Machine Learning-Based Prediction of Greenhouse Gas Emissions from Livestock Farming Systems

Abstract

Greenhouse gas (GHG) emissions from livestock farming significantly contribute to climate change through methane (CH₄), nitrous oxide (N₂O), and carbon dioxide (CO₂) released during enteric fermentation, manure management, and feed production. Accurate prediction of these emissions is essential for developing sustainable livestock management strategies and supporting environmental policy decisions. This study presents a machine learning-based framework for predicting GHG emissions using livestock production, feed composition, manure characteristics, climatic variables, and farm management practices. The proposed methodology incorporates data preprocessing, feature engineering, correlation analysis, feature selection, and hyperparameter optimization to improve predictive performance. Multiple machine learning models, including Linear Regression, Random Forest, Support Vector Regression, Extreme Gradient Boosting, and LightGBM, are comparatively evaluated using standard regression metrics such as R², RMSE, MAE, and MAPE. Feature importance analysis is performed to identify the most influential emission drivers and enhance model interpretability. Experimental findings demonstrate that ensemble learning models achieve superior prediction accuracy and robustness compared with conventional regression approaches while effectively capturing nonlinear relationships among environmental and management variables. The proposed framework provides an efficient decision-support tool for emission monitoring, precision livestock farming, and sustainable agricultural planning, contributing to climate-smart farming practices and informed environmental management.