Department of Agronomy, Bayero University Kano, Kano, Nigeria.
Department of Earth and Environmental Sciences, Division of Soil and Water Management, KU Leuven, Leuven, Belgium.
PLoS One. 2019 Feb 19;14(2):e0200118. doi: 10.1371/journal.pone.0200118. eCollection 2019.
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.
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。
