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Changes in tail posture detected by a 3D machine vision system are associated with injury from damaging behaviours and ill health on commercial pig farms

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  • Richard B D’Eath
  • Simone Foister
  • Mhairi Jack
  • Nicola Bowers
  • Qiming Zhu
  • David Barclay
  • Emma M Baxter

Abstract

To establish whether pig tail posture is affected by injuries and ill health, a machine vision system using 3D cameras to measure tail angle was used. Camera data from 1692 pigs in 41 production batches of 42.4 (±16.6) days in length over 17 months at seven diverse grower/finisher commercial pig farms, was validated by visiting farms every 14(±10) days to score injury and ill health. Linear modelling of tail posture found considerable farm and batch effects. The percentage of tails held low (0°) or mid (1–45°) decreased over time from 54.9% and 23.8% respectively by -0.16 and -0.05%/day, while tails high (45–90°) increased from 21.5% by 0.20%/day. Although 22% of scored pigs had scratched tails, severe tail biting was rare; only 6% had tail wounds and 5% partial tail loss. Adding tail injury to models showed associations with tail posture: overall tail injury, worsening tail injury, and tail loss were associated with more pigs detected with low tail posture and fewer with high tails. Minor tail injuries and tail swelling were also associated with altered tail posture. Unexpectedly, other health and injury scores had a larger effect on tail posture- more low tails were observed when a greater proportion of pigs in a pen were scored with lameness or lesions caused by social aggression. Ear injuries were linked with reduced high tails. These findings are consistent with the idea that low tail posture could be a general indicator of poor welfare. However, effects of flank biting and ocular discharge on tail posture were not consistent with this. Our results show for the first time that perturbations in the normal time trends of tail posture are associated with tail biting and other signs of adverse health/welfare at diverse commercial farms, forming the basis for a decision support system.

Suggested Citation

  • Richard B D’Eath & Simone Foister & Mhairi Jack & Nicola Bowers & Qiming Zhu & David Barclay & Emma M Baxter, 2021. "Changes in tail posture detected by a 3D machine vision system are associated with injury from damaging behaviours and ill health on commercial pig farms," PLOS ONE, Public Library of Science, vol. 16(10), pages 1-17, October.
  • Handle: RePEc:plo:pone00:0258895
    DOI: 10.1371/journal.pone.0258895
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    References listed on IDEAS

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    1. Kaitlin Wurtz & Irene Camerlink & Richard B D’Eath & Alberto Peña Fernández & Tomas Norton & Juan Steibel & Janice Siegford, 2019. "Recording behaviour of indoor-housed farm animals automatically using machine vision technology: A systematic review," PLOS ONE, Public Library of Science, vol. 14(12), pages 1-35, December.
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