Naked barley (Hordeum vulgare L.) grain threshes freely from its hull during harvesting and cleaning. Much of the available naked barley germplasm is unadapted to U.S. barley growing regions, and few genotypes have been selected to thrive under organic systems. The goal of this research was to characterize a set of spring naked barley genotypes for agronomic traits in the northern United States under organic conditions and estimate the degree of genotype × environment interaction (GEI). To achieve this goal, a multienvironment trial was conducted over 3 yr. Experiments were grown under organic conditions where traits including grain yield, test weight, plant height, heading date, and threshability were evaluated. Contributions of environment, genotype, and GEI to the phenotypic variance were calculated. In the tested germplasm, test weight, plant height, heading date, and threshability were found to have higher variance attributed to genotypic effects compared to GEI. Grain yield variance attributed to GEI was five times greater than that of genotype alone. Genotype + GEI (GGE) and location-grouping (LG) biplots showed two sets of environments where naked barley genotypes performed similarly. Sensitivity analysis by Finlay–Wilkinson regression found that grain yield was highly sensitive to changes in environment mean yield. These results show that selection for grain yield should be conducted within mega-environments to leverage GEI patterns. Heading date, plant height, test weight, and threshability can be selected across mega-environments.

中文翻译：

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美国北部有机裸大麦农艺性状和谷物脱粒性的遗传特征

裸大麦 ( Hordeum vulgare L.) 在收割和清洁过程中，谷物从外壳上自由脱粒。许多可用的裸大麦种质不适应美国大麦种植区，很少有基因型被选择在有机系统下茁壮成长。本研究的目的是在有机条件下表征美国北部农艺性状的一组春季裸大麦基因型，并估计基因型×环境相互作用（GEI）的程度。为了实现这一目标，我们进行了 3 年多的多环境试验。实验是在有机条件下种植的，其中评估了包括谷物产量、容重、株高、抽穗日期和脱粒性在内的性状。计算了环境、基因型和 GEI 对表型方差的贡献。在供试种质中，容重、株高、抽穗期、与 GEI 相比，发现可脱粒性具有更高的变异性，这归因于基因型效应。归因于 GEI 的谷物产量差异是单独基因型的五倍。基因型 + GEI (GGE) 和位置分组 (LG) 双图显示了裸大麦基因型表现相似的两组环境。Finlay-Wilkinson回归的敏感性分析发现，粮食产量对环境平均产量的变化高度敏感。这些结果表明，谷物产量的选择应该在大环境中进行，以利用 GEI 模式。可以在大型环境中选择抽穗日期、株高、测试重量和可脱粒性。归因于 GEI 的谷物产量差异是单独基因型的五倍。基因型 + GEI (GGE) 和位置分组 (LG) 双图显示了裸大麦基因型表现相似的两组环境。Finlay-Wilkinson回归的敏感性分析发现，粮食产量对环境平均产量的变化高度敏感。这些结果表明，谷物产量的选择应该在大环境中进行，以利用 GEI 模式。可以在大型环境中选择抽穗日期、株高、测试重量和可脱粒性。归因于 GEI 的谷物产量差异是单独基因型的五倍。基因型 + GEI (GGE) 和位置分组 (LG) 双图显示了裸大麦基因型表现相似的两组环境。Finlay-Wilkinson回归的敏感性分析发现，粮食产量对环境平均产量的变化高度敏感。这些结果表明，谷物产量的选择应该在大环境中进行，以利用 GEI 模式。可以在大型环境中选择抽穗日期、株高、测试重量和可脱粒性。Finlay-Wilkinson回归的敏感性分析发现，粮食产量对环境平均产量的变化高度敏感。这些结果表明，谷物产量的选择应该在大环境中进行，以利用 GEI 模式。可以在大型环境中选择抽穗日期、株高、测试重量和可脱粒性。Finlay-Wilkinson回归的敏感性分析发现，粮食产量对环境平均产量的变化高度敏感。这些结果表明，谷物产量的选择应该在大环境中进行，以利用 GEI 模式。可以在大型环境中选择抽穗日期、株高、测试重量和可脱粒性。