By Dr. Ira Parsons and Sydney Hurd et.al
Virtual fencing is not a new concept, but advancements in technology have expanded their popularity and usability in livestock production (Anderson, 2007). A team of researchers at the West River Research and Extension Center has partnered with SARE and a local sheep producer to learn more about the grazing management and herd health using virtual fencing and UAV (drone) technology. Using NoFence® collars, The team is building an integrated database to combine virtual fencing data with forage quality and UAV imagery alongside animal health records to better understand the grazing ecosystem (Brennan et al., 2024; Parsons et al., 2026).
Individual animal movement patterns are used to monitor grazing pressure across the pasture (Parsons et al., 2024). Individual variations relative to others in the animal’s peer group, and to itself may be signs of illness or other stressors such as reduced forage availability. The goal of this project is to identify what changes in behavior that may indicate a need for management intervention to improve pasture management, increase animal performance, or treat animal diseases. These abnormal movement patterns can be used to train a predictive model. With enough data, the goal is for the system to recognize early signs of illness and send an alert to producers, allowing them to intervene sooner and address health concerns before they become more severe.
Data Collection
We are currently in the second year of collecting movement data from sheep equipped with virtual fencing collars. Throughout the grazing season, we collect weekly forage sampling in the pastures in before, during, and after grazing. This allows the team to monitor changes in forage availability, utilization, and recovery from each rotation. Drone flights over the pastures provide aerial imagery and spatial data that will provide another layer of information for evaluating grazing patterns and pasture conditions.
Individual animal location data collected from the collars will be used to construct animal movement patterns and then will be combined with the pasture forage and drone data, along with animal health records. These integrated data sets are being used by the team to train and grazing and animal health models to identify changes in animal behavior that are early indicators for changing forage quality or the onset of animal illness.
Source : sdstate.edu