Calibration and evaluation of the Sustainable Grazing Systems pasture model for predicting native grass aboveground biomass production in southern Africa

• Published in: African journal of range & forage science 2021-12, Vol.38 (S1), p.S28-S40
• Main Authors: Svinurai, Walter, Hassen, Abubeker, Tesfamariam, Eyob, Ramoelo, Abel, Cullen, Brendan
• Format: Article
• Language: English
• Subjects: aboveground biomass; biomass production; Eragrostis curvula; forage and feed science; grass biomass; grasses; indigenous species; pastures; process-based modelling; rangelands; remote sensing; simulation models; Southern Africa; Urochloa mosambicensis
• ISSN: 1022-0119; 1727-9380; 1727-9380
• DOI: 10.2989/10220119.2021.1875501

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 and grass species were conducted to calibrate and evaluate the model, respectively. The model simulated a growth pattern typical for grasses native to local rangelands. The SGS model represented measured grass biomass moderately well (R 2 = 0.57) at reasonable average error (RMSE, 820 kg DM ha −1 ), despite huge discrepancy in measured (mean = 3 877 kg DM ha −1 ) and simulated (mean = 3 071 kg DM ha −1 ) biomass. Model predictions were also significantly correlated with remotely sensed grass biomass (R 2 = 0.46) at reasonable overall performance error (RMSE, 981 kg DM ha −1 ). The integrated workflow developed for calibrating and evaluating the pasture simulation model can benefit model users in data-constrained environments.