Abstract
BackgroundThe modern dairy industry routinely generates data on production and disease. Therefore, the use of these cheap and at times even “free” data to predict a given state of welfare in a cost-effective manner is evaluated in the present study. Such register data could potentially be used in the identification of herds at risk of having animal welfare problems. The present study evaluated the diagnostic performance of four routinely registered indicators for identifying herds with high lameness prevalence among 40 Danish dairy herds. Indicators were extracted as within-herd annual means for a one-year period for cow mortality, bulk milk somatic cell count, proportion of lean cows at slaughter and the standard deviation (SD) of age at first calving. The target condition “high lameness prevalence” was defined as a within-herd prevalence of lame cows of ≥ 16% (third quartile). Diagnostic performance was evaluated by constructing and analysing Receiver Operating Characteristic curves and their area under the curve (AUC) for single indicators and indicator combinations. Sensitivity (Se) and specificity (Sp) of the indicators were assessed at the optimal cut-off based on data and compared to a set of predefined cut-off levels (national annual means or 90-percentile).ResultsCow mortality had the highest AUC (0.76), while adding the three other indicators to the model did not yield significant increase in AUC. Cow mortality and SD of age at first calving had highest Se (100%, 95% confidence interval (CI): 72–100%), while highest Sp was found for the proportion of lean cows at slaughter (83%, 95% CI: 66–93%). The highest differential positive rate (DPR = 0.53) optimizing both Se and Sp was found for cow mortality. Optimal cut-off points were lower than the presently used pre-defined cut-offs.ConclusionsThe selected register-based indicators proved to be able to identify herds with high lameness prevalences. Optimized cut-offs improved the predictive ability and should therefore be preferred in official control schemes.
Highlights
The modern dairy industry routinely generates data on production and disease
The present study shows that selected register based secondary data have a predictive ability to discriminate between high and low prevalences of lameness
The present study shows that the quantitative assessment of register data can be used as a screening tool for direct cross-sectional measures
Summary
The use of these cheap and at times even “free” data to predict a given state of welfare in a cost-effective manner is evaluated in the present study Such register data could potentially be used in the identification of herds at risk of having animal welfare problems. The present study evaluated the diagnostic performance of four routinely registered indicators for identifying herds with high lameness prevalence among 40 Danish dairy herds. The Danish welfare control programme uses register-based indicators to identify livestock herds at risk of welfare problems based on a set of risk parameters from the national databases. This initial screening is followed by a control visit by the authorities in selected herds. The initial screening is based on certain cut-offs for the given parameters, but there is a need to investigate how sensitive these cut-offs are and how optimized cut-offs would perform instead, for the official selection of herds and in other welfare aspects such as commercial welfare assurance schemes
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
