Abstract:
Rangelands are extremely important for livestock grazing purposes in South Africa. Grazing should thus be regulated in order to conserve grass, shrubs and trees thereby ensuring sustainability of rangelands. In South Africa, the existing national grazing capacity estimate was developed in 1993 and updated in 2005 using National Oceanic and Atmospheric Administration Advanced Very High Resolution Radiometer (NOAA-AVHRR) satellite data. Largely due to changing land use practices (as well as changing data availability), there exists a clear need to create a new estimate, making use of current available data. For Limpopo, a province shown to be prone to recent land degradation, droughts and climate change, developing such an updated carrying capacity (CC) product (adjusted monthly according to monitoring data and seasonal forecasts) may help support more sustainable agricultural practices.
The main objectives of the study are to update current CC products and to create deviation maps from CC for several years with relevant data. For estimation of the CC product, input data have included Satellite Pour l'Observation de la Terre (SPOT) VEGETATION Dry Matter Productivity (DMP), vegetation map of 2009 and downscaled coupled model data (ECHAM4.5–MOM3-DC2). A tree density product of 2003, observed rainfall and secondary ground truth data are also used.
Study results show that Remote Sensing (RS) and Geographic Information System (GIS) technology, Earth Observation System (EOS) data and products, climate data and ground truth data are successfully used in a series of steps, processes with modelling to ultimately estimate grazing capacity. It is clear that rainfall is a primary determinant of DMP. The Coupled General Circulation Model (CGCM) shows that the December-January-February (DJF) rainfall season is important as a predictor season for the November through to April (NDJFMA) DMP growing season for the Limpopo Province. This model can discriminate high and low DMP (and GC) seasons. This study shows that the DMP product can, with certain assumptions, be used as a proxy for grass biomass. There is a strong drive towards the application of seasonal forecasts in agriculture. This project demonstrates the development of a tailored forecast, an avenue that should be explored in enhancing relevance of forecasts to agricultural production.
