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.

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.

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.