Community-based management as a driver of adoption of village poultry improvement technologies: empirical evidence from Benin

Abstract

In most developing countries, village poultry are raised in a scavenging system which gives the impression that all the birds in the village belong to the same flock. Therefore, actions targeting the entire community (Community-Based Management, CBM) could better contribute to improving the productivity of village poultry, notably through the adoption of technologies similar to those used in intensive poultry. The objective of this paper was to assess the effect of CBM, as well as the socioeconomic and institutional factors, on the adoption of village poultry improvement technologies. This was addressed using a logit model and data collected on 405 village poultry-keepers in Benin. Results indicate that the adoption of village poultry technologies depends on the availability of village poultry vaccinators (VPVs) and breeders’ experience (education level and participation in CBM). Farmers from experimental villages are also more willing to adopt various technologies. This indicates that when farmers have the information and technical support through an approach based on the community, i.e. CBM, they can change their behavior, overcome traditional ways of poultry farming. Furthermore, the adoption of the technologies is successful when the strategies used are based on the involvement of private veterinarians for the supply of VPVs.

Keywords:

• Benin
• community-based management
• village poultry vaccinator
• village poultry improvement technology
• adoption
• logit

Reviewing Editor:

• Pedro González-Redondo
• University of Seville
• Spain

SUBJECTS:

• Meat & Poultry; Decision Analysis; Regression Analysis and Multivariate Statistics; Test Development
• Validity & Scaling Methods; Motivation; Perception; Rural Development; Information / Knowledge Management; Management of Technology & Innovation

Introduction

Village poultry is a widespread traditional activity in most of developing countries, with a high share of the meat supply. Indeed, nearly all village dwellers in these countries, including the poor and landless, are owners of poultry which also represent 80–90% of poultry products in some countries (Mack et al., 2005; Alabi et al., 2006; Wong et al., 2017; Hailemichael et al., 2017; FAO (Food & Agriculture Organization of the United Nations) and IFAD (International Fund for Agricultural Development), 2022). The poultry raised ranges from a single bird up to several hundred and includes a wide range of birds from indigenous to commercial breeds (notably chickens, ducks, pigeons, guinea fowls, quails, turkeys etc.) (MAEP (Ministère de l’Agriculture de l’Elevage et de la Pêche), 2017; FAO (Food & Agriculture Organization of the United Nations) and IFAD (International Fund for Agricultural Development), 2022).

However, the productivity of village poultry is low in addition to poor breeding levels and a survival rate estimated at 30% (Sodjinou et al., 2012; Kulla et al., 2021; Chemuliti et al., 2023). This low productivity is mainly due to the farming practices based on total or partial scavenging in which birds seek their feed in the nature. The scavenging leads to a mixture of poultry from various famers giving the impression that all the birds in the village belong to the same flock (Sodjinou et al., 2012).

To improve this productivity, various technologies, notably those used in intensive poultry farming, were promoted through the community-based management (CBM) and other projects, including henhouse and chick-house construction using locally available materials, improved cockerels, poultry vaccination, and the use of improved or supplementary feed. The choice of the CBM is due to the fact that the mode of production based on the scavenging of the poultry obliges the producers to decide together on the technologies of improvement of the poultry. For example, the effort of a single individual to vaccinate his own poultry would be wasted if other farmers in the community did not do the same. Furthermore, the supply of vaccines by the private sector in the rural areas is limited mainly due to infrastructural challenges and weak demand, in addition to the fact that the vaccines come in large dose quantities, which are inappropriate for most farmers who own small flock sizes (Chemuliti et al., 2023). For this purpose, innovated approaches (such as CBM) are needed in order to successfully deliver animal productivity improvement technologies in such systems (Enahoro et al., 2021; Kulla et al., 2021).

CBM covers a range of situations that typically relate to scavenging systems with farmers within the village having a common interest to work together for improvement of their farming system (Sodjinou et al., 2012; Mueller et al., 2015). Its implementation relies on the installment of a ‘poultry interest group’ whose members usually have weekly meetings during which they not only share experiences in poultry production but also receive training in the basic techniques of village poultry management (Sodjinou et al., 2012; FAO (Food & Agriculture Organization of the United Nations) and IFAD (International Fund for Agricultural Development), 2022). CBM develops confidence in local communities as it is based on existing management and breeding practices, where the whole community flock is treated as one (Haile et al., 2019). By targeting CBM, development agencies aim to help villagers to think together and share experiences in poultry production.

The objective of this study is to assess the effect of community-based management of village poultry, as well as the socioeconomic and institutional factors, which influence the adoption of these technologies. Indeed, the introduction of village poultry improvement technologies has met with only partial success. Some of them (e.g. henhouse) are widely used by poultry-keepers, while others (e.g. improved cockerels) have not had much success (Mahoro et al., 2017). Understanding the reasons that underlie this situation is necessary in order to contribute to an efficient dissemination of the technologies. This information is important to prioritize the factors that affect adoption decisions and to provide insight into pathways to increase the awareness and use of village poultry improvement technologies.

Methodology

Modeling village poultry improvement technologies adoption

Two types of technology adoption exist, namely individual (farm-level) adoption and aggregate adoption. Feder et al. (1985) define adoption at the level of the individual farmer as the degree of use of a new technology by the farmer when he or she has full information about the new technology. Aggregate adoption is measured by the aggregate level of use of a specific innovation within a given region or a given population (Feder et al., 1985). This study mainly targets the former, i.e. the adoption of village poultry improvement technologies at the farm-level, where the adopter of a given technology is the person using this technology at the time of the survey. In other words, the study is based on a cross-sectional research design where data was collected from poultry producers during a single visit.

The basic problems faced by farmers when it comes to new technology are choices and tradeoffs. Differences in adoption decisions are often due to the fact that farmers do not have the same resource endowments, they have different objectives, different preferences, different cultures, as well as different educational and socio-economic backgrounds (Tambi et al., 1999). As a consequence, some producers may use the technology while others may not. In such circumstances, farmers’ responses to the innovation or new technology can be explained using the theory of the maximization of expected utility subject to constraints (e.g. credit).

