Breeding for Resilience: Interpreting Animal Behaviour With Machine Learning

Smallholder livestock systems face increasing challenges due to climate variability, particularly heat stress, which impacts animal health, welfare and productivity. Traditional productivity measurements, such as milk yield or growth rate, are labour-intensive, costly and fail to capture an animal’s overall adaptability. In response, the International Livestock Research Institute (ILRI) and Scotland’s Rural College (SRUC) are pioneering a novel phenotyping approach using low-cost sensors, video analysis and artificial intelligence (AI). By integrating data on animal movement, behaviour, physiological responses and environmental conditions, they are developing digital twins: real-time digital representations of animals’ health and comfort. This method provides a scalable, cost-effective proxy for fitness and resilience, enabling more accurate and rapid genetic selection suited to smallholder environments. Beyond breeding, the system supports animal management and policy planning by offering timely, actionable insights. This approach to phenotyping could revolutionise livestock improvement strategies in resource-constrained settings.