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Key message: Crop simulation helps to analyze environmental impacts on crops and provides year-independent context information. This information is of major importance when deciding which cultivar to choose at sowing time. Plant breeding programs design new crop cultivars which, while developed for distinct populations of environments, are nevertheless grown over large areas during their time in the market. Over its cultivation area, the crop is exposed to highly diverse stress patterns caused by climatic uncertainty and multiple management options, which often leads to decreased expected crop performance. In this study, we aim to assess how finer spatial management of genetic resources could reduce the yield variance explained by genotype × environment interactions in a set of cropping environments and ultimately improve the efficiency and stability of crop production. We used modeling and simulation to predict the crop performance resulting from the interaction between cultivar growth and development, climate and soil conditions, and management practices. We designed a computational experiment that evaluated the performance of a collection of commercial sunflower cultivars in a realistic population of cropping conditions in France, built from extensive agricultural surveys. Distinct farming locations sharing similar simulated abiotic stress patterns were clustered together to specify environment types. We then used optimization methods to search for cultivars × environments combinations leading to increased yield expectations. Results showed that a single cultivar choice adapted to the most frequent environment-type in the population is a robust strategy. However, the relevance of cultivar recommendations to specific locations was gradually increasing with the knowledge of pedo-climatic conditions. We argue that this approach while being operational on current genetic material could act synergistically with plant breeding as more diverse material could enable access to cultivars with distinctive traits, more adapted to specific conditions. [ABSTRACT FROM AUTHOR]

Key message: This study compares five models of GWAS, to show the added value of non-additive modeling of allelic effects to identify genomic regions controlling flowering time of sunflower hybrids. Abstract: Genome-wide association studies are a powerful and widely used tool to decipher the genetic control of complex traits. One of the main challenges for hybrid crops, such as maize or sunflower, is to model the hybrid vigor in the linear mixed models, considering the relatedness between individuals. Here, we compared two additive and three non-additive association models for their ability to identify genomic regions associated with flowering time in sunflower hybrids. A panel of 452 sunflower hybrids, corresponding to incomplete crossing between 36 male lines and 36 female lines, was phenotyped in five environments and genotyped for 2,204,423 SNPs. Intra-locus effects were estimated in multi-locus models to detect genomic regions associated with flowering time using the different models. Thirteen quantitative trait loci were identified in total, two with both model categories and one with only non-additive models. A quantitative trait loci on LG09, detected by both the additive and non-additive models, is located near a GAI homolog and is presented in detail. Overall, this study shows the added value of non-additive modeling of allelic effects for identifying genomic regions that control traits of interest and that could participate in the heterosis observed in hybrids. [ABSTRACT FROM AUTHOR]

Restriction fragment length polymorphism and isoenzyme markers were used to investigate quantitative trait loci involved in sunflower resistance to mycelial extension of Sclerotinia sclerotiorum on leaves and capitula. Seed weight, oil content and flowering data were also evaluated. Four quantitative trait loci were demonstrated for leaf resistance and two for capitulum resistance. One of these zones appears involved in resistance to both types of S. sclerotiorum attack while the others appear specific for resistance of one part of the plant. Two quantitative trait loci were detected for seed weight, three for oil content and three for flowering date. Individual quantitative trait loci explained 9% to 48% of the phenotypic variability, confirming the polygenic basis of the quantitative traits studied. Overall, the quantitative trait loci explain 60% of the genetic variation for leaf resistance and 38% for capitulum resistance to S. sclerotiorum. One linkage group is particularly interesting since it includes quantitative trait loci for all the five quantitative traits measured. Hypotheses for linkage versus pleiotropy and consequences of all the results in resistance breeding are discussed. [ABSTRACT FROM AUTHOR]