Annual Net Primary Productivity of Different Functional Groups as Affected by Different Intensities of Rainfall Reduction in the Semiarid Grasslands of the Gauteng Province in South Africa

Abstract

Rainfall variability is expected to change the soil water regime thereby impacting negatively on rangeland species composition, productivity and ecosystem services. The aim of this study was to assess the impact of different intensities of rainfall reduction (RR) on vegetation annual net primary productivity (ANPP). Twenty 7 × 7 m experimental plots with different intensities of RR structures consisting of transparent acrylic bands were built on a natural grassland. The interspaces between acrylic bands varied in size to intercept different intensities of ambient rainfall (0, 15, 30 and 60%) as RR treatments, with each RR treatment replicated five times in a complete randomised block design. A fixed 1 m2 quadrat was marked at the centre of each plot and the ANPP within the quadrats was determined by harvesting the quadrant at the end of the growing season. Generally, as compared to the control (ambient rainfall intensity) the overall grass ANPP (P > 0.05) showed resilience to lower and moderate intensities of (15 and 30%) RR, but at a severe intensity of RR (60%) the ANPP was significantly reduced. Compared to the control the percentage contribution of grasses towards the overall ANPP increased at a lower intensity of RR (15%). In contrast, the percentage contribution of forbs towards the overall ANPP significantly reduced at lower intensity of RR. Within the grass species, however, those grasses that decrease when the veld is undergrazed or overgrazed (decreaser grass species) showed resilience at lower intensity (15 and 30%) of RR, while at a severe intensity of RR the ANPP of decreaser grasses were significantly reduced (1841 vs. 220 kg DM/ha). Those grasses that increase with undergrazing or overgrazing (increaser I or increaser II grass species) recorded a higher ANPP at moderate intensity of RR (30% RR) than at a higher intensity of RR, while the difference between 60% RR and 0% RR in terms of increaser grasses ANPP were not significant (P > 0.05) (650 kg DM/ha). Up to 88% reduction in ANPP were recorded for decreaser grass species at severe intensity of RR as compared to the control the corresponding reduction in ANPP noted for increaser grasses were relatively less (up to 56% reduction in ANPP at 60% RR vs. 0% RR). Generally, the overall ANPP yield of the semiarid grassland in Gauteng province showed resilience to a low intensity of RR (15% RR) and moderate intensity of RR (30% RR) partly due to a shift in the species composition of grasses from decreasers to increasers ecological groups, as well as due to a decrease and an increased contribution of forb functional groups at a lower and moderate intensity of RR, respectively.

A Systematic Review of Climate Change Risks to Communal Livestock Production and Response Strategies in South Africa

“…However, to enhance livestock resilience to climate change, a better understanding of how the rural farming fraternity is affected is needed (Maluleke et al, 2020). Effective policy and practice require sector-specific data about the nature and magnitude of impacts triggered by climate change (Lottering et al, 2020b;Magandana et al, 2021). As the extent to which climate change impacts vary across different livestock systems (Vetter et al, 2020;, understanding these differences is critical to formulating policy and practices (Maluleke et al, 2020).…”

Climate Factors Contribute to Grassland Net Primary Productivity

“…In the semi-arid regions, the paper focused more on the response of natural grasslands to the increasing climatic constraints, fluctuations in temperature and precipitation. The approaches chosen were empirical [1][2][3] providing evidence for dose-response relationships for management effects and environmental changes. These are essential to derive parameters for process-based modelling as shown by Giannitsopoulos [4], as well as for understanding ecological phenomena and implementing observed changes in functional groups [3] and functional types [5].…”

“…Nanzad and his team demonstrate in their study for Mongolia that models are a crucial component in the analysis of groundand space-based evidence [5]. Magandana et al should follow up their nice experimental results [3] by collaborating with mathematical modellers to raise their analysis to another level, to enable an impact assessment at the sub-continental scale.…”