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在数据匮乏环境下校准 CERES-玉米基因型特定参数的方法。

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

机构信息

Department of Agronomy, Bayero University Kano, Kano, Nigeria.

Department of Earth and Environmental Sciences, Division of Soil and Water Management, KU Leuven, Leuven, Belgium.

出版信息

PLoS One. 2019 Feb 19;14(2):e0200118. doi: 10.1371/journal.pone.0200118. eCollection 2019.

Abstract

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.

摘要

大多数作物模拟模型都需要使用基因型特定参数(GSP),这些参数提供了 G×E×M 互作的基因型组成部分。GSP 的估计是大多数建模工作中最困难的方面,因为它需要昂贵且耗时的田间实验。GSP 也可以使用来自育种评估实验的多年和多地点数据进行估计。本研究的目的如下:i)使用校准实验和现有育种品种评估试验数据,确定 10 种新发布的玉米品种在尼日利亚萨凡纳的 GSP;ii)比较使用实验和育种数据生成的 GSP 的准确性;iii)评估 CERES-Maize 模型以模拟籽粒和组织氮含量。对于实验评估,在 2016 年的雨季和旱季在尼日利亚萨凡纳进行了 8 个不同的实验。还收集了 2 年和 7 个地点的育种评估数据。使用在不同氮素水平(0、60 和 120kg N ha-1)下进行的 4 年实验数据评估校准的 GSP。对于使用实验数据进行模型校准,计算的模型效率(EF)值范围为 0.88-0.94,决定系数(d-index)范围为 0.93-0.98。时间序列数据的校准产生的 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。所有测量变量的预测偏差均低于平均值的 17%。使用实验和育种试验进行模型评估,导致观测值与模拟值之间具有良好的一致性(低 RMSE、高 EF 和 d-index 值),包括籽粒产量、组织和籽粒氮含量。得出的结论是,CERES-Maize 模型的校准精度更高,来自详细的实验。如果不可用,也可以使用来自许多地点和种植日期的育种试验收集的数据,虽然精度较低,但仍可接受。

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