As already indicated, this section addresses the impact of Farmer Field Fora on farmer innovativeness.
Farmer Field Fora
Farmer Field Fora (FFF) of the Root and Tuber Improvement and Marketing Programme (RTIMP
SeeSeeRoot and Tuber Improvement and Marketing Programme (RTIMP)
) in Ghana are based on the successful implementation of the Root and Tuber Improvement Programme (RTIP)
SeeSeeRoot and Tuber Improvement Programme (RTIP)
between 1999 and 2005. The RTIMP was initiated as a follow-up project, with major funding from the International Fund for Agricultural Development (IFAD)
SeeSeeInternational Fund for Agricultural Development (IFAD)
. The RTIMP supports root and tuber crop production, increased commodity chain linkages and upgrading of technologies and skills within the value chain. The aim is to enhance income and food security to improve livelihoods
of the rural poor and to build a market to ensure profitability at all levels of the value chain.
The RTIMP used the FFF as a platform for mutual learning among stakeholders in the root and tuber value chain, particularly farmers, extension agents and researchers
. The main aim of FFF is to “build the capacities of farmers to become experts in the development of technologies and managerial practices to solve specific problems within the agro-ecological context of farming” (Gbadugui and Coulibaly 2010). It is a variant of the well-known Farmer Field School (FFS), a participatory extension model. The FFS approach was first introduced in Indonesia in the late 1980s by the FAO to help farmers deal with the pesticide-induced pest problems in irrigated rice, but has since spread to at least 78 countries and is highly promoted by many development agencies (Braun et al. 2006). Though it was mainly introduced to promote integrated pest management (IPM) practices
in rice farming, its methods have been adapted to suit different farming activities and even non-farm topics in Africa (Braun et al. 2006; Davis et al. 2012). Unlike FFS, which gives little or no attention to farmer-developed innovations (Reij and Waters-Bayer 2001), FFF provides an opportunity for farmers to experiment with their own innovations, thereby strengthening their decision-making and innovation capacities.
The RTIMP-FFF in Ghana, which started in 2006, aims at improving farmer innovation and productivity of root and tuber crops in major production districts of the country. In each participation district, the FFF was developed for the most important root or tuber crop. This study is based on the sweet potato FFF in ten communities in three northern districts of Ghana. The main actors include researchers, extension agents, business advisors, farmers and processors, and they are all placed on an equal footing. During a participatory rural appraisal, the farmers determine the theme of the FFF, thereby ensuring that their priorities are addressed. The thematic areas normally selected by the farmers include improved crop varieties, integrated pest management (IPM
), improved cultivation practices and integrated soil fertility management. There are also discussion sessions on non-farm topics. Each forum consists of a group of 30–40 farmers together with other key actors who meet regularly (usually weekly) in the field during a growing season. They engage in comparative experimentation using three plots: farmers practice (FP),
SeeSeeFarmers practice (FP)
integrated crop management (ICM) and participatory action research (PAR)
SeeSeeParticipatory action research (PAR)
, with the assistance of a facilitator who stimulates critical thinking and discussions and ensures active participation.
The participating farmers experiment with their own innovations or test new ideas on the PAR plots. Conventional practices and improved innovations are implemented on the FP and ICM plots, respectively.
There are many studies looking at the impact of farmer field schools (FFS
SeeSeeFarmer field schools (FFS)
) on outcome variables such as empowerment, technology adoption, household income and food security, but with inconclusive findings (for a review, see Braun et al. 2006; Davis et al. 2012, Table 10.1). Within this vast literature, however, there is little, if any, on the farmer innovation effects of FFS. This chapter provides empirical evidence on the potential of FFF, a variant of FFS, in stimulating innovation-generating behavior among farm households.
Empirical Method
We are interested in estimating the effect of FFF participation on farmer innovation
. The challenge is that participation in FFF is voluntary; hence, farmers self-select to participate. Thus, participating farmers may differ systematically from non-participants in observed characteristics such as education, age and wealth, and unobserved characteristics such as entrepreneurship, risk behavior or motivation which might lead to biased estimates of the effect of FFF on innovation. Due to the self-selection bias, participants and non-participants are not directly comparable. To minimize this problem, we use propensity score matching (PSM), a non-parametric technique suggested by Rosenbaum and Rubin (1983). It involves matching FFF participants with non-participants who are similar in terms of observable characteristics (Caliendo and Kopeinig 2008). Though it only accounts for observables, it is less restrictive, as it does not impose any functional form assumption, which is a challenge with other estimation techniques, such as instrumental variable regression. We also try to minimize the bias stemming from unobserved heterogeneity by controlling for household risk preferences
.
