Abstract
Identifying where and how grazing animals are active is crucial for informed decision-making in livestock and conservation management. Virtual fencing systems, which use animal-mounted location tracking sensors to automatically monitor and manage the movement and space-use of livestock, are increasingly being used to control grazing as part of Precision Livestock Farming (PLF) approaches. The sensors used in virtual fencing systems are often able to capture additional information beyond animal location, including activity levels and environmental information such as temperature, but this additional data is not always made available to the end user in an interpretable form. In this study we demonstrate how a commercial virtual fencing system (Nofence®) can be used to map the spatiotemporal distribution of livestock activity levels in the context of grazing. We first demonstrate how Nofence® activity index measurements correlate strongly with direct in-situ observations of grazing intensity by individual cattle. Using methods adapted from movement ecology for analysis of home range, we subsequently demonstrate how space-use and cumulative and average activity levels of grazing cattle can be spatially mapped and analyzed over time using two different approaches: a simple but computationally efficient cell-count method and a novel adapted version of a more complex Brownian Bridge Movement Model. We further highlight how the same sensors can also be used to map spatiotemporal variations in temperature. This study highlights how data generated from virtual fencing systems could provide valuable additional insights for livestock managers, potentially leading to improved production efficiencies or conservation outcomes.