The Dynamics and Role of Gender in High-Value Avocado Farming in Kenya

Abstract

We used two-wave panel data obtained from avocado growers in Murang'a County in Kenya to examine, through the perspective of gender, the dynamics of farmers' participation in avocado production and marketing organizations (PMOs), and test whether understanding group dynamics is important for analyzing contract farming. Using a multinomial logit (MNL) model, we identify the characteristics of men and women participating in PMOs categorized as early adopters, dis-adopters, late adopters, and non-adopters. We focus on dis-adopters and late adopters because these categories are most often ignored in the literature. Moreover, without considering the dynamics, we verify the influencing factors of PMOs by estimating a random-effects logit model that controls for unobserved heterogeneity across households. Furthermore, we estimate a sequential-choice model to test whether the process of selection into group membership affects the process of selection into contracting. Our results reveal heterogeneity with regard to household, farm, and resource characteristics across categories of farmers and between gender groups. Besides, the results reveal that group and contracting dynamics are related, and ignoring the former leads to biased estimates of the determinants of contracting dynamics. Policy efforts should focus on supporting women farmers to enhance their participation in PMOs, which ultimately affects contracting. Improving access to high-yielding avocado varieties and building capacity in orchard management would enhance women's decisionmaking including group participation, contracting, and marketing. Low-cost agricultural credit may also improve women's ownership of improved avocado trees and hence their participation in high-value markets.

Effects of Avocado Farming on Livelihoods and Biodiversity: Perspectives of Smallholder Farmers in Hai and Rungwe Districts, Tanzania

“…They also revealed that group and contracting dynamics were related and recommended policy efforts to focus on supporting women farmers to enhance their participation in avocado production and marketing organisations (PMOs) PMOs, which ultimately affects contracting. Muriithi & Kabubo-Mariara (2022) also underscore the need to improve access to high-yielding avocado varieties and build capacity in orchard management that would enhance women's decision-making including group participation, contracting, and marketing. In addition, they recommend the provision of low-cost agricultural credit that would also improve women's ownership of improved avocado trees and hence their participation in high-value markets.…”

Determinants of Adoption and Dis-Adoption of Integrated Pest Management Practices in the Suppression of Mango Fruit Fly Infestation: Evidence from Embu County, Kenya

“…Multinomial logit is used in cases where the dependent variable has many choices [8]. Other models used are the negative binomial regression, logistic regression, and Poisson [17]. These models use cross-sectional data and are not suitable for the current study, which uses panel data.…”

“…The variable was specified as a dummy variable; a farmer using MAT was assigned one and zero otherwise for the adoption model, while one who used the technology before and stopped was assigned one and zero otherwise in the dis-adoption model (Table 1). The choice for independent variables included in the adoption and dis-adoption models was informed by the literature on agricultural technology adoption and, particularly, the adoption of fruit fly IPM and, in the context of our study [8,[10][11][12][13][14][15]17], includes demographic characteristics, household resources, access to information, social capital, and networking and technology attributes.…”

“…Even though a significant portion of the literature on technology adoption has focused on factors influencing adoption, there exists limited evidence on the factors influencing technology dis-adoption, since most studies have treated dis-adopters as nonadopters [8,[10][11][12][13][14][15]17]. Similarly, there is limited information on fruit fly IPM dis-adoption.…”

“…Wangithi et al [8] assessed the determinants of fruit fly IPM dis-adoption in Kenya using cross-sectional data, but did not fully examine the drivers of adoption or explore the dynamics of adoption. In addition, previous studies on fruit fly IPM adoption [8,[10][11][12][13][14][15]17] have not considered IPM-technology-specific factors, such as cost of IPM, quality of IPM, and unavailability of IPM when assessing the determinants of adoption of the technology. This study fills these gaps by assessing the determinants of fruit fly IPM adoption, as well as their dis-adoption using duration analysis.…”