A Spatially Explicit Approach for Targeting Resource-Poor Smallholders to Improve Their Participation in Agribusiness: A Case of Nyando and Vihiga County in Western Kenya

摘要: The majority of smallholder farmers in Sub-Saharan Africa face myriad challenges to participating agribusiness markets. However, how the spatially explicit factors interact influence household decision choices at local level is not well understood. This paper’s objective identify, map, and analyze spatial dependency heterogeneity that impede poor smallholders from Using researcher-administered survey questionnaires, we collected geo-referenced data 392 households Western Kenya. We used three geostatistics methods Geographic Information System data—Global Moran’s I, Cluster Outliers Analysis, geographically weighted regression. Results show impeding exhibited autocorrelation was linked context. identified distinct clusters (hot spots cold clusters) were statistically significant. affirm play a crucial role influencing farming decisions households. paper has demonstrated geospatial analysis using disaggregated could help identification resource-poor neighborhoods. To improve smallholders’ participation agribusiness, recommend policymakers design targeted interventions are embedded context informed by locally expressed needs.