what works in development. View Show abstract Household Dietary diversity Score (HDDS) for Measurement of Household food Access: Indicator Guide. Food and Nutrition Technical Assistance Article * Jan 2005 * A. Swindale * P. Bilinsky View Figures and facts about food insecurity and malnutrition around the world * Jan 2022 FAO, (2022). Figures and facts about food insecurity and malnutrition around the world Show more RECOMMENDED PUBLICATIONS Discover more Technical Report Full-text available CLIMATE-SMART AGRICULTURE IN BENIN: NEED ASSESSMENT REPORT December 2022 * Alice Bonou * Achille E Assogbadjo * Avakoudjo Hospice Gérard Gracias * [...] * F.J. Chadare View full-text Conference Paper Full-text available IMPACT OF FLOOD ON THE LIVELIHOOD OF FARMERS IN SEMI-ARID ZONE OF BENIN REPUBLIC August 2014 * Alice Bonou Fluvial flooding has become a widely distributed and devastating natural disaster that has caused significant damages both economically and socially. Since 2007, Benin has experienced frequent floods. In semi-arid zone of Benin republic, the last flooding events occurred in August 2012 and 2013, when many farmers lost most of their crops. Yet, no studies were conducted to show the effect of these ... [Show full abstract] frequent flooding events on the livelihood of the farmers. To fill in this gap, a survey is conducted in Benin, a small country south of the Sahel. Two townships are chosen: Malanville and Karimama because of their location in downstream. In this region, our focus is the villages near a river. A total of 19 villages was chosen with 12 farmers interviewed in each village, leading to a total of 228 farmers who are interviewed. Then the sampling rate is 8.79%. The questionnaire includes open and closed questions. The econometric framework adopted is the Rubin Causal Model that has emerged as the standard approach for evaluating change effect using an observational data. Then two methods are used for comparison purpose: Propensity Score Matching Method and Instrumental Methods to measure the impact of the 2012 flood on farmers’ revenue in semi-arid zone of Benin republic. Results show firstly, that 86.4% (197 farmers) of farmers surveyed had their farms damaged by flooding in 2012. In this subset, the average flooded size of farm per household after 2012 flooding is about 2.4 hectares. Overall the econometric model indicates that flooding has a negative and significant impact on expected income from the harvest per hectare, about an average USD80 per farmer (Propensity Score Mtethod) and USD159 (Instrumental Variable Method). To cope with this bad situation, farmers develop many adaptation and prevention strategies as the shifting of the cultural calendar and the diversification of activities. View full-text Conference Paper Full-text available AGRICULTURAL TECHNOLOGY ADOPTION AND RICE VARIETAL DIVERSITY: A LOCAL AVERAGE TREATMENT EFFECT (LATE... September 2013 * Alice Bonou The aim of this study was to assess the impact of adoption of new high-yielding varieties (NERICA) of rice on its varietal diversity in Benin. The database was from Impact Assessment unit of AfricaRice and concerns 304 producers of rice. Overall the study covered twenty-four villages over three districts: Dassa-Zounmè, Glazoué and Savalou. Data analysis was carried out using the econometric ... [Show full abstract] approach based on the Local Average Effect of Treatment (LATE) framework. Overall, estimation of impact showed that at village level the indexes of in-situ (on farm) conservation of varietal diversity of rice are the same in NERICA and Non-NERICA villages. Moreover, at farmer level, the average impact of NERICA adoption on number of modern rice varieties of the sub-population of NERICA potential adopters is 0.8. NERICA’s rice varieties had positively impacted the in situ conservation of varietal diversity. Our findings indicated that it is worth extending diffusion of NERICA varieties in Benin. View full-text Conference Paper POVERTY AND HAPPINESS ASSESSMENT UNDER FLOODING EVENT IN BENIN September 2014 * Alice Bonou Read more Last Updated: 14 Jul 2024 Discover the world's research Join ResearchGate to find the people and research you need to help your work. Join for free ResearchGate iOS App Get it from the App Store now. Install Keep up with your stats and more Access scientific knowledge from anywhere or Discover by subject area * Recruit researchers * Join for free * Login Email Tip: Most researchers use their institutional email address as their ResearchGate login PasswordForgot password? Keep me logged in Log in or Continue with Google Welcome back! Please log in. Email · Hint Tip: Most researchers use their institutional email address as their ResearchGate login PasswordForgot password? Keep me logged in Log in or Continue with Google No account? 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