In the PSM, a probit regression
was estimated using several covariates to obtain a household’s propensity to participate in FFF. These covariates comprise household socio-demographic and economic variables (e.g., age, gender and education of the household head; household size and dependency ratio; access to services and the wealth position of the household). It also includes households’ risk preferences.Footnote 1 We then use the propensity scores obtained in the first stage to match participants and non-participants in FFF. As a matching algorithm, we used kernel matching
with a bandwidth of 0.3, but,
for the robustness check, radius matching with a caliper of 0.05 and nearest-neighbor matching are also employed.Footnote 2 We conducted a matching quality test
(Rosenbaum and Rubin 1983) to check if the balancing property is satisfied. Based on the kernel matching,Footnote 3 the test result (in Appendix 2) shows that, in contrast to the unmatched sample, there are no statistically significant differences in covariates between participants and non-participants in FFF after matching. Thus, the balancing requirement is satisfied. Using the PSM, we compute the average treatment effect on the treated (ATT):
$$ AT{T}^{PSM}=E\left[Y(1)\ \Big|\ FFF=1,\ P(X)\right]\ \hbox{-}\ E\left[Y(0)\ \Big|\ FFF=0,\ P(X)\right] $$
(10.1)
where Y(1) and Y(0) are the outcome variable (farmer innovativeness) for FFF participants and non-participants, respectively; FFF is a treatment indicator which is equal to 1 if the household is FFF participant and 0 otherwise; and P(X) indicates the probability of FFF participation given characteristics X, which is obtained from the probit regression. The ATT measures
the average difference in innovativeness between FFF participants and non-participants.
We use four different measures of the outcome variable, farmer innovativeness, to check if the results are sensitive to the indicator employed. The first (innovation_binary
) is a binary variable which is equal to one if the household has, in the past 12 months, implemented any of the four categories of farmer innovation (i.e., invention of new practices or technologies, adaptation of exogenous ideas, modification of common or traditional practices and experimentation with new ideas),
and 0 otherwise. The second (innovation_count
) is a count variable that indicates the number of different innovation-generating activities implemented by a household in the past 12 months. In the third and fourth measure of FI, we consider the varied importance of each of the four categories of farmer innovation and constructed an innovation index using weights. In the third measure of FI (innovation index 1
), we followed Filmer and Pritchett (2001) and used principal component analysis (PCA) to assign weights to each of the four innovation categories, and constructed a household innovation index. The final indicator (innovation index 2
) also involves the construction of a household innovation index, but using weights obtained through expert judgements. A stakeholder workshop was organized, and 12 agricultural experts in the study region assigned weights to the four innovation categories based on an agreed level of importance for each category. They assigned weights of 0.4, 0.2, 0.3 and 0.1 for invention, adaptation of exogenous ideas, modification of traditional practices and experimentation, respectively.
Data
The empirical analysis
is based on data for the 2011–2012 agricultural season obtained from a household survey in the districts of Bongo, Kassena Nankana East and Kassena Nankana West in the Upper East Region, one of the poorest administrative regions of Ghana. The districts fall within the Sudan savanna agro-ecological zone. The area is characterized by a prolonged dry season and erratic rainfall. Agriculture is the main income source and a cereal-legume cropping system is predominant in the study region. The major crops are millet, sorghum, maize, cowpea, rice and groundnut. Most households also rear livestock.
