Global Positioning Systems (GPSs) can collect tracking data to remotely monitor livestock well-being and pasture use. Supervised machine learning requires behavioral observations of monitored animals to identify changes in behavior, which is labor-intensive. Our goal was to identify animal behaviors automatically without using human observations. We designed a novel framework using unsupervised learning techniques. The framework contains two steps. The first step segments cattle tracking data using state-of-the-art time series segmentation algorithms, and the second step groups segments into clusters and then labels the clusters. To evaluate the applicability of our proposed framework, we utilized GPS tracking data collected from five cows in a 1096 ha rangeland pasture. Cow movement pathways were grouped into six behavior clusters based on velocity (m/min) and distance from water. Again, using velocity, these six clusters were classified into walking, grazing, and resting behaviors. The mean velocity for predicted walking and grazing and resting behavior was 44, 13 and 2 min/min, respectively, which is similar to other research. Predicted diurnal behavior patterns showed two primary grazing bouts during early morning and evening, like in other studies. Our study demonstrates that the proposed two-step framework can use unlabeled GPS tracking data to predict cattle behavior without human observations.

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.

Sensor technologies can identify modified animal activity indicating changes in health status. This study investigated sheep behavior before and after illness caused by mold-contaminated feed using tri-axial accelerometers. Ten ewes were fitted with HerdDogg biometric accelerometers. Five ewes were concurrently fitted with Axivity AX3 accelerometers. The flock was exposed to mold-contaminated feed following an unexpected ration change, and observed symptomatic ewes were treated with a veterinarian-directed protocol. Accelerometer data were evaluated 4 days before exposure (d −4 to −1); the day of ration change (d 0); and 4 days post exposure (d 1 to 4). Herddogg activity index correlated to the variability of minimum and standard deviation of motion intensity monitored by the Axivity accelerometer. Herddogg activity index was lower (p < 0.05) during the mornings (0800 to 1100 h) of days 2 to 4 and the evening of day 1 than days −4 to 0. Symptomatic ewes had lower activity levels in the morning and higher levels at night. After accounting for symptoms, activity levels during days 1 to 4 were lower (p < 0.05) than days −4 to 0 the morning after exposure. Results suggest real-time or near-real time accelerometers have potential to detect illness in ewes.

Bovine Ephemeral Fever (BEF), caused by an arthropod-borne rhabdovirus, is widespread in tropical and subtropical regions. It affects cattle with symptoms of fever, lameness, inappetence and in some situations can result in mortality. The goal of this study is to determine if accelerometer data can be used to identify the behavior patterns that occur when cattle become ill from BEF. Eight heifers in a separate experiment were monitored with 3-axis accelerometers sensors. Movement variation (MV) was calculated from accelerometer data (25 Hz) using 1-min epochs and then averaged hourly. Two different approaches, cosine similarity (CS) and deviation from previous behavioral patterns, were developed to autonomously detect patterns and recognize the onset of sickness in cattle using accelerometer data. Analyses show that one heifer had behavioral changes one day before the manager observed BEF, and another heifer had behavioral changes on the same day the manager observed BEF. The other six heifers did not display any BEF symptoms. To validate the efficacy of our analytical approaches, we employed them on a separate commercial herd of 73 cows where 4 of the 27 monitored cows were observed with BEF symptoms. Predictions were either on the day or even the day prior to the manager’s observation and diagnosis. There were likely no false positives in the first or second trials using the deviation algorithm with sum_deviation formula, but there were several false positives with the other algorithms. These case studies demonstrate the potential of accelerometer data to autonomously detect disease onset, in some cases before it was apparent to the human observer. However, more research is needed to minimize false positives that may occur from other similar diseases, abnormal weather events or cyclical changes in behavior such as estrus is required.