Evaluating AquaCrop model for simulating production of amaranthus (Amaranthus cruentus) a leafy vegetable, under irrigation and rainfed conditions

•AquaCrop crop model was calibrated and validated amaranthus, a leafy vegetable.•Pots and field experiments were carried out for parameterization, calibration and validation of the model.•The model was adequately calibrated for biomass and cumulative evapotranspiration (ET) for amaranthus under irrigation and rainfed conditions.•The prediction of soil water content by the model was moderate and needs improvement.•Datasets from other agro-ecological regions should be used to improve calibration and validation for this crop.

Amaranthus (Amaranthus spp.), a leafy vegetable in South Africa, has the potential to be cultivated as a crop, but is rarely cultivated because it easily grows naturally on any waste land. The crop tolerates adverse environmental conditions, but performs better with application of water and soil organic or inorganic fertilizers. The AquaCrop crop model was calibrated and validated for amaranthus under irrigation and rainfed conditions for this study. Field experiments were carried out during the 2008-09 and 2009-10 seasons under line source sprinkler system while pot experiment was carried out during the 2010-11 season. The pot and field data sets were used for parameterisation, calibration and validation of the model. The model was adequately calibrated for biomass and cumulative evapotranspiration (ET) for amaranthus under irrigation and rainfed conditions. However, pooled data across irrigation and rainfed conditions showed canopy cover (CC) was moderately simulated (root-mean-square error (RMSE)  = 20.8%; model efficiency (ME) = 0.11; R2 = 0.577; d index of agreement (d) = 0.746; mean absolute percentage error (MAPE) = 43.4%). During validation, the model was able to adequately predict biomass and cumulative evapotranspiration (ET) for amaranthus for pooled data of irrigation (Full irrigation = W5 & Moderate irrigation = W3) and rainfed (W1) with RMSE of 1.96 t ha−1 and 75.64 mm, ME of 0.89 and 0.76, R2 of 0.92 and 0.91, d index of agreement of 0.91 and 0.91 and MAPE of 24.1 and 37.6% respectively. The prediction of soil water content by the model was moderate (RMSE = 50.62 mm; ME = 0.19; R2 = 0.30; d = 0.67; MAPE = 40.09) and needs improvement. It is recommended that datasets from other agro-ecological regions be used to improve calibration and validation for this crop.