Exploring uses for an algorithmically generated Animal Welfare Indicator for welfare assessment of dairy herds

On-farm welfare assessment is time-consuming and costly. Assessing welfare using routinely collected herd data has been proposed as a more economical alternative. The online Animal Welfare Indicator (AWI), developed by a Norwegian dairy cooperative, applies an algorithm to routinely collected health, production, and management data to "indicate" aspects of animal welfare at herd level. The overall AWI score is based on 10 AWI subindicator scores, representative of elements of animal welfare such as claw health, udder health, and mortality. Our cross-sectional study explored 2 ways in which the AWI may enable more efficient welfare assessment of Norwegian dairy herds. First, we investigated using the AWI to reduce the duration of on-farm assessments by replacing on-farm measures. Second, we examined reducing the number of on-farm welfare assessments by using the AWI to predict which herds have poorer welfare with respect to specific on-farm measures. Using Spearman rank analyses, we investigated if the AWI scores for 157 herds were associated with 24 on-farm welfare variables measured contemporaneously by Welfare Quality assessment. The mortality AWI subindicator score and the percentage mortality in the previous 12 mo were moderately correlated, as were the udder health AWI subindicator score and the percentage high somatic cell count (SCC) in the previous 3 recordings. Only negligible or weak correlations were found between the other AWI scores and the on-farm assessment variables. We built Generalized Linear Models using AWI scores as independent variables to predict herds with poorer welfare. Herds were classified as having poorer welfare based on their results in specific on-farm welfare measures. We evaluated the models' predictive ability and accuracy. Moderately accurate models were built for predicting poorer herds with respect to high SCC, mortality, and moderate or severe lameness. The other models were less accurate. The AWI scores were generally unsuitable as replacements of on- farm welfare measures. The AWI subindicators for udder health and mortality could replace the on-farm welfare measures related to those 2 topics, but there was some overlap in the data used to calculate them. Despite a lack of independence, the use of those 2 AWI subindicators may marginally reduce the duration of on-farm assessments. A prediction model based on AWI scores showed potential for identifying herds with poorer welfare in terms of moderate or severe lameness, facilitating more efficient use of resources for on-farm lameness assessment. As a consequence of the data used in the AWI, it was only reflective of health-related welfare outcomes.