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Options for calibrating CERES-maize genotype specific parameters under data-scarce environments

机译:在数据稀缺环境下校准CERES-玉米基因型特定参数的选项

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摘要

Most crop simulation models require the use of Genotype Specific Parameters (GSPs) which provide the Genotype component of G×E×M interactions. Estimation of GSPs is the most difficult aspect of most modelling exercises because it requires expensive and time-consuming field experiments. GSPs could also be estimated using multi-year and multi locational data from breeder evaluation experiments. This research was set up with the following objectives: i) to determine GSPs of 10 newly released maize varieties for the Nigerian Savannas using data from both calibration experiments and by using existing data from breeder varietal evaluation trials; ii) to compare the accuracy of the GSPs generated using experimental and breeder data; and iii) to evaluate CERES-Maize model to simulate grain and tissue nitrogen contents. For experimental evaluation, 8 different experiments were conducted during the rainy and dry seasons of 2016 across the Nigerian Savanna. Breeder evaluation data were also collected for 2 years and 7 locations. The calibrated GSPs were evaluated using data from a 4-year experiment conducted under varying nitrogen rates (0, 60 and 120kg N ha-1). For the model calibration using experimental data, calculated model efficiency (EF) values ranged between 0.88–0.94 and coefficient of determination (d-index) between 0.93–0.98. Calibration of time-series data produced nRMSE below 7% while all prediction deviations were below 10% of the mean. For breeder experiments, EF (0.58–0.88) and d-index (0.56–0.86) ranges were lower. Prediction deviations were below 17% of the means for all measured variables. Model evaluation using both experimental and breeder trials resulted in good agreement (low RMSE, high EF and d-index values) between observed and simulated grain yields, and tissue and grain nitrogen contents. It is concluded that higher calibration accuracy of CERES-Maize model is achieved from detailed experiments. If unavailable, data from breeder experimental trials collected from many locations and planting dates can be used with lower but acceptable accuracy.
机译:大多数作物模拟模型都需要使用基因型特定参数(GSP),该参数提供G×E×M相互作用的基因型成分。 GSP的估计是大多数建模练习中最困难的方面,因为它需要昂贵且耗时的现场实验。也可以使用来自种鸡评估实验的多年和多地点数据来估算GSP。开展这项研究的目的是:i)使用校准实验数据和育种者品种评估试验的现有数据,确定尼日利亚大草原地区10个新发布的玉米品种的GSP; ii)比较使用实验和育种者数据生成的GSP的准确性; iii)评估CERES-玉米模型以模拟谷物和组织中的氮含量。为了进行实验评估,在2016年的雨季和干旱季节,尼日利亚大草原地区进行了8个不同的实验。还收集了2年和7个地点的育种者评估数据。校准的GSP使用在不同氮含量(0、60和120kg N ha -1 )下进行的为期4年的实验数据进行评估。对于使用实验数据进行的模型校准,计算出的模型效率(EF)值在0.88-0.94之间,而确定系数(d-index)在0.93-0.98之间。校准时间序列数据可使nRMSE低于7%,而所有预测偏差均低于平均值的10%。对于育种实验,EF(0.58–0.88)和d-index(0.56-0.86)范围较低。对于所有测量变量,预测偏差均低于平均值的17%。使用实验和育种试验进行的模型评估在观察到的和模拟的谷物产量以及组织和谷物的氮含量之间达成了良好的一致性(低RMSE,高EF和d-指数值)。结论是,通过详细的实验,获得了CERES-Maize模型更高的校准精度。如果不可用,则可以以较低但可以接受的准确性使用从许多位置和播种日期收集的育种者试验数据。

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