Accurate prediction of sugarcane yield is essential for optimizing agronomic inputs and enhancing productivity in commercial and smallholder production systems. This study compared the predictive performance of Response Surface Methodology (RSM) and conventional Multiple Linear Regression models for estimating sugarcane yield from agronomic and environmental variables. Field-level data on soil pH, organic matter content, fertilizer application rate, irrigation level, and planting density were analysed. A second-order RSM model was fitted using a Central Composite Design with 32 factorial points, 10 axial points and 2 centre points, while a multiple linear regression model was estimated under the conventional framework. Model performance was evaluated using the coefficient of determination (R2), root mean square error (RMSE), mean absolute error (MAE), and Akaike Information Criterion (AIC). Results indicated that RSM outperformed conventional regression models in capturing interaction and quadratic effects, yielding higher predictive accuracy and lower error metrics. The quadratic Response Surface Model (R2 = 0.975, adjusted R2 = 0.954) performed well compared to the Multiple Linear Regression (R2 = 0.922, adjusted R2 = 0.912), achieving lower prediction errors (RMSE = 1.26, MAE = 0.98) and a substantially lower Akaike Information Criterion (AIC = 29.7 vs. 62.0), demonstrating greater efficiency for sugarcane yield estimation. The findings demonstrate the robustness of RSM for modelling complex agronomic systems and highlight its suitability for yield optimization strategies in sugarcane production. The study concluded that Response Surface Methodology is a superior modelling approach for sugarcane yield prediction and optimization, and recommends its integration into decision-support systems to guide fertilizer, irrigation, soil pH, organic matter content, and planting density management for improved productivity and climate-resilient sugarcane production.
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