70b70352-8ca2-4b7f-aafa-221f8807078e urn:uuid BARC Gridded Yield Data Archive - version 1 USDA-ARS Hydrology and Remote Sensing Laboratory 301-504-7490 301-504-6542 10300 Baltimore Avenue, Bldg 007 Rm 104 BARC-W Beltsville Maryland 20705 USA ARS-LCB-DM@usda.gov 2025-01-14T20:35:53.941411Z 2025-07-25T16:26:21.533Z 2023-08-18T21:38:08.043Z ISO 19115-3 Geographic information -- Metadata -- Part 3: XML schema implementation for fundamental concepts ISO 19115-3:2018(E) https://geodata.nal.usda.gov:/geonetwork/srv/api/records/45c391c4-8a7c-4617-9bee-4ada7baaec6e EPSG:32618 WGS 84 / UTM zone 18N USDA Beltsville Agricultural Research Center (BARC) Gridded Yield Data Archive, version 1 2024-03-15 USDA-ARS Hydrology and Remote Sensing Laboratory, Beltsville, Maryland, USA https://www.ars.usda.gov/northeast-area/beltsville-md-barc/beltsville-agricultural-research-center/hydrology-and-remote-sensing-laboratory/ USDA-ARS, Research Support Services, Beltsville, Maryland, USA The USDA Beltsville Agricultural Research Center (BARC) Gridded Yield Data Archive is a multi-year, gridded yield data archive derived from yield monitor data acquired from over 40 production fields in Beltsville, MD. Production of the archive involved the projection of the yield monitor data into a geographic coordinate system suitable for mapping, the identification and removal of data outliers, and the production of gridded, spatially interpolated yield maps using block Kriging. All map products were produced in common, nonproprietary file formats for easy access. The purpose of the USDA BARC Gridded Yield Data Archive is to support the implementation of precision farming techniques such as the development of site-specific variable rate prescriptions as well as remote sensing applications, including the selection of field sites for long-term agricultural experiments and the assessment of crop modeling approaches. USDA-ARS, Hydrology and Remote Sensing Laboratory, Beltsville, Maryland, USA USDA-ARS, Hydrology and Remote Sensing Laboratory, Beltsville, Maryland, USA 301-504-7490 301-504-6542 10300 Baltimore Avenue, Bldg 007 Rm 104 BARC-W Beltsville Maryland 20705 USA ars-lcb-dm@usda.gov farming environment -76.9562 -76.8081 39.0013 39.0466 2014-01-01 2021-12-31 BARC Yield Archive - years 2014 and 2016-2021 https://doi.org/10.15482/USDA.ADC/28950746 Data files https://doi.org/10.15482/USDA.ADC/28950746 Data files https://geodata.nal.usda.gov/geonetwork/srv/api/records/70b70352-8ca2-4b7f-aafa-221f8807078e/attachments/agg220474-fig-0001-m.jpg Earth Science > Biosphere > Terrestrial Ecosystems > Agricultural Lands crop yield agriculture farming precision agriculture productivity kriging remote sensing long-term agricultural experiments BARC OPE3 Continent > North America > United States of America Earth sciences > Physical geography and environmental geoscience Agricultural, veterinary and food sciences > Agriculture, land and farm management > Agricultural spatial analysis and modelling Agricultural, veterinary and food sciences > Agriculture, land and farm management > Agricultural land management Fields of Research (FoR) Classification FoR Classification 2020-06-30 creation 1.3.6 publisher Figshare https://figshare.com/articles/dataset/Fields_of_Research_FoR_Classification/10305443 HTTP Web Browser Fields of Research (FoR) Classification The 2008 ANZSRC FoR classification allows R&D activity to be categorised according to the field of research. In this respect, it is the methodology used in the R&D that is being considered. information Agroecosystems & Environment > Management Maps & Multimedia > GIS Data and Images Plants & Crops > Crop production Agroecosystems & Environment > Plant and animal > Harvest yield and quality Ag Data Commons Keywords USDA NAL Ag Data Commons Keywords 2016 publication ADC Keywords hierarchy v4 pointOfContact USDA National Agricultural Library voice facsimile 10301 Baltimore Ave Beltsville MD 20705 United States of America nal-adc-curator@ars.usda.gov https://nal.usda.gov