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Among 50 environments in the United States, we screened 35 hairy vetch (Vicia villosa Roth.) lines for traits of interest to cover cropping. We analyzed the influence of genotype, environment, and the genotype‐by‐environment interaction (G × E) on biomass, vigor, winter survival, emergence, flowering time, and nitrogen fixation. To explore how environments and G × E impacted each trait, we associated environment predictions and G × E loadings with weather and soil parameters. Environment had the largest influence on all traits, representing more than half of the variance. Environment predictions were significantly associated with weather and/or soil parameters for each trait. Biomass was associated with growing degree days, winter survival with freezing degree days without snow cover, growth stage with shortwave radiation, and emergence with soil texture. The G × E interaction was larger than genotypic variance for all traits except for winter survival and flowering time. The G × E interaction loadings were associated with soil sand content for biomass, air temperature for fall vigor and emergence, and snow cover for winter survival. Although it represented the smallest proportion of total variance, genetic effects were significant for all traits except for emergence, Ndfa, %N, and C:N. New hairy vetch breeding lines were superior to all commercially available lines for biomass and winter survival. Biomass harvest timing did not significantly change line rank, indicating that top‐performing lines can be used in diverse management systems. To select for high nitrogen contribution to subsequent crops, breeding programs can indirectly select for biomass rather than expensively evaluating symbiotic nitrogen fixation. Core Ideas: New hairy vetch varieties have higher biomass and winter survival than check cultivars.Genotype‐by‐environment interactions were larger than genetic effects for biomass, vigor, and emergence.Weather and soil parameters that were associated with G × E could be used to optimize selection.There are few tradeoffs in selecting multiple cover crop traits in hairy vetch.Across our sites, aboveground biomass accurately predicted total fixed nitrogen (N). [ABSTRACT FROM AUTHOR]

Malnutrition due to macro‐ and micro‐nutrient deficiencies is one of the major global health concerns, especially in developing countries. Using genomics‐assisted breeding to enhance the nutritional value of important crops such as chickpea (Cicer arietinum L.) can help to address the problem. In this study, we conducted genome‐wide association studies to identify genes associated with protein, starch, oil, and fiber in chickpea to create resources to speed the breeding process. The USDA kabuli chickpea mini‐core of 88 accessions was genotyped using genotyped‐by‐sequencing, and 36,645 single nucleotide polymorphisms (SNPs) were identified across the eight chromosomes of the chickpea genome. A genome‐wide marker‐trait analysis using the FarmCPU model was conducted to identify SNP markers that can enable marker‐assisted breeding for seed protein, fiber, oil, and starch concentrations. The most significantly associated markers for seed protein concentration (p = 8.82E‐12), starch (p = 2.79E‐12), fiber (p = 7.65E‐12), and oil (p = 1.37E‐08) were found on chromosomes 1, 2, 6, and 7, controlling 11%, 12%, 20%, and 16% of the phenotypic variation, respectively. Validation of the SNP markers in a broader set of plant genetic resources and environments will be needed to determine their usefulness in breeding for end‐use characteristics. Core Ideas: Variation in seed protein, oil, fiber, and starch concentration exists in kabuli chickpea.Genome‐wide association study uncovered single nucleotide polymorphisms (SNPs) and gene candidates for seed quality traits.These SNPs may be useful for the marker‐assisted selection of improved seed quality in chickpea. [ABSTRACT FROM AUTHOR]

Background and objectives: Research and breeding for pea protein extraction are likely to play an important role as the demand for pea protein increases. Developing pea cultivars with high protein concentration could be a top breeding priority. Identification of the traits to select for and understanding the relationships between them will optimize the breeding process. Protein was extracted from 26 green and 30 yellow pea genotypes grown in four and two environments, respectively. Findings: During dehulling/decortication, approximately 15.6% of the pea seed was removed as seed coat, with a low of 10.1% and a high of 20.4%. The protein concentration of split pea flour varied between 18.9% and 29.3%. Split pea had 1.4 percentage point higher protein than the whole pea. Flour protein concentration varied among environments, with Fairfield, WA having the highest mean values. Protein isolate yield ranged from 14.8% to 21.1%, with a mean of 17.8%. Protein recovery from split pea flour ranged from 57.4% to 76.3% across all pea types, with a mean of 65.7%. On average, about 61.6% of the initial sample ended up in the starch‐rich byproduct fraction. The remaining 20.6% the initial samples lost as soluble material during the extraction process. Correlation analysis indicated that flour protein concentration was positively correlated with protein isolate yield and purity but negatively with yield of the starch‐rich byproduct. Conclusions: Increasing protein concentration leads to increased protein yield and reduced the loss as byproduct during the extraction process. Further studies are required to understand the relationships of protein isolate yield and purity with functional properties of protein. Significance and novelty: The associations among traits imply that the flour protein concentration could be used in the selection process. Increasing protein concentration could be one key input in proving pea cultivars for protein isolation. [ABSTRACT FROM AUTHOR]

Societal Impact Statement: Lentil is a staple in many diets around the world and growing in popularity as a quick‐cooking, nutritious, plant‐based source of protein in the human diet. Lentil varieties are usually grown close to where they were bred. Future climate change scenarios will result in increased temperatures and shifts in lentil crop production areas, necessitating expanded breeding efforts. We show how we can use a daylength and temperature model to identify varieties most likely to succeed in these new environments, expand genetic diversity, and give plant breeders additional knowledge and tools to help mitigate these changes for lentil producers. Summary Lentil (Lens culinaris Medik.) is cultivated under a wide range of environmental conditions, which has led to diverse phenological adaptations and resulted in a decrease in genetic variability within breeding programs due to reluctance in using genotypes from other environments.We phenotyped 324 genotypes across nine locations over three years to assess their phenological response to the environment of major lentil production regions and to predict days from sowing to flowering (DTF) using a photothermal model.DTF was highly influenced by the environment and is sufficient to explain adaptation. We were able to predict DTF reliably in most environments using a simple photothermal model, however, in certain site‐years, results suggest there may be additional environmental factors at play. Hierarchical clustering of principal components revealed the presence of eight groups based on the responses of DTF to contrasting environments. These groups are associated with the coefficients of the photothermal model and revealed differences in temperature and photoperiod sensitivity.Future climate change scenarios will result in increased temperature and/or shifts in production areas. The ability to use the photothermal model to identify genotypes most likely to succeed in these new environments has important social impacts with respect to traditional cropping systems. [ABSTRACT FROM AUTHOR]