The sample included FFF participants, non-participants from FFF communities and non-participants from control communities. We interviewed 409 households from 17 villages using a stratified random sampling. We first obtained from the district RTIMP project officers a list of all the 24 villages in the three districts where FFF had been implemented between 2008 and 2011. Then, we randomly selected ten participating villages across the three districts. We interviewed about 16–21 participants from each of these villages, resulting in a total of 185 FFF participants. We also obtained a list of all households in each of the FFF participating villages and randomly sampled and interviewed 99 non-participants across these villages. Since these non-participants are located in the FFF villages, they may potentially be exposed to some of the effects of FFF. To obtain a group of control farmers devoid of potential spillovers, we randomly selected seven villages (from the same three districts) that had similar infrastructural services and socio-economic conditions but not in close proximity to the FFF communities. Out of these, we randomly selected 125 farm households from a household list obtained from the District Agricultural Offices. Thus, our final sample
consisted of 185 FFF participants and 224 non-participants, making a total of 409 sample farmers.
Data collection was conducted by experienced enumerators who were highly trained for this research. Interviews were conducted with the aid of pre-tested questionnaires and were supervised by the first author. The questionnaire captured data on household and plot characteristics, off-farm income earning activities, innovation-generating activities and access to infrastructural services, information and social interventions. The respondents were mainly FFF participants or household heads in the presence of other available household members.
Descriptive Statistics
In this section, we focus on four categories of farmer innovations. These are: developing new techniques or practices (hereafter, invention), adding value or modifying indigenous or traditional practices, modifying or adapting external techniques or practices to local conditions or farming systems and informal experimentation with original or external ideas. Thus, innovators are farm households who have implemented any of these four categories of innovation-generating activities during the 12 months prior to the survey.
Figure 10.1 presents the share of households that implemented the four categories of innovation-generating activities and compares the results between participants and non-participants. Informal experimentation, which was implemented by 25 % of the sample households, constitutes the most practiced activity. A similar trend is observed when we compare the innovation activities of FFF participants and non-participants. This is expected, as experimentation is the first stage of most innovation processes. The figure also shows that, relative to non-participants, FFF participants implemented more innovation-generating activities in each of the the four categories, which seems to suggest that FFF participation enhances innovation capacity. Examples of innovations include: informal trials with or introduction of new crops or varieties in a community; testing and modification of planting distance and cropping pattern; using plant extracts as insecticide; new formulations of animal feed and new herbal remedies in the treatment of livestock diseases (ethnoveterinary practices); developing and using new farming tools; storage of farm products using local grasses; and new methods of compost preparation.
Table 10.3 outlines the description and mean values of the outcome indicators and variables used in estimating the propensity scores. The table shows that about 42 % of the sample households conducted at least one innovation-generating activity in the past 12 months.
Table 10.3 Description and
summary statistics of variables
Probability of
FFF Participation
As mentioned, the first step in the PSM technique is the probit estimation of the propensity to participate in FFF, and the result is presented in
Table 10.4. The result shows that FFF participation is influenced by household characteristics such as age, gender of household head and household size. Participants are likely to be younger, and come from male-headed households of large size. Membership in a social group and credit accessibility also positively influence FFF participation. The negative and significant effect of road distance indicates that households living close to all-weather roads have a higher probability of participating in FFF. It is interesting to note that all the wealth-related covariates (i.e., land holding, productive assets, livestock holding and off-farm income) are not statistically significant. This seems to suggest that participation in FFF is inclusive of both resource-rich and resource-poor households. Finally, the result shows that a household’s risk preferences do not affect FFF participation.
Table 10.4 Probit estimation of the propensity score
Effect of FFF Participation on Farmer Innovation
The estimated ATT is presented in Table 10.5.
We find positive and significant effect of FFF participation on farmer innovation irrespective of the matching algorithm or how the outcome variable is measured. Using the kernel matching approach, for instance, the results show that the rate of innovation generation by FFF participants is 13.4 percentage points higher relative to matched non-participants. Furthermore, FFF participants are more likely to implement between 0.24 and 0.31 more innovations than non-participants, depending on the matching technique. Overall, the results suggest that FFF participation consistently and robustly enhances innovativeness in farm households.
Table 10.5 PSM estimation of the effect of FFF participation on farmer innovation
We also conducted tests on the sensitivity of estimates to unobservable factors (Rosenbaum 2002). Running mhbounds for binary outcome variables (Becker and Caliendo 2007), for example, we obtained a critical value of gamma, Γ = 1.40 for kernel matching (model 1) which indicates that the ATT of 0.134 would be questionable only if matched pairs differ in their odds of FFF participation by a factor of 40 %.