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Can accelerometer ear tags identify behavioural changes in sheep associated with parturition?

E S Fogarty1, D L Swain1, G M Cronin2

  • 1Institute for Future Farming Systems, CQ University, Rockhampton QLD 4701 Australia.

Animal Reproduction Science
|May 17, 2020
PubMed
Summary

Machine learning (ML) accurately classified sheep behavior using accelerometers, detecting significant changes in ewe activity and posture around lambing. This technology can help monitor parturition in pasture-based ewes.

Keywords:
AccelerometerMEMSMachine learningParturitionRemote monitoring

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Area of Science:

  • Animal Behaviour
  • Machine Learning Applications
  • Precision Livestock Farming

Background:

  • On-animal sensor systems offer potential for monitoring ewes during parturition.
  • Reducing ewe and lamb mortality risk is a key objective in sheep farming.

Purpose of the Study:

  • To investigate machine learning (ML) behavior classification using ear-borne accelerometers to monitor sheep around lambing.
  • To assess changes in sheep behavior and activity patterns before and after parturition.

Main Methods:

  • Attached accelerometers to 27 ewes in a paddock.
  • Classified data using three ethograms: general behaviors, active behavior, and body posture.
  • Analyzed daily and hourly behavioral data and posture changes using linear mixed-effects models.

Main Results:

  • Significant changes in ewe behavior were observed around parturition, with decreased grazing/lying and increased standing/walking.
  • Hourly analysis revealed increased walking and posture changes, indicating heightened restlessness.
  • Most behavioral metrics returned to pre-partum levels shortly after lambing.

Conclusions:

  • Machine learning effectively identifies behavioral shifts in ewes during parturition.
  • Accelerometers and ML can monitor parturition in pasture-based sheep.
  • Further algorithm development is recommended for precise lambing onset detection.