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The modeling of genotype × environment interactions (GEI) is important to understand how new crops perform in different environments and management systems. Naked barley (Hordeum vulgare L.) is a type of barley where the hull threshes freely from the grain and can be used for multiple end uses, including food, malt, and animal feed. We examined the performance of a winter naked barley trial grown in organic conditions across three regions and 3 years in northern latitude regions of the United States. We recorded yield, test weight, heading date, plant height, threshability, winter survival, barley stripe rust (Puccinia striiformis f. sp. hordei Westend), barley scald (Rhynchosporium commune Zaffarano, McDonald and Linde sp. nov), and spot blotch (Cochliobolus sativus, anamorph Bipolaris sorokiniana (S. Ito & Kurib.). Barley stripe rust, plant height, heading date, preharvest sprouting, and threshability had high heritability and low GEI. Grain yield, test weight, scald, and winter survival had high GEI and were analyzed using the additive main effect and multiplicative interaction (AMMI) model, genotype plus GEI biplot (GGE), and Finlay–Wilkinson (FW) stability analysis. The environmental patterns explained by the AMMI and GGE models indicated that selection for high GEI traits should occur within target environments and across multiple years within the target environments. Environmental groups were separated by Oregon as the first group and Wisconsin and New York as the second group. Core Ideas: Barley stripe rust, plant height, heading date, preharvest sprouting, and threshability had high heritability and low genotype × environment interactions.Grain yield, test weight, scald, and winter survival had high genotype × environment interaction effects.Environmental groupings were separated into OR as the first group and NY and WI as the second group. [ABSTRACT FROM AUTHOR]

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. Core Ideas: There is five‐times greater genotype × environment interaction (GEI) than genetic variance for grain yield. Biplots define two mega‐environments in the northern United States for naked barley grain yield. Other assessed agronomic traits and threshability are more affected by genotype than GEI. [ABSTRACT FROM AUTHOR]

In plant breeding, the goal of genomewide selection is to predict the merit of unobserved individuals, particularly those in the next breeding generation. Predictions of these individuals in unobserved or future environments would be of additional use to a breeder. For many of the complex traits targeted in breeding, this may require management of genotype × environment interactions by, for example, using data from homogeneous groups of environments. Our objectives were to assess the accuracy of genomewide predictions in unobserved environments both within and between breeding generations; we aimed to compare training sets that included data from all possible environments with those that included data from (a) decreasingly similar environments or (b) discrete clusters of similar environments. A 183‐line spring barley (Hordeum vulgare L.) training population and 50‐line offspring test population were phenotyped in 29 location–year environments for grain yield, heading date, and plant height. Environmental similarities were measured using phenotypic data, geographic distance, or environmental covariables. When using training data from more, but decreasingly similar environments, prediction accuracy increased, but marginal gains declined; in some cases, accuracy declined with additional data. Clusters of environments informed by phenotypes (i.e., phenotypic correlations or multiplicative models) typically improved prediction accuracy within a generation, but not between generations (offspring population). Our study suggests that, as an alternative to using data from all available environments, informative subsets may be advantageous for genomewide predictions within a single breeding generation, but not between generations. [ABSTRACT FROM AUTHOR]