ANIMAL BEHAVIOR AND WELL-BEING SYMPOSIUM: Optimizing outcome measures of welfare in dairy cattle assessment

In most countries producing milk, industry- or other stakeholder-driven initiatives are in place to improve welfare and overall dairy farming sustainability. Those initiatives typically include a system of verification of reaching targets and progress over time. Reliable indicators are a fundamental requirement to provide public assurance and allow improvement on farms. Assessing dairy cattle welfare through outcome measures of welfare is done today through visual evaluations, including those of lameness, injuries, hygiene, and body condition. Numerical scoring charts for visual evaluation have been validated, and training programs have been developed to achieve high repeatability of assessors. Sampling strategies have been validated to determine how many animals and how many days are required to obtain reliable estimates of prevalence. However, visual evaluations require long periods of data collection, and multiple visits on farm, along with repeated checks of assessors to ensure repeatability over time, are, in turn, very costly to implement. An attractive alternative is relying on automated measures as activity monitors are becoming common on commercial farms; among those, lying time retains the most attention. The use of herd lying time in both free-stall and tie-stall situations has been validated. Current research is looking at relationships between lying time and other outcome measures of welfare, as well as lying time and risk factors for welfare in the environment (e.g., poor stall configuration or hoof trimming routine). We are not quite yet ready to rely solely on lying time to assess welfare; however, activity monitoring could certainly contribute to early detection of health and welfare issues (e.g., frequency of visits to the robotic milking system). Another interesting avenue is the development of early outcome measures of welfare and, possibly, remote indicators, for example, performance data collected routinely in Dairy Herd Improvement agencies' databases. The rationale is that a herd with good health and high longevity should be at lower risk of poor welfare. Research is needed to identify predictors and their conditions of use, allowing us to discriminate good vs. poor welfare status, at both the individual and herd levels. Finally, milk samples are already collected routinely to check quality and safety. It would be convenient to be able to predict cow welfare status directly with the milk using biomarkers, but again, we are not there