The Census of Agriculture, produced by the United States Department of Agriculture (USDA), provides a complete count of America's farms, ranches and the people who grow our food. The census is conducted every five years, most recently in 2022, and provides an in-depth look at the agricultural industry.This layer was produced from data obtained from the USDA National Agriculture Statistics Service (NASS) Large Datasets download page. The data were transformed and prepared for publishing using the Pivot Table geoprocessing tool in ArcGIS Pro and joined to county boundaries. The county boundaries are 2022 vintage and come from Living Atlas ACS 2022 feature layers.Dataset SummaryPhenomenon Mapped: 2022 Cattle ProductionCoordinate System: Web Mercator Auxiliary SphereExtent: 48 Contiguous United States, Alaska, and HawaiiSource: USDA National Agricultural Statistics ServicePublication Date: 2022AttributesNote that some values are suppressed as "Withheld to avoid disclosing data for individual operations", "Not applicable", or "Less than half the rounding unit". These have been coded in the data as -999, -888, and -777 respectively.Cattle, (Excl Cows) - Inventory - Inventory Of Cattle, (Excl Cows): (1 To 9 Head)Cattle, (Excl Cows) - Inventory - Inventory Of Cattle, (Excl Cows): (10 To 19 Head)Cattle, (Excl Cows) - Inventory - Inventory Of Cattle, (Excl Cows): (20 To 49 Head)Cattle, (Excl Cows) - Inventory - Inventory Of Cattle, (Excl Cows): (50 To 99 Head)Cattle, (Excl Cows) - Inventory - Inventory Of Cattle, (Excl Cows): (100 To 199 Head)Cattle, (Excl Cows) - Inventory - Inventory Of Cattle, (Excl Cows): (200 To 499 Head)Cattle, (Excl Cows) - Inventory - Inventory Of Cattle, (Excl Cows): (500 Or More Head)Cattle, (Excl Cows) - InventoryCattle, (Excl Cows) - Operations With Inventory - Inventory Of Cattle, (Excl Cows): (1 To 9 Head)Cattle, (Excl Cows) - Operations With Inventory - Inventory Of Cattle, (Excl Cows): (10 To 19 Head)Cattle, (Excl Cows) - Operations With Inventory - Inventory Of Cattle, (Excl Cows): (20 To 49 Head)Cattle, (Excl Cows) - Operations With Inventory - Inventory Of Cattle, (Excl Cows): (50 To 99 Head)Cattle, (Excl Cows) - Operations With Inventory - Inventory Of Cattle, (Excl Cows): (100 To 199 Head)Cattle, (Excl Cows) - Operations With Inventory - Inventory Of Cattle, (Excl Cows): (200 To 499 Head)Cattle, (Excl Cows) - Operations With Inventory - Inventory Of Cattle, (Excl Cows): (500 Or More Head)Cattle, (Excl Cows) - Operations With InventoryCattle, Calves - Operations With Sales - Sales Of Calves: (1 To 9 Head)Cattle, Calves - Operations With Sales - Sales Of Calves: (10 To 19 Head)Cattle, Calves - Operations With Sales - Sales Of Calves: (20 To 49 Head)Cattle, Calves - Operations With Sales - Sales Of Calves: (50 To 99 Head)Cattle, Calves - Operations With Sales - Sales Of Calves: (100 To 199 Head)Cattle, Calves - Operations With Sales - Sales Of Calves: (200 To 499 Head)Cattle, Calves - Operations With Sales - Sales Of Calves: (500 Or More Head)Cattle, Calves - Operations With SalesCattle, Calves - Sales, Measured In Head - Sales Of Calves: (1 To 9 Head)Cattle, Calves - Sales, Measured In Head - Sales Of Calves: (10 To 19 Head)Cattle, Calves - Sales, Measured In Head - Sales Of Calves: (20 To 49 Head)Cattle, Calves - Sales, Measured In Head - Sales Of Calves: (50 To 99 Head)Cattle, Calves - Sales, Measured In Head - Sales Of Calves: (100 To 199 Head)Cattle, Calves - Sales, Measured In Head - Sales Of Calves: (200 To 499 Head)Cattle, Calves - Sales, Measured In Head - Sales Of Calves: (500 Or More Head)Cattle, Calves - Sales, Measured In HeadCattle, Calves, Veal, Raised Or Sold - Number Of OperationsCattle, Cows - InventoryCattle, Cows - Operations With InventoryCattle, Cows, Beef - Inventory - Inventory Of Beef Cows: (1 To 9 Head)Cattle, Cows, Beef - Inventory - Inventory Of Beef Cows: (10 To 19 Head)Cattle, Cows, Beef - Inventory - Inventory Of Beef Cows: (20 To 49 Head)Cattle, Cows, Beef - Inventory - Inventory Of Beef Cows: (50 To 99 Head)Cattle, Cows, Beef - Inventory - Inventory Of Beef Cows: (100 To 199 Head)Cattle, Cows, Beef - Inventory - Inventory Of Beef Cows: (200 To 499 Head)Cattle, Cows, Beef - Inventory - Inventory Of Beef Cows: (500 Or More Head)Cattle, Cows, Beef - InventoryCattle, Cows, Beef - Operations With Inventory - Inventory Of Beef Cows: (1 To 9 Head)Cattle, Cows, Beef - Operations With Inventory - Inventory Of Beef Cows: (10 To 19 Head)Cattle, Cows, Beef - Operations With Inventory - Inventory Of Beef Cows: (20 To 49 Head)Cattle, Cows, Beef - Operations With Inventory - Inventory Of Beef Cows: (50 To 99 Head)Cattle, Cows, Beef - Operations With Inventory - Inventory Of Beef Cows: (100 To 199 Head)Cattle, Cows, Beef - Operations With Inventory - Inventory Of Beef Cows: (200 To 499 Head)Cattle, Cows, Beef - Operations With Inventory - Inventory Of Beef Cows: (500 Or More Head)Cattle, Cows, Beef - Operations With InventoryCattle, Cows, Milk - Inventory - Inventory