Opportunities to monitor animal welfare using the five freedoms with precision livestock management on rangelands

Advances in technology have led to precision livestock management, a developing research field. Precision livestock management has potential to improve sustainable meat production through continuous, real-time tracking which can help livestock managers remotely monitor and enhance animal welfare in extensive rangeland systems. The combination of global positioning systems (GPS) and accessible data transmission gives livestock managers the ability to locate animals in arduous weather, track animal patterns throughout the grazing season, and improve handling practices. Accelerometers fitted to ear tags or collars have the potential to identify behavioral changes through variation in the intensity of movement that can occur during grazing, the onset of disease, parturition or responses to other environmental and management stressors. The ability to remotely detect disease, parturition, or effects of stress, combined with appropriate algorithms and data analysis, can be used to notify livestock managers and expedite response times to bolster animal welfare and productivity. The “Five Freedoms” were developed to help guide the evaluation and impact of management practices on animal welfare. These freedoms and welfare concerns differ between intensive (i.e., feed lot) and extensive (i.e., rangeland) systems. The provisions of the Five Freedoms can be used as a conceptual framework to demonstrate how precision livestock management can be used to improve the welfare of livestock grazing on extensive rangeland systems.

Extensive Sheep and Goat Production: The Role of Novel Technologies towards Sustainability and Animal Welfare

Simple Summary

New technologies have been recognized as valuable in controlling, monitoring, and managing farm animal activities. It makes it possible to deepen the knowledge of animal behavior and improve animal welfare and health, which has positive implications for the sustainability of animal production. In recent years, successful technological developments have been applied in intensive farming systems; however, due to challenging conditions that extensive pasture-based systems show, technology has been more limited. Nevertheless, awareness of the available technological solutions for extensive conditions can increase the implementation of their adoption among farmers and researchers. In this context, this review addresses the role of different technologies applied to sheep and goat production in extensive systems. Examples related to precision livestock farming, omics, thermal stress, colostrum intake, passive immunity, and newborn survival are presented; biomarkers of metabolic diseases and parasite resistance breeding are discussed.

Abstract

Sheep and goat extensive production systems are very important in the context of global food security and the use of rangelands that have no alternative agricultural use. In such systems, there are enormous challenges to address. These include, for instance, classical production issues, such as nutrition or reproduction, as well as carbon-efficient systems within the climate-change context. An adequate response to these issues is determinant to economic and environmental sustainability. The answers to such problems need to combine efficiently not only the classical production aspects, but also the increasingly important health, welfare, and environmental aspects in an integrated fashion. The purpose of the study was to review the application of technological developments, in addition to remote-sensing in tandem with other state-of-the-art techniques that could be used within the framework of extensive production systems of sheep and goats and their impact on nutrition, production, and ultimately, the welfare of these species. In addition to precision livestock farming (PLF), these include other relevant technologies, namely omics and other areas of relevance in small-ruminant extensive production: heat stress, colostrum intake, passive immunity, newborn survival, biomarkers of metabolic disease diagnosis, and parasite resistance breeding. This work shows the substantial, dynamic nature of the scientific community to contribute to solutions that make extensive production systems of sheep and goats more sustainable, efficient, and aligned with current concerns with the environment and welfare.

Animal Welfare Implications of Digital Tools for Monitoring and Management of Cattle and Sheep on Pasture

Simple Summary

Monitoring the welfare of cattle and sheep in large pastures can be time-consuming, especially if the animals are scattered over large areas in semi-natural pastures. There are several technologies for monitoring animals with wearable or remote equipment for recording physiological or behavioural parameters and trigger alarms when the acquired information deviates from the normal. Automatic equipment allows continuous monitoring and may give more information than manual monitoring. Ear tags with electronic identification can detect visits to specific points. Collars with positioning (GPS) units can assess the animals’ movements and habitat selection and, to some extent, their health and welfare. Digitally determined virtual fences, instead of the traditional physical ones, have the potential to keep livestock within a predefined area using audio signals in combination with weak electric shocks, although some individuals may have difficulties in responding as intended, potentially resulting in reduced animal welfare. Remote technology such as drones equipped with cameras can be used to count animals, determine their position and study their behaviour. Drones can also herd and move animals. However, the knowledge of the potential effects on animal welfare of digital technology for monitoring and managing grazing livestock is limited, especially regarding drones and virtual fences.