WWW:LINK-1.0-http--link Web Browser USDA National Agricultural Library The National Agricultural Library is one of the national libraries of the United States and houses one of the world's largest collections devoted to agriculture and its related sciences. information Ag Data Commons Metadata Contact Erin Antognoli * Ag Data Commons Metadata Librarian A controlled vocabulary of keywords to classify dataset records in Ag Data Commons Access Constraints: None None None Software used for the BARC Yield Data Archive - Microsoft Windows 10 Enterprise; Ag Leader’s Spatial Management System (SMS) software <https://www.agleader.com/>; ESRI ArcGIS Version 10.6 <https://www.esri.com/>; Google Earth <https://earth.google.com/>; and Gamma Design Software’s GS+ Geostatistics for the Environmental Sciences. SMS software was used to convert data from Ag Leader format (.yld) to the point shapefile format (.shp). ArcGIS was used to project coordinate systems, clip points to polygon boundaries, filter data based on shapefile attribute values, and output text files (.csv). Google Earth was used to produce yield map graphics (.jpg). GS+ was used to model the spatial autocorrelation of the “cleaned” grain yield data and to predict interpolated data values on a regular grid, exported in ASCII Grid and GeoTIFF formats (.asc, .tif). Dulaney, W. P., Anderson, M. C., Gao, F., Stern, A., Moglen, G., Meyers, G., Daughtry, C. S. T., White, W., Akumaga, U., & Showalter, J. (2024). Development of a gridded yield data archive for farm management and research at the USDA Beltsville Agricultural Research Center. In Agrosystems, Geosciences & Environment (Vol. 7, Issue 1). Wiley. https://doi.org/10.1002/agg2.20474 GeoTIFF 2024 USDA-ARS, LTAR Network, Lower Chesapeake Bay ARS-LCB-DM@usda.gov Alex White ASCII ESRI Shapefile Text JPEG USDA-ARS Hydrology and Remote Sensing Laboratory 301-504-7490 10300 Baltimore Avenue, Bldg 007 Rm 104 BARC-W Beltsville Maryland 20705 USA ARS-LCB-DM@usda.gov https://doi.org/10.1002/agg2.20474 WWW:LINK Development of a gridded yield data archive for farm management and research at the USDA Beltsville Agricultural Research Center https://doi.org/10.15482/USDA.ADC/28950746 WWW:LINK Data files USDA Beltsville Agricultural Research Center (BARC) Gridded Yield Data Archive, version 1 Raw yield data from combine harvester, projected into planar UTM coordinates (Zone 18 North, meters), and exported to shapefile (.shp) and text (.csv) formats. Raw Yield Data 2025 USDA-ARS, Hydrology and Remote Sensing Laboratory Beltsville Maryland USDA-ARS, Research Support Services The first step in processing the yield monitor data involved conversion of the data values in Ag Leader’s proprietary file format (.yld) to the point shapefile format (.shp). The raw yield monitor data are in spherical latitude and longitude coordinates and need to be projected into a planar coordinate system to ensure that map distance measures are the same in both horizontal and vertical dimensions. ArcGIS was used to project the latitude and longitude yield monitor data into planar UTM coordinates (Zone 18 North, meters). The ArcGIS clipping tool was used to extract only those yield data values that fell within the boundaries of the field of interest. The yield data values and their corresponding Northing (m) and Easting (m) UTM coordinates were output to a text file (.csv). 2014-01-01 2021-12-31 Valid ("clean") yield data, filtered using ArcGIS geoprocessing tools to remove outliers and data points near field edges Cleaned Yield Data 2025 USDA-ARS, Hydrology and Remote Sensing Laboratory Beltsville Maryland USDA-ARS, Research Support Services Data were cleaned using ArcGIS geoprocessing tools to complete the following steps. Field border shapefiles were buffered by 7 m interior to the field’s boundary, and the resultant buffered boundary shapefile was used to clip the combine harvester’s grain yield data values. Threshold yield filters were used for each crop that excluded data values greater than the maximum obtainable yield expected in the Mid-Atlantic region (i.e., corn > 17.3 Mg/ha, grain sorghum and soybeans > 10.1 Mg/ha, and wheat and barley > 6.5 Mg/ha). Data points were also filtered to only include those for which the combine header was full, as indicated by the swath width and yield offset parameters. Neighborhood statistics were used to validate data points by confirming the number of data points within their circular sample neighborhood was at least 25% of the highest number of data points in the sample neighborhoods over the entire field. 