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Economic Determinants of IR2793-80-1 Rice Yield in Bunyala Irrigation Scheme, Kenya: Evidence from Comparative Production Function Modelling

2026 · International journal of research and innovation in social science · Vol 10, pp. 2290-2303 · 0 citations

Abstract

Rice productivity in Kenya remains below domestic demand despite continued investment in irrigation and improved varieties. This study analysed the economic determinants of IR2793-80-1 paddy yield among smallholder farmers in Bunyala Irrigation Scheme, Kenya, and examined whether the commonly used Cobb-Douglas production function adequately represented the observed input-output relationships. An explanatory cross-sectional survey design was adopted. A target sample of 333 farmers was determined, 266 questionnaires were administered, and 258 complete responses were retained for analysis. Paddy yield was measured in kilograms per acre; inorganic fertiliser was converted into formulation-adjusted nutrient quantity per acre; farm size was measured as acreage planted with IR2793-80-1; labour as person-days per acre; and productive credit as borrowed funds used directly for rice production per acre. Descriptive statistics and Spearman correlation preceded Ordinary Least Squares estimation of Linear, Cobb-Douglas and centred Translog production functions. The Cobb-Douglas benchmark explained 35.8% of variation in logged yield, whereas the centred Translog model explained 50.9%. The flexible terms significantly improved explanatory power (ΔR² = .151; F-change = 7.484, p < .001), while AIC and BIC also favoured the Translog specification. In the preferred model, land (B = .217, p < .001), labour (B = .151, p < .001) and productive credit (B = .030, p < .001) had positive first-order effects at the mean input combination. Fertiliser and credit exhibited significant positive curvature, land showed diminishing curvature, and the labour-credit interaction was negative and significant. The findings demonstrate that IR2793-80-1 yield is characterised by nonlinear and interdependent input relationships. The study recommends coordinated nutrient management, manageable acreage, timely labour organisation and production-linked credit rather than isolated increases in individual inputs.

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