Automatically measured variables related to tenderness of hoof placement and weight distribution are valuable indicators for lameness in dairy cowsReport as inadecuate




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(2017)APPLIED ANIMAL BEHAVIOUR SCIENCE.189.p.13-22 Mark abstract As lameness detection in dairy cattle using visual locomotion scoring is cumbersome and subjective, research efforts are dedicated to develop automatic lameness detection systems. 'Tender hoof placement' and the distribution of the body weight over the four legs are possible lameness indicators, but no research exists on how to derive from automatically measured gait characteristics. This study aims to derive new variables related to the (i) landing, full weight bearing and lifting phases of a stance time and the (ii) time spent on combinations of legs during the different phases of the gait cycle from cow gait recordings on a dedicated pressure mat, known as the Gaitwise. Data of 9 non-lame, 11 mildly lame and 12 severely lame cows were gathered. For all measurements, each variable was calculated per leg or combination of legs, after which the group means of each variable were compared between the three lameness statuses using a one-way ANOVA analysis. Landing and lifting variables indicated that the proportion of time for hoof placement and hoof lifting during the total stance time was longer in lame cows, and that the proportion of full weight bearing time was shorter. Lame cows were thus more careful to place and retract the hind feet in the case of a hind-lame leg. Support time variables indicated that lame cows increased the percentage triple support time (i.e time spent with three feet on the ground during walking) and lowered the percentage double support (i.e. time spent with two legs on the ground). Also, double support combinations on the same side of the body were preferred above diagonal combinations. The newly defined gait variables indeed reflect tenderness of hoof placement and body weight distribution and hence seem useful for discriminating between non-lame, mildly lame and severely lame cows. However, several of these interesting variables may have to be combined to obtain automatic lameness detection with sufficient accuracy. (C) 2017 Elsevier B.V. All rights reserved.

Please use this url to cite or link to this publication: http://hdl.handle.net/1854/LU-8520107



Author: Tim Van De Gucht, Wouter Saeys, Stephanie Van Weyenberg, Ludwig Lauwers , Koen Mertens, Leen Vandaele, Jurgen Vangeyte and Annelie

Source: https://biblio.ugent.be/publication/8520107



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