Summary
This narrative review synthesises evidence on the integration of intelligent systemisation—comprising real-time monitoring, machine learning, and IoT technologies—into livestock farming. The authors demonstrate that these technologies can simultaneously improve animal welfare through early disease detection, optimise resource allocation in feeding and climate control, and reduce environmental impacts, positioning intelligent automation as a substantive pathway toward more productive, humane, and ecologically sustainable livestock operations.
Regional applicability
The findings are potentially applicable to UK livestock systems, where there is growing policy interest in farm automation and animal health monitoring, though adoption barriers including capital costs, digital infrastructure, and skills gaps may limit implementation. UK producers could particularly benefit from the early disease detection and resource optimisation findings given existing pressures on profitability and environmental regulation.
Key measures
Animal health indicators, disease detection timing, feeding efficiency, operational costs, environmental footprint reduction, waste minimisation, energy use optimisation
Outcomes reported
The review reports that intelligent systemisation technologies enhance livestock well-being through real-time health monitoring and early disease detection, optimise feeding efficiency, reduce operational costs through automation, and minimise environmental impacts by reducing waste and ecological footprint. Specific metrics on productivity gains, welfare improvements, cost reductions, and environmental savings were synthesised across case studies.
Topic tags
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