Welfare Science

Farm Animal Welfare Metrics and Data Science

Data science is transforming farm animal welfare assessment, enabling analysis of large welfare datasets to identify patterns, benchmarks, and improvement priorities.

Key Facts

Outcome-based welfare measures are increasingly preferred over input-based regulations
Machine learning can identify animals at risk from sensor and behavioural data
Cross-farm benchmarking reveals performance variation and best practice
Supply chain data integration allows retailers to trace welfare to farm level
Standardisation of welfare metrics is essential for meaningful comparison

Welfare Considerations

Data science approaches to farm animal welfare have potential to transform our understanding and improve outcomes at scale. Moving from input-based rules to outcome-based metrics measured at slaughter and on farm allows welfare to be objectively quantified. Data transparency enables consumer choice and supply chain accountability.

What You Can Do

Support organisations developing open welfare data standards
Advocate for mandatory welfare outcome reporting by large food companies
Choose food companies with transparent, data-driven welfare reporting