Time-Consuming, but Necessary: A Wide Range of Measures Should Be Included in Welfare Assessments for Dairy Herds

Animal welfare assessments that measure welfare outcomes, including behavior and health, can be highly valid. However, the time and skill required are major barriers to their use. We explored whether feasibility of welfare outcome assessment for dairy herds may be improved by rationalizing the number of measures included. We compared two approaches: analyzing whether strong pairwise associations between measures existed, enabling the subsequent exclusion of associated measures; and identifying possible summary measures-iceberg indicators-of dairy herd welfare that could predict herd welfare status. A cross-sectional study of dairy herd welfare was undertaken by a single assessor on 51 English farms, in which 96 welfare outcome measures were assessed. All measures showed at least one pairwise association; percentage of lame cows showed the most (33 correlations). However, most correlations were weak-moderate, suggesting limited scope for excluding measures from protocols based on pairwise relationships. A composite measure of the largest portion of herd welfare status was then identified via Principal Component Analysis (Principal Component 1, accounting for 16.9% of variance), and linear regression revealed that 22 measures correlated with this. Of these 22, agreement statistics indicated that percentage of lame cows and qualitative descriptors of calmness and happiness best predicted Principal Component 1. However, even these correctly classified only similar to 50% of farms according to which quartile of the Principal Component 1 they occupied. Further research is recommended, but results suggest that welfare assessments incorporating many diverse measures remain necessary to provide sufficient detail about dairy herd welfare.

Automated summary

Animal welfare assessments that measure welfare outcomes, such as behavior and health, can be complex and time-consuming. This study explored the feasibility of improving welfare outcome assessments for dairy herds by rationalizing the number of measures included. While weak to moderate correlations were found between measures, a composite measure was identified through Principal Component Analysis, but even the best predictors could only classify around 50% of farms accurately. Further research is recommended to better understand the diverse measures needed for detailed dairy herd welfare assessments.