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.

The disc pasture meter (DPM) is a tool that is widely used to estimate aboveground standing grass biomass (ASB) in a rapid and non-destructive manner. Depending on the species composition, rangeland condition and local climate, available DPM calibrations may fail in providing precise measurements for a certain area. We calibrated a DPM for the arid dunefield savanna of the south-western Kalahari as found in the Northern Cape province of South Africa and bordering Namibia and Botswana. Assessments were done at 30 sites and restricted to inter-dune areas. The site selection accounted for different grazing conditions to ensure a better representation of the variability in ASB in the region. We determined grass species composition, basal cover and dry matter production in relation to height readings of the DPM. The grass sward was dominated by the species Stipagrostis ciliata, S. obtusa and Schmidtia kalahariensis, with a mean density of 25 cm point-to-tuft distances. Based on 90 paired observations of ASB an...

Rainfall and stocking densities are key drivers of vegetation functioning and productivity in semi-arid regions. Below-average rainfall is of particular importance in the agricultural sector as it limits fodder availability for livestock in the short term, and can influence potential agricultural productivity through inducing the mortality of palatable perennial plant species in the long term. Changes in rainfall trends are not equal for all regions and must be quantified locally to sustainably manage natural grazing resources. The Upper Karoo has experienced considerable fluctuations in rainfall for many thousands of years, with relatively wetter and drier phases. This study analysed farm records from the past eight decades at eight sites in the Upper Karoo. The aim was to provide detailed and comparable descriptions to generate a more general understanding of rainfall at a spatial scale over this entire bioregion and to assess recent farming conditions within a historical rainfall context. A steadily increasing trend in rainfall of approximately 10 mm per decade has been observed in the Upper Karoo over the past eight decades, and the 30-year period from 1989 to 2018 was generally characterised by above-average rainfall. Presently, the farming conditions are more favourable compared to historical rainfall recorded before the year 1989.

As the primary land use in the Karoo, extensive small-stock farming contributes significantly towards the agricultural economy of the region. However, the sustainability of this practice has been questioned for many years. This led to the establishment of a long-term stocking-density trial in 1988 at the Carnarvon Research Station. The trial was designed on a three-camp rotational grazing system under four stocking densities (8, 7, 5.5 and 4 ha SSU–1). Following continuous treatment over 28 years, the results from a once-off assessment in February 2016 portrayed no significant differences between treatments with regard to plant height, cover, species diversity, ecological and grazing index scores. Animal production varied among treatments where the mean production per hectare was found to be the highest under the high stocking-density treatment. Due to the absence of historical data, the results from this study are not conclusive with regards to changes in the vegetation dynamics over time and therefore d...

Rangelands represent about 25% of the Earth’s land surface but are under severe pressure. Rangeland degradation is a gradually increasing global environmental problem, resulting in temporary or permanent loss of ecosystem functions. Ecological rangeland studies aim to determine the productivity of rangelands as well as the severity of their degradation. Rigorous in situ assessments comprising visual identification of plant species are required as such assessments are perceived to be the most accurate way of monitoring rangeland degradation. However, in situ assessments are expensive and time-consuming exercises, especially when carried out over large areas. In situ assessments are also limited to areas that are accessible. This study aimed to evaluate the effectiveness of multispectral (MS) and hyperspectral (HS) remotely sensed, unmanned aerial vehicle (UAV)-based data and machine learning (random forest) methods to differentiate between 15 dominant Nama Karoo plant species to aid ecological impact surveys. The results showed that MS imagery is unsuitable, as classification accuracies were generally low (37.5%). In contrast, much higher classification accuracies (>70%) were achieved when the HS imagery was used. The narrow bands between 398 and 430 nanometres (nm) were found to be vital for discriminating between shrub and grass species. Using in situ Analytical Spectral Device (ASD) spectroscopic data, additional important wavebands between 350 and 400 nm were identified, which are not covered by either the MS or HS remotely sensed data. Using feature selection methods, 12 key wavelengths were identified for discriminating among the plant species with accuracies exceeding 90%. Reducing the dimensionality of the ASD data set to the 12 key bands increased classification accuracies from 84.8% (all bands) to 91.7% (12 bands). The methodology developed in this study can potentially be used to carry out UAV-based ecological assessments over large and inaccessible areas typical of Karoo rangelands.

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.