Simulating future rangeland production in central South Africa

[摘要] English: A large part (69%) of South Africa's surface is suitable for grazing resulting in livestock farming being the largest agricultural sector in the country. Rangelands are an important resource for a stock farmer as it provides a cheap food source for the livestock midst it is in a good condition. In order to feed an ever-increasing population, better rangeland management practices are needed to ensure food security. Adaptation strategies should address climate variability and change, which is already suspected to be the main cause for variable crop yields and rangeland production. It is therefore imperative to investigate what the effect of climate change will be on rangeland production in the long run. Thus, the main aim of this study was to assess the historical and future rangeland production in the Bloemfontein area of South Africa, which falls within the dry Themeda-Cymbopogon veld type and is deemed representative of the central grassland biome.Observed climate data was sourced from the South African Weather Service (SAWS) station at Bloemfontein Airport for the historical base period (1980/81 �?2009/10). Simulated climate data was also obtained for the base and three future periods (i.e. current period (2010/11 �?2039/40), near future (2040/41 �?2069/70) and distant future (2070/71 �?2098/99)) from five Global Climate Models (GCMs) using two Representative Concentration Pathways (RCPs). Here RCP 4.5 and 8.5 respectively represented intermediate and high greenhouse gas emission pathways. Measured rangeland production data was obtained from the Sydenham Experimental Farm outside Bloemfontein for the historical base period. PUTU VELD (PV) was used to simulate rangeland production for the base and future time periods. Inputs included rainfall (mm), minimum and maximum temperature (°C), sunshine hours (h) and evapotranspiration (mm.d-1) at daily intervals, where the latter was estimated using the Hargreaves-Samani method. PV outputs included maximum dry matter production (DMPmax), the date of occurrence of DMPmax (Dtp) and the number of moisture stress days (MSD).Results showed a weak positive trend in measured DMPmax over the historical base period. It should be stressed that the results of this study should not be interpreted or extrapolated outside the context of this document since the validation of PV over the historical base period yielded poor results (R2 = 0.28), revealing possible seriousoverfitting issues. PV was also found to generally underestimate DMPmax when using GCM data as input when compared to runs employing SAWS data. Dtp showed a weak negative trend, implying a tendency for Dtp to occur slightly earlier in the season with time, while MSD revealed weak linear trends over the base period. Using 3-month running means of the Niño 3.4 anomalies as predictor of standardized DMPmax showed real promise as approximately 17.5% of the variation in DMPmax could be explained by the variation in the July-August-September (JAS) Niño 3.4.With respect to the future periods, the results showed that on average DMPmax will decrease slightly over time under RCP 4.5, while it will increase under RCP 8.5. In terms of grazing capacity, both RCPs revealed that more land will be needed per animal for sustainable farming. The Dtp showed a general shift to later in the growing season under both RCPs. It was also noted that although both RCPs had more MSDs when compared to the base period, there were larger differences observed under RCP 8.5.It was suggested that active monitoring and good rangeland improvement techniques be utilised by livestock farmers to ensure a good rangeland condition with adequate food supply for livestock. Future work should focus on evaluating other rangeland production models for this region.

[发布机构] University of the Free State