Evaluation of the performance of register data as indicators for dairy herds with high lameness prevalence

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.

The relationship between common data-based indicators and the welfare of Swiss dairy herds

The assessment of dairy cow welfare has become increasingly important in recent years. Welfare assessments that use animal-based indicators, which are considered the most direct indicators, are time consuming and therefore not feasible for assessments on a large number of farms. One approach to reducing this effort is the use of data-based indicators (DBIs) calculated from routine herd data. The aim of this study was to explore the relationship between common DBIs and the welfare of 35 dairy herds to evaluate the feasibility of a data-based welfare prediction method. For this purpose, the WelfareQuality® (WQ) protocol was used to assess the welfare of dairy cows on 35 Swiss farms, for each of which 10 commonly used DBIs were calculated from herd data. Spearman's rank correlations were used to investigate the relationship between DBIs and WQ criteria and measurements. The study found only a few statistically weak associations between DBIs and animal welfare, with no associations for measurements or criteria of resting comfort and appropriate behavior. Thus, the multidimensional welfare definition is insufficiently covered, and the present publication does not support the approach of a purely data-based prediction of dairy welfare status at the farm level. Instead, the regular calculation of DBIs that are indicative of isolated animal welfare problems or metrics of animal health could allow monitoring of these specific areas of animal welfare.

Data-Based Variables Used as Indicators of Dairy Cow Welfare at Farm Level: A Review

Simple Summary

In recent years, the interest in the use of data from routine herd records in the monitoring of dairy welfare at farm level has increased. This review compiles 13 papers to outline the current potential of data-based variables for animal welfare monitoring. All the identified studies showed associations between data-based variables and farm-level dairy cow welfare and therefore provide a first indication of the possible use and suitability of data-based variables for welfare monitoring. However, we found that the definitions of animal welfare, its assessment on the farm, and the data-based variables varied considerably. Consequently, the current state of research does not allow a conclusive assessment of the potential of data-based variables for animal welfare monitoring. Therefore, future research is needed to clarify the potential of data-based variables. Harmonisation of the data-based variables and the use of valid measurements that reflect the multidimensionality of welfare could contribute to increased comparability between the studies.

Abstract

During the last years, the interest in data-based variables (DBVs) as easy-to-obtain, cost-effective animal welfare indicators has continued to grow. This interest has led to publications focusing on the relationship between DBVs and animal welfare. This review compiles 13 papers identified through a systematic literature search to provide an overview of the current state of research on the relationship between DBVs and dairy cow welfare at farm level. The selected papers were examined regarding their definition of animal welfare and classified according to this definition into three categories: (a) papers evaluating DBVs as predictors of animal welfare violations, (b) papers investigating the relationship between DBVs and animal-based measurements, and (c) papers investigating the relationship of DBVs to scores of welfare assessments like the Welfare Quality protocol or to overall welfare scores at farm level. In addition, associations between DBVs and indicators of animal welfare were extracted, grouped by the type of DBV, and examined for replications that may confirm the associations. All the identified studies demonstrated associations between DBVs and animal welfare. Overall, the first indications of a possible suitability of DBVs for predicting herds with animal welfare violations as well as good or poor animal welfare status were given. The evaluation of relationships between DBVs and animal-based measurements (ABMs) found mortality-based DBVs to be frequently associated with ABMs. However, owing to varying definitions of animal welfare, the use of different variants of DBVs, and different methods used to assess DBVs, the studies could only be compared to a limited extent. Future research would benefit from a harmonisation of DBVs and the use of valid measurements that reflect the multidimensionality of welfare. Data sources rarely investigated so far may have the potential to provide additional DBVs that can contribute to the monitoring of cow welfare at farm level.