GNSS technology and its application for improved reproductive management in extensive sheep systems

Fogarty, Eloise S.; Manning, Jaime; Trotter, Mark; Schneider, Derek; Thomson, Peter; Bush, R. D.; Cronin, G. M.

doi:10.1071/an14032

Cited by 15 publications

(9 citation statements)

References 26 publications

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“…GPS collars have also been used to track shelter utilisation in Merino ewes [26], where sheep used sheltered areas significantly more often than the remainder of the paddock. Additionally, GNSS technology has been used to identify the onset of oestrus, through a period of increased movement speed followed by a return to 'normal' activity [118].…”

Section: Gps Trackingmentioning

confidence: 99%

Technologies for the automated collection of heat stress data in sheep

et al. 2021

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The automated collection of phenotypic measurements in livestock is becoming increasingly important to both researchers and farmers. The capacity to non-invasively collect real-time data, provides the opportunity to better understand livestock behaviour and physiology and improve animal management decisions. Current climate models project that temperatures will increase across the world, influencing both local and global agriculture. Sheep that are exposed to high ambient temperatures experience heat stress and their physiology, reproductive function and performance are compromised. Body temperature is a reliable measure of heat stress and hence a good indicator of an animals’ health and well-being. Non-invasive temperature-sensing technologies have made substantial progress over the past decade. Here, we review the different technologies available and assess their suitability for inferring ovine heat stress. Specifically, the use of indwelling probes, intra-ruminal bolus insertion, thermal imaging and implantable devices are investigated. We further evaluate the capacity of behavioural tracking technology, such as global positioning systems, to identify heat stressed individuals based on the exhibition of specific behaviours. Although there are challenges associated with using real-time thermosensing data to make informed management decisions, these technologies provide new opportunities to manage heat stress in sheep. In order to obtain accurate real-time information of individual animals and facilitate prompt intervention, data collection should be entirely automated. Additionally, for accurate interpretation on-farm, the development of software which can effectively collect, manage and integrate data for sheep producer’s needs to be prioritised. Lastly, understanding known physiological thresholds will allow farmers to determine individual heat stress risk and facilitate early intervention to reduce the effects in both current and subsequent generations.

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Section: Gps Trackingmentioning

confidence: 99%

Technologies for the automated collection of heat stress data in sheep

et al. 2021

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“…Although no papers have formally utilised accelerometers to measure vigilance, it is theoretically possible. A global navigation satellite system (GNSS) estimates an animal's location by triangulating signals from orbiting satellites (Fogarty et al 2015). Visualisation of GNSS data could reveal changes in ambulatory pattern, distance to conspecifics, and step length.…”

Section: Referencementioning

confidence: 99%

Abstracts from the Proceedings of the 33rd Biennial Conference of the Australian Association of Animal Sciences

2021

Anim. Prod. Sci.

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During the career of Professor Eric Underwood, the feeding of livestock was closely linked to the availability of local resources. He solved problems by making the connections between the nutrient content of soil and livestock health, but while his research had global impact, I am sure he didn't foresee either the scale of the global feed trade or the environmental impact that livestock systems would have on the world 50 years on from his retirement in 1970.The livestock industry in 2020 is very different from what it was in 1970: global meat production is ~336 million tonnes per year (FAOSTAT averaged over 2016, 17 and 18) compared to the 92 million tonnes produced globally 50 years before. In Australia, the proportional contribution (by weight) of sheep and goat meat to total meat production has decreased from 35 to 16% over that time period and the annual import (mainly from Argentina) of soybean cake in 2016-18 averaged 743,000 tonnes out of a total of 895,000 tonnes of imported feed. Thus, even with Australia's land mass, feeding its livestock sector has a long-range footprint.As we know, livestock production also has global impact through the emission of greenhouse gases (GHGs) into the atmosphere -the relationship also influenced by diet. Gerber et al. (2013) published an extensive assessment of the global contribution of emissions of GHGs from the livestock sector in 2013: beef and cattle milk production accounted for 41 and 20% of the emissions, while pig meat and poultry meat and eggs contributed 9 and 8% respectively. The expansion of pasture and feed crops into forests accounted for 9%.Awareness of the negative environmental impacts of livestock production systems started to become an important topic for research in the 1990s, as policies to limit the environmental impact of agriculture were introduced (e.g. Nitrate Vulnerable Zones in the European Union). The publication of Livestock's Long Shadow in 2006 raised global awareness of the long-range environmental impacts of livestock production systems and since then millions of dollars have been invested in quantifying those impacts. Looking at agricultural production simply through an environmental lens is not sufficient though, since global agreement to the 2030 Agenda for Sustainable Development with 17 Sustainable Development Goals (SDGs). These goals are to be delivered as a package and include 'zero hunger' and 'economic growth', both goals to which livestock production makes a significant contribution (Mehrabi et al. 2020).Research questions thus need to address the potential consequences on all goals, of pathways for making progress towards individual goals. Eric Underwood solved animal health problems through his research, providing solutions based on nutritional knowledge. In the last 50 years animal science has added greatly to that knowledge and supported the development and implementation of technologies which have facilitated the 3.5-fold increase in global meat production. Agriculture and trade policies have also played a significant ro...

