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.

Efficiently measuring and mapping green herbage mass using remote sensing devices offers substantial potential benefits for improved management of grazed pastures over space and time. Several techniques and instruments have been developed for estimating herbage mass, however, they face similar limitations in terms of their ability to distinguish green and senescent material and their use over large areas. In this study we explore the application of an active, near infrared and red reflectance sensor to quantify and map pasture herbage mass using a range of derived spectral indices. The Soil Adjusted Vegetation Index offered the best correlation with green dry matter (GDM), with a root mean square error of prediction of 288 kg/ha. The calibrated sensor was integrated with a Global Positioning System on a 4-wheel motor bike to map green herbage mass. An evaluation of representative, truncated transects indicated the potential to conduct rapid assessments of the GDM in a paddock, without the need for full paddock surveys.

Knowledge of the aboveground biomass (AGB) of large pasture fields is invaluable as it assists graziers to set stocking rate. In this preliminary evaluation, we investigated the response of Sentinel-1 (S1) Synthetic Aperture Radar (SAR) data to biophysical variables (leaf area index, height and AGB) for native pasture grasses on a hilly, pastoral farm. The S1 polarimetric parameters such as backscattering coefficients, scattering entropy, scattering anisotropy, and mean scattering angle were regressed against the widely used morphological parameters of leaf area index (LAI) and height, as well as AGB of pasture grasses. We found S1 data to be more responsive to the pasture parameters when using a 1 m digital elevation model (DEM) to orthorectify the SAR image than when we employed the often-used Shuttle Radar Topography 30 m and 90 m Missions. With the 1m DEM analysis, a significant quadratic relationship was observed between AGB and VH cross-polarisation (R² = 0.71), and significant exponential relationships between polarimetric entropy and LAI and AGB (R² = 0.53 and 0.45, respectively). Similarly, the mean scattering angle showed a significant exponential relationship with LAI and AGB (R² = 0.58 and R² = 0.83, respectively). The study also found a significant quadratic relationship between the mean scattering angle and pasture height (R² = 0.72). Despite a relatively small dataset and single season, the mean scattering angle in conjunction with a generalised additive model (GAM) explained 73% of variance in the AGB estimates. The GAM model estimated AGB with a root mean square error of 392 kg/ha over a range in pasture AGB of 443 kg/ha to 2642 kg/ha with pasture LAI ranging from 0.27 to 1.87 and height 3.25 cm to 13.75 cm. These performance metrics, while indicative at best owing to the limited datasets used, are nonetheless encouraging in terms of the application of S1 data to evaluating pasture parameters under conditions which may preclude use of traditional optical remote sensing systems.

The present study examined how shelter availability, altitude and temperature influence paddock utilisation by pregnant and lambing Merino ewes. Global positioning systems (GPS) attached to collars allowed continuous tracking of ewes’ positions within two paddocks, and environmental conditions were also monitored throughout this time using temperature loggers. Animal tracking devices (UNEtracker GPS collars) were used in the spring (September–November) of 2008 (51 days) and 2009 (43 days), 14 days post-shearing, to monitor movement of pregnant grazing fine-wool Merino ewes (5 per paddock per year). The data were used to examine sheep use of lone trees, interior shelter, perimeter shelter and remainder of the paddock during three distinct diurnal activity periods, namely night camping, morning grazing and afternoon grazing. Regular use of shelter was consistently recorded in the two experimental years and in both paddocks. The ewes consistently used sheltered areas and both the leeward and windward sides of shelter, particularly during high sheep chill days. The sheep used the sheltered areas significantly more often than they used the remainder of the paddock, which was devoid of shelter except for lone trees. Night camping did not occur at the highest altitude, but predominantly where shelter was also located. The present study has demonstrated the consistent use of shelter by sheep. The shelter-seeking behaviour of the ewes a month post-shearing suggests that these animals are more sensitive to weather conditions than has been previously reported.