Objectives: Advanced tools and resources are needed to efficiently and sustainably produce food for an increasing world population in the context of variable environmental conditions. The maize genomes to fields (G2F) initiative is a multi-institutional initiative effort that seeks to approach this challenge by developing a flexible and distributed infrastructure addressing emerging problems. G2F has generated large-scale phenotypic, genotypic, and environmental datasets using publicly available inbred lines and hybrids evaluated through a network of collaborators that are part of the G2F's genotype-by-environment (G × E) project. This report covers the public release of datasets for 2014-2017. Data description:Datasets include inbred genotypic information; phenotypic, climatic, and soil measurements and metadata information for each testing location across years. For a subset of inbreds in 2014 and 2015, yield component phenotypes were quantified by image analysis. Data released are accompanied by README descriptions. For genotypic and phenotypic data, both raw data and a version without outliers are reported. For climatic data, a version calibrated to the nearest airport weather station and a version without outliers are reported. The 2014 and 2015 datasets are updated versions from the previously released files [1] while 2016 and 2017 datasets are newly available to the public.

ObjectivesCrop improvement relies on analysis of phenotypic, genotypic, and environmental data. Given large, well-integrated, multi-year datasets, diverse queries can be made: Which lines perform best in hot, dry environments? Which alleles of specific genes are required for optimal performance in each environment? Such datasets also can be leveraged to predict cultivar performance, even in uncharacterized environments. The maize Genomes to Fields (G2F) Initiative is a multi-institutional organization of scientists working to generate and analyze such datasets from existing, publicly available inbred lines and hybrids. G2F’s genotype by environment project has released 2014 and 2015 datasets to the public, with 2016 and 2017 collected and soon to be made available.Data descriptionDatasets include DNA sequences; traditional phenotype descriptions, as well as detailed ear, cob, and kernel phenotypes quantified by image analysis; weather station measurements; and soil characterizations by site. Data are released as comma separated value spreadsheets accompanied by extensive README text descriptions. For genotypic and phenotypic data, both raw data and a version with outliers removed are reported. For weather data, two versions are reported: a full dataset calibrated against nearby National Weather Service sites and a second calibrated set with outliers and apparent artifacts removed.

Plant breeders face the challenge of genotype × environment interaction (G × E) in comprehensively breeding for expanded geographic regions. An improved understanding of G × E sensitivity of traits and the environmental features that effectively discriminate among genotypes will enable more efficient breeding efforts. In this study of 31 maize (Zea mays L.) inbreds grown in 36 environments that are part of the Genomes to Fields Initiative, we measured 14 traits, including flowering date, height, and yield components (i.e., ear and kernel dimensions) to (i) identify traits that are the most sensitive indicators of G × E; (ii) determine how geographic location and weather factors influence environments’ discriminability of inbreds; and (iii) detect patterns of stability in better and worse discriminating environments. Genotype × environment interaction explained between 9.0–20.4% of the phenotypic variance with greater effects in the yield‐component traits. Discriminability of environments varied by trait. Midwest locations (where 26 of the 31 inbreds were developed) were among the most discriminating environments for more traits, while environments in the West and East tended to be less discriminating. Weather factors during silking were significantly different between the most and least discriminating environments more often than average weather across the season or during the period from planting to silking. Stability of genotypes varied by trait, and performance was usually not correlated with stability. The dissection of complex traits, such as yield into component traits, appears to be a useful approach to understand how environmental factors differentially affect phenotype.

Growing crops with improved N use efficiency (NUE) could decrease fertilizer inputs and the negative environmental effects of N overuse. We measured nine agronomic and yield component traits under N‐limiting and nonlimiting treatments for 250 two‐row and 250 six‐row barley (Hordeum vulgare L.) breeding lines. We calculated four stress indices to approximate NUE. To identify quantitative trait loci (QTLs), we conducted association mapping (AM) for these phenotypic data with 3072 single nucleotide polymorphism markers. All traits and two stress indices—the geometric mean and the stress tolerance index—exhibited significant genetic variation within the two‐row and six‐row panels. Of the 25 QTLs identified in the two‐row panel and 31 identified in the six‐row panel, only four were common to both. Four of the QTLs detected in the two‐row panel and 10 detected in the six‐row panel were identified under the N‐limiting treatment or for a stress index, but not under the nonlimiting treatment, signaling that they may be associated with NUE. We detected a QTL for grain protein concentration (GPC) on chromosome 6H that has been mapped previously in barley and is collinear with the well‐characterized Gpc‐B1 locus in wheat. Groups of lines defined by marker haplotypes at this locus exhibited significant differences in GPC but not in grain yield. These results support that breeding strategies for improving NUE include crossing between two‐ and six‐row parents with complementary alleles, phenotypic selection based on stress indices, marker‐assisted selection for desirable alleles, and genomic selection to capture small‐effect loci.

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