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.

Behaviour is a useful indicator of an individual animal’s overall wellbeing. There is widespread agreement that measuring and monitoring individual behaviour autonomously can provide valuable opportunities to trigger and refine on-farm management decisions. Conventionally, this has required visual observation of animals across a set time period. Technological advancements, such as animal-borne accelerometers, are offering 24/7 monitoring capability. Accelerometers have been used in research to quantify animal behaviours for a number of years. Now, technology and software developers, and more recently decision support platform providers, are integrating to offer commercial solutions for the extensive livestock industries. For these systems to function commercially, data must be captured, processed and analysed in sync with data acquisition. Practically, this requires a continuous stream of data or a duty cycled data segment and, from an analytics perspective, the application of moving window algorithms to derive the required classification. The aim of this study was to evaluate the application of a ‘clean state’ moving window behaviour state classification algorithm applied to 3, 5 and 10 second duration segments of data (including behaviour transitions), to categorise data emanating from collar, leg and ear mounted accelerometers on five Merino ewes. The model was successful at categorising grazing, standing, walking and lying behaviour classes with varying sensitivity, and no significant difference in model accuracy was observed between the three moving window lengths. The accuracy in identifying behaviour classes was highest for the ear-mounted sensor (86%–95%), followed by the collar-mounted sensor (67%–88%) and leg-mounted sensor (48%–94%). Between-sheep variations in classification accuracy confirm the sensor orientation is an important source of variation in all deployment modes. This research suggests a moving window classifier is capable of segregating continuous accelerometer signals into exclusive behaviour classes and may provide an appropriate data processing framework for commercial deployments.

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.

A common strategy to reduce predator attack on livestock is the deployment of guardian alpacas. However, little research has been conducted on the behaviour of this species while housed with other livestock. This study monitored two male alpacas cohabitating with 180 lambing ewes in order to quantify field behaviour in two phases. Phase one assessed diurnal patterns of alpacas and lambing ewes using Global Navigation Satellite System (GNSS) collars recording data over 41 days, in combination with observational recordings. Phase two developed an alpaca behavioural ethogram through continuous observations from 05:30 to 19:30 h over a 3-day period. The two alpacas shared similar behaviours with commonality of distance travelled, and both species exhibited an increase in activity level based on speed between the times of 05:00 and 17:00 h. The GNSS data indicated that the alpacas flocked with the ewes at night sharing the same resting location, however, would spend time during the day on the outskirts of the paddock. Alpacas were observed to spend the majority of the observation period in two behavioural states: grazing (57%) and resting (27%). As a result of this study we were able to catalogue a range and frequency of field behaviours which alpacas exhibit while cohabitating with lambing ewes. However, further research is needed to determine in more detail how these behaviours correspond with the effectiveness of this species as a livestock guardian.

Identifying the licking behaviour in beef cattle may provide a means to measure time spent licking for estimating individual block supplement intake. This study aimed to determine the effectiveness of tri-axial accelerometers deployed in a neck-collar and an ear-tag, to characterise the licking behaviour of beef cattle in individual pens. Four, 2-year-old Angus steers weighing 368 ± 9.3 kg (mean ± SD) were used in a 14-day study. Four machine learning (ML) algorithms (decision trees [DT], random forest [RF], support vector machine [SVM] and k-nearest neighbour [kNN]) were employed to develop behaviour classification models using three different ethograms: (1) licking vs. eating vs. standing vs. lying; (2) licking vs. eating vs. inactive; and (3) licking vs. non-licking. Activities were video-recorded from 1000 to 1600 h daily when access to supplement was provided. The RF algorithm exhibited a superior performance in all ethograms across the two deployment modes with an overall accuracy ranging from 88% to 98%. The neck-collar accelerometers had a better performance than the ear-tag accelerometers across all ethograms with sensitivity and positive predictive value (PPV) ranging from 95% to 99% and 91% to 96%, respectively. Overall, the tri-axial accelerometer was capable of identifying licking behaviour of beef cattle in a controlled environment. Further research is required to test the model under actual grazing conditions.