Automatically Recorded Performance and Behaviour Parameters as Risk Factors for Lameness in Dairy Cattle

• Main Authors: K. Schindhelm, I. Lorenzini, M. Tremblay, D. Dopfer, S. Reese, B. Haidn
• Format: Article
• Language: English
• Published: AIDIC Servizi S.r.l. 2017-06-01
• Series: Chemical Engineering Transactions
• Online Access: https://www.cetjournal.it/index.php/cet/article/view/1362

Lameness is one of the major welfare issues in dairy cattle, but because of the increasing amount of automation in modern dairy farms and consequently a decreasing surveillance rate per individual, lameness often is recognized only when a severe, very obvious change in gait can be observed. This usually occurs after a long period of time during which the animal endures suffering from pain caused by the hoof lesion, seeing as cows will try to hide signs of disease (in this case: an obvious limp) as long as possible. This study evaluated automatically recorded performance and behaviour parameters as possible risk factors for lameness that could be used for an automatic lameness alert in order to detect lameness cases earlier. The study herd consisted out of 60 Simmental cattle housed in a free stall barn. The herd was observed continuously from April 2014 to April 2015 using different sensors to measure performance (milk yield, live weight, feed intake) and behaviour (feeding behaviour, standing, lying and activity index) related parameters, adding up to a total of 27 variables. During the data collection period the whole herd was locomotion scored weekly according to Sprecher et al. (1997) (1-5, 1 = healthy, 5 = severely lame) in order to assess lameness in every individual. After dividing the data into a training and a test dataset, the training set was fitted into a regularized regression method (Elastic Net) including relevant interaction terms. The preliminary model fitted on the training dataset had an AUC (Area under the Receiver Operating Characteristic Curve) of 0.95 with a sensitivity of 0.95 (95 %-confidence interval: 0.92-0.97) and a specificity of 0.83 (95 %-confidence interval: 0.79-0.86) and the same model fitted on the training data resulted in an AUC of 0.84 with a sensitivity of 1.00 (95 %-confidence interval: 0.19-1.00) and a specificity of 0.80 (95 %- confidence interval: 0.71-0.87). The most important risk factors were ‘feeding duration (day/night ratio)’, ‘lying time (day/night ratio)’, ‘milk yield*feed intake’ and ‘milk yield*lying time’.