Assessing the Impact of a Farmer Field Schools Project in East Africa

Transcription

1 Assessing the Impact of a Farmer Field Schools Project in East Africa Kristin Davis International Food Policy Research Institute PO Box 5689 Addis Ababa, Ethiopia Tel ; Fax k.davis@cgiar.org Ephraim Nkonya International Food Policy Research Institute Daniel Ayalew International Food Policy Research Institute Edward Kato International Food Policy Research Institute Abstract Farmer field schools (FFS) are a popular education and extension approach worldwide. However, there is limited or conflicting evidence as to their effect on productivity, poverty, and empowerment, especially in East Africa. This study attempts to provide rigorous evidence to policymakers and other stakeholders on the effectiveness of FFS in reducing poverty and empowering farmers. The researchers examined a FFS project in Kenya, Tanzania, and Uganda. Using a variety of methods, including a household survey, the authors describe participation in FFS and its effects on productivity, empowerment, and poverty. They find that households with younger heads and those who were also members of credit and savings organizations tended to participate in field schools. Female membership was 50%. Reasons for not joining FFS included lack of time and information. Adoption was significantly higher among the FFS farmers for nearly all of the major technologies, with the major technologies being improved crop varieties, soil fertility management, pest control, and livestock management. FFS had a significantly larger impact on crop productivity in Kenya than in Tanzania and Uganda; however, in the latter countries, women farmers productivity was significantly higher than men s. Regarding poverty, there were differences between the three countries and also between FFS and non-ffs in various poverty indicators. While qualitative data suggest that FFS contribute to empowerment of individual farmers, these differences were not very apparent in the survey results, and more refined means should be used to show such evidence in future. Key Words: Farmer field schools, impact, East Africa 136

2 Introduction Agricultural education and extension are a critical means of addressing rural poverty, since these institutions have the mandate to transfer technology, support learning, assist farmers in problem-solving, and enable farmers to become more actively embedded in the agricultural knowledge and information system (Christoplos & Kidd, 2000, p. 11). With almost one billion small-scale farmers worldwide, extension is urgently seeking for the best ways to support these farmers in terms of information, technology, advice, and empowerment. One highly successful extension and education approach worldwide is the farmer field schools (FFS) approach, now in place in at least 78 countries (Braun, Jiggins, Röling, van den Berg, & Snijders, 2006). Farmer field schools are traditionally an adult education approach: a method to assist farmers to learn in a nonformal setting within their own environment. FFS are schools without walls where groups of farmers meet weekly with facilitators. They are a participatory method of learning, technology development, and dissemination (FAO, 2001) based on adult learning principles such as experiential learning (Davis & Place, 2003). The approach is an interactive and practical method of training, and empowers farmers to be their own technical experts on major aspects of localized farming systems. Farmers are facilitated to use critical thinking to conduct their own research, diagnose and test problems, and come up with solutions. The FFS groups of about 25 farmers typically meet regularly (weekly or bi-weekly) during a crop or animal cycle for a half-day of discussion and field work. Started in Indonesia in 1989, FFS have expanded throughout many parts of Sub-Saharan Africa. In Kenya alone, there are over 1,000 FFS with 30,000 farmer graduates (FAO-KARI- ILRI, 2003). FFS started in East Africa with the Food and Agriculture Organization s Special Programme for Food Security (SPFS), which ended in Following the SPFS project, the Food and Agriculture Organization (FAO) Global Integrated Pest Management (IPM) Facility then started the East African Sub-regional Project for Farmer Field Schools in 1999 in Kenya, Tanzania, and Uganda in eight pilot districts (Kimani & Mafa, n.d.). The overall topic was integrated production and pest management (IPPM), and was supported by the International Fund for Agricultural Development (IFAD). This ended in 2002, and a second expansion phase of the project started in October 2005 and ran for three years. The goal of the expansion phase of the project was to enhance the livelihoods of farmers in Eastern and Southern Africa through the development and expansion of a low-cost, sustainable, and broad-based model for farmer education and empowerment. The IFAD-FAO FFS project has been working in Busia, Bungoma, and Kakamega Districts in Kenya; Bukoba, Muleba, and Missenyi Districts in Tanzania, and Busia, Kabermaido, and Soroti Districts in Uganda. These areas were chosen based on (a) relevance of crops and farming systems; (b) the need to develop interface between smallholders and extension activities; (c) testing FFS under the new decentralized district structures; and (d) potential linkage with ongoing IFAD extension activities (IFAD, 1998). While many donors, governments, and nongovernmental organizations (NGOs) are enthusiastically promoting farmer field schools in Sub-Saharan Africa, there are growing concerns and interest among stakeholders and donors regarding applicability, targeting, costeffectiveness, and impact of the FFS approach. There have been relatively few efforts to document FFS impact in a systematic manner, and therefore extension actors often find themselves with many questions in relation to when, where, and how FFS should be applied. Although a popular method the new orthodoxy, according to Leeuwis, Röling, and Bruin (1998) much of what is written on FFS in East Africa deals mainly with the 137

