MAPPING FARMERS' NETWORKS FOR EFFECTIVE AGRICULTURAL INFORMATION SHARING IN WESTERN KENYA

Abstract

Agricultural information sharing is crucial for improved productivity and economic development, especially in developing countries. This study aimed to utilize Social Network Analysis (SNA) as a tool to map, measure, and analyze social relationships among farmers and their networks in Western Kenya. The research identified opinion leaders, influencers, and clusters in the network, using SNA metrics such as density, degree, and clustering coefficient. The study was conducted in two villages of Nambale Sub-county in Busia County, Kenya, where farming is characterized by low input and low output. The research revealed clear distinctions between villages in terms of SNA measures and provided insights into network structures, cohesion, and shape. A step-by-step approach was provided for applying SNA to information flow, and the analyses were presented using UCINET and Net-draw software. The study found that SNA offers a valuable approach to information sharing and can be useful in identifying key actors in the network. Respondent-driven sampling design was adopted for data collection, and the study highlights the importance of network boundaries and sampling strategy. The authors conclude that the methodology can be effectively applied to evaluate interventions to improve information networks. The use of SNA in agricultural extension is significant, yet its use is limited, and this study highlights the benefits and strengths of this approach in facilitating information flow among farmers. A broad range of potential applications of this tool is possible, including using it to help design interventions to promote adoption of agricultural innovations