Modeling and Evaluation of AquaCrop for Maize (zea mays L.) under Full and Deficit Irrigation in Semi-Arid Tropics

Abstract

FAO AquaCrop is a simulation model that predicts the effects of soil, climate, water and crop growth on water productivity, yield and its attributes of various crops. In the present study, performance evaluation of AquaCrop model for maize was assessed for rabi maize during 2015 at Water Technology Centre, College of Agriculture, Rajendranagar, Hyderabad. The experiment was laid in a randomized block design with eight treatments in three replications. The treatments comprised of surface and drip irrigation schedules based on Epan viz., surface irrigation at 0.6 IW/CPE ratio (T1), 0.8 IW/CPE ratio (T2), 1.0 IW/CPE ratio (T3), 1.2 IW/CPE ratio (T4), drip irrigation at 0.6 Epan (T5), 0.8 Epan (T6), 1.0 Epan (T7) and 1.2 Epan (T8). The model was evaluated using crop data resulted from the experiment under varying water application methods and levels. Simulation performance was assessed with statistical parameters viz., statistical co-efficient of determination (R2), prediction error (Pe), model efficiency (E), root mean square error (RMSE) and mean absolute error (MAE). The model results are in quite agreement with practical values for grain yield, biomass and water productivity with model efficiency of 0.99, 0.92 and 0.71, coefficient of determination (R2) of 0.90, 0.91 and 0.93 with an RMSE of 0.24, 0.10 and 0.05, respectively. The model prediction errors in simulation of grain yield, biomass and water productivity under all treatments ranged from 1.4% to 11.9%, 1.4% to 16.1% and 4.85% to 25.9%, respectively. The highest and lowest prediction accuracy for grain yield, biomass and water productivity were in drip irrigation at 1.2 Epan and surface irrigation at 0.6 IW/CPE ratios. It is inferred that FAO AquaCrop model is suitable for predicting grain yield, biomass, water productivity and green canopy cover with acceptable range of under and over predictions for maize in semi-arid tropical climate.