A typology of adopters and nonadopters of improved sorghum seeds in Tanzania: A deep learning neural network approach

Abstract

Abstract For more than three decades, the direction of agricultural research and extension efforts have been toward developing improved seeds for agricultural transformation in Sub-Saharan Africa. Despite these efforts and substantial investment in physical and human capital, the adoption of improved seeds has remained marginal. One of the factors constraining adoption is limited choices among heterogeneous small-scale farmers often targeted by fit-for-all agricultural technologies. In this paper, we typify small-scale sorghum producers in Tanzania based on the socio-economic characteristics of farmers that include a propensity for adoption and intensity of adoption. The two variables are predicted using a deep learning neural network with back-propagation. The visualization of identified adopters and nonadopters groups is achieved using t-distributed stochastic neighbor embedding. Knowing the typology of farmers is a critical first step when the goal is scaling-up the adoption process through tailored advisory services. Results show that sorghum producers in Tanzania are heterogeneous, and there is a need for developing targeted agricultural innovations and public policies that serve specific groups of farmers. Since Tanzania agricultural policies are formulated at the national level, there must be room for adjustment by regional and district levels authorities to reflect local demand for services.