title: In silico system analysis of physiological traits determining grain yield and protein concentration for wheat as influenced by climate and crop management.
abstract: 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.
authors: 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 pierre.martre@clermont.inra.fr.
authors: Pierre
authors: P
authors: Martre
authors: 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.
authors: Jianqiang
authors: J
authors: He
authors: 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.
authors: Jacques
authors: J
authors: Le Gouis
authors: Department of Computational and Systems Biology, Rothamsted Research, Harpenden, Herts AL5 2JQ, UK.
authors: Mikhail A
authors: MA
authors: Semenov
mesh_terms: D002980:Climate; D003198:Computer Simulation; D018556:Crops, Agricultural / Q000254:growth & development* / Q000502:physiology; D002523:Edible Grain / Q000254:growth & development*; D008954:Models, Biological; D009584:Nitrogen / Q000378:metabolism; D010940:Plant Proteins / Q000378:metabolism*; D018526:Plant Transpiration; D011336:Probability; D019655:Quantitative Trait, Heritable; D012987:Soil / Q000737:chemistry; D014908:Triticum / Q000254:growth & development / Q000378:metabolism / Q000502:physiology*; D014867:Water / Q000737:chemistry
chemical_list: D010940:Plant Proteins; D012987:Soil; D014867:Water; D009584:Nitrogen
keywords: Crop growth model; genetic adaptation; grain protein concentration; grain yield; interannual variability; sensitivity analysis; wheat (Triticum aestivum L.); yield stability