3 methodology or case studies. Thus it is as yet unclear what the long-term impacts of FFS are. Much is still unknown about the approach and the issues pertinent to extension, such as poverty reduction, sustainability, participation, and financing. Purpose and Objectives In order to document the IFAD-FAO FFS experience, as well as to provide robust evidence for policymakers, donors, farmers, and implementation actors on if and how FFS can contribute to poverty alleviation, productivity, and local empowerment, the International Food Policy Research Institute (IFPRI) and IFAD engaged in a rigorous evaluation of the project. The purpose of the study was to improve practice in FFS and extension education to lead to improved livelihoods of farmers in East Africa, and to provide robust evidence on the impact of FFS. Specific objectives of the study that will be covered in this paper are found below. (1) Determine whether the poor, women, and other marginalized groups participate equally in FFS. (2) Determine the effectiveness of FFS in achieving outcomes regarding poverty, empowerment, gender, and productivity. (3) Determine the impacts of household capital endowment level social characteristics on FFS (including human, capital, capital, and financial capital). Methods and Data Sources This study used a variety of methods and approaches to assess a FFS project in Kenya, Tanzania, and Uganda. This paper presents some early results. The overall design employed was a longitudinal impact evaluation. Both qualitative and quantitative methods were used to collect and analyze data. Econometric analysis was conducted using double difference (DD) and propensity score-matching (PSM) approaches, explained below. Qualitative methods included analysis of secondary data, semi-structured interviews with key informants, and document analysis. An additional method of gaining information and promoting participatory dialog among stakeholders was through workshop, , and internet discussion forums. An inception workshop for stakeholders was held in early 2008, and a final workshop in early 2009 to present initial findings and to get feedback from stakeholders. A survey instrument was used to collect data at the household level. The instrument was a closed-ended questionnaire, modified from a baseline survey instrument administered by the project in 2006 (modifications included more data on production and income). The instrument was field tested during a three-day training exercise with the enumerators and local researchers in each of the three countries. Survey respondents were the same as those from the original baseline survey (see Alokit- Olaunah, 2006; Nkuba, Thomas, & Duveskog, 2007; and Odendo, Duveskog, & Khisa, 2006). The sampling procedure for that survey, as described in Nkuba et al. (2007), was a two-stage random sampling technique. A list of all newly-registered FFS (as of 2006) in the IFAD-FAO FFS project districts made up the sampling frame. A total of 20 FFS per country were chosen, the number being proportional to the number of FFS in each district and diversity of agroecological zones. Next, lists of households were used to randomly select household members, the number of members being interviewed being proportional to the total membership in FFS. To obtain non-ffs households, a list of all villages in the administrative locale where the selected FFS households were located was obtained. Two villages were then randomly selected 138

4 in each locale. List of households in those villages were drawn up, and households randomly sampled. Table 1 shows the number of respondents sampled in each district. Data were checked, entered into the Statistical Package for the Social Sciences (SPSS) at the country level by data assistants, cleaned, and analyzed using Stata Version 10. Review of the instrument by a panel of experts helped to assure face and content validity, and pilot testing and careful training of enumerators helped to ensure face, content, and criterion validity. Member checks were also used through a workshop held in early 2009 and subsequent discussion with a wider range of stakeholders, where findings were presented and discussed to obtain feedback and to ensure validity of results. Reliability was ensured through use of local languages, pilot testing, enumerator training, and triangulation using different methods to collect data. Measurement errors were dealt with by checking means, medians, and outliers, especially on variables where high measurement errors were expected. Non-response errors were dealt with by stating the number of responses on each variable. Table 1 Household Sampling Country District Number of farmers sampled Total FFS farmers Non-FFS farmers Kenya Kakamega Bungoma Busia Total Tanzania Bukoba Missenyi Muleba Total Uganda Soroti Kabermaido Busia Total All Total Quasi-Experimental Methods With impact evaluation, it is difficult to attribute outcomes to interventions such as FFS, since many other factors could affect these outcomes. For example, if a FFS objective is to increase adoption of improved crop varieties, there are many other factors that can affect adoption unrelated to the FFS, including other extension programs. To address these challenges, several quasi-experimental methods have been developed to net out the impacts of other factors. The common approach is to use panel data with baseline data, which measure the outcome before the intervention, and follow up data that measure the outcome after a certain amount of time. Ideally, the impact of an intervention needs to be measured by observing the outcome with and without the intervention. For example if the outcome is crop yield (Y), the impact of FFS could be measured by observing the actual yield of the farmers participating in FFS (participants) and their expected yield if they did not participate in FFS. This is referred to in the impact literature as the average effect of the treatment on the treated (ATT), that is, ATT = (y 1 p = 1) (y 0 p = 0), where p = participation in the program (p = 139

