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Nearest Neighbor Soil Water Retention Estimator

    The k-nearest neighbor (k-NN) technique is a non-parametric technique that can be used to make predictions of discrete (class-type) as well as continuous variables. The k-NN technique and many of its derivatives belong to the group of .lazy learning algorithms.. It is lazy, as it passively stores the development data set until the time of application; all calculations are performed only when estimations need to be generated.

    SHOOTGRO

      SHOOTGRO emphasizes the development and growth of the shoot apex of small-grain cereals such as winter and spring wheat (Triticum aestivum L.) and spring barley (Hordeum vulgare L.). To better incorporate the variability typical in the field, up to six cohorts, or age classes, of plants are followed using a daily time step.

      CCE Nitrogen Index Tool

        The Nitrogen Index is a tool written in the programming language Java that is used to calculate nitrogen uptake and leaching in farming techniques.

        Soil Series Classification Database (SC)

          The USDA-NRCS Soil Series Classification Database contains the taxonomic classification of each soil series identified in the United States, Territories, Commonwealths, and Island Nations served by USDA-NRCS. Along with the taxonomic classification, the database contains other information about the soil series, such as office of responsibility, series status, dates of origin and establishment, and geographic areas of usage.

          Genomes To Fields 2016

            Phenotypic, genotypic, and environment data for the 2016 field season: The data is stored in [CyVerse](http://datacommons.cyverse.org/browse/iplant/home/shared/commons_repo/curated/GenomesToFields_G2F_2016_Data_Mar_2018).

            Genomes To Fields 2015

              Phenotypic, genotypic, and environment data for the 2015 field season: The data is stored in [CyVerse](http://datacommons.cyverse.org/browse/iplant/home/shared/commons_repo/curated/Carolyn_Lawrence_Dill_G2F_Mar_2017).

              Soil Use - Hydric Soils database

                The Hydric Soils section presents the most current information about hydric soils. It updates information that was previously published in *Hydric Soils of the United States* and coordinates it with information that has been published in the *Federal Register*. It also includes the most recent set of field indicators of hydric soils. The database selection criteria are selected soil properties that are documented in Soil Taxonomy and were designed primarily to generate a list of potentially hydric soils from soil survey databases. Only criteria 1, 3, and 4 can be used in the field to determine hydric soils; however, proof of anaerobic conditions must also be obtained for criteria 1, 3, and 4 either through data or best professional judgment (from *Tech Note 1*). The primary purpose of these selection criteria is to generate a list of soil map unit components that are likely to meet the hydric soil definition.

                Data from: Soil organic carbon and isotope composition response to topography and erosion in Iowa

                  The dataset includes topographic information, soil properties, and 137Cs levels collected from a 15 ha cropland under soybean/maize (C3/C4) rotation in June 2002. The cropland is located in the central-western part of the Walnut Creek watershed, Story County, Iowa. 128 sampling locations were collected and three soil samples were obtained using a 3.2 cm-diameter push probe from the 0 to 30 cm soil layer within a 1 m × 1 m quadrat at each sampling location. Deeper soil samples were collected from 30 to 50 cm layers in locations where sediment deposition was observed. The three samples from each sampling location were mixed and analyzed to determine soil properties, SOC content and its carbon (C) isotope composition (C12 to C13 ratio), and 137Cs levels. For landscape topography of each sampling location, topographic metrics were derived from a digital elevation mode using LiDAR (Light Detection and Ranging) data. These data are useful in investigating the fate of eroded SOC in croplands and its responses to landscape topography.

                  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.