Use of Multiple Environment Variety Trials Data to Simulate Maize Yields in the Ogallala Aquifer Region: A Two Model Approach

Sharda Vaishali; Mekonnen Mesfin M.; Ray Chittaranjan; Gowda Prasanna H.

摘要

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.

机译：随着长期目标在奥加拉拉蓄水层区域（OAR）优化利用地下水来维持粮食生产系统，该研究进行校准决策支持系统农业技术转让（DSSAT）和AQUACROP作物模拟平台来模拟玉米产量使用历史数据集区域范围。当地作物生长数据和作物管理措施模型的校准是重要的，但通常这种反季节作物生长信息不可用。本研究确定采用玉米品种试验的数据，在OAR的CSM-农作物估产，通过资源与环境综合玉米和AQUACROP模型的评估的可能性。这些模型进行了校准，并在内布拉斯加州三个县进行测试。这两个模型1988年期间则用来模拟灌溉玉米产量至2015年的所有三个县。评价这些作物模型的性能标准包括统计参数和图形分析。这两款车型的性能，然后用从现场各种测试结果和历史悠久的国家农业统计服务收益率观测产量相比。结果表明校准DSSAT模型的产量和产率观察之间的差小于10％，并且范围从740到1820公斤/公顷AQUACROP根均方误差。观测和模拟内布拉斯加县的产量也表示相信在校准作物模型与季节的产量数据，典型的最终和使用这些模型时，详细的反季节作物生长观测数据不可用学习农作物生产区域尺度之间的长期比较。

Use of Multiple Environment Variety Trials Data to Simulate Maize Yields in the Ogallala Aquifer Region: A Two Model Approach

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