Assessing the performance of the FAO AquaCrop model to estimate maize yields and water use under full and deficit irrigation with focus on model parameterization
Several maize field experiments, including deficit and full irrigation, were performed in Ribatejo region, Portugal and were used to assess water stress impacts on yields using the AquaCrop model. The model was assessed after its parameterization using field observations relative to leaf area index (LAI), crop evapotranspiration, soil water content, biomass and final yield data and also using default parameters. LAI data were used to calibrate the canopy cover (CC) curve. Results showed that when the CC curve is properly calibrated, with root mean square errors (RMSE) smaller than 7.4%, model simulations, namely relative to crop evapotranspiration and its partition, show an improved accuracy. The model performance relative to soil water balance simulation revealed a bias in estimation but low estimation errors, with RMSE<13% of the total available soil water. However the model tends to overestimate transpiration and underestimate soil evaporation. A good model performance was obtained relative to biomass and yield predictions, with RMSE lower than 11% and 9% of the average observed biomass and yield, respectively. Overall results show adequacy of AquaCrop for estimating maize biomass and yield under deficit irrigation conditions, mainly when an appropriate parameterization is adopted. The model showed less good performance when using the default parameters but errors are likely acceptable when field data are not available.
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