Literature DB >> 22959932

Validation of an automated method to count steps while cows stand on a weighing platform and its application as a measure to detect lameness.

N Chapinal1, C B Tucker.   

Abstract

Weight shifting between legs and steps taken when cows stand may be a useful tool to assess cow comfort and lameness. Weight shifting is assessed by measuring the distribution of weight applied to each leg when standing on a weighing platform, whereas frequency of steps is traditionally measured with live observation or video recording. The objectives of this study were to validate an automated method to count steps from weight distribution measurements (experiment 1) and to assess the accuracy of the frequency of steps in detecting lameness (experiment 2). In experiment 1, 6 nonlame multiparous cows stood on a weighing platform covered with either concrete or rubber (1h/cow per surface) while stepping behavior was video recorded. Receiver operating characteristic curves were constructed, using the steps observed in the video recordings as the gold standard, to calculate the optimal threshold (based on the sum of sensitivity and specificity) of the weight applied to a leg to define a step. Optimal thresholds were similar between surfaces. The optimal thresholds, when pooling the 2 surfaces, were 127 and 98 kg for the front and rear pair for legs, respectively, with a specificity and sensitivity ≥0.96. Thresholds were used to construct an algorithm to count steps. In experiment 2, 57 cows (26 of them considered lame according to their gait score) stood for 15 min on the weighing platform. Frequency of steps taken with the front and rear pair of legs was calculated from the weight distribution measurements using the algorithm calculated in experiment 1. Lame cows took more steps per minute with the rear legs than did nonlame cows (1.6 vs. 1.0 steps/min; SE of the difference=0.2). As previously shown for weight shifting, the frequency of steps taken with the rear legs was a good predictor of lameness (area under the curve of the receiver operating characteristic curve=0.67; 95% confidence interval=0.52, 0.81). A positive relationship was observed between the frequency of steps and weight shifting (measured as SD of the weight applied over time to the legs) in both the front (R(2)=0.35) and rear (R(2)=0.49) legs, yet the slopes differed from 1 and the intercepts differed from 0, indicating that the 2 measures were related but not the same. In conclusion, weighing platforms can accurately calculate the frequency of steps automatically, and this measure shows promise as a tool to assess lameness.
Copyright © 2012 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

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Year:  2012        PMID: 22959932     DOI: 10.3168/jds.2012-5742

Source DB:  PubMed          Journal:  J Dairy Sci        ISSN: 0022-0302            Impact factor:   4.034


  5 in total

Review 1.  Precision Technologies to Address Dairy Cattle Welfare: Focus on Lameness, Mastitis and Body Condition.

Authors:  Severiano R Silva; José P Araujo; Cristina Guedes; Flávio Silva; Mariana Almeida; Joaquim L Cerqueira
Journal:  Animals (Basel)       Date:  2021-07-30       Impact factor: 3.231

Review 2.  Lameness Detection in Dairy Cows: Part 2. Use of Sensors to Automatically Register Changes in Locomotion or Behavior.

Authors:  Annelies Van Nuffel; Ingrid Zwertvaegher; Stephanie Van Weyenberg; Matti Pastell; Vivi M Thorup; Claudia Bahr; Bart Sonck; Wouter Saeys
Journal:  Animals (Basel)       Date:  2015-08-28       Impact factor: 2.752

Review 3.  Lameness Detection in Dairy Cows: Part 1. How to Distinguish between Non-Lame and Lame Cows Based on Differences in Locomotion or Behavior.

Authors:  Annelies Van Nuffel; Ingrid Zwertvaegher; Liesbet Pluym; Stephanie Van Weyenberg; Vivi M Thorup; Matti Pastell; Bart Sonck; Wouter Saeys
Journal:  Animals (Basel)       Date:  2015-08-28       Impact factor: 2.752

Review 4.  Association between Lameness and Indicators of Dairy Cow Welfare Based on Locomotion Scoring, Body and Hock Condition, Leg Hygiene and Lying Behavior.

Authors:  Mohammed B Sadiq; Siti Z Ramanoon; Wan Mastura Shaik Mossadeq; Rozaihan Mansor; Sharifah Salmah Syed-Hussain
Journal:  Animals (Basel)       Date:  2017-11-05       Impact factor: 2.752

5.  A Machine Vision-Based Method for Monitoring Scene-Interactive Behaviors of Dairy Calf.

Authors:  Yangyang Guo; Dongjian He; Lilong Chai
Journal:  Animals (Basel)       Date:  2020-01-22       Impact factor: 2.752

  5 in total

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