This analysis reveals socioeconomic factors significantly influence farming practices in agro-pastoralist communities, suggesting holistic support may enhance food security.
Agro-pastoralist communities residing in dryland regions rely on livestock and crop production as their main source of livelihood. However, information on their socioeconomic characteristics and how they shape their farming practice is very limited. This hinders policy interventions in addressing constraints affecting farmers consequently making them vulnerable in the challenging arid and semi-arid conditions. This study was conducted in dryland areas of West Pokot County in Kenya. It involved multistage sampling techniques, stratified systematic random sampling to draw a representative sample. Data on socioeconomic variables and farming practices were collected using questionnaires and focused group discussions. Descriptive statistics and logistic regression were used to analyse the data. The research findings showed that household heads were mostly male dominated (82%), educated (59%), had an average age of 38 years and with a mean family size of seven members. Crop production and livestock sales were identified as the primary source of income by 84% and 45% of the respondents, respectively. Farming practices showed that crop farming was small-scale in less than 2 acres of land (75%), mainly under mixed cropping systems (69%) and incorporating organic manure (62%) using hand hoe (56%) and tractor (42%) for farming operations. The logistic regression analysis revealed significant influence of socioeconomic factors on farming practices, helping in understanding the adoption of adaptive strategies for improving soil health and farm productivity. These practices promote agricultural resilience by addressing challenges such as climate variability while supporting food security, nutrition, and sustainable livelihoods in the fragile dryland ecosystems. This study recommends deliberate multi-sectoral support for inclusive farmer extension services, trainings, access to farm inputs and mechanization to improve operational efficiency and optimize crop production.
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Alkamoi et al. (2025) studied this question.
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