Multimodal animal health monitoring in extensive livestock production systems

Animal production in extensive livestock systems faces significant health and welfare challenges due to variable environments, diverse climatic conditions, and practical constraints that limit close animal monitoring. By "extensive livestock systems", we refer to production systems characterized by large herd sizes, open-range grazing, and limited direct animal supervision, typical of beef cattle, sheep, and goat farming in rangeland environments. Conventional approaches, including visual inspection and periodic veterinary assessment, often provide incomplete and delayed insights into animal health status, limiting timely intervention for infectious and metabolic diseases. Recent advances in wearable sensors, imaging technologies, genomic testing, omics profiling, and environmental monitoring offer new opportunities for continuous, data-driven surveillance of livestock. However, when applied in isolation, these modalities capture only partial aspects of the complex biological and environmental processes that influence animal health and disease progression. Multimodal monitoring integrates these diverse data streams to provide a more comprehensive and dynamic representation of animal health. This enables earlier detection of disease risk, improved welfare outcomes, and enhanced support for veterinary and on-farm decision-making. Ultimately, such integration empowers farmers to achieve earlier and more precise interventions, reduce veterinary costs, and improve overall animal welfare and productivity in extensive systems. This review synthesizes current approaches to multimodal monitoring in extensive livestock systems, explores data integration strategies, and evaluates key challenges for practical implementation, including cost, scalability, and data interoperability. We conclude by outlining future research directions that prioritize feasibility, affordability, and farmer-centered design to facilitate real-world adoption.