Abstract

The opportunities for natural animal behaviours in pastures imply animal welfare benefits. Nevertheless, monitoring the animals can be challenging. The use of sensors, cameras, positioning equipment and unmanned aerial vehicles in large pastures has the potential to improve animal welfare surveillance. Directly or indirectly, sensors measure environmental factors together with the behaviour and physiological state of the animal, and deviations can trigger alarms for, e.g., disease, heat stress and imminent calving. Electronic positioning includes Radio Frequency Identification (RFID) for the recording of animals at fixed points. Positioning units (GPS) mounted on collars can determine animal movements over large areas, determine their habitat and, somewhat, health and welfare. In combination with other sensors, such units can give information that helps to evaluate the welfare of free-ranging animals. Drones equipped with cameras can also locate and count the animals, as well as herd them. Digitally defined virtual fences can keep animals within a predefined area without the use of physical barriers, relying on acoustic signals and weak electric shocks. Due to individual variations in learning ability, some individuals may be exposed to numerous electric shocks, which might compromise their welfare. More research and development are required, especially regarding the use of drones and virtual fences.

Legal Complexities of Animal Welfare in Australia: Do On-Animal Sensors Offer a Future Option?

Simple Summary

‘Good animal welfare’ has evolved in recent decades to recognise behavioural, physiological and health factors, acknowledging that an animal may have good clinical health and be productive, though their welfare may be poor. The five freedoms and domains of animal welfare provide internationally recognised frameworks against which to evaluate practices to shape evidence-based standards which recognise both the physical and mental health needs of animals to provide a balanced view of an animal’s ability to cope in its environment. Whilst there are many techniques to measure animal welfare, the challenge lies with how best to align these with future changes in definitions and expectations, advances in science, legislative requirements and technology improvements. Substantial literature discusses the use of technology for improving animal monitoring, management and productivity on and off farm, though little has been published in relation to using such technologies to support legislative compliance and drive overall improvements in animal welfare. This article discusses the current legislation around animal welfare (with a focus on the Australian red meat sector); the impact of public expectation of welfare standards and production practices; and the current and future opportunity for on-animal sensors to support animal welfare, monitoring, management and compliance.

Abstract

The five freedoms and, more recently, the five domains of animal welfare provide internationally recognised frameworks to evaluate animal welfare practices which recognise both the physical and mental wellbeing needs of animals, providing a balanced view of their ability to cope in their environment. Whilst there are many techniques to measure animal welfare, the challenge lies with how best to align these with future changes in definitions and expectations, advances in science, legislative requirements, and technology improvements. Furthermore, enforcement of current animal welfare legislation in relation to livestock in Australia and the reliance on self-audits for accreditation schemes, challenges our ability to objectively measure animal welfare. On-animal sensors have enormous potential to address animal welfare concerns and assist with legislative compliance, through continuous measurement and monitoring of an animal’s behavioural state and location being reflective of their wellbeing. As reliable animal welfare measures evolve and the cost of on-animal sensors reduce, technology adoption will increase as the benefits across the supply chain are realised. Future adoption of on-animal sensors by producers will primarily depend on a value proposition for their business being clear; algorithm development to ensure measures are valid and reliable; increases in producer knowledge, willingness, and trust in data governance; and improvements in data transmission and connectivity.

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

On-animal sensor systems provide an opportunity to monitor ewes during parturition, potentially reducing ewe and lamb mortality risk. This study investigated the capacity of machine learning (ML) behaviour classification to monitor changes in sheep behaviour around the time of lambing using ear-borne accelerometers. Accelerometers were attached to 27 ewes grazing a 4.4 ha paddock. Data were then classified based on three different ethograms: (i) detection of grazing, lying, standing, walking; (ii) detection of active behaviour; and (iii) detection of body posture. Proportion of time devoted to performing each behaviour and activity was then calculated at a daily and hourly scale. Frequency of posture change was also calculated on an hourly scale. Assessment of each metric using a linear mixed-effects model was conducted for the 7 days (day scale) or 12 h (hour scale) before and after lambing. For all physical movements, regardless of the ethogram, there was a change in the days surrounding lambing. This involved either a decrease (grazing, lying, active behaviour) or peak (standing, walking) on the day of parturition, with most values returning to either pre-partum or near-pre-partum levels (all P < 0.001). Hourly changes also occurred for all behaviours (all P < 0.001), the most marked being increased walking behaviour and frequency of posture change. These findings indicate ewes were more restless around the time of parturition. Further application of this research should focus on development of algorithms that can be used to identify onset of lambing and/or time of parturition in pasture-based ewes.

Potential for autonomous detection of lambing using global navigation satellite system technology

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 &lt; 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 &lt; 0.001). Day of lambing was also evident by reduced spatial paddock utilisation (P &lt; 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.

