Artificial intelligence is rapidly entering dairy barns and poultry houses. Computer vision systems monitor lameness and body condition, acoustic models infer thermal and respiratory stress, sensor networks support herd- and flock-level digital twins, and cloud platforms translate these data into management recommendations. These tools are increasingly framed as climate-smart technologies that support feed efficiency, earlier health interventions, and tighter environmental control across livestock value chains. Yet the energy use, carbon intensity, and hardware lifecycles of the AI systems themselves remain largely invisible in livestock sustainability frameworks. Current policy instruments in the European Union and United States promote AI as a sustainability enabler, but rarely require disclosure of AI's computational energy use, emissions, and material impacts as a condition for “climate-smart” claims. This creates a governance gap as AI becomes embedded in climate programs and supply-chain narratives. This Perspective argues that climate-smart livestock policy must move from AI-for-agriculture to Green-AI-for-agriculture, where computational sustainability is integral to mitigation and adaptation. Drawing on recent work in AI regulation, digital agriculture, sustainable computing, and agri-food data spaces, we propose five guiding principles for governing Green AI in livestock systems: energy transparency, boundary harmonization, architectural proportionality, climate-first design, and FAIR compute metadata. Building on these principles, we outline a three-tier framework differentiating expectations for smallholders, commercial operations, and export-oriented value chains. A 2026–2030 roadmap identifies how funders, regulators, technology providers, and supply-chain actors can embed computational carbon into climate-smart livestock strategies without excluding producers with limited digital capacity.
Suresh Neethirajan (Wed,) studied this question.