Potential for autonomous detection of lambing using global navigation satellite system technology
Eloise S. Fogarty
A E , David L. Swain A , Greg M. Cronin B , Luis E. Moraes C , Derek W. Bailey D and Mark G. Trotter A
+ Author Affiliations
- Author Affiliations
A Institute for Future Farming Systems, Central Queensland Innovation and Research Precinct, CQ University,630 Ibis Avenue, Rockhampton, Qld 4701, Australia.
B The University of Sydney, Faculty of Science – SOLES, 425 Werombi Road, Camden, NSW 2570, Australia.
C Department of Animal Sciences, The Ohio State University, 2029 Fyffe Road, Columbus, OH 43210, USA.
D Animal and Range Sciences Department, New Mexico State University, Las Cruces, NM 88003, USA.
E Corresponding author. Email: e.fogarty@cqu.edu.au
Animal Production Science 60(9) 1217-1226 https://doi.org/10.1071/AN18654
Submitted: 16 October 2018 Accepted: 4 November 2019 Published: 18 February 2020
Abstract
Context: On-animal sensing systems are being promoted as a solution to the increased demand for monitoring livestock for health and welfare. One key sensor platform, global navigation satellite system (GNSS) positioning, provides information on the location and movement of sheep. This information could be used to detect partition in sheep, a key period of time when both ewes and lambs are at risk. The development of algorithms based on key behavioural features could provide alerts to sheep managers to enable intervention when problems arise.
Aims: To investigate the use of GNSS monitoring as a method for detecting behavioural changes in sheep in the period around parturition.
Methods: GNSS collars were attached to 40 late gestation ewes grazing a 3.09 ha paddock in New Zealand. Several metrics were derived: (i) mean daily speed, (ii) maximum daily speed, (iii) minimum daily speed, (iv) mean daily distance to peers, and (v) spatial paddock utilisation by 95% minimum convex polygon. Speed metrics and distance to peers were also evaluated at an hourly scale for the 12 h before and 12 h after lambing.
Key results: Minimum daily speed peaked on the day of parturition (P < 0.001), suggesting animals may have been expressing more agitation and did not settle. Isolation was also evident during this time, with postpartum ewes located further from their peers than pre-partum ewes (P < 0.001). Day of lambing was also evident by reduced spatial paddock utilisation (P < 0.001).
Conclusions: This study demonstrates that GNSS technology can be used to detect parturition-related behaviours in sheep at a day scale; however, detection at the hour scale using GNSS is not possible.
Implications: This research highlights the opportunity to develop predictive models that autonomously detect behavioural changes in ewes at parturition using GNSS. This could then be extended to identify ewes experiencing prolonged parturition, for example dystocic birth enabling intervention which would improve both production and welfare outcomes for the sheep industry.
Additional keywords: behaviour monitoring, ewe, GNSS, GPS, Ovis aries.
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