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Central Plains Experimental Range Study for Long-Term Agroecosystem Research in Nunn, Colorado

    The Central Plains Experimental Range (CPER) is a site with the The Long-Term Agroecosystem Research (LTAR) Network, which consists of 18 sites across the continental United States (US) sponsored by the US Department of Agriculture, Agricultural Research Service, universities and non-governmental organizations. LTAR scientists seek to determine ways to ensure sustainability and enhance food production (and quality) and ecosystem services at broad regional scales. They are conducting common experiments across the LTAR network to compare traditional production strategies (“business as usual or BAU) with aspirational strategies, which include novel technologies and collaborations with farmers and ranchers.

    sharpshootR: A Soil Survey Toolkit

      Miscellaneous soil data management, summary, visualization, and conversion utilities to support soil survey operations within the USDA-NRCS. Many of the functions are highly specialized and inherit default arguments from the names used by the various NCSS (National Cooperative Soil Survey) databases.

      soilDB: Soil Database Interface

        soilDB is one of the Algorithms for Quantitative Pedology (AQP) suite of R packages, and comprises a collection of functions for reading data from USDA-NCSS soil databases including SoilWeb, Series Extent Explorer, and Soil Data Explorer.

        Algorithms for Quantitative Pedology (AQP)

          Algorithms for Quantitative Pedology (AQP) is a collection of code, ideas, documentation, and examples wrapped-up into several R packages. The AQP suite of R packages are used to generate figures for SoilWeb, Series Extent Explorer, and Soil Data Explorer.

          Useful to Usable: Developing usable climate science for agriculture

            Useful to Usable (U2U): Transforming Climate Variability and Change Information for Cereal Crop Producers, was a USDA-funded research and extension project designed to improve the resilience and profitability of U.S. farms in the Corn Belt amid a changing climate. Over a six-year period from April 2011 - April 2017, 122 faculty, staff, graduate students, and undergraduate students from ten Midwestern universities contributed to this interdisciplinary project. Our team integrated expertise in applied climatology, crop modeling, agronomy, cyber-technology, agricultural economics, sociology, Extension and outreach, communication, and marketing to improve the use and uptake of climate information for agricultural decision making. Together, and with members of the agricultural community, we developed a series of decision support tools, resource materials, and training methods to support data-driven decision making and the adoption of climate-resilient practices.

            Rapid Carbon Assessment (RaCA)

              The Rapid Carbon Assessment (RaCA) was initiated by the USDA-NRCS Soil Science Division in 2010 with the following objectives: * To develop statistically reliable quantitative estimates of amounts and distribution of carbon stocks for U.S. soils under various land covers and to the extent possible, differing agricultural management. * To provide data to support model simulations of soil carbon change related to land use change, agricultural management, conservation practices, and climate change. * To provide a scientifically and statistically defensible inventory of soil carbon stocks for the U.S.

              Pulse Crop Database Resources

                Genomic, Genetic and Breeding Resources for Pulse Crop Improvement. The Pulse Crop Database (PCD), formerly the Cool Season Food Legume Database (CSFL),contains genes, genomes, germplasm, maps, markers, QTL/MTL, species information, and transcripts. Crops supported include Adzuki bean, Bambara bean, Chickpea, Common bean, Cowpea, Faba bean, Lentil, Lupin, Pea, Pigeon pea, Vetch, and others.

                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.