Thus, let $U_{i1}$ represent the expected utility that a given farmer $i$ would receive from adopting a new technology and $U_{i0}$ the expected utility gained from adopting the traditional farming practice. The i^th farmer adopts the new technology if $U_{i1}>U_{i0}$; i.e. if the expected utility obtained from the new technology exceeds that of the old one (Chebil et al., 2009). Following Verbeek (2004), for each farmer $i$, we can write the utility difference between adopting and not adopting as a function of observed characteristics, $x_i$ say, and unobserved characteristics, ${\epsilon }_i$ say. Assuming a linear additive relationship, for the utility difference we obtain: (1) $$y_i^{\text{*}}=U_{i1}-U_{i0}=x_i\beta +{\epsilon }_i$$(1)

Because $y_i^{*}$ is unobserved, it is referred to as a latent variable. In the case of village poultry improvement, the decision to adopt or not adopt a given technology can be framed as binary-choice models, which assume that individuals are faced with a choice between two alternatives: the technology is adopted ($y_i=1$) or not adopted ($y_i=0$). These models (often derived from Equation (1)(1) $$y_i^{\text{*}}=U_{i1}-U_{i0}=x_i\beta +{\epsilon }_i$$(1) ) essentially describe the probability that $y_i=1$. The general functional form is defined as follows (Wooldridge, 2013): (2) $$P\left(y_i=1x_i\right)=G\left(x_i,\beta \right),$$(2) where $G$ is a function with a value strictly between zero and one. Verbeek (2004) argues that, usually, one should restrict attention to functions of the form $G\left(x_i,\beta \right)=F(x_i\beta )$.

Various nonlinear functions have been suggested for the function $F$ in order to make sure that the probabilities are between zero and one. The most widely used are the standard normal and standard logistic distribution functions. The former results in the probit model (Verbeek, 2004): (3) $$F\left(w\right)={{\displaystyle \int }}_{-\infty }^w\frac{1}{\sqrt{2\pi }}\text{exp}\left\{-\frac{1}{2}{t}^2\right\}\mathit{\text{dt}}$$(3) and the second, i.e. standard logistic distribution function, leads to the logit model (Verbeek, 2004): (4) $$F\left(w\right)=\frac{e^w}{1+e^w}=\frac{e^{x_i\beta }}{1+e^{x_i\beta }}\text{\hspace{0.33em}\hspace{0.33em}with\hspace{0.33em}\hspace{0.33em}}\hspace{0.17em}w=x_i\beta .$$(4)

The two distributions are very close to each other, except at the tails, unless the samples are large (so that we have enough observations at the tails) (Maddala, 1983). Thus, the choice between these two models does not seem to make much difference (Greene, 2008). In this study, the logit model was used to identify the factors which influence the adoption of village poultry improvement technologies.

In the innovation adoption literature, especially in animal health management decisions, factors influencing poultry improvement technologies adoption can be regrouped into five categories (Chilonda & Van Huylenbroeck, 2001): (i) characteristics specific to small-scale farmers (e.g. age, attitudes, knowledge, objectives); (ii) characteristics specific to their farms (e.g. availability of land, labor, size of the livestock resource); (iii) economic factors (markets for outputs and inputs, level of input and product prices); (iv) institutional setting (veterinary delivery system, infrastructure, credit, information sources, and extension services); and (v) biophysical factors (diseases, parasites, climatic factors).

Data used

The data used in this study were collected in two of the poorest regions of Benin: Donga in the North and Mono in the South. In each province, two districts where poultry-based interventions have been implemented during the past decade were selected. In each district, discussions with resource-persons (development agents, extension agents and researchers) enabled us to identify experimental villages (i.e. villages where CBM was or is implemented): in total 8–10 in each district. Based on these results, two categories of village were considered in each district: two experimental villages and one non-experimental village. So, in total, eight experimental and four non-experimental villages were selected for the study.

In each selected village, a census of household producing village poultry was carried out. In the experimental villages, poultry breeders were grouped into two categories: participants and non-participants in CBM. Afterwards, 25 participants and 8–10 non-participants were randomly selected, while in each non-experimental village, 20 breeders were randomly selected. In sampled household, when the head of the surveyed household is married, poultry farming is practiced by the wife (wives) or the husband or both. For this study, in each of the households, all members (the wife, wives and/or the husband) who produce poultry were interviewed. For this reason, the sample size of the poultry breeders (Table 1) is higher than the number of households selected.

Table 1. Distribution of the sample according to the participation in community-based management.

Region	Non-participant of non-experimental village	Non-participant of experimental village	Participant	Total
South	51	84	93	228
North	39	55	83	177
Total	90	139	176	405

In total, data used in this study were collected on 405 poultry-keepers using three main tools: focus group discussions (two in each village surveyed), the ‘observation’ and structured questionnaires. The observation mainly targeted technologies such as the henhouse, the chick-house, traditional methods of treating poultry, and products used for poultry feeding.

The dataset includes breeders’ socio-economic characteristics, poultry management practices, the adoption of various village poultry improvement technologies (notably vaccination, chick-house, henhouse, improved cockerel, and the use of improved or complementary feed), peasants’ knowledge of these different innovations, their appreciation/perception of the innovations, the distance between farmers’ household and the nearest market.

Data analysis

Qualitative data were analyzed using content analysis. This allows us not only to identify main themes but also to discuss and analyze these themes in depth.

For quantitative data, descriptive statistics (means, standard deviation, percentages) were used to describe the breeders interviewed as well as the village poultry improvement technologies. The logit model is used to identify the factors which influence the adoption of village poultry improvement technologies. The output variables are the adoption status (adopt or not adopt), where the adopter is the person using the technology at the time of the survey. Thus, in the model, a given outcome variable was coded with the value 1 to indicate that the farmer adopts the technology and zero otherwise. Five types of technologies were targeted: improved cockerel, henhouse, chick-house, improved feeding, and vaccination of village poultry. The logit of Equation (4)(4) $$F\left(w\right)=\frac{e^w}{1+e^w}=\frac{e^{x_i\beta }}{1+e^{x_i\beta }}\text{\hspace{0.33em}\hspace{0.33em}with\hspace{0.33em}\hspace{0.33em}}\hspace{0.17em}w=x_i\beta .$$(4) was used. (5) $$F\left(w\right)=\frac{e^{x_i\beta }}{1+e^{x_i\beta }}$$(5)

In this equation, ${\beta }_{}$ are parameters to be estimated and $x_i$ are independent variables for a given farmer $i_{}$. Independent variables used in this study include the gender, age, education of the breeder, household size, access to credit, village status (experimental or non-experimental village), and the regional dummy. These variables are listed in Table 2 along with hypotheses on how each characteristic might affect the adoption of village poultry technologies.

