Ottawa Research and Development Center, Agriculture and Agri-Food Canada, Building 20, 960 carling Ave., Ottawa, Ontario, KA1 0C6, Canada.
Sci Rep. 2019 May 9;9(1):7130. doi: 10.1038/s41598-019-43683-9.
Due to the presence of genotype by environment interaction (GE), no crop cultivar performed the best in all regions. Therefore, the growing regions of a crop must be divided into sub-regions or mega-environments, and specifically adapted cultivars must be bred and deployed in each mega-environment. Meaningful mega-environment delineation must be based on repeatable GE patterns, which can be extracted from multi-year, multi-location crop variety trials. In regional crop variety trials, usually the same set of genotypes are tested across locations within a year, but different sets of genotypes are tested in different years, leading to highly unbalanced multi-year data. Such data are abundant for all crops and regions; but there has been no way to fully utilize them for mega-environment delineation. This paper presents a new method that allows utilization of existing variety trial data to identify repeatable GE patterns, to delineate mega-environments, and to understand the scope of unrepeatable GE at a location and within a mega-environment.
由于存在基因型与环境互作(GE),没有任何一个作物品种在所有地区都表现最佳。因此,必须将作物的种植区域划分为亚区或大环境,并在每个大环境中培育和部署专门适应的品种。有意义的大环境划分必须基于可重复的 GE 模式,这些模式可以从多年、多点的作物品种试验中提取。在区域性作物品种试验中,通常在一年内在不同地点测试相同的一组基因型,但在不同年份测试不同的基因型,导致多年数据高度不平衡。这种数据对于所有作物和地区都很丰富;但一直没有办法充分利用它们来进行大环境划分。本文提出了一种新方法,可以利用现有品种试验数据来识别可重复的 GE 模式,划分大环境,并了解在一个地点和一个大环境内不可重复 GE 的范围。