Search Results (8)

The effect of weather on the welfare of cattle grazing rangelands has received little study. The objective of this case study was to evaluate the effectiveness of a commercial rumen temperature bolus in monitoring changes in cattle body temperature and behavior during the summer on Arizona rangelands. Ten 2-year-old Corriente heifers were monitored by using SmaXtec Classic Boluses from 1 June to 29 August 2023. The bolus and weather data were averaged and analyzed on 3 and 24 h time scales. The bolus outputs included an activity index, a water intake index, the reticular temperature (RT) and the adjusted reticular temperature (ART, adjusted for drinking events). Weather metrics included the wet bulb globe temperature (WBGT), relative humidity (RH), ambient temperature (AT), wind speed, solar load and temperature–humidity index (THI). Weather variables were independently evaluated as a fixed continuous effect with linear, quadratic and cubic functions. The relative humidity and WBGT were better predictors of bolus metrics than other weather variables. Using 24 h data, the ART initially decreased by 0.4 °C as the WBGT increased from 2 °C to 15 °C, but the ART increased by over 0.15 °C with increasing WBGTs up to 24 °C. As the relative humidity increased, a proprietary bolus activity index initially increased with increasing RH to 45%, remained relatively constant until 65% RH and then increased at more humid levels. A proprietary water intake index decreased with increasing RH. Commercial rumen boluses have the potential to monitor body temperature and identify periods when cattle behavior may be affected by hot weather. Full article

Water-based nutrient injection technology, widely used in sectors like viticulture, hydroponics, and intensive animal systems, has previously seen limited application in livestock production. Early mechanical dispensers for nutrients, such as non-protein nitrogen (NPN) and phosphorus (P), were prone to malfunction, leading to inconsistent dosing and potential livestock health risks. This contributed to skepticism and slow adoption among producers. However, recent technological advancements have renewed interest in water-based supplementation for grazing animals. This case study assessed the use of water injection technology to deliver nutrients and a methane-reducing compound to cattle on a commercial cattle station under extensive grazing conditions. A total of 120 steers [initial liveweight (LW) 322.5 ± 28.3 kg] were assigned to three groups: water only (Control), a water supplement containing nutrients such as nitrogen and phosphorus, known as uPRO GREEN^® (Green), and uPRO GREEN^® combined with Agolin Ruminant L^® (Blue). The experiment lasted 90 days, during which LW was continuously monitored via a walk-over weighing system, and water disappearance was measured at the mob level. Methane emissions were forecasted using dry matter intake estimates based on observed animal growth rates. Additionally, 24 steers were equipped with on-animal sensors with GPS to monitor behavioral changes. The results indicate that despite the potential reduction in water intake (Control and Green: 948.1 and 973.5 L/d, respectively, versus 547.5 L/d for Blue), there were no negative effects on growth (mean average daily gain of 1.32 kg/d) or animal behaviors. The predicted methane emission of 209.04 g CH₄/head/day could potentially be reduced by 10–15% with the compound used in the current trial. These findings suggest that water-based supplementation can be used to optimize nutrient delivery and a methane-reducing compound without compromising cattle productivity in extensive grazing environments. In addition, the potential enteric methane mitigation presents an opportunity for livestock producers to generate additional revenue through carbon credits or to create new markets for beef with low greenhouse gas emissions when cattle consume methane-reducing compounds. Full article

The ICARUS (International Cooperation for Animal Research Using Space) satellite IoT system was launched in 2020 to observe the life of animals on Earth: their migratory routes, living conditions, and causes of death. These findings will aid species conservation, protect ecosystem services by animals, measure weather and climate, and help forecast the spread of infectious zoonotic diseases and possibly natural disasters. The aim of this article is to explain the system design of ICARUS. Essential components are ‘wearables for wildlife’, miniature on-animal sensors, quantifying the health of animals and the surrounding environment on the move, and transmitting artificially intelligent summaries of these data globally. We introduce a new class of Internet-of-things (IoT) waveforms—the random-access, very-low-power, wide-area networks (RA-vLPWANs) which enable uncoordinated multiple access at very-low-signal power and low-signal-to-noise ratios. RA-vLPWANs used in ICARUS solve the problems hampering conventional low-power wide area network (LPWAN) IoT systems when applied to space communications. Prominent LPWANs are LoRA, SigFox, MIOTY, ESSA, NB-IoT (5G), or SCADA. Hardware and antenna aspects in the ground and the space segment are given to explain practical system constraints. Full article

