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The Ag Data Commons is migrating

The Ag Data Commons is migrating to a new institutional portal on Figshare. The current system is available for search and download only. The new platform is open for submission with assistance from Ag Data Commons curators. Please contact NAL-ADC-Curator@usda.gov, if you need to publish or update your datasets.

SGA (farm)

    Stored Grain Advisor (SGA) is a decision support system for managing insect pests of farm-stored wheat. The program predicts the likelihood of insect infestation, and recommends appropriate preventative actions . It also provides advice on how to sample and identify insect pests of stored wheat. SGA Pro was designed for use in commercial elevators as part of the Areawide IPM Project for stored grain. Grain samples are taken with a vacuum probe and processed over an inclined sieve. SGA Pro analyzes the insect data, grain temperatures and moistures, and determines which bins need to be fumigated.

    Data from: Measured and simulated carbon dynamics in Midwestern US corn-soybean rotations

      The dataset includes information on soil properties collected from two conventionally managed fields under corn-soybean rotation in October 2005 and October 2016, respectively. The fields are located in Story County, Central Iowa. 42 sampling locations per field and year were sampled within a 50 m × 50 m grid, and 1 to 2 samples per location were collected using a hydraulic soil sampler (d= 38.2 mm) from the 0 - 120 cm soil layer. The samples were analyzed to determine carbon and nitrogen concentration, and soil pH in five soil layers (0-15, 15-30, 30-60, 60-90, and 90-120 cm). Presented is the raw data per location (mean of duplicates) with which carbon and nitrogen content can be calculated with either the equivalent soil mass method or by using bulk density.

      Agricultural Research Word Vectors

        This model was originally trained for use in a recommendation system to the Ag Data Commons that will automatically link viewers of one dataset to other directly relevant datasets and research papers that they may be interested in. It was also used to determine the similarities and differences between projects within ARS’ National Programs and create a visualization layer to allow leaders to explore and manage their programs easily.

        WinFlume 1.06.0006

          WinFlume is a Windows-based computer program used to design and calibrate long-throated flume and broad-crested weir flow measurement structures. The software was developed through the cooperative efforts of the Bureau of Reclamation, the Agricultural Research Service, and the International Institute for Land Reclamation & Improvement . Primary funding for WinFlume's development has come from the Bureau of Reclamation's Water Conservation-Field Services Program.

          DRIFTSIM

            DRIFTSIM can be used to determine the effects of major drift-causing factors on the mean drift distances up to 656 feet from the release point for individual water droplets or classes of droplets.

            SGA Pro (elevator storage)

              Stored Grain Advisor (SGA) is a decision support system for managing insect pests of farm-stored wheat. The program predicts the likelihood of insect infestation, and recommends appropriate preventative actions . It also provides advice on how to sample and identify insect pests of stored wheat. SGA Pro was designed for use in commercial elevators as part of the Areawide IPM Project for stored grain. Grain samples are taken with a vacuum probe and processed over an inclined sieve. SGA Pro analyzes the insect data, grain temperatures and moistures, and determines which bins need to be fumigated.

              Data from: Estimation of genetic parameters and their sampling variances for quantitative traits in the type 2 modified augmented design

                The type 2 modified augmented design (MAD2) is an efficient unreplicated experimental design used for evaluating large numbers of lines in plant breeding and for assessing genetic variation in a population. Statistical methods and data adjustment for soil heterogeneity have been previously described for this design. In the absence of replicated test genotypes in MAD2, their total variance cannot be partitioned into genetic and error components as required to estimate heritability and genetic correlation of quantitative traits, the two conventional genetic parameters used for breeding selection. We propose a method of estimating the error variance of unreplicated genotypes that uses replicated controls, and then of estimating the genetic parameters. Using the Delta method, we also derived formulas for estimating the sampling variances of the genetic parameters. Computer simulations indicated that the proposed method for estimating genetic parameters and their sampling variances was feasible and the reliability of the estimates was positively associated with the level of heritability of the trait. A case study of estimating the genetic parameters of three quantitative traits, iodine value, oil content, and linolenic acid content, in a biparental recombinant inbred line population of flax with 243 individuals, was conducted using our statistical models. A joint analysis of data over multiple years and sites was suggested for genetic parameter estimation. A pipeline module using SAS and Perl was developed to facilitate data analysis and appended to the previously developed MAD data analysis pipeline (http://probes.pw.usda.gov/bioinformatics_tools/MADPipeline/index.html).

                Low-Disturbance Manure Incorporation

                  The LDMI experiment (Low-Disturbance Manure Incorporation) was designed to evaluate nutrient losses with conventional and improved liquid dairy manure management practices in a corn silage (*Zea mays*) / rye cover-crop (*Secale cereale*) system. The improved manure management treatments were designed to incorporate manure while maintaining crop residue for erosion control. Field observations included greenhouse gas (GHG) fluxes from soil, soil nutrient concentrations, crop growth and harvest biomass and nutrient content, as well as monitoring of soil physical and chemical properties. Observations from LDMI have been used for parameterization and validation of computer simulation models of GHG emissions from dairy farms (Gaillard et al., submitted). The LDMI experiment was performed as part of the Dairy CAP.