Assuring dairy cattle welfare : towards efficient assessment and improvement

Summary

In many countries, there is an increasing interest to assure the welfare of production animals. On-farm assessment of dairy cattle welfare, however, is time-consuming and, therefore, expensive. Besides this, effects of housing and management interventions that are aimed at improving welfare can be conflicting for different indicators of dairy cattle welfare. The research described in this thesis aimed to contribute to assurance of dairy cattle welfare by evaluating strategies to improve time-efficiency of welfare assessment and by identifying housing and management interventions for welfare improvement. Results presented are based on an observational study among 194 selected Dutch dairy herds. From these herds, data relating to housing, management, and indicators of the Welfare Quality (WQ) protocol for dairy cattle was collected on-farm, and routine herd data (RHD), relating to demography, management, milk production, milk composition, and fertility, was extracted from several national databases. Because in many countries RHD are regularly collected from dairy farms, it was hypothesized that RHD could be used to identify herds with potentially poor animal welfare and, therefore, reduce the number of on-farm assessments that are needed to identify these herds. Results of the literature review showed that variables of RHD have been associated with almost half of the welfare indicators in the WQ protocol for dairy cattle. When RHD and welfare data collected in the observational study were used to evaluate the value of RHD for predicting dairy cattle welfare at the herd level, predictions based on RHD for welfare indicators varied from less to highly accurate. For most welfare indicators, therefore, RHD can serve as a pre-screening test for detecting herds with poor welfare and reduce the number of on-farm assessments. In order to decide whether a herd should be visited following a pre-screening, however, value judgments about the overall welfare of herds need to be made. This requires combining welfare indicators in an overall score that reflects the multidimensional nature of welfare and the relative importance of indicators. The relative importance of indicators was evaluated for welfare classification of our study herds based on the WQ multicriteria evaluation model. Results showed that a limited number of indicators had a strong influence on classification of herds, and classification was not very sensitive to indicators of good health, such as prevalence of severely lame cows. As a different strategy for improving time-efficiency of welfare assessment, reduction of the time per on-farm assessment of the WQ protocol for dairy cattle was explored. Reduction of on-farm assessment time was simulated by omitting welfare indicators from the WQ protocol, and replacing observed values of omitted indicators by predictions based on remaining welfare indicators in the protocol. Because results showed that agreement between predicted and observed values of indicators was poor to moderate, it was concluded that this strategy has little potential to reduce on-farm assessment time. To contribute to knowledge of housing and management interventions that may lead to improvement of dairy cattle welfare, housing and management factors associated with various indicators in the WQ protocol were identified and compared. Surface of the lying area and pasturing in summer were commonly associated with the prevalence of lameness, lesions or swellings, and dirty hindquarters, but no common risk factors were identified for the average frequency of displacements and other welfare indictors. In conclusion, the present work shows that routine herd data can be used to improve time-efficiency of welfare assessment, whereas replacing welfare indicators by predictions based on other welfare indicators cannot. The WQ multicriteria evaluation model for classification of dairy cattle welfare has limitations in its current form. A softer surface of the lying area and pasturing in summer can enhance simultaneous improvement of multiple welfare indicators.