Sensor-based detection of parturition in beef cattle grazing in an extensive landscape: a case study using a commercial GNSS collar
T. M. Williams
A , D. F. A. Costa A * , C. S. Wilson A , A. Chang A , J. Manning A , D. Swain A and M. G. Trotter A
A CQUniversity Institute for Future Farming Systems, Rockhampton, Qld, Australia.
Animal Production Science - https://doi.org/10.1071/AN21528
Submitted: 8 October 2021 Accepted: 20 December 2021 Published online: 3 February 2022
© 2022 The Author(s) (or their employer(s)). Published by CSIRO Publishing
Abstract
Context: Neonate management remains a key issue in extensive beef production systems where producers are faced with substantial environmental and management challenges that limit their ability to monitor and manage livestock in a timely manner. Parturition is a critical event and can affect the calf health and survival, particularly in the perinatal period (up to 48 h after birth). As such, monitoring parturition using precision livestock technologies may provide producers with additional tools to manage their cattle and mitigate the impacts of neonatal mortality in extensive beef systems.
Aims: The purpose of this study was to determine whether data from a global navigation satellite system (GNSS) collar could be used to detect parturition events in extensively grazed beef cattle.
Methods: Forty-eight Bos taurus cows (583.5 kg body weight ± 9.25 s.e.m.) were allocated to a 28 ha paddock between 8 January 2021 and 6 March 2021 during the calving season. Thirty of the animals were fitted with GNSS-equipped collars (Smart Paddock, Vic., Australia) that captured data at 10 min intervals. Parturition events were recorded daily by visual observation. Collected data were used to calculate key predictive features related to calving behaviour. Derived features were compared and assessed for changes in the period surrounding parturition.
Key results: Increases were observed in distance to nearest neighbour and to herd aggregate features, and decreases were observed in paddock utilisation and distance travelled features in the lead-up to calving (P < 0.05). Furthermore, the number of animals within a 20 m radius decreased significantly (P < 0.05) in the lead up to parturition, supporting known isolation behaviours.
Conclusions: With further development of predictive algorithms, on-animal sensors may be valuable in the prediction of calving events in extensive beef production systems.
Implications: Remote management and monitoring with on-animal sensor technologies, such as GNSS collars and tags, will provide producers with an additional means of monitoring their animals, while overcoming many of the management challenges associated with extensive grazing operations.
Keywords: birth events, calf loss, calving, on-animal sensors, precision technology, rangelands, smart-tags, welfare implications.
References
Anderson DM, Estell RE, Cibils AF (2013) Spatiotemporal cattle data – a plea for protocol standardization. Positioning 4, 115–136.| Spatiotemporal cattle data – a plea for protocol standardization.Crossref | GoogleScholarGoogle Scholar |
Bailey DW, Trotter MG, Tobin C, Thomas MG (2021) Opportunities to apply precision livestock management on rangelands. Frontiers in Sustainable Food Systems 5, 93
| Opportunities to apply precision livestock management on rangelands.Crossref | GoogleScholarGoogle Scholar |
Bunter KL, Johnston DJ, Wolcott ML, Fordyce G (2013) Factors associated with calf mortality in tropically adapted beef breeds managed in extensive Australian production systems. Animal Production Science 54, 25–36.
| Factors associated with calf mortality in tropically adapted beef breeds managed in extensive Australian production systems.Crossref | GoogleScholarGoogle Scholar |
Chang AZ, Swain DL, Trotter MG (2020a) Calf loss in northern Australia: a systematic review. The Rangeland Journal 42, 9–26.
| Calf loss in northern Australia: a systematic review.Crossref | GoogleScholarGoogle Scholar |
Chang AZ, Swain DL, Trotter MG (2020b) Towards sensor-based calving detection in the rangelands: a systematic review of credible behavioral and physiological indicators. Translational Animal Science 4, txaa155
| Towards sensor-based calving detection in the rangelands: a systematic review of credible behavioral and physiological indicators.Crossref | GoogleScholarGoogle Scholar | 33928238PubMed |
Cosby A, Manning J, Fogarty ES, Wilson CS, Islam N, Trotter MG (2021) Assessing real time tracking technologies to integrate with identification methods and national traceability requirements. (Meat and Livestock Australia)
Fogarty ES, Swain DL, Cronin GM, Moraes LE, Bailey DW, Trotter M (2021) Developing a simulated online model that integrates GNSS, accelerometer and weather data to detect parturition events in grazing sheep: a machine learning approach. Animals 11, 303
| Developing a simulated online model that integrates GNSS, accelerometer and weather data to detect parturition events in grazing sheep: a machine learning approach.Crossref | GoogleScholarGoogle Scholar | 33503953PubMed |
Fogarty ES, Swain DL, Cronin GM, Moraes LE, Bailey DW, Trotter MG (2020) Potential for autonomous detection of lambing using global navigation satellite system technology. Animal Production Science 60, 1217–1226.
| Potential for autonomous detection of lambing using global navigation satellite system technology.Crossref | GoogleScholarGoogle Scholar |
Heglund NC, Taylor CR (1988) Speed, stride frequency and energy cost per stride: how do they change with body size and gait? Journal of Experimental Biology 138, 301–318.
| Speed, stride frequency and energy cost per stride: how do they change with body size and gait?Crossref | GoogleScholarGoogle Scholar |
Hijmans RJ (2021) geosphere: spherical trigonometry. R package version 1.5-14. Available at https://CRAN.R-project.org/package=geosphere.
