A remotely sensed weight gain model for sheep in the semi-arid Karoo shrublands of South Africa

The monitoring of animal weight gain is expensive as it often involves the rounding up of animals over large areas and long distances. Such monitoring is an arduous process that causes stress related health problems and weight loss in animals. The aim of this study was to evaluate the use of remotely sensed vegetation indices for modelling sheep weight gain in semi-arid rangelands. The temporal and spatial patterns of grazing were investigated using Sentinel-2 imagery, collar data obtained from a global position system (GPS), and data of sheep weight related to grazing hotspots. Historical animal weight data were compared statistically with nine commonly used spectral indices extracted from Sentinel-2 imagery to determine how vegetation conditions relate to sheep weight gain. Sheep appeared to adapt their grazing behaviour according to time of the year, with the average distance walked per sheep per day in line with previous studies. In contrast to distance walked, sheep at lower stocking densities used less grazing area than at higher densities. The normalised difference vegetation index (NDVI) proved to best model liveweight changes. By combining remote sensing (RS) and GPS data, our understanding of sheep grazing patterns and sheep weight gain was improved. This can lead to the optimisation of production potential through precision farming. The finding has applications for studies conducted on non-reproductive sheep in semi-arid Karoo rangeland systems of South Africa. Because the model is both cost-effective and replicable, it offers a long-term monitoring template for livestock studies elsewhere.