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Application and evaluation of aquacrop, dssat and simple model in modelling yield water use of selected underutilised cereal crops.


The study compared yield, biomass, and water use (WU) for maize, sorghum, and millet simulated using three crop models of varying complexity: AquaCrop, DSSAT and the SIMPLE model. The hypothesis was that there is no significant difference between simple and complex models of estimating yield, biomass and WU. A standard set of crop parameters was used to develop crop files for all three models. Similar soil, climate and management descriptions attained from the Ukulinga Research Farm were used across the models. Six general circulation models (GCMs) were used as climate input data to model past, present, mid-, and late-century climate change impacts on cereal crops. The effect of irrigation (as a management practice) on yield and water use was assessed using the mid-century projections. The performance of the three models was observed to be statistically different. Based on the mean bias error, all models overestimated yield, but the lowest overestimation was with AquaCrop (0.22 t/ha) followed by DSSAT (0.24 t/ha) and the SIMPLE model (0.69 t/ha). Other statistical indicators, viz., RMSE and R2, illustrate that the simulation of yield and WP in AquaCrop was more satisfactory than DSSAT and the SIMPLE model. Across all the time scales, it was observed that AquaCrop simulated the highest yield and biomass, and the SIMPLE model simulated the lowest yield across the GCMs, which were inconsistent. Applying a higher amount of irrigation at more frequent intervals resulted in higher yield, biomass and WP. AquaCrop showed the highest simulated mean yield for maize (8.34 t/ha), millet (6.86 t/ha) and sorghum (5.28 t/ha). Highest WP was observed under AquaCrop for maize (21 kg/ha/mm) and millet (15.10 kg/ha/mm), the SIMPLE model for sorghum (13.37 kg/ha/mm). The study confirms that DSSAT requires relatively more input data but does not always perform more satisfactorily. The SIMPLE model requires fewer input requirements than AquaCrop and DSSAT; however, it is less sensitive to management changes. AquaCrop had relatively incomparable results to DSSAT and the SIMPLE model and was observed as the most suitable model for simulating yield, biomass, and WU of the selected cereal NUS under climate change and irrigation management scenarios. Before their application, it is essential to calibrate crop growth parameters for local conditions or use parameters from local field studies when applying complex crop models such as DSSAT specifically for marginal environments, such as South Africa. On the other hand, AquaCrop performed reasonably well with minimal input requirements, confirming its application in datalimited and marginal environments. However, it is recommended that there must be calibration for all the models using inputs specific to locations.


Masters Degree. University of KwaZulu-Natal, Pietermaritzburg.