Of Milk Cows: (1 To 9 Head)Cattle, Cows, Milk - Inventory - Inventory Of Milk Cows: (10 To 19 Head)Cattle, Cows, Milk - Inventory - Inventory Of Milk Cows: (20 To 49 Head)Cattle, Cows, Milk - Inventory - Inventory Of Milk Cows: (50 To 99 Head)Cattle, Cows, Milk - Inventory - Inventory Of Milk Cows: (100 To 199 Head)Cattle, Cows, Milk - Inventory - Inventory Of Milk Cows: (200 To 499 Head)Cattle, Cows, Milk - Inventory - Inventory Of Milk Cows: (500 Or More Head)Cattle, Cows, Milk - InventoryCattle, Cows, Milk - Operations With Inventory - Inventory Of Milk Cows: (1 To 9 Head)Cattle, Cows, Milk - Operations With Inventory - Inventory Of Milk Cows: (10 To 19 Head)Cattle, Cows, Milk - Operations With Inventory - Inventory Of Milk Cows: (20 To 49 Head)Cattle, Cows, Milk - Operations With Inventory - Inventory Of Milk Cows: (50 To 99 Head)Cattle, Cows, Milk - Operations With Inventory - Inventory Of Milk Cows: (100 To 199 Head)Cattle, Cows, Milk - Operations With Inventory - Inventory Of Milk Cows: (200 To 499 Head)Cattle, Cows, Milk - Operations With Inventory - Inventory Of Milk Cows: (500 Or More Head)Cattle, Cows, Milk - Operations With InventoryCattle, >= 500 Lbs - Operations With Sales - Sales Of Cattle >= 500 Lbs: (1 To 9 Head)Cattle, >= 500 Lbs - Operations With Sales - Sales Of Cattle >= 500 Lbs: (10 To 19 Head)Cattle, >= 500 Lbs - Operations With Sales - Sales Of Cattle >= 500 Lbs: (20 To 49 Head)Cattle, >= 500 Lbs - Operations With Sales - Sales Of Cattle >= 500 Lbs: (50 To 99 Head)Cattle, >= 500 Lbs - Operations With Sales - Sales Of Cattle >= 500 Lbs: (100 To 199 Head)Cattle, >= 500 Lbs - Operations With Sales - Sales Of Cattle >= 500 Lbs: (200 To 499 Head)Cattle, >= 500 Lbs - Operations With Sales - Sales Of Cattle >= 500 Lbs: (500 Or More Head)Cattle, >= 500 Lbs - Operations With SalesCattle, >= 500 Lbs - Sales, Measured In Head - Sales Of Cattle >= 500 Lbs: (1 To 9 Head)Cattle, >= 500 Lbs - Sales, Measured In Head - Sales Of Cattle >= 500 Lbs: (10 To 19 Head)Cattle, >= 500 Lbs - Sales, Measured In Head - Sales Of Cattle >= 500 Lbs: (20 To 49 Head)Cattle, >= 500 Lbs - Sales, Measured In Head - Sales Of Cattle >= 500 Lbs: (50 To 99 Head)Cattle, >= 500 Lbs - Sales, Measured In Head - Sales Of Cattle >= 500 Lbs: (100 To 199 Head)Cattle, >= 500 Lbs - Sales, Measured In Head - Sales Of Cattle >= 500 Lbs: (200 To 499 Head)Cattle, >= 500 Lbs - Sales, Measured In Head - Sales Of Cattle >= 500 Lbs: (500 Or More Head)Cattle, >= 500 Lbs - Sales, Measured In HeadCattle, Heifers, >= 500 Lbs, Milk Replacement, Production Contract - Operations With ProductionCattle, Heifers, >= 500 Lbs, Milk Replacement, Production Contract - Production, Measured In HeadCattle, Incl Calves - Inventory - Inventory Of Cattle, Incl Calves: (1 To 9 Head)Cattle, Incl Calves - Inventory - Inventory Of Cattle, Incl Calves: (10 To 19 Head)Cattle, Incl Calves - Inventory - Inventory Of Cattle, Incl Calves: (20 To 49 Head)Cattle, Incl Calves - Inventory - Inventory Of Cattle, Incl Calves: (50 To 99 Head)Cattle, Incl Calves - Inventory - Inventory Of Cattle, Incl Calves: (100 To 199 Head)Cattle, Incl Calves - Inventory - Inventory Of Cattle, Incl Calves: (200 To 499 Head)Cattle, Incl Calves - Inventory - Inventory Of Cattle, Incl Calves: (500 Or More Head)Cattle, Incl Calves - InventoryCattle, Incl Calves - Operations With Inventory - Inventory Of Cattle, Incl Calves: (1 To 9 Head)Cattle, Incl Calves - Operations With Inventory - Inventory Of Cattle, Incl Calves: (10 To 19 Head)Cattle, Incl Calves - Operations With Inventory - Inventory Of Cattle, Incl Calves: (20 To 49 Head)Cattle, Incl Calves - Operations With Inventory - Inventory Of Cattle, Incl Calves: (50 To 99 Head)Cattle, Incl Calves - Operations With Inventory - Inventory Of Cattle, Incl Calves: (100 To 199 Head)Cattle, Incl Calves - Operations With Inventory - Inventory Of Cattle, Incl Calves: (200 To 499 Head)Cattle, Incl Calves - Operations With Inventory - Inventory Of Cattle, Incl Calves: (500 Or More Head)Cattle, Incl Calves - Operations With InventoryCattle, Incl Calves - Operations With Sales - Sales Of Cattle, Incl Calves: (1 To 9 Head)Cattle, Incl Calves - Operations With Sales - Sales Of Cattle, Incl Calves: (10 To 19 Head)Cattle, Incl Calves - Operations With Sales - Sales Of Cattle, Incl Calves: (20 To 49 Head)Cattle, Incl Calves - Operations With Sales - Sales Of Cattle, Incl Calves: (50 To 99 Head)Cattle, Incl Calves - Operations With Sales - Sales Of Cattle, Incl Calves: (100 To 199 Head)Cattle, Incl Calves - Operations With Sales - Sales Of Cattle, Incl Calves: (200 To 499 Head)Cattle, Incl Calves - Operations With Sales - Sales Of Cattle, Incl Calves: (500 Or More Head)Cattle, Incl Calves - Operations With SalesCattle, Incl Calves - Sales, Measured In US Dollars ($)Cattle, Incl Calves - Sales, Measured In Head - Sales Of Cattle, Incl Calves: (1 To 9 Head)Cattle, Incl Calves - Sales, Measured In Head - Sales Of Cattle, Incl Calves: (10 To 19 Head)Cattle, Incl

