Martre Pierre, He Jianqiang, Le Gouis Jacques, Semenov Mikhail A
INRA, UMR1095 Genetic, Diversity and Ecophysiology of Cereals, 5 chemin de Beaulieu, Clermont-Ferrand F-63100, France Blaise Pascal University, UMR1095 Genetic, Diversity and Ecophysiology of Cereals, Aubière F-63177, France
INRA, UMR1095 Genetic, Diversity and Ecophysiology of Cereals, 5 chemin de Beaulieu, Clermont-Ferrand F-63100, France Blaise Pascal University, UMR1095 Genetic, Diversity and Ecophysiology of Cereals, Aubière F-63177, France.
J Exp Bot. 2015 Jun;66(12):3581-98. doi: 10.1093/jxb/erv049. Epub 2015 Mar 24.
Genetic improvement of grain yield (GY) and grain protein concentration (GPC) is impeded by large genotype×environment×management interactions and by compensatory effects between traits. Here global uncertainty and sensitivity analyses of the process-based wheat model SiriusQuality2 were conducted with the aim of identifying candidate traits to increase GY and GPC. Three contrasted European sites were selected and simulations were performed using long-term weather data and two nitrogen (N) treatments in order to quantify the effect of parameter uncertainty on GY and GPC under variable environments. The overall influence of all 75 plant parameters of SiriusQuality2 was first analysed using the Morris method. Forty-one influential parameters were identified and their individual (first-order) and total effects on the model outputs were investigated using the extended Fourier amplitude sensitivity test. The overall effect of the parameters was dominated by their interactions with other parameters. Under high N supply, a few influential parameters with respect to GY were identified (e.g. radiation use efficiency, potential duration of grain filling, and phyllochron). However, under low N, >10 parameters showed similar effects on GY and GPC. All parameters had opposite effects on GY and GPC, but leaf and stem N storage capacity appeared as good candidate traits to change the intercept of the negative relationship between GY and GPC. This study provides a system analysis of traits determining GY and GPC under variable environments and delivers valuable information to prioritize model development and experimental work.
基因型×环境×管理的复杂互作以及性状间的补偿效应阻碍了谷物产量（GY）和谷物蛋白质浓度（GPC）的遗传改良。本研究对基于过程的小麦模型SiriusQuality2进行了全局不确定性和敏感性分析，旨在确定可提高GY和GPC的候选性状。选取了欧洲三个具有对比性的地点，并使用长期气象数据和两种氮（N）处理进行模拟，以量化可变环境下参数不确定性对GY和GPC的影响。首先使用Morris方法分析了SiriusQuality2的所有75个植物参数的总体影响。确定了41个有影响的参数，并使用扩展傅里叶幅度敏感性测试研究了它们对模型输出的个体（一阶）和总体影响。参数的总体影响主要由它们与其他参数的相互作用决定。在高氮供应下，确定了一些对GY有影响的参数（例如辐射利用效率、籽粒灌浆的潜在持续时间和叶龄期）。然而，在低氮条件下，超过10个参数对GY和GPC表现出相似的影响。所有参数对GY和GPC都有相反的影响，但叶和茎的氮储存能力似乎是改变GY和GPC之间负相关截距的良好候选性状。本研究对可变环境下决定GY和GPC的性状进行了系统分析，并为优先开展模型开发和实验工作提供了有价值的信息。
