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Predicting yield response to water is important in rainfed agriculture. The objective of this study was to calibrate and test AquaCrop for simulating yield of 3 sorghum genotypes (PAN8816, a hybrid; Macia, an open-pollinated variety; and Ujiba, a landrace) grown during the 2013/14 and 2014/15 planting seasons (early, optimal and late planting dates). Variables considered during model evaluation included canopy cover (CC), biomass (B) and yield (Y). The model was able to simulate CC ( R 2 ≥ 0.710; root mean square error (RMSE) ≤ 22.73%; Willmott’s d-index ( d ) ≥ 0.998), biomass accumulation ( R 2 ≥ 0.900; RMSE ≤ 10.45%; d ≥ 0.850), harvest index ( R 2 ≥ 0.902; RMSE ≤ 7.17%; d ≥ 0.987) and yield ( R 2 ≥ 0.945; RMSE ≤ 3.53%; d ≥ 0.783) well for all genotypes and planting dates after calibration. AquaCrop over-estimated biomass and crop yield. The relatively good simulations produced by the minimum data input calibration confirm AquaCrop’s simplicity and suitability for use in places where extensive datasets may be unavailable. Biomass and yield overestimation resulting from the use of the minimum data input calibration suggests that other parameters (water productivity, canopy sensitivity to water stress and water stress coefficient) are required to improve canopy and yield predictions for sorghum genotypes. Keywords: modelling, parameterization, minimum data input calibration, sorghum, water availability

Calibration and testing of AquaCrop for selected sorghum genotypes