Harnessing big data for climate‑smart livestock systems: A perspective on resilient animal‑source food security

Climate change is reshaping livestock production through chronic heat stress, hydrologic extremes, feed and water volatility, and shifting burdens of climate-sensitive diseases. At the same time, the sector is known as a source of methane and nitrous oxide, creating a dual imperative for adaptation and mitigation. This review synthesizes evidence on climate risks to animal health, welfare, and productivity and evaluates how big data, spanning the Internet of Things (IoT), remote sensing, cloud computing, and artificial intelligence (AI), can convert heterogeneous streams of environmental and biological data into farm-to-policy decisions timely. Drawing on international case studies, we map a practical “digital stack” for climate-smart livestock systems: (1) continuous sensing of animals and housing environments; (2) data integration across on-farm sensors, weather and hydrologic services, and satellite products; (3) predictive analytics for early warning of heat stress, disease emergence, and feed shortages; and (4) automated or decision-supported interventions (cooling, precision feeding, vaccination targeting, and logistics). We outline an architecture for a big-data-enabled early warning system and show how precision nutrition, health surveillance, and pasture biomass forecasting can simultaneously safeguard productivity and lower emissions intensity. At systems scale, we identify governance and infrastructure requirements, such as interoperable data standards, Findable, Accessible, Interoperable, Reusable (FAIR) principles, and open Application Programming Interfaces (APIs), to break vendor lock-in, improve model reproducibility, and aggregate farm-level signals into actionable regional dashboards. We also highlight priority use cases for genetic improvement under warming climates by linking genomics and longitudinal performance data (heat tolerance, disease resistance, feed efficiency). Collectively, the evidence indicates that embedding sensing, prediction, and adaptive control into routine operations can reduce climate-related losses, improve animal welfare, and provide credible metrics for sustainability reporting. We conclude with a policy and investment roadmap that prioritizes national data platforms, targeted incentives for smallholder adoption, and cross-sector partnerships to scale climate-smart livestock technologies. This perspective offers veterinarians, agricultural engineers, and decision-makers an integrated, actionable framework to future-proof animal-source food security under accelerating climate change.