Validation of the AquaCrop model for irrigated rice production under varied water regimes in Bangladesh

Abstract

Crop growth simulation models of varying complexity have been developed to predict the effects of soil, water, nutrients and climate on biomass and grain yields and water use efficiency of different crops. In this study, the AquaCrop model was calibrated and validated for rice crop growth modeling under different irrigation water regimes at the Bangladesh Rice Research Institute, Gazipur, Bangladesh during the 2008–09 and 2009–10 winter (dry) seasons. Three irrigation water regimes were examined: irrigation with continuous standing water (CSW), and irrigation at 3 or 5 days after water disappearance (3 or 5 DAWD) from the field as potential water saving adaptations. Model performance was evaluated in terms of prediction error (Pe), coefficient of determination (R2), the normalized root mean squared error (NRSME), the Nash–Sutcliffe model efficiency coefficient (EF) and Willmott’s index of agreement (d). The model calibration yielded 0.94<R2<0.99, 14.5<NRMSE<21.6, 0.83<EF<0.95 and 0.97<d<0.99 in simulating canopy cover (CC) percentage and above-ground biomass. Model validation yielded 0.98<R2<0.99, 8.6<NRMSE<12.9, 0.94<EF<0.97 and d=0.99 in simulating CC percentage and above-ground biomass. In calibration and validation, respectively, the prediction errors for grain yield varied from 5.55 to 7.70% and 8.22 to 11.54%, and for biomass production from 2.62 to 5.19% and 7.95 to 11.15%, indicating good model performances. Based on crop yield, water use and its use efficiency, the IR69515-KKN-4-UBN-4-2-1-1 genotype showed better productivity in the dry season under the 3 DAWD irrigation water regime compared to the other examined treatments, which was shown by both the experimental data and the model simulations using FAO recommended conservative model parameters. The FAO AquaCrop model was able to predict rice growth and yield with acceptable accuracy under different water regimes, making this model a suitable candidate to facilitate local scenario studies related to irrigation scheduling, yield prediction or studies related to climate change and adaptation.