wseas-12190-20261012064045-6a40da 20261012064045311 wseas/wseas wseas.group@gmail.com WSEAS International Journal of Computational and Applied Mathematics & Computer Science 2769-2477 06 10 2026 06 10 2026 6 Adebayo O. P Department of Statistics Federal University of Agriculture and Technology Okeho Oyo State NIGERIA Ahmed I Department of Statistics Nasarawa State University Pmb 1022 Keffi Nasarawa State NIGERIA Garba I. M Department of Agriculture Phoenix University Agwada Pmb 1019 Keffi Nasarawa State NIGERIA Mustaph A. D National Bureau of Statistics NIGERIA Spatial heterogeneity is a fundamental characteristic of agricultural landscapes, and accurately modelling its impact on crop yield is critical for precision agriculture. This study presents a comparative analysis of spatial statistical and machine learning approaches to model yield variability in a field exhibiting strong spatial dependence. Initial geostatistical analysis confirmed significant positive spatial autocorrelation (Moran's I = 0.7355, p < 2.2e-16). A series of models were evaluated, progressing from linear and polynomial trend surfaces to spatial regression (Lag and Error), mixed effects, and a Random Forest ensemble. The results demonstrate that while traditional spatial models effectively captured and explained the spatial autocorrelation, a Random Forest model significantly outperformed all others in predictive accuracy, achieving a 31% lower Root Mean Square Error (36.05) than the best spatial regression model and explaining 71.02% of the variance. Among the spatial models, a quadratic trend surface with a row × column interaction provided the most parsimonious fit, revealing an accelerating yield increase along rows and a U-shaped pattern across columns. 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