Table 2. Hypothesized determinants of breeders’ decision to adopt poultry improvement technologies.

Variables	Label (and measure)	Ho sign	Rationale
EXPVIL	Experimental village (1 = yes, 0 = no)	+	Breeders in experimental villages have more information about the new technologies
REGION	Region (1 = North, 0 = South)	±	Cultural, agro-climatic and economic differences
GENDER	Gender of the breeder (1 = male, 0 = female)	+	Females often have less access to productive resources and the introduction of new technologies often requires additional resources
AGE	Age of the breeder (Years)	+	Producers become increasingly open to new technologies until a certain age but their openness decreases again with a further increasing age
AGE2	Square of age ([years]^2)	−
EDUC	Formal education of the breeder (1 = educated, 0 = none)	+	Education increases the ability to understand and benefit from new technologies
HHSIZE	Household size	+	Labor increases the willingness to adopt the technologies Labor availability increases the potential benefits from new technologies, because new technologies often require additional labor
CREDIT	Obtained credit (1 = yes, 0 = no)	+	Breeders who have access to credit are more likely to have sufficient financial means to introduce new technologies

We do not use the participation in CBM as explanatory variable in the adoption model, because this is clearly an endogenous decision variable and hence, would cause inconsistent estimates of the effects of all explanatory variables. In contrast, we include the village status (EXPVIL), with 1 for experimental village and 0 otherwise, as explanatory variable in the adoption model. This variable is clearly exogenous, because it cannot be influenced by individual farmers. Hence, the coefficients of the village status measure the combined effect of living in an experimental village and having the option to participate in CBM. Both effects are expected to be positive. Having the option to participate in CBM implies that the farmers have the possibility to get informed about and trained in the new technologies, which should increase the probability that they adopt these technologies. Even farmers not participating in CBM but living in experimental villages, i.e. where CBM has been implemented, are more often exposed to the new technologies than farmers in non-experimental villages, because they certainly talk to some of their participating neighbors about the new technologies and they can see some of their participating neighbors adopting these technologies. Hence, farmers in experimental villages are expected to have a higher propensity to adopt the new technologies no matter whether they participate in CBM or not, i.e. the sign of this variable is expected to be positive.

A dummy variable is included for the region (REGION, with 1 = North and 0 = South), which allows us to control for cultural, agro-climatic and economic differences that could affect the likelihood of adopting the new technologies. For instance, the price of vaccinations is lower in the South (about FCFA 25 per dose per bird) than in the North (FCFA 50 per dose per bird). Thus, we assume that this may encourage the adoption of vaccination more in the South than the North. On the other hand, as most farmers in the North already introduced the vac­cination of other livestock (cattle), they might be more open to vaccinate also their poultry than farmers in the South (Sodjinou, 2011). Hence, this variable can have either a positive or negative effect on the adoption of vaccination and the other new technologies.

Concerning gender (GENDER); we use the gender of the farmer rather than using the gender of the household head (the conventional practice in most adoption studies). This allows us to assess the behavior of female breeders regarding village poultry improvement technologies adoption in female-headed as well as male-headed households. Following Rabe et al. (2021), men are more likely to adopt new technologies because they have more access to productive resources than women. Thus, we assume that they will be less likely to adopt the technologies, i.e. the variable gender will have a positive sign.

Following Sall et al. (2000), ‘age (AGE), a proxy for farming experience, implies that knowledge gained over time from working in an uncertain production environment may help in evaluating information, thereby influencing adoption decisions.’ In this study, the relationship between age and adoption is expected to be positive for young farmers and negative for old farmers. In other words, we assume that producers are opened to new technologies until a certain age after which they become less open until they reach old age. To allow this nonlinear relationship, the square of the breeder’s age (AGE2) is included in the adoption model.

According to Feder et al. (1985), farmers with better education (EDUC) are often earlier adopters of modern technologies and apply modern inputs more efficiently. Education may enhance the farmer’s ability to efficiently allocate inputs across competing uses, and to select the ‘best’ technology mix (Polson & Spencer, 1991). This is in agreement with Adebiyi et al. (2019) and Dovonou et al. (2021) who showed that the level of education positively influences the adoption of agricultural technologies. This variable is supposed to have a positive influence on the adoption of village poultry technologies.

In the village poultry technologies adoption model, we use household size (HHSIZE) as a simple measure of labor availability. We assume that this factor will have a positive effect on the adoption of poultry technologies, since adopting new technology often implies a need for additional labor. Indeed, following Feder et al. (1985), new technologies may increase the seasonal demand for labor, so that adoption is less attractive for those with limited family labor or those operating in areas with less access to labor markets.

The access to credit (CREDIT) can have a positive effect on the adoption of various village poultry technologies. Indeed, Feder et al. (1985) show that credit is an important determinant for the adoption of new technologies. Access to credit is supposed to allow the producer to better cope with the additional costs generated by the technologies; and therefore would positively affect the adoption of village poultry technologies (Kpadenou et al., 2020). This is in line with Assogba et al. (2017) and Baba et al. (2016) who argue that credit is a determining factor in the adoption of soil fertility technologies. The main problem, however, is that measuring access to credit is not an easy task. Doss (2006) argues that the best measure would be whether there is a source of credit available to the producer; i.e. a source of loan for which the producer is eligible, at a reasonable cost, in terms of time and money. However, Doss (2006) notes that such a measure is often unavailable, but one solution is to include a measure of whether the farmer had ever received credit. This measure is still not perfect, but it is a better measure of access than the simpler question of whether the farmer used credit in the current period (Doss, 2006). Therefore, we used this method in this study.

It is worth noting that, the estimation of the coefficients is carried out with the maximum likelihood (ML) method using the software Stata/SE 15.1 for Windows (StataCorp, 2017). As the model might suffer from heteroskedasticity, i.e. a non-constant variance of the variable $y$ given the covariates $x$, we use an ML procedure that automatically accounted for this.

