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首页> 美国卫生研究院文献>Journal of Experimental Botany >In silico system analysis of physiological traits determining grain yield and protein concentration for wheat as influenced by climate and crop management
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In silico system analysis of physiological traits determining grain yield and protein concentration for wheat as influenced by climate and crop management

机译：在计算机系统中分析受气候和作物管理影响的小麦的生理特性确定小麦的籽粒产量和蛋白质浓度

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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.
机译：大基因型×环境×管理相互作用和性状间的代偿效应阻碍了籽粒产量（GY）和籽粒蛋白质浓度（GPC）的遗传改良。在此，对基于过程的小麦模型SiriusQuality2进行了全球不确定性和敏感性分析，目的是确定可增加GY和GPC的候选性状。选择了三个对比鲜明的欧洲站点，并使用长期天气数据和两个氮（N）处理进行了模拟，以量化参数不确定性对可变环境下GY和GPC的影响。首先使用Morris方法分析了SiriusQuality2的所有75个工厂参数的总体影响。确定了41个有影响力的参数，并使用扩展傅里叶幅度灵敏度测试研究了它们的个体（一阶）和对模型输出的总体影响。这些参数与其他参数的交互作用决定了总体效果。在高氮供应下，确定了一些与GY有关的影响参数（例如辐射利用效率，潜在的籽粒充填持续时间和叶轮节律）。但是，在低氮下，> 10的参数对GY和GPC表现出相似的影响。所有参数对GY和GPC的影响相反，但叶片和茎N的贮藏能力似乎是改变GY和GPC之间负相关关系的良好候选者。这项研究提供了在可变环境下确定GY和GPC的性状的系统分析，并提供了有价值的信息以优先进行模型开发和实验工作。

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