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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: Generation and analysis of blueberry transcriptome sequences from leaves, developing fruit, and flower buds from cold acclimation through deacclimation

      There has been increased consumption of blueberries in recent years fueled in part because of their many recognized health benefits. Blueberry fruit is very high in anthocyanins, which have been linked to improved night vision, prevention of macular degeneration, anti-cancer activity, and reduced risk of heart disease. Very few genomic resources have been available for blueberry, however. Further development of genomic resources like expressed sequence tags (ESTs), molecular markers, and genetic linkage maps could lead to more rapid genetic improvement. Marker-assisted selection could be used to combine traits for climatic adaptation with fruit and nutritional quality traits.

      National Animal Nutrition Program (NANP) Modeling: Animal Performance Information

        This page allows you to download data from the National Animal Nutrition Program animal performance data repository directly into an Excel file. Component data sets: [NRC Dairy Report](https://animalnutrition.org/node/12); [Lofgreen Garrett](https://animalnutrition.org/node/13); [Environmental Stress](https://animalnutrition.org/node/27): [to follow]; [Beef Digestiblity](https://animalnutrition.org/beef-digestibility): [to follow]; [USDA Beltsville Agricultural Research Center](https://animalnutrition.org/usda-barc).

        Sodium Monitoring Dataset

          The Agricultural Research Service of the US Department of Agriculture (USDA) in collaboration with other government agencies has a program to track changes in the sodium content of commercially processed and restaurant foods. Results of these monitoring activities are shared once a year in the [Sodium Monitoring Dataset](https://www.ars.usda.gov/ARSUserFiles/80400525/Sodium/Copy%20of%20SodiumMonitoringDatasetUpdatedJuly2616.xlsx) and [USDA National Nutrient Database for Standard Reference](https://www.ars.usda.gov/Services/docs.htm?docid=8964) and once every two years in the [Food and Nutrient Database for Dietary Studies](https://www.ars.usda.gov/Services/docs.htm?docid=12068).

          NUOnet (Nutrient Use and Outcome Network) database

            The Nutrient Uptake and Outcomes (NUOnet) database will be able to help establish baselines on nutrient use efficiencies; processes contributing to nutrient losses; and processes contributing to optimal crop yield, nutritional and organoleptic quality. This national database could be used to calculate many different environmental indicators from a comprehensive understanding of nutrient stocks and flows.

            USDA National Nutrient Database for Standard Reference Dataset for What We Eat In America, NHANES (Survey-SR)

              The dataset, Survey-SR, provides the nutrient data for assessing dietary intakes from the national survey What We Eat In America, National Health and Nutrition Examination Survey (WWEIA, NHANES). Historically, USDA databases have been used for national nutrition monitoring (1). Currently, the Food and Nutrient Database for Dietary Studies (FNDDS) (2), is used by Food Surveys Research Group, ARS, to process dietary intake data from WWEIA, NHANES. Nutrient values for FNDDS are based on Survey-SR. Survey-SR was referred to as the "Primary Data Set" in older publications. Early versions of the dataset were composed mainly of commodity-type items such as wheat flour, sugar, milk, etc. However, with increased consumption of commercial processed and restaurant foods and changes in how national nutrition monitoring data are used (1), many commercial processed and restaurant items have been added to Survey-SR.