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Uniform Soybean Tests, Northern Region

    The Uniform Soybean Tests, Northern Region, in place since 1941, evaluate yield, disease resistance, and quality traits of public breeding lines from northern states of the USA and Canadian provinces. The annual reports which compile the test results (PDF format) are available, and new reports are added annually.

    Uniform Soybean Tests, Southern States

      The Uniform Soybean Tests, Southern States, in place since 1943, evaluate yield, disease resistance, and quality traits of public breeding lines from the southern states of the USA. The annual reports which compile the test results (PDF format) are available, and new reports are added annually.

      The Triticeae Toolbox

        [The Triticeae Toolbox](https://triticeaetoolbox.org/) (T3) webportal hosts data generated by the Triticeae Coordinated Agricultural Project (CAP), funded by the National Institute for Food and Agriculture (NIFA) of the United States Department of Agriculture (USDA). T3 contains SNP, phenotypic, and pedigree data from wheat and barley germplasm in the Triticeae CAP integrating rapidly expanding DNA marker and sequence data with traditional phenotypic data.

        Data from: Identification of Single-Nucleotide Polymorphic Loci Associated with Biomass Yield under Water Deficit in Alfalfa (Medicago sativa L.) Using Genome-Wide Sequencing and Association Mapping

          Alfalfa is a worldwide grown forage crop and is important due to its high biomass production and nutritional value. However, the production of alfalfa is challenged by adverse environmental factors such as drought and other stresses. Developing drought resistance alfalfa is an important breeding target for enhancing alfalfa productivity in arid and semi-arid regions. In the present study, we used genotyping-by-sequencing and genome-wide association to identify marker loci associated with biomass yield under drought in the field in a panel of diverse germplasm of alfalfa.

          Data from: Proteomics analysis reveals novel host molecular mechanisms associated with thermotherapy of 'Ca. Liberibacter asiaticus'-infected citrus plants

            The goal of this study is to identify potential Las resistance/tolerance-related genes in citrus plants for application in breeding or genetic engineering programs, and apply comparative proteomics analysis via 2-DE and mass spectrometry to elucidate the molecular processes associated with heat-induced mitigation of HLB in citrus plants.

            Data from: Genetic Architecture of Resistance to Stripe Rust in a Global Winter Wheat Germplasm Collection

              Virulence shifts in populations of *Puccinia striiformis* f. sp. *tritici* (*Pst*), the causal pathogen of wheat stripe rust, are a major challenge to resistance breeding. The majority of known resistance genes are already ineffective against current races of *Pst*, necessitating the identification and introgression of new sources of resistance. Germplasm core collections that reflect the range of genetic and phenotypic diversity of crop species are ideal platforms for examining the genetic architecture of complex traits such as resistance to stripe rust. We report the results of genetic characterization and genome-wide association analysis (GWAS) for resistance to stripe rust in a core subset of 1175 accessions in the National Small Grains Collection (NSGC) winter wheat germplasm collection, based on genotyping with the wheat 9K single nucleotide polymorphism (SNP) iSelect assay and phenotyping of seedling and adult plants under natural disease epidemics in four environments.

              Arctic Peregrine Falcon Abundance on Cliffs Along the Colville River, Alaska, 1981-2002 and Covariate Input Files

                This data set consists of fourteen data files. Rcode_arctic_peregrine_abundance.R contains R code that was used to analyze Arctic peregrine falcon data collected between 1981 and 2002. The code primarily uses the R package "UNMARKED" and is based on the Dail-Madsen model for estimating population abundance. To run this code in an R environment, download the file and open it in an R interpreter (such as RStudio). The remaining files are all covariate matrices that act as inputs to the R code.