Literature DB >> 26040626

Lameness detection via leg-mounted accelerometers on dairy cows on four commercial farms.

V M Thorup1, L Munksgaard2, P-E Robert1, H W Erhard1, P T Thomsen2, N C Friggens1.   

Abstract

Lameness in dairy herds is traditionally detected by visual inspection, which is time-consuming and subjective. Compared with healthy cows, lame cows often spend longer time lying down, walk less and change behaviour around feeding time. Accelerometers measuring cow leg activity may assist farmers in detecting lame cows. On four commercial farms, accelerometer data were derived from hind leg-mounted accelerometers on 348 Holstein cows, 53 of them during two lactations. The cows were milked twice daily and had no access to pasture. During a lactation, locomotion score (LS) was assessed on average 2.4 times (s.d. 1.3). Based on daily lying duration, standing duration, walking duration, total number of steps, step frequency, motion index (MI, i.e. total acceleration) for lying, standing and walking, eight accelerometer means and their corresponding coefficient of variation (CV) were calculated for each week immediately before an LS. A principal component analysis was performed to evaluate the relationship between the variables. The effects of LS and farm on the principal components (PC) and on the variables were analysed in a mixed model. The first four PC accounted for 27%, 18%, 12% and 10% of the total variation, respectively. PC1 corresponded to Activity variability due to heavy loading by five CV variables related to standing and walking. PC2 corresponded to Activity level due to heavy loading by MI walking, MI standing and walking duration. PC3 corresponded to Recumbency due to heavy loading by four variables related to lying. PC4 corresponded mainly to Stepping due to heavy loading by step frequency. Activity variability at LS4 was significantly higher than at the lower LS levels. Activity level was significantly higher at LS1 than at LS2, which was significantly higher than at LS4. Recumbency was unaffected by LS. Stepping at LS1 and LS2 was significantly higher than at LS3 and LS4. Activity level was significantly lower on farm 3 compared with farms 1 and 2. Stepping was significantly lower on farms 1 and 3 compared with farms 2 and 4. MI standing indicated increased restlessness while standing when cows increased from LS3 to LS4. Lying duration was only increased in lame cows. In conclusion, Activity level differed already between LS1 and LS2, thus detecting early signs of lameness, particularly through contributions from walking duration and MI walking. Lameness detection models including walking duration, MI walking and MI standing seem worthy of further investigation.

Entities:  

Keywords:  accelerometer; dairy cow; health monitoring; lameness detection; principal component analysis

Mesh:

Year:  2015        PMID: 26040626     DOI: 10.1017/S1751731115000890

Source DB:  PubMed          Journal:  Animal        ISSN: 1751-7311            Impact factor:   3.240


  18 in total

1.  Classification and Analysis of Multiple Cattle Unitary Behaviors and Movements Based on Machine Learning Methods.

Authors:  Yongfeng Li; Hang Shu; Jérôme Bindelle; Beibei Xu; Wenju Zhang; Zhongming Jin; Leifeng Guo; Wensheng Wang
Journal:  Animals (Basel)       Date:  2022-04-20       Impact factor: 2.752

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

4.  Lameness Affects Cow Feeding But Not Rumination Behavior as Characterized from Sensor Data.

Authors:  Vivi M Thorup; Birte L Nielsen; Pierre-Emmanuel Robert; Sylvie Giger-Reverdin; Jakub Konka; Craig Michie; Nicolas C Friggens
Journal:  Front Vet Sci       Date:  2016-05-10

5.  Kinematic gait characteristics of straight line walk in clinically sound dairy cows.

Authors:  M Tijssen; F M Serra Braganςa; K Ask; M Rhodin; P H Andersen; E Telezhenko; C Bergsten; M Nielen; E Hernlund
Journal:  PLoS One       Date:  2021-07-21       Impact factor: 3.240

6.  Use of Extended Characteristics of Locomotion and Feeding Behavior for Automated Identification of Lame Dairy Cows.

Authors:  Gian Beer; Maher Alsaaod; Alexander Starke; Gertraud Schuepbach-Regula; Hendrik Müller; Philipp Kohler; Adrian Steiner
Journal:  PLoS One       Date:  2016-05-17       Impact factor: 3.240

Review 7.  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

8.  Supporting the Development and Adoption of Automatic Lameness Detection Systems in Dairy Cattle: Effect of System Cost and Performance on Potential Market Shares.

Authors:  Tim Van De Gucht; Stephanie Van Weyenberg; Annelies Van Nuffel; Ludwig Lauwers; Jürgen Vangeyte; Wouter Saeys
Journal:  Animals (Basel)       Date:  2017-10-08       Impact factor: 2.752

9.  Grazing Cow Behavior's Association with Mild and Moderate Lameness.

Authors:  Niall W O'Leary; Daire T Byrne; Pauline Garcia; Jessica Werner; Morgan Cabedoche; Laurence Shalloo
Journal:  Animals (Basel)       Date:  2020-04-11       Impact factor: 2.752

Review 10.  Alterations in sick dairy cows' daily behavioural patterns.

Authors:  I Dittrich; M Gertz; J Krieter
Journal:  Heliyon       Date:  2019-11-22
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