Abstract
The majority of smallholder farmers in Sub-Saharan Africa face myriad challenges to participating in agribusiness markets. However, how the spatially explicit factors interact to influence household decision choices at the local level is not well understood. This paper’s objective is to identify, map, and analyze spatial dependency and heterogeneity in factors that impede poor smallholders from participating in agribusiness markets. Using the researcher-administered survey questionnaires, we collected geo-referenced data from 392 households in Western Kenya. We used three spatial geostatistics methods in Geographic Information System to analyze data—Global Moran’s I, Cluster and Outliers Analysis, and geographically weighted regression. Results show that factors impeding smallholder farmers exhibited local spatial autocorrelation that was linked to the local context. We identified distinct local spatial clusters (hot spots and cold spots clusters) that were spatially and statistically significant. Results affirm that spatially explicit factors play a crucial role in influencing the farming decisions of smallholder households. The paper has demonstrated that geospatial analysis using geographically disaggregated data and methods could help in the identification of resource-poor households and neighborhoods. To improve poor smallholders’ participation in agribusiness, we recommend policymakers to design spatially targeted interventions that are embedded in the local context and informed by locally expressed needs.
Highlights
Smallholder farmers are important drivers of food security, poverty reduction, and livelihoods in rural and peri-urban areas in developing countries
The findings correlate with average food insecurity of 40% for both counties reported in the Kisumu and Vihiga County Integrated Development Plans (2018–2022)
This confirm10sotfh1a9t in both study areas, there is presence of spatial clustering and patterns that could not be the result of mcoampps,lethtee shpoattsiaplortasnadnodmconledsspooftdsaatrae.as are statistically significant local spatial clusters of high values and lRowesuvlatsluoefs,CrleusspteecrtiavnedlyO
Summary
Smallholder farmers are important drivers of food security, poverty reduction, and livelihoods in rural and peri-urban areas in developing countries. Many factors interact to impede their access to and participation in agribusiness markets, including high poverty levels, lack of access to productive resources, and low endowment of human, financial, physical, and socio-economic livelihood capitals, among others [4,5,6,7,8,9] The outcome of these factor interactions within individual households is most pronounced at the local level (i.e., farms and neighborhoods). Their spatial manifestation can be observed from the resulting diverse smallholder farming typologies across rural landscapes [10]. Tuhne eaaimrtohf lthoicsaplaspperawtiaasl tfhaucstotorms athp,aatnianlyflzue eanndcegesom-vaislulahliozledgeeroghroapuhsiecahlloyledxsp’lidciet cisions to participatede(oterrmnionta)nitns, iangdriebteuctsiinngetshse ipnretsheenctewoof ssttautisdtiycaallryeasisg.nificant local spatial patterns in Nyando and Vihiga study areas This was done to unearth local spatial factors that influence smallholder. The topography of Nyando is predominantly flat while Vihiga’s is undulating in the east and gently flat in the west
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