5 1 if program participant, and p = 0 if non-participant); Y 1 = yield of the participant after participating in the program; Y 0 = yield of the same participant if he or she did not participate. According to Figure 1, ATT = B C. FFS Participant Non-participant Figure 1. Measuring the impact of FFS Yield of FFS participant before FFS (A) Yield of non-ffs participant before FFS (D) Yield after FFS (B) Yield after FFS without FFS (C) Yield after FFS (E) Yield after FFS if they had participated in FFS (F) However, (y 0 p = 0) = C is unobservable since the participant cannot simultaneously participate and not participate in FFS. Several methods have been used to find counterfactuals that could be used to measure the equivalent of (y 0 p = 0). The common approach is to find a control group with similar observable characteristics as the participants. The characteristics considered are those that affect participation in the program and outcome of interest. Using this non-participant control group accounts for other factors that could have also affected yield or any other outcome (Heckman, Ichimura, Smith, & Todd, 1998). The impact of other factors that affect the outcome of interest is eliminated by subtracting the changes in yield of FFS non-participants before and after FFS from the change in yield of the FFS participants, that is, ATT = (B A) (E D). Figure 1 presents the framework used to measure impacts of interventions. The fact that FFS are joined by choice and are purposively located geographically means that there is no randomization of treatment of location or participation. This leads to several biases. Self-selection bias is where characteristics of the participant may lead certain kinds of farmers to join a program (such as those with higher education). Since placement is not random, there is also potential placement bias, where sites may be purposively selected due to high poverty or proximity to markets. Matching methods are one way to address self-selection bias. The econometric analysis in this study was thus based on matched (comparable) samples using propensity score matching (PSM), which matches participants and non-participants based on observable characteristics that affect participation in the program and the outcome being measured (Rosenbaum & Rubin, 1983; Smith & Todd, 2001; 2005). Using PSM ensures that the results do not compare outcomes for respondents who are not really comparable. This reduces the bias due to inclusion of noncomparable observations (Eliasson, 2006). The propensity scores were calculated using P(X i ) = E(Di X i ) where X i is a vector of pre-treatment covariates, which includes variables that affect both participation in the FFS and outcomes (e.g. yield, income, empowerment, etc.). One shortcoming of PSM is that it does not address self-selection bias due to unobservable characteristics. The participant and comparison groups may differ in unobservable characteristics, even though they are matched in terms of observable characteristics (Heckman et al., 1998). The authors thus addressed the problem of selection on unobservable characteristics 140

6 by combining PSM with the double difference (DD) estimator (Duflo, Mullainathan, & Bertrand, 2004). The DD estimator compares changes in outcome measures (i.e., change from before to after the FFS) between program participants and non-participants, rather than simply comparing outcome levels at one point in time. The advantage of the DD estimator is that it nets out the effects of any factors (whether observable or unobservable) that have fixed (time-invariant) and additive impacts on the outcome indicator (Ravallion, 2005). By combining PSM with the DD estimator, differences in pre-ffs observable characteristics can be controlled for. Results The household survey collected information on crops, income, perceptions of empowerment, and household characteristics. Table 2 compares characteristics of FFS and non- FFS farmers. Table 2 shows that FFS members in Kenya were about 66 percent female, while Tanzanian female members accounted for 31% of the total, and Ugandan female FFS members 50%. In Kenya and Ugandan about 60% of FFS members had only primary education, while in Tanzania 80% had only primary education. Tanzanian FFS members also had the lowest level of tertiary education (0.7%). Table 2 Characteristics of Individual FFS Farmers Country Male (%) Female (%) Avg. age (SD) No education (%) Kenya (N=300) (11.79) Tanzania (N=284) (13.06) Uganda (N=267*) (11.91) All (N=851)* (12.28) Note. * For education, N=850 for all and N=266 for Uganda **College, university Primary education (%) Secondary education (%) Tertiary education** (%) A probit regression model shows the determinants of participation in FFS (Table 3). Gender of the household head did not have a significant impact on participation in FFS in Kenya and Tanzania, demonstrating that participation in FFS was equally available to women and men farmers in the two countries. Households whose head had primary or secondary education were more likely to participate in FFS in Kenya than those with no formal education. In Tanzania, level of education had no impact on participation in FFS. 141

