From Prediction to Practice: A Systematic Review of the Decision Support Gap in Artificial Intelligence and Machine Learning for Environmentally Sustainable Livestock Production

Abstract

Artificial intelligence (AI) and machine learning (ML) are promoted as tools for improving the environmental sustainability of livestock production, but it remains unknown whether the models reported in this literature are used to support real-world decisions. We systematically reviewed studies that applied AI or ML to environmental sustainability outcomes in livestock production. Four databases were searched in January 2026, and 165 of 1994 screened records were included. The protocol was registered, but the main outcome was defined after data extraction, so the analysis is exploratory. A study counted as delivering decision support only if it named a specific decision and used its model to compare options or recommend an action. The authors of 96 studies (58.2%) stated that their work could support decisions, but only 26 studies (15.8%) met our definition. This gap appeared in 10 of 11 application areas. The gap held under both a broader and a stricter definition, and when a second coder repeated the coding. Only 30 studies (18.2%) tested their model outside the setting where it was developed, and 17 (10.3%) included an optimization step, which turns a prediction into a choice. Linking ML with mechanistic farm models is a promising approach that still needs testing. We also describe the main components a practical decision tool would need.