<p>Our research work extends the application of ensemble learning models for crop yield prediction by incorporating interpretability techniques to explain stacked regression models. Building on top of the prior work focused on predicting yields in the Davangere district using ensemble methods like Ridge Regression, Random Forest, XGBoost, CatBoost, LightGBM, and Gradient Boosting, our work emphasizes model transparency and explainability. While ensemble methods excel in predictive accuracy, their complexity often obscures the reasoning behind predictions, limiting their practical adoption by stakeholders in agriculture. To address this challenge, Local Interpretable Model-Agnostic Explanations (LIME) and SHapley Additive explanations (SHAP) were applied to a stacked regression framework combining the strengths of multiple ensemble models. These interpretability techniques were used to identify key features influencing crop yields, such as soil health, weather conditions, and crop type. The stacked regression model achieved an impressive accuracy of 99.64%, highlighting its effectiveness in capturing complex relationships in the dataset. By elucidating feature importance and the localized effects of variables, the study bridges the gap between high-performing predictive models and actionable agricultural insights. The findings demonstrate that while stacked regression improves accuracy, LIME and SHAP effectively clarify the contributions of individual features, providing intuitive explanations for both global trends and specific predictions. By making advanced models interpretable, this research advances the practical application of ensemble learning in agriculture, promoting transparency and trust. It underscores the critical role of interpretability in fostering sustainable practices, optimizing resource allocation, and adapting to environmental changes.</p>