This EnviroAtlas dataset summarizes by county the number of farm operations with cattle and the number of heads they manage. The data come from the Census of Agriculture, which is administered every five years by the US Department of Agriculture (USDA), and include the years 2002, 2007, 2012, and 2017. The Census classifies cattle managed on operations as beef cows, dairy cows, or other cattle (which encompasses heifers, steers, bulls, and calves). Data regarding all three categories are displayed in this layer. Operations are categorized into small, medium, or large, based on how many heads they manage. For each county and Census year, the dataset reports the number of farm operations that manage cattle, the number of heads on their property at the end of the Census year, and a breakdown of the operations into small, medium, and large. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).

The environmental impacts of beef cattle production and their effects on the overall sustainability of beef have become a national and international concern. Our objective was to quantify important environmental impacts of beef cattle production in the United States. Surveys and visits of farms, ranches and feedlots were conducted throughout seven regions (Northeast, Southeast, Midwest, Northern Plains, Southern Plains, Northwest and Southwest) to determine common practices and characteristics of cattle production. These data along with other information sources were used to create about 150 representative production systems throughout the country, which were simulated with the Integrated Farm System Model using local soil and climate data. The simulations quantified the performance and environmental impacts of beef cattle production systems for each region. A farm-gate life cycle assessment was used to quantify resource use and emissions for all production systems including traditional beef breeds and cull animals from the dairy industry. Regional and national totals were determined as the sum of the production system outputs multiplied by the number of cattle represented by each simulated system. The average annual greenhouse gas and reactive N emissions associated with beef cattle production over the past five years were determined to be 243 ± 26 Tg carbon dioxide equivalents (CO2e) and 1760 ± 136 Gg N, respectively. Total fossil energy use was found to be 569 ± 53 PJ and blue water consumption was 23.2 ± 3.5 TL. Environmental intensities expressed per kg of carcass weight produced were 21.3 ± 2.3 kg CO2e, 155 ± 12 g N, 50.0 ± 4.7 MJ, and 2034 ± 309 L, respectively. These farm-gate values are being combined with post farm-gate sources of packing, processing, distribution, retail, consumption and waste handling to produce a full life cycle assessment of U.S. beef. This study is the most detailed, yet comprehensive, study conducted to date to provide baseline measures for the sustainability of U.S. beef. Resources in this dataset:Resource Title: Appendix A. Supplementary Data - Tables S1 to S8 (docx). File Name: Web Page, url: https://ars.els-cdn.com/content/image/1-s2.0-S0308521X18305675-mmc1.docx Direct download, docx.