The likelihood-ratio test is used for the test of the null hypothesis that all of the coefficients associated with independent variables are simultaneously equal to zero. We used the z-statistic to test the statistical significance of single covariates, H₀: ${\beta }_k=0$. The z-statistic is equal to the estimate divided by its standard error: $z={\widehat{\beta }}_k/{\widehat{\sigma }}_{{\hat{\beta }}_k}$ (Wooldridge, 2013). The test of joint significance of AGE and AGE2 is performed using the Likelihood-ratio test. This test compares the log-likelihood values of the models with and without the factors AGE and AGE2, and tests whether this difference is statistically significant. The likelihood-ratio (LR) test statistic is given by: (6) $${\xi }_{\mathit{\text{LR}}}=2\left[\text{log}\hspace{0.17em}L(\widehat{\theta })-\hspace{0.17em}\text{log}\hspace{0.17em}L(\widehat{\theta })\right],$$(6) where $\widehat{\theta }$ is the unrestricted ML estimator (i.e. with factors AGE and AGE2) and $\widehat{\theta }$ is the constrained ML estimator (i.e. without factors AGE and AGE2) obtained by maximizing the log-likelihood function $\hspace{0.17em}\mathrm{\text{log}}\hspace{0.17em}L(\theta )$.

The set of parameters β in Equation (5)(5) $$F\left(w\right)=\frac{e^{x_i\beta }}{1+e^{x_i\beta }}$$(5) reflects the impact of changes in $x$ on the probability of adoption of the technology (Greene, 2008). One way to interpret these parameters, and to ease comparison across different models, is to consider the partial derivative of the probability that $y$ equals one with respect to a continuous explanatory variable, $x_k$, say. For the logit model used in this study, we obtain: (7) $$\frac{\partial L(x^{\prime }\beta )}{\partial x_k}=\frac{e^{x^{\prime }\beta }}{{(1+e^{x^{\prime }\beta })}^2}{\beta }_k$$(7)

Five of the variables used in the logit model are dummy variables. For these dummy variables, Equation (7)(7) $$\frac{\partial L(x^{\prime }\beta )}{\partial x_k}=\frac{e^{x^{\prime }\beta }}{{(1+e^{x^{\prime }\beta })}^2}{\beta }_k$$(7) is inappropriate, since the derivative is with respect to a small change. The appropriate marginal effect for these binary independent variables, say, $d$, would be (Greene, 2008): (8) $$\text{Prob}[y=1|{\overline{x}}_{(d)},d=1]-\text{Prob}[y=1|{\overline{x}}_{(d)},d=0]$$(8) where ${\overline{x}}_{(d)}$ denotes the means of all the other variables in the model.

Results and discussion

Description of interviewed poultry-keepers

Females represent about 42% of producers, with 48% of the participants in CBM compared to 42 and 31% for non-participants of experimental and non-experimental villages, respectively (Table 3). The average age of participants and non-participants in the CBM does not differ significantly. Concerning the level of education, roughly 33% of the producers have received formal education. Among the participants in CBM, 38% had received a formal education compared to 25 and 36% for non-participants of experimental and non-experimental villages, respectively.

Table 3. Some characteristics of poultry-keepers according to the participation in Community-Based Management.

Characteristics	Non-participant of non-experimental village (n = 90)	Non-participant of experimental village (n = 139)	Participant (n = 176)	Total (n = 405)
Gender of the breeder (%)
Female	31.1	42.4	47.7	42.2
Male	68.9	57.6	52.3	57.8
Age of the livestock-keeper (year)	43.0 (15.4)	43.0 (13.7)	45.3 (13.5)	44.0 (14.0)
Education (% of formal educated)	35.6	24.5	37.5	32.6
Obtained credit (% of yes)	5.6	5.0	77.8	36.8
Household size (number)	7.8 (4.2)	8.2 (4.1)	8.0 (4.6)	8.0 (4.3)
Type of poultry reared
Chicken	65.7	58.3	57.9	59.7
Chicken and duck	11.0	9.4	11.4	10.6
Chicken and guinea fowl	13.3	19.4	22.2	19.3
Chicken, duck and guinea fowl	10.0	12.9	8.5	10.4

( ): Figures in parentheses are standard deviations.

About 37% of the producers have access to credit, with about 78% of the participants in CBM received credit compared to 5 and 6% for the non-participants of experimental and non-experimental villages, respectively. Household size is almost the same (about 8 people per household) for participants and non-participants in CBM.

All the breeders interviewed are involved in chicken breeding. However, 11% of them also produce ducks, 19% also have guinea fowl and 10% have chicken, duck and guinea fowl.

Description of village poultry improvement technologies

As mentioned above, five types of technologies were targeted: vaccination of village poultry, improved feeding, henhouse and chick-house construction, and improved cockerel. The adoption rates of these new technologies are presented in Table 4.

Table 4. ‘Proportions of the breeders who adopted the new technologies.

		Non-participant of non-experimental village (n = 90)	Non-participant of experimental village (n = 139)	Participant (n = 176)	Total (n = 405)
Poultry vaccination (% of yes)		31.1	36.7	63.6	47.2
Appreciation of the vaccination (by breeders who vaccinate their birds)	Satisfied	56.8	85.3	84.2	80.5
	Little satisfied	43.2	14.7	15.8	19.5
Possess chick-house (% of yes)		16.7	13.7	46.6	28.6
Possess henhouse (% of yes)		47.8	59.0	89.8	69.9
Improved feed (% of yes)		6.7	21.6	50.0	30.6
Possess improved cockerels (% of yes)		5.6	8.6	13.6	10.1

Poultry vaccination

Modern treatment of village poultry is based on the use of vaccine and other medicine commonly used in intensive poultry production. It is a preventive measure, which supposes that the vaccination should be done before the birds are host to the disease, in particular Newcastle disease which is the most devastating disease of poultry in many regions (FAO (Food & Agriculture Organization of the United Nations) and IFAD (International Fund for Agricultural Development), 2022). The vaccination is performed by village poultry vaccinators (VPVs) and sometimes by veterinarians. About 47% of the breeders used to vaccinate their poultry, with about 64% for participants in CBM compared to 31% for non-participants in non-experimental villages (Table 4). This rate of poultry vaccination is higher than the value found at the national level. Indeed, at the national level, the rate of poultry vaccination is low. For example, in 2007, the national average rate of vaccination was 11% (DE/MAEP (Direction de l’Elevage Ministère de l’Agriculture de l’Elevage et de la Pêche), 2008). In a study carried out in the South and Center of Benin, Sedegan et al. (2023) found a vaccination rate of 13.3%.

