The CERES-Maize model performance was investigated in simulating maize phenology, grain yield, soil–water, evapotranspiration, and water productivity under different irrigation and nitrogen (N) levels under a variable rate lateral (linear)-move sprinkler irrigation system. The irrigation levels were rainfed, full irrigation treatment (FIT) and 75% FIT. The N levels were 0, 84, 140, 196, and 252 kg/ha. The field experiment was conducted in the form of split plots with the irrigation levels as the main treatment and N levels as a sub-main treatment. The root mean squared error (RMSE), normalized RMSE (RMSE_n), R², T test, and model prediction error (P_e) statistics were used to evaluate the performance and accuracy of the model. Calibration of the model was done using the data of 2011 and 2012 and validation of the model was conducted for 2013 and 2014 by considering days after planting to flowering (DAPF), days after planting to maturity (DAPM), grain yield, crop evapotranspiration (ETc), water productivity (WP), and soil water content (SWC). The DAPF simulations based on the average values of P_e (0%), RMSE (2 days), and RMSE_n (3%) and the DAPM simulation results based on the average values of P_e (2%), RMSE (4 days), and RMSE_n (3%) showed that the model had an acceptable accuracy. In calibration years, RMSE, RMSE_n, and R², respectively, were 0.57 ton/ha, 5%, and 0.91; and in validation years, the same statistics, respectively, were 0.86 ton/ha, 10% and 0.94, indicating good performance of the model in estimating the grain yield. Good accuracy was observed in the estimation of ETc and WP. In most cases, the model accuracy was greatest for 75% FIT and FIT treatments than the stressed conditions in the rainfed treatment. The model accuracy can be enhanced by improving the model coefficients in response to low levels of water and N supply. R² values obtained in rainfed (0.83), 75% FIT (0.81) and FIT (0.67) treatments in calibration years and R² values in rainfed (0.75), 75% FIT (0.77) and FIT (0.86) treatments in validation years showed that the model predicted the SWC relatively well. The comparison of ETc values with respect to N levels showed that there was no considerable difference between levels of N applications impact(s) on the ETc magnitude in the rainfed treatment. Comparison of different levels of N in rainfed and FIT showed that the application of 252 kg/ha of N resulted in 2.37 kg/m³ and 2.56 kg/m³ of WP, respectively, which was significantly different from other levels of N fertilizer applications. In general, CERES-Maize model can be a useful tool for predicting plant phenology, grain yield, ETc, WP, and SWC for the conditions similar to those presented in this research. The CERES-Maize model can provide valuable data and information for sustainable maize production by examining the long-term grain yield and WP, which can be beneficial to growers, advisors, and stakeholders to enhance the maize production efficiency by accounting for irrigation and N management strategies.

研究了 CERES-Maize 模型在可变速率横向（线性）移动喷灌系统下不同灌溉和氮 (N) 水平下模拟玉米物候、谷物产量、土壤-水、蒸散量和水分生产率的性能。灌溉水平为雨养、充分灌溉处理（FIT）和75% FIT。氮水平为 0、84、140、196 和 252 千克/公顷。田间试验以裂区形式进行，以灌溉水平为主处理，施氮水平为次主处理。使用均方根误差(RMSE)、归一化均方根误差(RMSE _n )、R ²、T检验和模型预测误差( P _e )统计来评估模型的性能和准确性。使用 2011 年和 2012 年的数据对模型进行了校准，并通过考虑种植后至开花天数（DAPF）、种植后至成熟天数（DAPM）、谷物产量、作物蒸散量（ ETc）、水生产力（WP）和土壤含水量（SWC）。DAPF 模拟基于P _e (0%)、RMSE (2 天) 和 RMSE _n (3%)的平均值，DAPM 模拟结果基于P _e (2%)、RMSE (4天）和 RMSE _n（3%）表明该模型具有可接受的精度。校准年中，RMSE、RMSE _n和 R ²分别为 0.57 吨/公顷、5%和 0.91；在验证年，相同的统计数据分别为0.86吨/公顷、10%和0.94，表明该模型在估算粮食产量方面表现良好。ETc 和 WP 的估计具有良好的准确性。在大多数情况下，75% FIT 和 FIT 处理的模型精度比雨养处理的应激条件下最高。通过改进模型系数来响应低水平的水和氮供应，可以提高模型的精度。校准年雨养 (0.83)、75% FIT (0.81) 和 FIT (0.67) 处理中获得的 R 2 值以及验证年雨^{养(0.75)、75% FIT (0.77) 和 FIT (0.86) 处理中获得的 R 2}值表明该模型对 SWC 的预测相对较好。ETc 值与氮水平的比较表明，雨养处理中施氮水平对 ETc 大小的影响没有显着差异。雨养和FIT不同施氮水平的比较表明，施氮量为252 kg/ha时，施氮量为2.37 kg/m ³和2.56 kg/m ³WP 分别与其他施氮水平显着不同。一般来说，CERES-Maize 模型可以成为预测植物物候、谷物产量、ETc、WP 和 SWC 的有用工具，适用于与本研究中提出的条件类似的条件。CERES-Maize 模型可以通过检查长期粮食产量和 WP 为可持续玉米生产提供有价值的数据和信息，这有利于种植者、顾问和利益相关者通过考虑灌溉和氮管理来提高玉米生产效率策略。