7 Table 3 Determinants of Participation in FFS in East Africa (Probit) Household human capital endowment Kenya Tanzania Uganda Female household head ** Level of education of household head (cf no formal education) Primary 0.715* ** Secondary 0.989** ** Post-secondary Level of education of spouse of household head (cf no formal education) Primary ** *** Secondary Post-secondary * Membership in non-ffs groups *** 1.346** Member credit and savings organization 0.794** 0.696*** 2.484** Off-farm income * Ln (household head age) ** ** ** Ln (household size) *** Dependency ratio 1.786*** *** Ln (distance to tarmac road) *** * Ln (distance to market/town) 0.905*** 0.272** *** Ln (land) size ** Constant *** 3.067* Number of observations R Note. * p < 0.10, ** p < 0.05, *** p < 0.01 The level of education of the spouse of the household head had no impact on participation in FFS in Kenya. In Tanzania and Uganda, primary education of the spouse was negatively related to participation. Likewise, post-secondary education of the spouse was negatively associated with likelihood to participate in FFS in Kenya, but had no impact in Tanzania and Uganda. The results demonstrate that FFS in Tanzania and Uganda were more accessible to households with less educated farmers. Membership in farmer groups other than FFS also increased the propensity to participate in FFS in Kenya and Tanzania, but had no impact in Uganda. Likewise, membership in credit and savings organizations increased the likelihood to participate in FFS in all three countries. This demonstrates the effectiveness of farmer groups in enhancing access to rural services. As expected, having off-farm income reduced the probability of participating in FFS in Kenya, but had no impact in Tanzania and Uganda. The opportunity cost of farmers with non-farm activities may be higher, and therefore farmers may not be able to participate in FFS activities and/or to adopt technologies promoted by FFS. In all three countries, households with younger heads were more likely to participate in FFS. Household size was also negatively related with the probability to participate in FFS in Kenya, but had no impact in Tanzania and Uganda. Dependency ratio, which is the ratio of number of dependents divided by the number of working adults, was positively related with the 142

8 probability to participate in FFS in Kenya, and negatively associated with participation in FFS in Uganda. Given that households with a higher dependency ratio were more likely to be poor than those with a lower dependency ratio, the results reveal the potential that FFS has for reducing poverty in Kenya. In summary, these results show quite a contrasting picture, with Uganda revealing unique characteristics that are different from Kenya and Tanzania. Results in Kenya show that households with better educated, younger heads who were members of farmer groups other than FFS were more likely to participate in FFS. In Tanzania, households with younger heads and those away from urban areas were more likely to participate in FFS. In Uganda, female-headed households, those with no formal education, and those closer to urban areas but away from tarmac roads were more likely to participate in FFS. In all three countries, younger farmers and those belonging to credit and savings organizations were more likely to participate in FFS. The authors also examined reasons that farmers gave for not participating in FFS. Table 4 gives the main reasons for not participating, which included lack of time, distance from the venue, and lack of information. Tanzania had an especially high number of respondents who stated that the venue was too far (27%). However, 40% of Tanzanian respondents stated that they would join soon, showing that while they were aware of and wanted to join FFS, distance and other issues prohibited them. A good number of Kenyan respondents claimed that they did not have enough time to join the FFS (65%), and many Ugandans (53%) that they lacked information. Table 4 Reasons for not Joining FFS (as % of non-ffs respondents) Reasons Kenya (n=88) Uganda (n=47) Tanzania (n=107) All (n=242) Lack of time/commitments Leadership not good enough * Plan to join soon Venue is far from home Lack of information on enrollment * Lack of capital * Want to first observe results No FFS around I am too old Note. *n = 135 for these cases. Tanzania had three unique responses; - corresponds to no response. Qualitative results also shed light on the issue of participation. Informants stated that The majority of FFS participants are drawn from the low/marginalized and middle income groups. The ground work sensitization, registration, training can enable people to be aware of the project and to target poor people. However, landless farm laborers or the very poor may not be able to participate, and the poor don t participate in leadership of FFS. Participation in the FFS appeared to lead to adoption of various technologies. Since there were many specific answers, answers were post-coded into the main categories of technologies shown in Table 5. Comparing FFS and non-ffs farmers across the entire project, there were significant differences in the mean value of adoption for all technology categories except post 143