About 81% of the producers (who used to vaccinate the birds) are satisfied with village poultry vaccination, with 84% of participants and 57% in non-participants of non-experimental villages (Table 4). This is in line with findings of other authors (Enahoro et al., 2021; Chemuliti et al., 2023) who noted that traditional poultry farmers are generally satisfied with the services of community vaccinators. But the main problem would be the availability and distribution of vaccines. The result is also in accordance with Thomsen (2005) who indicated that farmers recognize vaccination as being the most effective means of combating Newcastle disease, thus making this measure highly prioritized by farmers. However, smallholders make their own adjustments regarding the application of the measure, e.g. they may not be capable of accessing the funds needed for vaccinating every time their VPV decides to run a campaign, or they disagree as to when it should be done (Thomsen, 2005). Consequently, establishment of storage facilities at the village level would be of great importance for this purpose to encourage the adoption of vaccines and vaccination services for village poultry (Chemuliti et al., 2023). Furthermore, the very low coverage of the Beninese territory in veterinary pharmaceutical pharmacies with the low rate of qualified extension agents leads to low vaccination coverage of village poultry (Sedegan et al., 2023).

In addition, about 19% of breeders who vaccinate their birds are not satisfied with the VPV interventions for various reasons. First, certain producers indicate that the VPV are not always available, mainly because they have no salary. Second, in the surveyed villages in Northern Benin, producers blame the VPVs because they do not respect appointments. Third, some VPVs do not have equipment (for storage of the vaccines) and products (vaccines and antibiotic) are often unavailable at their level. Fourth, during the focus group discussion, producers stated that some VPVs do not master the timing for poultry vaccination, e.g. they vaccinate the birds when some are already infected or are ill. This bad timing increases the mortality rate of several birds, notably chickens. This then results in a decrease in the motivation of farmers to use modern treatment. Thomsen (2005) reports similar results stating that sometimes VPVs wait until they hear rumors of an approaching epidemic before they announce a campaign. However, amongst farmers who used to vaccinate their birds, 81% are satisfied with village poultry vaccination, with 84% of participants and 57% in non-participants of non-experimental villages. Fifth, the place to purchase the vaccines is far from the village. As a result, the VPVs increase the price of the vaccine to cover the cost of travelling and buying the vaccines. This situation reduces the producers’ ability to purchase the vaccines because of their low financial power.

These different situations linked to the use of modern vaccines have led certain producers to continue using traditional methods of treating poultry. Indeed, approximately 53% of the surveyed farmers continue to treat their birds with traditional methods, with about 36% for participants in CBM compared to 63 and 69% for non-participants of experimental and non-experimental villages, respectively. Traditional methods of village poultry diseases treatment are based on plants or various products purchased in local markets. The plants most used by the interviewed farmers are vernonia (Vernonia amygdalina), chili pepper (Capsicum frutescens) and basilica (Ocimum basilicum). The products usually purchased on the market include capsules, antibiotics (products normally used for the treatment of human disease), and glutamate (a white powder often used for seasoning sauces). Parasites are usually treated with ash and reptiles are driven out by surrounding the henhouse with carbide from welding shops.

Chick-house and henhouse construction

During the implementation of poultry-based projects, a model of the henhouse (constructed with locally available materials) is often suggested to the producers. However, farmers are not obliged to adopt this model type. Each producer can adapt the model to his own conditions and financial means. By allowing producers to adapt henhouses to their personal circumstances means that projects leave room for variability in the henhouses built. However, following Thomsen (2005), it is expected that the henhouses are in line with certain regulations regarding the optimal functioning of the structure. For example, henhouses have to have a door which is high enough for people to enter for cleaning purposes, as well as for some form of ventilation. Henhouses also have to contain nests, perches, drinking bowls and feeders, although such equipments are often badly kept. In some cases, breeders also possess modest cages for egg laying. During our study, we found that henhouses are made of clay and oil palm branches. Birds are also sheltered in small cages, which are often hung from the ceiling of the room or placed on the ground. Normally, each poultry keeper builds his henhouse himself, although women sometimes ask for their husband’s assistance. Young boys also participated in the building of the henhouse for the head of the household.

Table 4 shows the henhouses are available for 70% of poultry-keepers, with 90% for participants in CBM compared with 48% of the farmers of non-experimental villages. About 29% of the breeders have chick-houses, with 47% for participants in CBM compared to 14 and 17% for non-participants of experimental and non-experimental villages, respectively. These chick-houses are made of ribs of palm and are often cone-shaped.

Improved feeding

The improved feed (served mainly to adult birds) is just a combination of various locally available products such as corn, bones or shells of snails, small fishes, soy, salt and by-products of peanut oil. These products are ground down and are then served to birds in the morning, noon and/or afternoon, depending on the producer’s means.

About 31% of the breeders make improved feed to their birds, with 50% for the participants in CBM (Table 4). These measures are considerably less used by non-participants, particularly in non-experimental villages. Indeed, only 7% of the non-participants of non-experimental villages make improved feed to their birds, compared with approximately 22% of non-participants of experimental villages.

In this study, it is observed that traditionally, products such as chopped cassava, corn, bran of corn, rice, millet/sorghum, bean and by-products of gari (flour from cassava), are used to feed birds. For chicks, cereals are first crushed and for ducklings these crushed grains are coated with oil. Snails and worms (obtained from cattle excreta put in a pot for 3 to 4 days) are also used as feed. Termites are essentially used for the feeding of small guinea-fowl and sometimes chicks and ducklings. In some countries, such as Togo and Burkina Faso, producers have even developed techniques for producing termites, termite eggs and larvae (Farina et al., 1991; Pomalegni et al., 2017). As per Badjonama et al. (2020), the incorporation of termites, particularly dried termites, into feed helps improve the nutritional status, growth performance and profitability of village poultry.

These results are in agreement with Sonaiya and Swan (2004) and Kulla et al. (2021) who also noted that in village poultry farming, farmers often use by-products, leftovers from household meals, greens, insects, cereals or cereal bran as feed. However, these feeds are often insufficient in quantity and quality (particularly in terms of protein content) (Goromela et al., 2006). In most cases, the use of these poultry feeding practices aims to reduce feed expenses and ensure permanent availability of feed.

Improved cockerels

Improved males are only introduced for chickens through the ‘improved cockerels operation’ (Cordel, 2003; Chrysostome & Sodjinou, 2005). The aim of this introduction is to improve indigenous chicken performance: quantity of eggs produced and quantity of meat. Only 10% of the surveyed farmers have improved cockerels, with roughly 11% in the experimental villages and 6% in the non-experimental villages (Table 4). In the experimental villages, about 14% of the participants in CBM have improved cockerels compared to 9% of non-participants.