Sward architecture mediates ruminant grazing behavior in temperate grazing lands. Temperate grasses differ in their sward structure, which may influence the grazing behavior of cattle. We determined relationships between the grazing behavior of dairy heifers and the sward structure of the following temperate grasses: meadow fescue (Schedonorus pratensis (Huds.) P. Beauv.), orchardgrass (Dactylis glomerata L.), quackgrass (Elymus repens (L.) Gould), and reed canarygrass (Phalaris arundinacea L.). Vegetative-stage grasses were rotationally grazed by Holstein heifers (average initial body weight of 460 kg) during 5 day periods in the spring, summer, and fall of 2007 and 2008. The herbage dry matter (DM) allowance was twice the expected daily intake (11 kg DM animal⁻¹ d⁻¹). The sward characteristics were measured before grazing (e.g., the herbage height and mass, vertical distribution of leaf and stem fraction, and nutritive value). The grazing behavior of the heifers was quantified using automatic jaw movement recorders. In this study, the grass species had little effect on the grazing behavior. However, the bite rate was negatively correlated with the herbage mass, while the number of bites was positively correlated with the sward height and herbage mass. These results suggest that when herbage availability is not limited, grazing dairy heifers exhibit similar ingestive and rumination behavior across grass species and seasons, yet jaw movement dynamics may respond to the different characteristics of the swards. The results of this study provide the following benefits: (1) they inform managers about the jaw movement mechanics that can be expected of dairy heifers in temperate forage systems, showing that they are not limited by herbage allowance, and (2) they provide insight for future studies that employ on-animal sensors to evaluate foraging dynamics and animal performance outcomes in temperate forage pasture systems. Full article

In Australia, wild dogs are one of the leading causes of sheep losses. A major problem with managing wild dogs in Australia’s rangeland environments is that sheep producers are often unaware of their presence until injuries or deaths are observed. One option for earlier detection of wild dogs is on-animal sensors, such as Global Positioning System (GPS) tracking collars, to detect changes in the behaviour of sheep due to the presence of wild dogs. The current study used spatio-temporal data, derived from GPS tracking collars, deployed on sheep from a single rangeland property to determine if there were differences in the behaviour of sheep when in the presence, or absence, of a wild dog. Results indicated that the presence of a wild dog influenced the daily behaviours of sheep by increasing the daily distance travelled. Differences in sheep diurnal activity were also observed during periods where a wild dog was present or absent on the property. These results highlight the potential for on-animal sensors to be used as a monitoring tool for sheep flocks directly impacted by wild dogs, although further work is needed to determine the applicability of these results to other sheep production regions of Australia. Full article

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. Full article

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. Full article

Lameness is a clinical symptom associated with a number of sheep diseases around the world, having adverse effects on weight gain, fertility, and lamb birth weight, and increasing the risk of secondary diseases. Current methods to identify lame animals rely on labour intensive visual inspection. The aim of this current study was to determine the ability of a collar, leg, and ear attached tri-axial accelerometer to discriminate between sound and lame gait movement in sheep. Data were separated into 10 s mutually exclusive behaviour epochs and subjected to Quadratic Discriminant Analysis (QDA). Initial analysis showed the high misclassification of lame grazing events with sound grazing and standing from all deployment modes. The final classification model, which included lame walking and all sound activity classes, yielded a prediction accuracy for lame locomotion of 82%, 35%, and 87% for the ear, collar, and leg deployments, respectively. Misclassification of sound walking with lame walking within the leg accelerometer dataset highlights the superiority of an ear mode of attachment for the classification of lame gait characteristics based on time series accelerometer data. Full article