Meteorological variables affect fertility rate after intrauterine artificial insemination in sheep in a seasonal-dependent manner: a 7-year study

A total number of 48,088 artificial inseminations (AIs) have been controlled during seven consecutive years in 79 dairy sheep Spanish farms (41° N). Mean, maximum and minimum ambient temperatures (Ts), temperature amplitude (TA), mean relative humidity (RH), mean solar radiation (SR) and total rainfall of each insemination day and 15 days later were recorded. Temperature-humidity index (THI) and effective temperature (ET) have been calculated. A binary logistic regression model to estimate the risk of not getting pregnant compared to getting pregnant, through the odds ratio (OR), was performed. Successful winter inseminations were carried out under higher SR (P < 0.01) and summer inseminations under lower SR values (P < 0.05). Successful inseminations during the summer were performed under significantly lower maximum T (P < 0.01), while winter inseminations resulted in pregnancy when they were carried out under higher maximum (P < 0.05) and minimum Ts (P < 0.01). Up to five meteorological variables presented OR >1 (maximum T, ET and rainfall on AI day, and ET and rainfall on day 15), and two variables presented OR <1 (SR on AI day and maximum T on day 15). However, the effect of meteorological factors affected fertility in opposite ways, so T becomes a protective or risk factor on fertility depending on season. In conclusion, the percentage of pregnancy after AI in sheep is significantly affected by meteorological variables in a seasonal-dependent manner, so the parameters such as temperature reverse their effects in the hot or cold seasons. A forecast of the meteorological conditions could be a useful tool when AI dates are being scheduled.

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

The behaviour of Merino ewes during non-oestrus and oestrus were quantified using Global Navigation Satellite System (GNSS) tracking devices and direct visual observation. GNSS devices were attached to neck collars and deployed on mixed-age ewes (38 maiden and 40 experienced ewes) following hormonal oestrus synchronisation. The positional accuracy of the GNSS data was validated through a comparative study of GNSS estimates of each animal’s location compared with direct visual observations. Positional accuracy was estimated at 90–94%, for a 4-m and 6-m-buffer radius, respectively. Ewe speed of movement was calculated from the GNSS data and plotted against hour of the day to determine diurnal activity patterns during non-oestrus and oestrus days. Ewes showed increased speed of movement during the early morning of the anticipated day of oestrus compared with the non-oestrus day (P < 0.001). In addition, ewes that increased their speed of movement by 0.05 m/s received 1.4–28.4 times more mounts depending on the hour of the day (P = 0.02). Ewes also displayed an increased speed of movement in the period leading up to maximum sexual activity, defined as the hour in which ewes received the maximum number of mounts. Thereafter, ewe activity decreased. No difference in sexual activity was detected between maiden and experienced ewes. The present study has demonstrated a change in ewe diurnal activity at oestrus, suggesting the onset of sexual activity can be identified as a period of increased speed of movement followed by a return to ‘normal’ activity. The development of commercial remote autonomous monitoring technologies such as GNSS tracking to detect this change in behaviour could facilitate improved reproductive management of sheep in extensive systems.

The use of GNSS technology to identify lambing behaviour in pregnant grazing Merino ewes

This current study investigated whether pre-lambing behavioural changes could be identified with the use of global navigation satellite system (GNSS) technology. GNSS devices were deployed on 20 pregnant Merino ewes grazing a 1.6 ha paddock and their lambing activity was compared with the metrics derived from the spatial data. The aims were to evaluate the lambing event using the following three separate metrics: (1) mean daily speed (MDS) of ewes 7 days before and to 7 days after lambing, inclusive (n = 12); (2) mean hourly speed (MHS) 12 h before and 12 h after lambing, inclusive (n = 9); and (3) the mean distance the lambing ewe to her peers in the 7 days before and the 7 days after lambing (mean distance to peers (MDP); n = 9), inclusive. There was a significant (P < 0.01) difference between pre- and post-lambing MDS with average ± se MDS pre-lambing being faster than post-lambing (0.051 ± 0.0004 vs 0.047 ± 0.0005 m/s). Pre- and post-lambing MHS differed significantly (P < 0.05), with mean ± s.e. MHS pre-lambing being faster than post-lambing (0.049 ± 0.002 vs 0.038 ± 0.002 m/s). Mean distance to peers indicated that at the time of lambing, ewes were significantly (P < 0.01) further from their peers than at either pre- or post-lambing (83.6 ± 14.59 vs 35.2 ± 2.82 vs 35.6 ± 1.68 m). Despite MDS and MHS metrics indicating significant changes pre- and post-lambing, neither metric was able to identify the time of lambing. The MDP metric could not identify differences pre- and post-lambing but was useful at predicting lambing. The current study found that MDS and MHS metrics have the potential to determine a ‘trigger’ point that could identify parturition and therefore could be used to determine the day of lambing. Therefore, further research is required to determine if a combination of these metrics could identify pre-lambing activity that would enable informed management decisions to be made.