2017 Census Ag Atlas Maps

2017 Census of Agriculture Ag Atlas Maps are available for the following topics:

2017 Ag Atlas Maps
Map # Map Title Format
17-M207 Number of Farms: 2017 PDF GIF PNG
17-M208 Cattle and Calves - Inventory: 2017 PDF GIF PNG
17-M209 Milk Cows - Inventory: 2017 PDF GIF PNG
17-M210 Beef Cows - Inventory: 2017 PDF GIF PNG
17-M211 Hogs and Pigs - Inventory: 2017 PDF GIF PNG
17-M212 Sheep and Lambs - Inventory: 2017 PDF GIF PNG
17-M213 Number of Broilers and Other Meat-Type Chickens Sold: 2017 PDF GIF PNG
17-M214 Corn for Grain, Harvested Acres: 2017 PDF GIF PNG
17-M215 All Wheat for Grain, Harvested Acres: 2017 PDF GIF PNG
17-M216 Rice, Harvested Acres: 2017 PDF GIF PNG
17-M217 All Cotton, Harvested Acres: 2017 PDF GIF PNG
17-M218 Tobacco, Harvested Acres: 2017 PDF GIF PNG
17-M219 Soybeans for Beans, Harvested Acres: 2017 PDF GIF PNG
17-M220 Potatoes, Harvested Acres: 2017 PDF GIF PNG
17-M221 Vegetables, Acres Harvested for Sale: 2017 PDF GIF PNG
17-M222 Total Acres of Apples: 2017 PDF GIF PNG

Access county-level data and maps, as well as the entire data set, at the 2017 Ag Census Web Maps (to be released at a later date this year). The web maps correspond to some, but not all of the Ag Atlas static maps.



How the Maps Were Made

Geographic information system (GIS) and desktop publishing technologies were used in the production of these thematic maps. NASS developed an automated map production system to generate digital map files based on statistical data from the 2017 Census of Agriculture. The system utilized agricultural statistical data files, geographic area boundary files, land use/cover boundary files, map parameter data files, and customized GIS and statistical software to produce thematic dot-density maps. The customized software performed statistical calculations to allocate the number of dots for a geographic area. The software also executed other cartographic functions, including: assigning symbology to represent the data; randomly placing dots; creating and positioning map titles, legends and notes; and outputting individual maps to digital image files. Colors for the maps were selected with the assistance of ColorBrewer , an online tool for selecting map color schemes. The color schemes were developed by Dr. Cynthia A. Brewer at Pennsylvania State University.

The U.S. Census Bureau provided a generalized county boundary file and a county-level land area/perimeter data file. NASS modified the county boundary file to show the county-level geographic areas for which agriculture census statistics are reported. The statistical data and geographic areas were identified by Federal Information Processing Standards (FIPS) codes that allowed for a 1-to-1 correspondence between the data and the geographic area. NASS created a new land use/cover boundary file based on NASS’ Primary Sampling Unit (PSU) boundaries. The predominant land use/cover type for each PSU was determined from several sources: a 5-year composite of the NASS Cropland Data Layers for years 2008 – 2012, Moderate Resolution Imaging Spectroradiometer (MODIS) Irrigated Agriculture Dataset for the United States (Earth Resources Observation and Science Center, U.S. Geological Survey, 2008), Irrigated Lands from Remote Sensing (Mutlu Ozdogan, University of Wisconsin, Center for Sustainability and the Global Environment, 2001), Bureau of Land Management (BLM) national grazing allotment polygons (U.S. Department of the Interior, BLM, 2018), U.S. Forest Service grazing allotment polygons (U.S. Department of Agriculture, Forest Service, 2016), and randomly distributed point data of Non-Federal Grazing Land (U.S. Department of Agriculture, Natural Resources Conservation Service, National Resources Inventory, 2007). The continental U.S. and Hawaii were mapped at a scale of 1:21,000,000 and Alaska used a map scale of 1:63,000,000. The maps were projected using Albers Equal Area Conic projection.


Dot-Density Maps

The dot-density maps portray quantitative data as a dot which represents a number of the phenomenon found within the boundary of a geographic area. The pattern of distributed dots reflects the general locations where the phenomenon was most likely to occur. The pattern and number of dots within a geographic area reveal the density of the phenomenon. The traditional dot map symbolizes (positive) data with blue dots.

Placement of dots utilized land use/cover files, weighting factors, customized statistical algorithms, and GIS software to allocate and randomly place dots within geographic areas. A land use/cover file is a digital vector file containing 26 land use/cover categories. NASS statisticians assigned weighting factors to each land use/cover category based on the likelihood of a specific type of agricultural activity occurring within a category. The weighted land use/cover files were merged with the geographic area boundary files to produce weighted land use/cover filter files. Customized statistical software used the weighted land use/cover filter files and statistical data files to calculate and assign the number of dots for each weighted land use/cover polygon based on the dot value, polygon size, and assigned weighting factor. GIS software then randomly placed the specified number of dots within each weighted land use/cover polygon. Because dot positions were randomly determined, the dots do not show the actual locations of the phenomena.

The dot value assigned to a dot actually reflects a range of data values. For example, if the legend indicates that one dot equals 500 acres of apples, then in most cases, no dot is placed for county-level geographic areas with data values less than 250 acres of apples, one dot is placed for county-level geographic areas with data values ranging from 250 to 749 acres of apples, two dots are placed for county-level geographic areas with data values ranging from 750 to 1,249 acres of apples, and so on. This methodology can yield an undercount of dots at the state level, so dots are added to the map to reach the calculated state number of dots by including an extra dot for the county-level geographic areas with the largest positive remainder values. For example, if a state has a total of 1,390 apples acres, with county A having 240 acres of apples and county B having 1,150 acres of apples, then normally county A would receive no dots and county B would receive 2 dots, for a total of 2 dots. However, there should be a total of 3 dots shown at the state level. Therefore, an additional dot would be placed in county A, because the remainder in county A (240 - 0 = 240) is greater than the remainder in county B (1,150 - 1,000 = 150). NASS statisticians selected dot values such that widely varying dot densities for different areas are illustrated. Geographic areas with non-disclosed data are shown because a dot represents a range of data values rather than a specific data value.


Cartographic Boundary Files

The cartographic boundary files are generalized, digital vector files of state- and county-level geographic areas for which 2017 Census of Agriculture data are reported.  The cartographic boundary files can be used with Geographic Information System (GIS) software as a base for medium to small-scale thematic mapping.

These boundary files are not map images. They were developed to support USDA-NASS projects involving map production for the 2017 Agricultural Atlas of the United States.

The cartographic boundary files on this site are in ESRI™ shapefile format.

States only - Download Boundary ZIP file, view metadata

Counties only - Download Boundary ZIP file, view metadata

States and counties - Download Boundary ZIP file, (for metadata refer to states/counties metadata)

Last Modified: 04/30/2019