Optimizing outcome measures of welfare in dairy cattle assessment

Abstract Text:

In most countries producing milk, industry-, government- and/or other stakeholder-driven initiatives are in place to improve welfare and dairy farming sustainability, for example, by enhancing profitability and reducing environmental impact. Those initiatives typically include a system of verification of reaching targets and tracking progress over time. Reliable indicators of welfare are required to provide public assurance and allow improvement on farms. Assessing dairy cattle welfare through outcome measures is done today through visual evaluations, including lameness, injuries, hygiene and body condition. Numerical scoring charts have been validated, together with the development of training programs, 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 a long period of data collection and multiple visits to farms, along with follow-up checks of assessors to maintain repeatability over time, and in turn, are costly to implement. An attractive alternative is relying on automated measures collected from activity monitors that are becoming common on commercial farms; among those, lying time has gotten 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 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 or feeders). Another interesting avenue is the development of early outcome measures of welfare and, possibly, remote indicators, for example, performance data (milk production, reproductive success, longevity) collected routinely in DHI databases. The rationale being 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 to discriminate good vs. poor welfare status, both at the individual and herd level. Finally, milk samples are already collected in routine to check quality and safety. It would be extremely convenient to be able to predict cow welfare status directly in the milk using biomarkers; but again, we are not there yet.

Keywords: dairy cattle, welfare, outcome measure