U.S. flag

An official website of the United States government

Data from: Chapter 1, Introduction. U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990-2018

    The primary greenhouse gas (GHG) sources for agriculture are nitrous oxide (N2O) emissions from cropped and grazed soils, methane (CH4) emissions from ruminant livestock production and rice cultivation, and CH4 and N2O emissions from managed livestock waste. This dataset contains tabulated data from the figures and tables presented in Chapter 1, Introduction, of the report. Data are presented for Cropland Soils (N2O), Enteric Fermentation (CH4), Managed Livestock Waste (CH4 + N2O), Grazed Lands (CH4 + N2O), Rice Cultivation + Residue Burning (CH4 + N2O), Energy Use, Forests, Harvested Wood, Urban Trees, and Agricultural Soils.

    27 years of livestock production data under different stocking rate levels at the Central Grasslands Research Extension Center near Streeter, North Dakota

      The effects of stocking rate on livestock performance and profitability were monitored on 12 pastures at the Central Grasslands Research Extension Center (CGREC) near Streeter, ND from 1989 through 2015. These data were produced from an investigation of how the impacts of grazing intensity on native range, in addition an economic component, was included to determine grazing intensity effect on animal production.

      Legacy Phosphorus and Potassium Correlation Experiments: Qulin, Missouri

        Correlation experiments for P and K were conducted from 1968-1973 at a research farm in Qulin, Missouri to better define the relationships between soil tests, crop yields, and fertilizer treatments. Three crop rotations each were conducted for P and K trials (ranges C, D, E, F, G, and H), and included corn, soybean, wheat, cotton, and sorghum.

        iStitch: GUI-based Image Stitching Software

          GUI-based software coded in PYTHON to automate image stitching and alignment processes from a set of tile images for the high throughput image analytics by implementing a series of algorithms.

          LTAR Phosphorus Budget Summary

            Surface agronomic P budgets for 61 cropping systems using field-scale P flux data across 24 research sites in the United States and Canada. Data are representative of P inputs and outputs associated with the production of each crop in a respective rotation year, ranging from 1 to 10 rotation years. This dataset provides a comparison of field-scale soil surface P fluxes and phosphorus budgets across sites and cropping systems.

            Agricultural land use by field: Upper Mississippi River Basin 2010-2020

              This database is structured around individual farm fields as the unit of record, providing a framework that enables land use to be assessed at the same scale that agricultural land uses shift, at an annual time step, and at the scale at which conservation practices are implemented. It is beneficial to document agricultural land cover and its rates of change to understand responses of watershed, landscape, and agroecosystem processes to changes in land use and to identify viable approaches that can be customized for local adoption and mitigate environmental impacts from agricultural production.

              The Bronson Files, Dataset 3, Field 107, 2013

                Small dataset describing a unique rubber bush, in the context of greater published research Active optical proximal cotton canopy sensing spatial data and including additional related metrics canopy thermal and height are presented. Agronomic nitrogen and irrigation management related field operations are listed. Unique research experimentation intermediate analysis table is made available, along with raw data. The raw data recordings, and annotated table outputs with calculated VIs are made available. Plot polygon coordinate designations allow a re-intersection spatial analysis. Data was collected in the 2013 season at Maricopa Agricultural Center, Arizona, USA. High throughput proximal plant phenotyping via electronic sampling and data processing method approach is exampled. Acquired data using USDA Maricopa first mobile platforms, such as the Proximal Sensing Cart Mark 1. SAS and GIS compute processing output tables, including Excel formatted examples are presented, where data tabulation and analysis is available. The weekly proximal sensing data collected include canopy reflectance at six wavelengths, ultrasonic distance sensing of canopy height, and infrared thermometry. Limited soil sampling and final harvest information is included.