This result shows that the improved cockerel was not widely adopted (compared to the four other technologies analyzed above) because of various reasons, e.g. low resistance to disease and the socio-cultural role of indigenous poultry. Indeed, the study of Cordel (2003) and Chrysostome and Sodjinou (2005) showed that ‘improved cockerels operation’, financed by the Beninese Government, has produced interesting results: improvement in the weight of indigenous chickens and number of eggs laid. However, the main drawback of this operation is that it did not take into account the failures of similar operations carried out in the 1960s when the introduction of new genes¹ seriously impacted the phenotypic diversity, which is strongly valued in rural areas (Chrysostome & Sodjinou, 2005). Thus, the chickens with red, white or black plumage sought for traditional and ritual ceremonies became rare. Some farmers, therefore, consciously abandoned cockerels without care or killed them. Moreover, Chrysostome and Sodjinou (2005) state that the crossbreed obtained from the first generation are not adapted to traditional poultry rearing practices; consequently the rate of loss was high which is mainly due to the birds’ low level of resistance.

As stated by Chatterton and Chatterton (1982), the problem may be in the communication between government institutions and farmers (but also researchers), with the role of farmers (and researchers) being underestimated in the technology transfer process. The farmers may be socially and politically conservative, but they live a life that requires them and their families to take risks and to cope with the vagaries of the weather and markets in a manner that agricultural administrators would find extremely stressful (Chatterton & Chatterton, 1982). Kryger et al. (2010) argue that without considering the social and cultural aspects of smallholders’ livestock keeping, there is a risk that development interventions will fail to provide smallholders with the appropriate assistance. This is because where livestock keeping is concerned, smallholders do not act only on the basis of economic rationales, but also seek to fulfill their social and cultural obligations (Kryger et al., 2010).

Effect of community-based management on the adoption of the technologies

The introduction of Community-Based Management (CBM) in experimental villages (variable EXPVIL) has a positive effect on the adoption of all technologies analyzed in this study (Table 5). This effect is significant on the adoption of vaccination (at 5% level), improved feed, and henhouse (at 1% level). The implementation of the CBM in a village will, ceteris paribus, increase the probability of village poultry vaccination by 14 percentage points, the probability of henhouse adoption by 19 percentage points and the probability of using improved feed by 24 percentage points.

Table 5. Factors influencing the adoption of village poultry vaccination, chick-houses, henhouses, improved feed and improved cockerel: results of logit regression.

		Vaccination		Henhouse		Chick-house		Improved feed		Improved cockerel
Variable	Label	Coef.	Marg. Eff.	Coef.	Marg. Eff.	Coef.	Marg. Eff.	Coef	Marg. Eff.	Coef	Marg. Eff.
EXPVIL	Experimental village (1 = yes, 0 = no)	0.554** (0.282)	0.137** (0.068)	0.921*** (0.284)	0.190*** (0.064)	0.343 (0.349)	0.061 (0.058)	1.728*** (0.468)	0.235(0.043)	0.625 (0.525)	0.036 (0.026)
REGION	Region (1 = North, 0 = South)	0.650*** (0.248)	0.161*** (0.060)	0.601** (0.284)	0.109** (0.050)	−1.653*** (0.300)	−0.289*** (0.047)	−1.535*** (0.306)	−0.259*** (0.048)	−1.893*** (0.483)	−0.123*** (0.029)
GENDER	Gender of the breeder (1 = male, 0 = female)	0.095 (0.269)	0.024 (0.067)	0.726** (0.300)	0.138** (0.058)	0.722** (0.303)	0.130(0.053)	0.175 (0.306)	0.031 (0.054)	0.165 (0.422)	0.011 (0.027)
AGE	Age of the breeder (Years)	0.020 (0.042)	−0.002 (0.002)	0.041 (0.046)	0.001 (0.001)	0.040 (0.050)	0.001 (0.003)	0.107* (0.060)	−0.002 (0.002)	0.123 (0.090)	0.002 (0.004)
(AGE^2)/100	Square of AGE ([years]^2) divided by 100	−0.033 (0.043)	NN	−0.043 (0.047)	NN	−0.047 (0.051)	NN	−0.137** (0.065)	NN	−0.131 (0.098)	NN
EDUC	Formal education of the breeder (1 = educated, 0 = none)	0.790*** (0.269)	0.193*** (0.063)	0.443 (0.307)	0.079 (0.052)	0.148 (0.301)	0.028 (0.057)	0.680** (0.308)	0.129** (0.061)	0.514 (0.420)	0.037 (0.032)
HHSIZE	Household size	−0.026 (0.027)	−0.007 (0.007)	0.016 (0.032)	0.003 (0.006)	0.121*** (0.030)	0.023*** (0.005)	0.072** (0.030)	0.013** (0.005)	0.098** (0.038)	0.006*** (0.002)
CREDIT	Obtained credit (1 = yes, 0 = no)	1.289*** (0.251)	0.308*** (0.055)	1.784*** (0.338)	0.291*** (0.044)	1.549*** (0.280)	0.309*** (0.056)	1.567*** (0.281)	0.304(0.056)	0.077 (0.380)	0.005 (0.026)
(Constant)	Constant	−1.419 (0.973)	NN	−2.107** (1.047)	NN	−3.431*** (1.147)	NN	−5.126*** (1.352)		−5.931*** (1.989)

Marg. Eff.: marginal effects; Coef.: coefficient.

N = 405; likelihood-ratio (LR) chisquare(8) = 72.17***; log likelihood  =  −244.61; pseudo R² = 0.129; NN : not need.

Likelihood-ratio (LR) test for the joint significant of AGE and AGE2: LR chi-square = 1.75.

Value of AGE that maximizes the linear prediction = 32.33 years.

( ): Figures in parenthesis are standard errors.

*** Significant at 1%, ** Significant at 5%, * Significant at 10%.

The positive effect of CBM on the adoption of technology is consistent with our initial hypothesis formulated above and implies that producers from experimental villages are more likely to adopt new technologies than their counterparts from non-experimental villages. This can be explained by the fact that CBM promotes contact between producers, which facilitates discussions about production methods based on the participants’ experiences (e.g. the effect of henhouses, improved feed, and vaccination on the birds’ survival rate, see Sodjinou et al., 2012). This result is in line with Leight et al. (2022) who found that households exposed to the intervention are able to increase their use of poultry inputs (veterinary services, enhanced feeds, and deworming).

