Behaviour Analytics in Precision Animal Farming: Transforming Welfare Assessment, Disease Prediction and Sustainable Livestock Management

Behaviour analytics integrated with Big Data is transforming precision animal farming by enabling continuous, objective, and predictive welfare assessment. Traditional welfare frameworks such as the Five Freedoms, Welfare Quality, and the Five Domains recognize welfare as multidimensional, but on-farm application has been limited by manual observation and subjective scoring. Precision Livestock Farming (PLF) overcomes these constraints through wearable sensors, environmental monitoring, IoT infrastructure, and AI-driven analytics that allow real-time, individual-level monitoring. Behavioural indicators including activity, rumination, feeding duration, posture changes, locomotion, and spatial distribution function as digital biomarkers, often revealing welfare compromise or disease onset before clinical signs appear. Integration of accelerometers, GPS systems, and physiological sensors with machine-learning models such as LSTM networks and Gradient Boosting Regression Trees enables early prediction of mastitis, ketosis, and lameness, supporting timely intervention and reduced antibiotic reliance. Beyond disease detection, behavioural analytics facilitates positive welfare assessment by identifying stable activity rhythms, social cohesion, and adaptive responses to environmental conditions. Despite its promise, challenges remain in data integration, interoperability, ethical governance, farmer adoption, and validation of automated welfare indices. As precision systems evolve toward integrated welfare-intelligence platforms combining behavioural, physiological, and environmental datasets, Big Data-driven livestock management is advancing toward production systems that are efficient, transparent, welfare-centred, and aligned with sustainability and One Health objectives.