Calibration and evaluation of the Sustainable Grazing Systems pasture model for predicting native grass aboveground biomass production in southern Africa
Walter Svinurai, Abubeker Hassen, Eyob Tesfamariam, Abel Ramoelo, Brendan Cullen
African Journal of Range and Forage Science | NATL INQUIRY SERVICES CENTRE PTY LTD | Published : 2021
Simulation modelling of grass biomass production has gained huge attention since the early 2000s, but it has rarely been applied to southern African rangelands, due to limited data availability for model calibration and evaluation. This study was conducted to calibrate the Sustainable Grazing Systems (SGS) pasture model using measured and sourced data, to assess the reliability of model predicted biomass against field measured- and remotely sensed- grass aboveground biomass. Parameter sets were developed for crest-, mid- and foot-slope land types, and Urochloa mosambicensis and Eragrostis curvula grass species. Short- and long-term simulation experiments for all combinations of land types an..View full abstract
Awarded by National Research Foundation of South Africa
The Department of Research and Innovation Support of the University of Pretoria is hereby greatly appreciated for providing the postgraduate research bursary for this research. The National Research Foundation of South Africa is also acknowledged for partly funding the research. Management personnel at Nuanetsi cattle ranch are hereby thanked for permission to conduct this study at their property. The Chemistry and Soil Research Institute of the Department of Research and Specialist Services of Zimbabwe is appreciated for providing soil survey data for the Nuanetsi subcatchment. The Zimbabwe Sugar Association Experiment Station is also acknowledged for providing weather data used to process satellite estimates. The Earth Observation, Natural Resources and Environment department at the Council for Scientific and Industrial Research, Pretoria is acknowledged for permitting us to use infrastructure for processing remote sensing data. This work was supported by the National Research Foundation of South Africa (Grant no. 95734).