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Cassavabase

    The Next Generation Cassava Breeding (NEXTGEN Cassava) project aims to significantly increase the rate of genetic improvement in cassava breeding and unlock the full potential of cassava, a staple crop central to food security and livelihoods across Africa. The project will implement and empirically test a new breeding method known as Genomic Selection that relies on statistical modeling to predict cassava performance before field-testing, and dramatically accelerates the breeding cycle.

    REAP (Resilient Economic Agricultural Practices)

      REAP (Resilient Economic Agricultural Practices), formerly known as the Renewable Energy Assessment Project, was initially organized to quantitatively assess the impacts of crop residue (e.g., corn stover) on soil properties. The project's current vision is to revitalize soil health and resiliency, thereby enabling soil resources to meet expanding societal demands while safe-guarding planetary health. Goals include 1) Identifying physical, chemical, or biological parameters and index tools that quantify management effects on carbon sequestration and soil health; 2) Conducting coordinated, quantitative multi-location comparisons of business as usual vs. improved management practices designed to enhance nutrient use efficiency and soil health; 3) Identification of critical indicators and index tools to quantify site-specific soil health and water quality effects; 4) Developing, expanding, and coordinating among ARS teams providing data and databases needed to sustainably supply cellulosic-based bioenergy feedstock and other national natural resource and agricultural challenges.

      PeanutBase

        PeanutBase ([peanutbase.org](https://peanutbase.org)) is the primary genetics and genomics database for cultivated peanut and its wild relatives. It houses information about genome sequences, genes and predicted functions, genetic maps, markers, links to germplasm resources, and maps of peanut germplasm origins.

        Legume (Fabaceae) Fruits and Seeds Version 2

          This is an identification key to genera for seeds and fruits of the legume family. The coverage is world wide, and for each genus there are descriptions of the seeds and fruits, distribution data, and images. The interactive software system INTKEY is used for accessing the data and images. The key can be used for identifying to genus unknown legume samples or for querying the data and images for legume genera, and is designed for seed analysts, technicians, port inspectors, weed scientists, ecologists, botanists, and researchers who need to identify isolated legume fruits and seeds.

          U.S. National Fungus Collections

            The U.S. National Fungus Collections (BPI) are the “Smithsonian for fungi” and are the repository for over one million fungal specimens worldwide - the largest such collections in the world. The collection includes preserved organisms, their parts and products, and their associated data. Information associated with these specimens constitute an enormous data resource, especially about plant-associated fungi. The collections document fungi through time and space for the past 200 years. Data from the labels of more than 750,000 of the specimens have been entered into a database. These labels have information on the host on which the fungus was found and the locality in which the specimen was collected. Sixty percent of these specimens are from the United States and thus represent a large body of information about the fungi in this country.

            Data from: Soil Water Holding Capacity Mitigates Downside Risk and Volatility in US Rainfed Maize: Time to Invest in Soil Organic Matter?

              This dataset includes county-level annual data on maize (Zea mays L.) yield, soil physical and chemical characteristics, and mean weather data for 2000 through 2014 for IL, MI, MN and PA. The data were aggregated from public databases, including NASS Quick Stats, NOAA Climate Data Online, and the USDA-NRCS Web Soil Survey. U.S. counties were the experimental unit for this study, and all data are county-level averages. Covariances among county-level maize yield stability and environmental variability were analyzed using structural equation models (SEM) and linear mixed effects (LME) models.

              USDA LCA Commons Data Submission Guidelines

                This document provides instructions for editing and submitting unit process or product system models to the USDA LCA Commons life cycle inventory (LCI) database. The LCA Commons LCI database uses the openLCA life cycle modeling tool's database schema. Therefore, this document describes how to import and edit data in openLCA and name and classify flows such that they properly import into and operate in the database. This document also describes metadata or documentation requirements for posting models to the LCA Commons. This document is an evolving standard for LCA Commons data. As USDA-NAL continues to gain experience in managing a general purpose LCI database and global conventions continue to evolve, so too will the LCA Commons Submission Guidelines.