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Irrigation Residue Removal Study for Greenhouse gas Reduction through Agricultural Carbon Enhancement network and Resilient Economic Agricultural Practices in Lincoln, Nebraska

    USDA-ARS REAP Study (Ithaca, NE) - NEMEIRR Sustainable intensification of high-yielding production systems may help meet increasing demands for food, fuel, and fiber worldwide. Specifically, corn stover is being removed by producers for livestock purposes, and stover is also targeted as a primary 2nd generation biofuel feedstock. The NEMEIRR experimental objectives are to quantify how stover removal (no removal, moderate removal, high removal) and tillage management (no-till, disk) affect crop yields, soil organic carbon, soil greenhouse gas emissions, and other soil responses (microbial community structure, function; soil health). This experiment is conducted in a fully irrigated continuous corn system in the western Corn Belt, and soil and plant measurements have been taken since study establishment in 2001.

    Data from: A database for global soil health assessment

      The SoilHealthDB database integrates soil health measurements conducted in the field from sites across the globe. SoilHealthDC currently focuses on four main conservation management methods: cover crops, no-tillage, agro-forestry systems, and organic farming. These studies represent 354 geographic sites (i.e., locations with unique latitudes and longitudes) in 42 countries around the world. The SoilHealthDB includes 42 soil health indicators and 46 background indicators that describe factors such as climate, elevation, and soil type.

      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.