9 harvest handling and marketing, with the FFS participants having significantly higher adoption rates. Table 5 Technologies Adopted since Start of Project in all Countries (%) FFS (N=746) Non-FFS (N=283) Response M SD M SD p-value Improved varieties (any) Crop management Soil fertility management Soil and water conservation Pest control Agro-forestry Livestock breeds Livestock management Post harvest handling Marketing With regard to crop productivity, Figure 2 shows that FFS participants had a significant increase in the value of crop productivity per acre (80%). These differences were not significant in Tanzania or Uganda. However, if the productivity results are disaggregated by gender, women FFS participants had a significantly higher change in the value of crop productivity in Tanzania and Uganda, but not in Kenya (Figure 3). The FAO-IFAD FFS project, as many extension projects, was targeting poor farmers and attempting to reduce poverty among participants. If the data from each country are aggregated, not much difference is seen in the poverty indicators between FFS and non-ffs farmers. However, at the country level, certain significant differences are evident (Table 6). Tanzanian FFS members had a higher percentage of food insufficiency in the past 12 months, and were more likely to have poorer roofs and walls on their houses. This may be due to the targeting of poorer farmers by the project. More analysis is needed on this outcome. Qualitative data indicate effects on poverty through FFS. One informant stated that FFS ha[d] an impact on poverty reduction because the graduate farmer are able to solve their problems and to help other farmers in solving their problems. Empowerment was an important objective of the project, and there was evidence of this from key informant interviews. For instance, one informant stated On the basis of the FFS knowledge, farmers become independent and confident decision makers, experts in their own fields, irrespective of gender or position, hence contributing equally through observation, experimentation, and finding possible solutions. However, the results of empowerment are less clear from the survey data. Again, at the aggregated country level, the differences between FFS and non-ffs were not significant with regard to holding leadership positions in groups or 144

10 making complaints regarding public services. At the country level, differences are nonsignificant or significant only at the 10% level (Ugandan results are shown in Table 7). Note. Thick lines denote significant results. Figure 2. Impact of participation in FFS on value of crop produced per acre Note. Thick lines denote significant results. Figure 3. Impact of participation in FFS of female household members on crop productivity Table 6 Poverty Indicators for Tanzania FFS and Non-FFS Respondents (%) 145

11 FFS Non-FFS Response n M SD n M SD p- value Food insufficiency in the household during the last 12 months a Roof type = b Wall type = a Roof type a Roof type =1 if iron or galvanized sheets, or tiles; or 0 if grass thatch, polythene, plant fibers, or tins b Wall type =1 if bricks, cement blocks, unburned bricks, or plaster; or 0 if mud, banana or grass fiber, or tins Table 7 Empowerment Indicators on Individual Empowerment Indicators for Uganda (%) FFS Non-FFS Response n M SD n M SD Leadership position in any group Complaints made regarding public services p- value Educational Importance and Implications Understanding the impact of education and extension programs is crucial. Program managers and extension personnel, donors, governments, and other stakeholders want to know the impacts of their programs. However, showing impact is complex and difficult. This paper demonstrates a methodology that helps to show the impact of extension and education programs in developing countries by using qualitative data together with quantitative data that match participating and non-participating farmers both before and after the program. The paper shows that FFS can bring about positive development results for small-scale farmers. FFS appear to be effective in reaching women farmers (who made up 50% of the program participants) and other poor and marginalized small-scale farmers. Farmers were able to participate in FFS with only limited amounts of formal schooling. Lack of information, lack of time, and distance were the main factors inhibiting participation. The FFS farmers learned more and adopted significantly greater numbers of technologies than non-ffs farmers. Other positive results include increases in productivity, especially for women farmers in some cases. It is difficult to make judgments on poverty differences from this analysis since the project did target poor farmers, and further analysis is needed on this issue. The study also demonstrates some empowerment results of participating in FFS, although ways to measure this construct still need to be refined. References Alokit-Olaunah, C. (2006). Baseline survey for assessing socioeconomic characteristics of farmers participating in farmer field schools of Busia, Kabermaido and Soroti Districts of Eastern Uganda. Nairobi: Food and Agriculture Organization of the United Nations. 146

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