346 Sensor-Determined Behaviors and Pasture Intake Estimation in Extensive Grazing Systems

Wearable sensor devices to monitor livestock behavior and location in extensive grazing systems can overcome limitations to collection and use of behavior data. These data enable generation of new phenotypes for genetic parameter estimation and decision support tools. Technical challenges, including device hardware and location on-animal, sensor types and modalities, data and power management, and sensor networks to enable measurement of livestock phenotypes in extensive environments, are being addressed. Wearable sensors currently used for behavior classifications include accelerometers, magnetometers and/or gyroscope within inertial measurement units, and pressure and acoustic sensors. We primarily use tri-axial accelerometers because of their reliability and richness of data for feature extraction to classify behaviors. Behavior data combined with GPS also allows location, activity and behavior mapping. Development of livestock behavior classifiers using sensors requires annotation of time-synchronized behavior recordings using video and/or a behavior recording app (e.g. CSIRO AnnoLog). Various analytical methods are used to classify behaviors from sensor data, including supervised machine-learning applied to accelerometer data. Our devices generate data for concurrent classification of behaviors including grazing, ruminating, walking, resting and drinking with reliabilities ≥ 90%. Estimates of pasture intake using behavior data across a range of environments also require validation. We have a facility to concurrently generate benchmark estimates of pasture intake using chemical markers and/or biomass disappearance while recording behaviors using sensors. To date, our R&D using pastures ranging from nutritionally optimal to severely drought affected suggest time spent grazing accounts for up to 60% of variation in pasture intake by individual beef cattle. We are assessing other sources of variation including pasture removal events (bites, tears), classes of cattle, and pasture characteristics to determine if more variation in pasture intake can be explained within extensive grazing systems to enhance development of new traits and applications for precision management.

Method for Classifying Behavior of Livestock on Fenced Temperate Rangeland in Northern China

Different livestock behaviors have distinct effects on grassland degradation. However, because direct observation of livestock behavior is time- and labor-intensive, an automated methodology to classify livestock behavior according to animal position and posture is necessary. We applied the Random Forest algorithm to predict livestock behaviors in the Horqin Sand Land by using Global Positioning System (GPS) and tri-axis accelerometer data and then confirmed the results through field observations. The overall accuracy of GPS models was 85% to 90% when the time interval was greater than 300–800 s, which was approximated to the tri-axis model (96%) and GPS-tri models (96%). In the GPS model, the linear backward or forward distance were the most important determinants of behavior classification, and nongrazing was less than 30% when livestock travelled more than 30–50 m over a 5-min interval. For the tri-axis accelerometer model, the anteroposterior acceleration (–3 m/s2) of neck movement was the most accurate determinant of livestock behavior classification. Using instantaneous acceleration of livestock body movement more precisely classified livestock behaviors than did GPS location-based distance metrics. When a tri-axis model is unavailable, GPS models will yield sufficiently reliable classification accuracy when an appropriate time interval is defined.

Use of GPS tracking collars and accelerometers for rangeland livestock production research1

Over the last 20 yr, global positioning system (GPS) collars have greatly enhanced livestock grazing behavior research. Practices designed to improve livestock grazing distribution can now be accurately and cost effectively monitored with GPS tracking. For example, cattle use of feed supplement placed in areas far from water and on steep slopes can be measured with GPS tracking and corresponding impacts on distribution patterns estimated. Ongoing research has identified genetic markers that are associated with cattle spatial movement patterns. If the results can be validated, genetic selection for grazing distribution may become feasible. Tracking collars have become easier to develop and construct, making them significantly less expensive, which will likely increase their use in livestock grazing management research. Some research questions can be designed so that dependent variables are measured by spatial movements of livestock, and in such cases, GPS tracking is a practical tool for conducting studies on extensive and rugged rangeland pastures. Similarly, accelerometers are changing our ability to monitor livestock behavior. Today, accelerometers are sensitive and can record movements at fine temporal scales for periods of weeks to months. The combination of GPS tracking and accelerometers appears to be useful tools for identifying changes in livestock behavior that are associated with livestock diseases and other welfare concerns. Recent technological advancements may make real-time or near real-time tracking on rangelands feasible and cost-effective. This would allow development of applications that could remotely monitor livestock well-being on extensive rangeland and notify ranchers when animals require treatment or other management.