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Plant breeding programs expend significant resources on multilocation testing to evaluate genotypes for advancement or potential cultivar release. The selection of genotype entries for these trials is typically based on previous phenotypic data or predictions; yet locations, important contributors to nongenetic variation, are often chosen in a less data‐driven manner. Using agronomic and quality trait data from two long‐term regional barley (Hordeum vulgare L.) nurseries, our objectives were (a) to measure the precision, repeatability, and representativeness of test locations based on multitrait data and (b) to optimize the selection of test locations for use in future trials. When considering traits individually, ideal locations could be identified simply, but a combined analysis of 11 traits indicated that very few locations were broadly favorable, and considerable tradeoffs are necessary. We developed a flexible optimization procedure to select the locations based on their precision, repeatability, and representativeness for multiple traits while simultaneously constraining the total number of locations. Optimization led to a 58–75% reduction in the number of locations, and therefore phenotyping costs, with little loss in data utility. Importantly, our approach allowed locations to be selected for phenotyping different sets of traits (e.g., either agronomic or both agronomic and malting quality), mimicking the often nested structure of trait data collection. This approach may be useful for individual plant breeding programs or collaborations wishing to increase the resource efficiency of these important regional evaluation trials. Core Ideas: Test locations in regional barley breeding nurseries were evaluated for multiple traits.No single location was ever superior for genotypic evaluation of many traits.Optimization identified a reduced set of locations with little loss in phenotypic data utility. [ABSTRACT FROM AUTHOR]

Kernel shape and ß-glucan content are important attributes for oat (Avena sativa L.) millers. The identification of genomic regions controlling these traits, as well as their effect on phenotype, will enable rapid gain fromselection. The objectives of this study were (a) to identify the genomic regions associated with kernel shape in an oat panel adapted to subtropical environments and (b) to clarify the relationship between kernel shape and ß-glucan content in oat. An elite oat panel comprising 406 inbred lines was assessed in Londrina and Eldorado do Sul, Southern Brazil. Genome-wide association analyses were performed for each environment separately and also in a multi-environment analysis. Phenotypic and genetic correlation coefficients were estimated between kernel shape traits and ß-glucan content. Genomic regions influencing kernel length were identified onMrg06, Mrg21, and Mrg24. Kernel width and kernel thickness were genetically correlated and shared a quantitative trait locus, located on Mrg13 of the oat consensus map. Phenotypic and genetic correlations were found between kernel shape traits and ß-glucan content, indicating a negative relationship between kernel width or kernel thickness and ß-glucan content. Oat breeding programs that aim to maintain minimum levels of ß-glucan for nutrition and minimum kernel widths for milling will need to screen for both traits; otherwise, indirect selection will lead to unfavorable results. However, the negative correlation was not too strong (-0.4), suggesting that improvement for both traits can be made simultaneously. [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]