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Bush encroachment is a serious problem in savanna rangelands of southern Africa. There is a strong interest in practical and reliable assessment methods to quantify related vegetation changes in the woody layer such as the widely applied point-centred quarter (PCQ) methods. Several variations of these distance methods exist but their results differ due to differences in sampling effort and methodological accuracy. The aim of this study was to compare the performance of two recently developed adapted PCQ methods. These methods were used to estimate density, productivity and diversity of the woody layer of a semiarid savanna along a degradation gradient in the Kalahari rangelands. It was found that both adapted PCQ methods (APCQ10 and APCQ20, with the APCQ20 method using less recording points but a larger sampling area and higher sampling intensity per recording point) provided similar results for density, phytomass, available browse and browsing capacity in open, dense and encroached savanna types. Significant differences between the methods were obtained in differentiating height classes, which were, however, largely restricted to the woody layer above 2 m in open savanna types. There, applying the APCQ20 method avoided an under-sampling of larger shrubs and trees and increased precision in data assessment. This was confirmed by a better representation of species frequency distributions, as well as the density, phytomass and diversity status of the woody layer. These differences disappeared as the woody vegetation became denser with the APCQ10 method providing similar results to that of the APCQ20 method in densely vegetated and encroached savanna types. From a practical point of view, the APCQ10 method has a range of advantages in dense vegetation, where restricted movement impedes effective data collection. It is concluded that the APCQ20 method should be used to quantify open savanna communities, whereas the APCQ10 method is more suitable in dense stands of >1200 tree equivalents ha–1. Overall, the two APCQ methods were effective for assessing and monitoring woody savanna layers for management purposes but, for research, their accuracy still needs to be investigated in comparison to other assessment methods.

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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.

Rangeland monitoring aims to determine whether grazing management strategies meet the goals of sustainable resource utilization. The development of sustainable grazing management strategies requires an understanding of the manner in which grazing animals utilize available vegetation. In this study, we made use of livestock tracking, in situ observations and Sentinel-2 imagery to make rangeland scale observations of vegetation conditions in a semi-arid environment, to better understand the spatial relationships between vegetation conditions and sheep movement patterns. We hypothesized that sheep graze more selectively under low stocking rates—resulting in localized overgrazing. We also assessed the importance of image spatial resolution, as it was assumed localized effects of grazing will be best explained by higher resolution imagery. The results showed that livestock tend to congregate along drainage lines where soils are deeper. The findings demonstrate how the spatial analysis of remotely sensed data can provide a landscape-scale overview of livestock movement patterns. This study illustrates that high-resolution normalized difference vegetation index (NDVI) data can be used as a grazing management tool to determine the spatial variability of productive areas across the semi-arid Upper Karoo rangelands and identify preferred grazing areas.