Horváth A, Lénárt L, Csepreghy A, Madar M, Pálffy M, Szenci O (2021) A field study using different technologies to detect calving at a large-scale Hungarian dairy farm. Reproduction in Domestic Animals 56, 673–679.
| A field study using different technologies to detect calving at a large-scale Hungarian dairy farm.Crossref | GoogleScholarGoogle Scholar | 33529387PubMed |
Keceli AS, Catal C, Kaya A, Tekinerdogan B (2020) Development of a recurrent neural networks-based calving prediction model using activity and behavioral data. Computers and Electronics in Agriculture 170, 105285
Knight CW, Bailey DW, Faulkner D (2018) Low-cost global positioning system tracking collars for use on cattle. Rangeland Ecology & Management 71, 506–508.
| Low-cost global positioning system tracking collars for use on cattle.Crossref | GoogleScholarGoogle Scholar |
Lenth RV (2021) emmeans: estimated marginal means, aka least-squares means. R package version 1.7.0. Available at https://CRAN.R-project.org/package=emmeans.
McCosker KD, Fordyce G, O’Rourke PK, McGowan MR (2020) Reproductive performance of northern Australia beef herds. 2. Descriptive analysis of monitored reproductive performance. Animal Production Science
| Reproductive performance of northern Australia beef herds. 2. Descriptive analysis of monitored reproductive performance.Crossref | GoogleScholarGoogle Scholar |
McGowan, M, McCosker, K, Fordyce, G, Smith, D, O’Rourke, P, Perkins, N, Barnes, T, Marquart, L, Morton, J, Newsome, T, Menzies, D, Burns, B, Jephcott, S (2014) Northern Australian beef fertility project: Cash Cow (Final Report). Future Beef
O’Neill CJ, Bishop-Hurley GJ, Williams PJ, Reid DJ, Swain DL (2014) Using UHF proximity loggers to quantify male–female interactions: a scoping study of estrous activity in cattle. Animal Reproduction Science 151, 1–8.
| Using UHF proximity loggers to quantify male–female interactions: a scoping study of estrous activity in cattle.Crossref | GoogleScholarGoogle Scholar | 25449547PubMed |
Pearson C, Lush L, González LA (2020) Intravaginal devices and GNSS collars with satellite communication to detect calving events in extensive beef production in northern Australia. Remote Sensing 12, 3963
| Intravaginal devices and GNSS collars with satellite communication to detect calving events in extensive beef production in northern Australia.Crossref | GoogleScholarGoogle Scholar |
Pinheiro J, Bates D, DebRoy S, Sarkar D, R Core Team (2021) nlme: linear and nonlinear mixed effects models. R package version 3.1-153. Available at https://CRAN.R-project.org/package=nlme.
R Core Team (2021) ‘R: a language and environment for statistical computing.’ (R Foundation for Statistical Computing: Vienna, Austria). Available at https://www.R-project.org.
Rørvang MV, Nielsen BL, Herskin MS, Jensen MB (2018) Prepartum maternal behavior of domesticated cattle: a comparison with managed, feral, and wild ungulates. Frontiers in Veterinary Science 5, 45
| Prepartum maternal behavior of domesticated cattle: a comparison with managed, feral, and wild ungulates.Crossref | GoogleScholarGoogle Scholar | 29594159PubMed |
Rutten CJ, Kamphuis C, Hogeveen H, Huijps K, Nielen M, Steeneveld W (2017) Sensor data on cow activity, rumination, and ear temperature improve prediction of the start of calving in dairy cows. Computers and Electronics in Agriculture 132, 108–118.
| Sensor data on cow activity, rumination, and ear temperature improve prediction of the start of calving in dairy cows.Crossref | GoogleScholarGoogle Scholar |
Schirmann K, Chapinal N, Weary DM, Vickers L, von Keyserlingk MAG (2013) Short communication: rumination and feeding behavior before and after calving in dairy cows. Journal of Dairy Science 96, 7088–7092.
| Short communication: rumination and feeding behavior before and after calving in dairy cows.Crossref | GoogleScholarGoogle Scholar | 24054300PubMed |
Stephen CP, Norman ST (2021) Design and development of a biological implant for long term intravaginal retention in cattle. Australian Veterinary Journal 99, 326–333.
| Design and development of a biological implant for long term intravaginal retention in cattle.Crossref | GoogleScholarGoogle Scholar | 33954984PubMed |
Trotter M, Cosby A, Manning J, Thomson M, Trotter T, Graz P, Fogarty E, Lobb A, Smart A (2018) Demonstrating the value of animal location and behaviour data in the red meat value chain. (Meat and Livestock Australia)
van Beest FM, Rivrud IM, Loe LE, Milner JM, Mysterud A (2011) What determines variation in home range size across spatiotemporal scales in a large browsing herbivore? Journal of Animal Ecology 80, 771–785.
| What determines variation in home range size across spatiotemporal scales in a large browsing herbivore?Crossref | GoogleScholarGoogle Scholar |
Williams T, Wilson C, Wynn P, Costa D (2021) Opportunities for precision livestock management in the face of climate change: a focus on extensive systems. Animal Frontiers 11, 63–68.
| Opportunities for precision livestock management in the face of climate change: a focus on extensive systems.Crossref | GoogleScholarGoogle Scholar | 34676141PubMed |
