Low-power networks and GIS analyses for monitoring the site use of grazing cattle

• DOI: 10.1016/j.compag.2023.107897
• Publication Date: 2023-05-06T06:00:32Z
• Authors: Dominga Mancuso; Giulia Castagnolo; Monica C.M. Parlato; Francesca Valenti; Simona M.C. Porto
• Keywords: IoT; GPS; Tracking; Cow behaviour; GPS; Grazing cows; IoT; Kernel Density Estimation; Spatial analysis; Tracking;; Spatial analysis; Cow behaviour; Cow behaviour; GPS; Grazing cows; IoT; Kernel Density Estimation; Spatial analysis; Tracking; Grazing cows; Kernel Density Estimation

In the last years the use of wearable sensors for monitoring and tracking animals and, mostly, for early detection of diseases and improving the quality of production has shown increasing interest. Only recently, their appli- cation has become significant in extensive livestock systems, where the farmer-to-animal contact is less frequent than intensive ones. Furthermore, the extensive livestock systems can cause several environmental impacts, that are not easy to compute or model due to the difficulties in continuous long-distance monitoring of the herd. Internet of Things (IoT) technologies could be a valid solution to overcome these issues since they allow monitoring both remotely and in real time. In this regard, the aim of the present study was to prove the feasibility of an IoT-based low-power global positioning system (LP-GPS), developed for locating and tracking cows in extensive livestock systems. Two case studies were compared to prove the suitability of the proposed system with regard the battery life and loss samples. In this regard, the system was adopted to test its battery life and signal coverage after the installation of a SigFox repeater in the grazing area and to examine animals’ behaviours within the considered grazing areas by using the Kernel Density Estimation (KDE) tool available in Geographic Infor- mation Systems (GIS) software. As result the installation of a Sigfox repeater contributed to reduce losses of position-related samples, and it was possible to define further improvements of the developed system for detecting, locating, and tracking cows in extensive livestock systems. In details, it is important to analyse the behavioural activities of the cows, by combining motion sensors such as accelerometers GPS data to reach the most accurate way for measuring animal activity on extensive farm.