Machine Learning and Response Surface Methodology for Optimizing Olive Waste Compost in Sustainable Chickpea Production

Abstract

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.This study combines machine learning (ML) and response surface methodology (RSM) to optimize and predict the effects of compost made from olive mill waste cake residues (OMWC) on chickpea yield. Compost was applied to chickpeas irrigated with rainwater, and plant growth, phenology, and yield were monitored. Four modeling techniques RSM with Box-Behnken Design (RSM-BBD), artificial neural networks (ANN), support vector machines (SVM), and XGBoost, were employed to identify optimal compost application conditions. The RSM-BBD and ANN models showed superior predictive performance, with high coefficients of determination (R² = 0.9205 and 0.9718, respectively) and low root mean square error (RMSE = 8.0368 and 4.2833, respectively). In contrast, SVM and XGBoost showed lower accuracy. These results highlight the importance of selecting appropriate modeling approaches based on the problem and accuracy needs. This work advances understanding of crop yield prediction and supports sustainable agriculture through improved compost use, with clear practical implications for Moroccan chickpea production