INTEGRATING CLIMATE VARIABILITY AND SOIL DYNAMICS INTO HYBRID ENSEMBLE LEARNING MODELS FOR ADAPTIVE CROP YIELD PREDICTION IN SUB-SAHARAN AFRICA

Accurate crop yield prediction remains a cornerstone of sustainable agriculture and food security, particularly in regions vulnerable to climate fluctuations such as Sub-Saharan Africa. This study develops an adaptive hybrid ensemble learning model that integrates climatic and soil parameters to improve crop yield prediction accuracy. The proposed framework combines Decision Tree Regressor and Ridge Regression as base learners, while Linear Regression serves as a meta-model to optimize ensemble predictions. A dataset spanning 1990–2020 was analyzed and preprocessed using normalization and feature selection techniques based on agronomic significance. Model optimization was performed using GridSearchCV to fine-tune hyperparameters. Experimental results revealed that the stacking ensemble achieved superior performance, with an RMSE of 0.1318, MAE of 0.0804, and R² of 0.9766, outperforming individual models. The findings underscore the effectiveness of hybrid ensemble methods in modeling nonlinear agricultural systems and demonstrate the potential of machine learning to support data-driven agricultural decision-making. Future work will explore dynamic adaptation to real-time environmental data and regional transferability across diverse agricultural ecosystems.