INRA, UMR759 Laboratoire d'Ecophysiologie des Plantes sous Stress Environnementaux, Place Viala, F-34060 Montpellier, France
INRA, UMR759 Laboratoire d'Ecophysiologie des Plantes sous Stress Environnementaux, Place Viala, F-34060 Montpellier, France.
J Exp Bot. 2014 Nov;65(21):6179-89. doi: 10.1093/jxb/eru223. Epub 2014 Jun 19.
A crop model with genetic inputs can potentially simulate yield for a large range of genotypes, sites, and years, thereby indicating where and when a given combination of alleles confers a positive effect. We discuss to what extent current crop models, developed for predicting the effects of climate or cultivation techniques on a reference genotype, are adequate for ranking yields of a large number of genotypes in climatic scenarios with water deficit or high temperatures. We compare here the algorithms involved in 19 crop models. Marked differences exist in the representation of the combined effects of temperature and water deficit on plant development, and in the coordination of these effects with biomass production. The current literature suggests that these differences have a small impact on the yield prediction of a reference genotype because errors on the effects of different traits compensate each other. We propose that they have a larger impact if the crop model is used in a genetic context, because the model has to account for the genetic variability of studied traits. Models with explicit genetic inputs will be increasingly feasible because model parameters corresponding to each genotype can now be measured in phenotyping platforms for large plant collections. This will in turn allow prediction of parameter values from the allelic composition of genotypes. It is therefore timely to adapt crop models to this new context to simulate the allelic effects in present or future climatic scenarios with water or heat stresses.
具有遗传输入的作物模型可以潜在地模拟大范围基因型、地点和年份的产量,从而指示在何处以及何时给定的等位基因组合赋予了积极的影响。我们讨论了当前的作物模型在多大程度上可以用于在缺水或高温的气候情景下对大量基因型的产量进行排名,这些模型是为预测气候或栽培技术对参考基因型的影响而开发的。我们在这里比较了 19 个作物模型所涉及的算法。在温度和水分亏缺对植物发育的综合影响的表示以及这些影响与生物量生产的协调方面,存在明显的差异。当前的文献表明,这些差异对参考基因型的产量预测影响较小,因为不同性状的效应误差相互补偿。我们提出,如果作物模型在遗传背景下使用,这些差异的影响会更大,因为模型必须考虑到研究性状的遗传变异性。具有明确遗传输入的模型将越来越可行,因为现在可以在大型植物收集的表型平台上测量每个基因型对应的模型参数。这反过来又可以根据基因型的等位基因组成预测参数值。因此,及时调整作物模型以适应这种新情况,以模拟当前或未来水或热胁迫下的等位基因效应是及时的。