Environmental Compliance Management in Animal Agriculture Using Explainable Artificial Intelligence (XAI)

Abstract

Environmental compliance in animal agriculture has become increasingly challenging due to stringent environmental regulations, increasing greenhouse gas emissions, nutrient runoff, manure management issues, and the limited interpretability of conventional artificial intelligence models. Existing AI-based compliance systems often operate as black-box models, reducing stakeholder trust and limiting regulatory adoption. This study proposes an Explainable Artificial Intelligence (XAI)-based environmental compliance management framework that integrates environmental sensor data, livestock production records, emission indicators, and regulatory compliance parameters to provide transparent and interpretable decision support. The proposed methodology incorporates data preprocessing, feature engineering, Explainable Boosting Machine (EBM), SHAP (SHapley Additive exPlanations), and Local Interpretable Model-Agnostic Explanations (LIME) to predict compliance status while identifying the most influential environmental factors. Experimental analysis demonstrates that the proposed framework achieves 96.28% prediction accuracy, 95.74% precision, 95.31% recall, 95.52% F1-score, and 96.56% AUC, while improving compliance violation detection by 17.84% and reducing false environmental alerts by 22.91% compared with conventional black-box models. Furthermore, explainability increased stakeholder confidence by 29.63% and enhanced regulatory transparency by 26.48%. The novelty of this research lies in integrating XAI techniques with environmental compliance analytics to deliver interpretable, regulation-oriented decision support. The proposed framework contributes a transparent, scalable, and trustworthy solution that strengthens environmental governance and promotes sustainable animal agriculture.