The phenological and growth parameters of CERES-Maize were estimated using data from field variety trials in 2017 and 2018 to simulate hybrid maize (Zea mays L.) grown in Michigan. Multiple calibration methods used include GENCALC (Genotype Coefficient Calculator), GLUE (Generalized Likelihood Uncertainty Estimate), NMCGA (Noisy Monte Carlo Genetic Algorithm) and ensembling approach. Three irrigated sites were used for calibration while six rainfed sites for evaluation. Better results were obtained when using multiple years of data in calibration than using only a single year. Model evaluation also suggests that fixed soil root growth factor (SRGF) used in calibration (irrigated condition) tended to restrict root dynamics under rainfed condition. This resulted in substantial yield mismatch due to poorly modeled yields, although phenology was better predicted. Adjusting SRGF under rainfed condition resulted in better model evaluation for both years. Moreover, weighted averaging of genetic coefficients resulted in better predictions of phenology and yields.

使用2017年和2018年田间品种试验的数据估算CERES玉米的物候和生长参数，以模拟密歇根州种植的杂交玉米（Zea mays L.）。使用的多种校准方法包括GENCALC（基因型系数计算器），GLUE（广义似然不确定性估计），NMCGA（噪声蒙特卡洛遗传算法）和组合方法。三个灌溉地点用于校准，而六个雨育地点用于评估。在校准中使用多年数据比仅使用一年可获得更好的结果。模型评估还表明，在校准（灌溉条件）中使用的固定土壤根系生长因子（SRGF）往往会限制雨养条件下的根系动态。由于未正确建模的收益导致大量收益失配，尽管物候学可以更好地预测。在雨水条件下调节SRGF可以使两个年度的模型评估更好。此外，遗传系数的加权平均可以更好地预测物候和产量。