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“…Much of the research to date has been conducted using individual on-animal sensors such as Global Navigation Satellite System (GNSS) tracking, motion sensors (e.g., accelerometers, inertial monitoring units, pitch and roll sensors), jaw or bite sensors and physiological sensors [ 1 , 3 ]. In specific studies of sheep, on-animal sensor technologies have been applied to monitor various behaviours of interest, either within a particular context (e.g., lambing [ 4 , 5 , 6 ]; predation [ 7 ]; oestrus [ 8 ]) or more generally for basic behaviour recognition [ 9 , 10 , 11 , 12 , 13 ].…”

Section: Introductionmentioning

confidence: 99%

Developing a Simulated Online Model That Integrates GNSS, Accelerometer and Weather Data to Detect Parturition Events in Grazing Sheep: A Machine Learning Approach

Fogarty

Swain

Cronin

et al. 2021

Animals

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In the current study, a simulated online parturition detection model is developed and reported. Using a machine learning (ML)-based approach, the model incorporates data from Global Navigation Satellite System (GNSS) tracking collars, accelerometer ear tags and local weather data, with the aim of detecting parturition events in pasture-based sheep. The specific objectives were two-fold: (i) determine which sensor systems and features provide the most useful information for lambing detection; (ii) evaluate how these data might be integrated using ML classification to alert to a parturition event as it occurs. Two independent field trials were conducted during the 2017 and 2018 lambing seasons in New Zealand, with the data from each used for ML training and independent validation, respectively. Based on objective (i), four features were identified as exerting the greatest importance for lambing detection: mean distance to peers (MDP), MDP compared to the flock mean (MDP.Mean), closest peer (CP) and posture change (PC). Using these four features, the final ML was able to detect 27% and 55% of lambing events within ±3 h of birth with no prior false positives. If the model sensitivity was manipulated such that earlier false positives were permissible, this detection increased to 91% and 82% depending on the requirement for a single alert, or two consecutive alerts occurring. To identify the potential causes of model failure, the data of three animals were investigated further. Lambing detection appeared to rely on increased social isolation behaviour in addition to increased PC behaviour. The results of the study support the use of integrated sensor data for ML-based detection of parturition events in grazing sheep. This is the first known application of ML classification for the detection of lambing in pasture-based sheep. Application of this knowledge could have significant impacts on the ability to remotely monitor animals in commercial situations, with a logical extension of the information for remote monitoring of animal welfare.

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GNSS technology and its application for improved reproductive management in extensive sheep systems

Cited by 15 publications

References 26 publications

Technologies for the automated collection of heat stress data in sheep

Technologies for the automated collection of heat stress data in sheep

Abstracts from the Proceedings of the 33rd Biennial Conference of the Australian Association of Animal Sciences

Developing a Simulated Online Model That Integrates GNSS, Accelerometer and Weather Data to Detect Parturition Events in Grazing Sheep: A Machine Learning Approach

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