1. Machine Learning and Causal Analysis to Support Improved Crop Decision-making

While machine learning (ML) models, particularly Extreme Gradient Boosting (XGBoost) and Random Forest (RF), have demonstrated potential in generating accurate crop yield predictions, their practical adoption for on-farm decision support remains limited. A key challenge lies in their fundamentally associative nature, which, without additional tools, can reduce interpretability and diminish practitioner confidence. Explainable Artificial Intelligence (XAI) techniques like SHAP values address one... M. Chan Fu Wei, J.P. Molin, A. Colaço, L. Longchamps