Another explanation for the positive effect of CBM on the adoption of various innovations is that the networks which arise from the implementation of the approach not only reduce the cost of accessing information for small-scale farmers, but also expose farmers to the technologies and make them aware of their benefits. This confirms the observation of Doss (2006), who notes that the first reason why farmers do not adopt improved technologies is simply that they are not aware of them. Following Sedegan et al. (2023), the very low coverage of the Beninese territory in veterinary pharmaceutical pharmacies with the low rate of qualified supervisory agents tends to a low vaccination coverage of the village poultry. This is in agreement with the findings of Ahuja et al. (2003), who show that in rural Odisha (a state of India), the demand for animal health services was linked to the general awareness level of the household, but was not linked to the rate of subsidy of veterinary services or products if they existed. In other words, poor livestock owners show willingness to pay for animal health services, but may have lower awareness of, and access to, the services (Kryger et al., 2010). This indicates the positive influence of information, knowledge and training on the adoption of traditional poultry improvement technologies, and needs to be strengthened as part of the agricultural extension system (Hailemichael et al., 2017; Sariyev & Zeller, 2023). Furthermore, in rural Benin, farmers often have limited access to public extension services. The situation is even more complicated for poultry producers as extension agents are often uninterested in this type of farming. In such circumstances, farmers often obtain information about new technologies through cooperative or farmer association meetings (Adegbola, 2010). This is confirmed by the finding of Boahene et al. (1999) who showed that small-scale farmers with limited resources tend to invest in their social networks for information rather than in extension services. Collaboration through social networks allows farmers to obtain the same level of knowledge at a lower cost (Zirulia, 2012).

Since experimental villages also have VPVs, the positive effect of CBM can also be explained by the fact that these VPVs contribute to removing the difficulty of access to vaccines for small rural farmers. Indeed, vaccines against Newcastle disease are not available in small doses in the market, thus constituting a key factor which discourages most farmers with few birds from vaccinating (Chemuliti et al., 2023). Trained VPVs are able to buy large doses of vaccines and offer door-to-door vaccination services at a cost, during the vaccination campaign period (Chemuliti et al., 2023).

Region location and adoption of technologies

The adoption of all village poultry improvement technologies is significantly influenced by the region of residence. Village poultry-keepers surveyed in the northern part of the country have a higher propensity to adopt poultry vaccination than the breeders in the southern part. The probability of village poultry vaccination is, ceteris paribus, 16 percentage points higher in the North than in the South (Table 5). This can be explained by three main reasons. First, in the North, farmers are more involved in cattle rearing where vaccination is frequently used. Therefore, a certain propensity to vaccinate animal exists for these peasants. Second, the difference between the two regions can also be explained by the difference in the vaccine supply system used. Indeed, in the North, VPVs are regrouped in a network (or associations) with the support of the project PAMRAD (Project of Rural Development in Atacora and Donga provinces). The main role of these associations is to purchase and sell vaccines (and other veterinary products) to their members. They used to purchase their products from private veterinarians.

For poultry vaccination to be effective and sustainable, there is need to improve the supply and accessibility of breeders to vaccines and other veterinary products, for example through the establishment of storage facilities at community level with the involvement of private veterinarians. In this sense, community-based approaches such as CBM have an important role to play (Enahoro et al., 2021; Chemuliti et al., 2023).

Third, the difference between the North and the South can also be explained by the difference in the profitability of village poultry vaccination. Indeed, Cordel (2003) shows that the profit of the VPVs was higher in the North (FCFA 2,000 to FCFA 2,500 to vaccinate 100 chickens) than the South (FCFA 500 to vaccinate 100 chickens). Also, the monthly gross margin for VPVs in the North can reach FCFA 15,000 compared to FCFA 2,500 for VPVs in the South. This difference in the monthly margins of the VPVs can be explained by the quantity of vaccines marketed.

The regional location has also a significant effect both on the adoption of chick-houses, and on the adoption of henhouses. While, ceteris paribus, households in the North have an 11 percentage points higher probability of having a henhouse, they have, ceteris paribus, a 29 percentage points lower probability of having a chick-house compared to households in the South. The higher probability for adopting henhouses in the North may be explained, as mentioned above, by the fact that the producers in the North have greater manpower and greater access to credit and they are therefore more inclined to adopt the technology.

The breeders in the North have a significantly lower probability of adopting improved feed and improved cockerel. This means that breeders in the South are more willing to adopt improved feed than those in the North. Thus, ceteris paribus, the probability of improved feed adoption is 16 percentage points higher in the South than the North.

Effect of socioeconomic factors on the adoption of the technologies

Four socioeconomic factors were included in the logit model including the gender, the age, education of the producer and the household size. The gender of the breeder significantly influences the adoption of chick-houses as well as the adoption of henhouses, where male producers are more likely to provide shelter for their birds than female breeders (Table 5). In other words, the probability of henhouse adoption tends to be, ceteris paribus, 14 percentage points higher for male breeders (13 percentage points in case of chick-houses). This can be explained by the fact that male producers have greater access to labor and financial means than females. This result is in accordance with Houndonougbo (2005) who found that male smallholders have less financial constraints when constructing poultry housing than their female counterparts. Furthermore, Thomsen (2005) noted a difference in size and solidness between the structures raised by men and women. Thomsen (2005) argues that this difference can partly be explained by the actual physical work needed for construction with women depending on male assistance, whether it is their husbands or a hired work force (Nordhagen et al., 2019). In the latter case, women tend to compromise on the quality and size of the housing due to their limited financial means. However, even if female smallholders do not need to raise funds to pay outsiders to construct a poultry house for them, but can instead receive assistance from their husbands, they still end up with relatively small structures in awkward places, sometimes quite far away from their homes (Thomsen, 2005).

The difference between males and females regarding the adoption of henhouses may also be explained by the prestige associated with henhouses, which is sought by men. Thus, as noted by Thomsen (2005), to some men, a large and attractive looking poultry house may act as an important status symbol, so they are more than willing to invest considerable funds and work into its construction. This is not the case for women who have more economic vision. Indeed, to the women, poultry keeping is mostly about making money, or reinforcing social positions, and therefore a henhouse has a mainly functional purpose (Thomsen, 2005).

The age of the breeders has a significant effect on the adoption of improved feed. As the signs of both the coefficient of age as well as AGE² (square of age) are significantly different from zero, the probability of adoption of improved feed depends nonlinearly on the breeder’s age. The value of age that maximizes the linear prediction is 40.55 years. This implies that the effect of age on the probability of improved feed is inversely U-shaped. In other words, until the age of 40.55, the probability of adopting improved feed increases with the breeder’s age, but after 40.55, the probability of adopting improved feed decreases with the breeder’s age. To put it another way, producers are open to the use of improved feed for village poultry production until the age of 40.55, after which they become less receptive to the technology.