2014-01-01 2021-12-31 Block Kriging was used to spatially interpolate yield data points to a regular grid. Interpolated Yield Values 2025 USDA-ARS, Hydrology and Remote Sensing Laboratory Beltsville Maryland USDA-ARS, Hydrology and Remote Sensing Laboratory Spatial autocorrelation of the “cleaned” grain yield data was modeled, and block Kriging was used to spatially interpolate the data to produce yield maps with a 5 m × 5 m grid cell resolution. To assess the degree of confidence associated with the interpolated yield data values, maps of the error associated with the prediction of the interpolated grid cell values were produced. Both the interpolated grain yield and prediction error maps were produced in common file formats (i.e., .jpg, .tif, and ascii grid). 2014-01-01 2021-12-31 OVERVIEW: FOR MORE TECHNICAL DETAILS AND PROJECT INFORMATION: <https://doi.org/10.1002/agg2.20474> The USDA Beltsville Agricultural Research Center (BARC) Gridded Yield Data Archive is a multi-year, gridded yield data archive derived from yield monitor data acquired from over 40 production fields in Beltsville, MD. Production of the archive involved the projection of the yield monitor data into a geographic coordinate system suitable for mapping, the identification and removal of data outliers, and the production of gridded, spatially interpolated yield maps using block Kriging. All map products were produced in common, nonproprietary file formats for easy access. PURPOSE: The purpose of the USDA BARC Gridded Yield Data Archive is to support the implementation of precision farming techniques such as the development of site-specific variable rate prescriptions as well as remote sensing applications, including the selection of field sites for long-term agricultural experiments and the assessment of crop modeling approaches. SOFTWARE: Microsoft Windows 10 Enterprise; Ag Leader’s Spatial Management System (SMS) software <https://www.agleader.com/>; ESRI ArcGIS Version 10.6 <https://www.esri.com/>; Google Earth <https://earth.google.com/>; and Gamma Design Software’s GS+ Geostatistics for the Environmental Sciences. SMS software was used to convert data from Ag Leader format (.yld) to the point shapefile format (.shp). ArcGIS was used to project coordinate systems, clip points to polygon boundaries, filter data based on shapefile attribute values, and output text files (.csv). Google Earth was used to produce yield map graphics (.jpg). GS+ was used to model the spatial autocorrelation of the “cleaned” grain yield data and to predict interpolated data values on a regular grid. 2021-12-31T00:00:00 USDA-ARS Hydrology and Remote Sensing Laboratory 301-504-7490 301-504-6542 10300 Baltimore Avenue, Bldg 007 Rm 104 BARC-W Beltsville Maryland 20705 USA ARS-LCB-DM@usda.gov Metadata Access Constraints: No restrictions on the distribution or use of the metadata file Metadata Use Constraints: No restrictions on the distribution or use of the metadata file This metadata was automatically generated from the FGDC Content Standards for Digital Geospatial Metadata standard (version FGDC-STD-001-1998) using the 2015-07-24 version of the FGDC RSE to ISO 19115-2 transform. Translated from FGDC 2021-02-06T13:48:43.052-05:00 Updated for the 2023 annual release 2024-01-26T11:50:00-05:00 USDA-ARS Hydrology and Remote Sensing Laboratory 301-504-7490 301-504-6542 10300 Baltimore Avenue, Bldg 007 Rm 104 BARC-W Beltsville Maryland 20705 USA ARS-LCB-DM@usda.gov