With a long‐term goal to optimize use of groundwater in the Ogallala Aquifer Region (OAR) to sustain food production systems, this study was conducted to calibrate Decision Support System for Agrotechnology Transfer (DSSAT) and AquaCrop crop modeling platforms to simulate maize production at a regional scale using historic datasets. Calibration of the models with local crop growth data and crop management practices is important, but usually this in‐season crop growth information is not available. This study determined the possibility of using maize variety trial data for the evaluation of the CSM‐Crop Estimation through Resources and Environmental Synthesis‐Maize and AquaCrop models in the OAR. The models were calibrated and tested in three counties in Nebraska. Both the models were then used to simulate irrigated maize yield during 1988 to 2015 for all three counties. The criteria for evaluating the performance of these crop models included statistical parameters and graphical analysis. The performance of both models were then compared with the observed yield from field variety test results and historic National Agricultural Statistical Service yields. The results indicated that difference between yield of calibrated DSSAT model and observed yield was less than 10% and AquaCrop root mean square error ranged from 740 to 1,820 kg/ha. Long‐term comparison between observed and simulated Nebraska county yields also indicated confidence in calibrating crop models with typical end of season yield data and using these models for studying crop production at regional scales when detailed in‐season crop growth observed data are not available.

中文翻译：

---

利用多种环境试验数据模拟奥加拉拉含水层地区的玉米产量：两种模型方法

为了优化Ogallala含水层地区（OAR）的地下水使用量以维持粮食生产系统，该研究旨在校准农业技术转让决策支持系统（DSSAT）和AquaCrop作物建模平台以模拟玉米生产。使用历史数据集的区域规模。用当地作物生长数据和作物管理方法对模型进行校准很重要，但是通常无法获得该季节作物生长信息。这项研究确定了使用玉米品种试验数据通过OAR中的资源和环境综合玉米和AquaCrop模型评估CSM作物估计的可能性。在内布拉斯加州的三个县对模型进行了校准和测试。然后将这两个模型用于模拟三个县在1988年至2015年期间的灌溉玉米产量。评估这些作物模型性能的标准包括统计参数和图形分析。然后将这两种模型的性能与田间试验结果和国家农业统计局历史产量所观察到的产量进行比较。结果表明，经校正的DSSAT模型的产量与观察到的产量之间的差异小于10％，并且AquaCrop的均方根误差为740至1,820 kg / ha。