As expected, the education has a positive and significant effect (at 1% level) on the adoption of village poultry vaccination and the use of improved feed. This means that producers who have received a formal education are more likely to vaccinate their poultry and use improved feed than uneducated farmers. The probability that an educated person will adopt these technologies is, ceteris paribus, 13 percentage points (for improved feed) to 19 percentage points (for vaccination) higher than an uneducated person. Therefore, an improvement in the education level of producers can increase the adoption of vaccination and the use of improved feed.

The results confirm the important role that community-level education, particularly CBM, plays in the adoption of village poultry improvement technologies. Indeed, Weir and Knight (2004), in a study carried out in rural Ethiopia show that community-level education encourages uneducated farmers to adopt.

These results show that the effect of individual producer education on the adoption of vaccination is higher than the effect of CBM. On the other hand, the effect of CBM on the adoption of the henhouse and improved feed is higher than that of CBM. But in reality, as Weir and Knight (2004) stated, the effects of individual-level education and community education are complementary. Indeed, the former allows producers to read and better understand extension documents, which in turn, improve producer propensity to adopt innovations (Asfaw & Admassie, 2004; Bucci et al., 2019). Community-level education allows uneducated producers to have access to information (particularly through exchanges with their peers), enhancing thus the perceived benefit and usefulness of technologies, and even adopt it sooner than expected.

Household size has a positive and significant effect on the adoption of chick-houses, improved feed and improved cockerel. The adoption of these technologies is less attractive for farmers with limited family labor. Increasing the household size by 1 person increases, ceteris paribus, the probability of adopting these technologies by 2 percentage points for chick-house, 1.3 percentage points for improved feed and 0.6 percentage point for improved cockerel. This positive effect can be explained by the fact that the use of these technologies requires daily labor, in particular for the care of chicks (as well as chick-house), and improved cockerels in order to facilitate their adaptation to their new environment. The production of improved feed as well as the construction of chick-house require additional labor, which is often unavailable in small households. This result is in line with Udimal et al. (2017) who, in a study on the adoption of the Nerica rice variety in Ghana, showed that larger households were more likely to adopt Nerica than farmers with low availability of labor. Indeed, family labor sometimes constitutes the first recourse of the farmer when he adopts a technology which leads to an increase in the workforce (Tede et al., 2023). In other words, the availability of family labor is a relief for rural producer since they can immediately rely on it when needed (Udimal et al., 2017).

Effect of other institutional factors on the adoption of the technologies

Table 5 results indicate that there was a statistically significant difference in access to credit and the adoption of poultry vaccination (at 1% level), e.g. access to credit increases the probability that farmers will adopt village poultry vaccination by 31 percentage points. Compared with the other variables used in our logit model, access to credit has the highest impact on vaccination adoption. This result is in accordance with Feder et al. (1985) who argue that credit is an important determinant for the adoption of new technologies. This implies that improving producers’ access to credit will increase the adoption of poultry vaccination. As in the case of vaccination, access to credit is the main factor which influences the adoption of henhouses and chick-houses. Indeed, access to credit has a positive and significant influence on the adoption of henhouses and chick-houses. Thus, the probability of henhouse adoption increases by, ceteris paribus, 29 percentage points when farmers have access to credit, and 31 percentage points in the case of chick-houses.

The adoption of improved feed is highly and significantly (at 1% level) influenced by the producer’s access to credit. To put it another way, breeders’ access to credit might increase the probability of adopting this technology by 30 percentage points. Contrary to the four technologies analyzed above, access to credit does not have a significant effect on improved cockerel adoption. This result corroborates those of Tede et al. (2023), for whom access to credit strengthens the decision of producers to adopt new technologies. This is also in line with Hailemichael et al. (2017) who found that factors related to institutional support enhanced the use of poultry vaccines (and other veterinarian products) and scale of operation of poultry.

Conclusion and implications

In conclusion, the CBM approach brings some changes to the peasant’s behavior concerning the management of village poultry farming. Farmers from experimental villages are also more likely to adopt various village poultry improvement technologies such as vaccination, improved feed, henhouses and chick-houses. This indicates that when the farmer has the information and technical support through an approach based on the community, i.e. CBM, he can change his behavior over the traditional poultry farming to obtain increased profits. The main policy implication of this result is that the government or development actors must invest in the dissemination of information and assistance on village poultry improvement in particular through community-based approaches.

Education also has a positive and significant effect on the adoption of village poultry improvement technologies. Farmers in the South are more likely to adopt improved cockerel, chick-houses and improved feed than those in the North. On the other hand, producers in the North are more likely to adopt henhouses and poultry vaccination than those in the South. Our results also suggest that decisions concerning the adoption of village poultry improvement technologies also depend on access to resources, notably credit. The main policy implication is that it might be important for the government or development actors to improve the access to credit for farmers.

The experience of the producer in the vaccination of other animal species and the strategy used (e.g. VPVs organized in association, involvement of private veterinarians in the supply of VPVs with vaccines and other veterinary products) foster the adoption of village poultry vaccination. The policy implication of this result is that it might be beneficial to organize VPVs in associations also in other regions of the country and to put them in touch with private veterinarians.

Author contributions

The author conceptualizes the research, designs the methodology, hires enumerators with him for the field data collection and writes the report. The author read and approved the final manuscript.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

Additional information

Funding

The Danish International Development Agency (DANIDA) funded this study through the second phase of Benin Agricultural Sector Development Support Program (PADSA2).

Notes on contributors

Epiphane Sodjinou

Epiphane Sodjinou is a citizen of Benin. He is an Associate Professor in Agricultural Economics at the University of Parakou (Benin). Before joining the University of Parakou, Epiphane worked as a junior agricultural economist researcher at Agricultural Policy Analysis Unit (PAPA) of the National Agricultural Research Institute of Benin (INRAB). He holds an Agricultural Economics Engineer in Faculty of Agricultural Sciences from the University of Abomey-Calavi, an M.Sc. in Applied Statistics from the Department of Agricultural Sciences of Gembloux, Belgium, and a Ph.D. in Development Economics from the University of Copenhagen. His research interests include production economics, impact assessment, and agricultural policy analysis.

Notes

1 The main breeds used were Rhode Island Red and Plymouth Rock.