AJA VOL (3) ISSUE 4, 2016: 76-81 Azarian Journal of Agriculture www.azarianjournals.ir Azarian Journals Research article ISSN:2383-4420 76 Application of AquaCrop model for maize under water and nitrogen managements in a humid environment Ebrahim Amiri 1 , Meysam Abedinpour *2 Article Info ABSTRACT Accepted: 25 Aug. 2016 Globally, it is well debated fact that the water productivity in agriculture needs to be raised in order to meet the increasing demand for the feed and food production, which will double by 2050. Simulation models have been developed for predicting the effects of soil, water and nutrients on growth and water productivity of different crops. In this study, AquaCrop model was calibrated for grain maize (Single Cross 260) using drip irrigation system under varying irrigation and nitrogen levels. The intervals of irrigation were 6 days (F 1 ), 12 days (F 2 ) and 18 days (F 3 ) which combined with different nitrogen levels of 0 (N 1 ), 120 (N 2 ), 180 (N 3 ) and 240 kg ha -1 . Root Mean Square Error (RMSE), normalized Root Mean Square Error (RMSE n ), Mean Absolute Error (MAE), Prediction error (Pe) and coefficient of determination (R 2 ) were used to test the model performance. The model was calibrated for simulating maize grain and biomass yield for all treatment levels with the prediction error 0.37<Pe<1.5 percent, 0.87<R 2 <0.92 and 0.8<RMSE<1.37 t ha -1 . The results of the study showed that the AquaCrop model simulated aboveground biomass and grain yield in normal conditions more accurately than moderate and severe water stress conditions. Keywords: AquaCrop model, Calibration, Maize, Nitrogen, Interval irrigation INTRODUCTION 1 resh water is an indispensable natural resource, which plays a vital role in the development of any country. Thus, on one hand, failure to develop and implement the technologies to enhance water productivity will result in use of more water in future to sustain the present level of agricultural production and on the other hand, use of water in excess of that required for crop growth will have a significant negative impact on ecosystem and livelihood of the region (FAO 2008). Improving crop water productivity for increasing maize production most importance to obtain more yield per drop with declining irrigation resources. As a result, water allocation has become one of the most vexing problems faced by policy makers. In many water scarce countries, irrigation is the dominant user of water. Water withdrawal for 1 Associate professor, Islamic Azad University of Lahijan, Lahijan,Iran 2 Assistant Professor, Kashmar Higher Education Institute, Kashmar, Iran *Email: abedinpour_meysam@yahoo.com agricultural purposes accounts for about 75 per cent of all usages in developing countries and the FAO has predicted a 14 per cent net increase in use of water to meet the food demands by the year 2030 as compared to year 2000 (FAO 2008). The water- driven crop growth models assume a linear relation between biomass growth rate and transpiration through a water productivity (WP) parameter (Tanner and Sinclair 1983; Steduto and Albrizio 2005). This approach avoids the subdivision into different hierarchical levels, which results in a less complex structure and reduces the number of input parameters (Steduto et al. 2009). One of the major advantages of the water-driven module over radiation-driven is the opportunity to normalize the WP parameter for climate (both the evaporative demand and the atmospheric CO 2 concentration) in the former which, therefore, has a greater applicability in different locations under varying spatio-temporal settings (Steduto and Albrizio 2005; Steduto et al. 2007). Crop models viz. CERES-Maize (Jones and Kiniry 1986), WOFOST model, CropSyst (Stockle et al. 2003) and the Hybrid-Maize model (Yang et al., 2004) have been used for prediction of yield of maize crop. Most of these models, however, are quite sophisticated; require advanced modeling skills for their calibration and subsequent operation, and require large number of model input parameters. Some F