Table S1. Important characteristics of farms and ranches simulated throughout seven regions of the U.S.

Table S2. Important characteristics of representative finishing facilities simulated in seven regions of the U.S.

Table S3. Important characteristics of dairy farms simulated throughout seven regions of the U.S.

Table S4. Summary of 25 years of weather data (daily solar radiation, daily mean temperature, annual precipitation and daily wind speed)1 used to simulate beef cattle operations in each area of the eastern regions.

Table S5. Soil characteristics used for locations simulated across the U.S.

Table S6. Cattle numbers by state and region as obtained or estimated from NASS (2017).

Table S7. Cattle numbers by state and region divided between traditional beef and dairy breeds as obtained or estimated from NASS (2017).

Table S8. Important resource inputs and emissions from representative cow-calf, stocker / background and feedlot operations expressed per unit of final carcass weight (CW) produced.

This dataset provides information on the number of milk cows, production of milk per cow and total milk production by state and region in the United States from the year 1970 to 2021.

Circular management of beef supply chains holds great promise for improving sustainability from grazing agroecosystem to dinner plate. In the United States and Canada, one approach to circularity entails transporting manure nutrients from cattle produced in feedlots back to the grazing agroecosystems where they originated to enrich haylands for further grazing cattle production. We provide data to assess this strategy centered around three grazing agroecosystems: Florida, New Mexico, and the provincial assemblage of Manitoba, Saskatchewan, Alberta, British Columbia. We describe four datasets that can be used to estimate the potential nutrient utilization of hay fed to grazing cattle in the three grazing agroecosystems and the magnitudes of feedlot manure nutrients available for transport back to them. We found that although biogeography and management differ among the three grazing agroecosystems, the hay allocated for grazing cattle represented approximately 65% of the total harvested hay produced per agroecosystem after accounting for harvest losses, and that on average all three areas exported about 450,000 cattle annually for feedlot, pasture, and slaughter to states across the US. Although we highlight only three grazingland settings, our approach relies on methods that could ultimately be scaled nationally and internationally, with applicability to other animal industries for which circular management is an aspiration for sustainability outcomes.

The Quick Stats Database is the most comprehensive tool for accessing agricultural data published by the USDA National Agricultural Statistics Service (NASS). It allows you to customize your query by commodity, location, or time period. You can then visualize the data on a map, manipulate and export the results as an output file compatible for updating databases and spreadsheets, or save a link for future use. Quick Stats contains official published aggregate estimates related to U.S. agricultural production. County level data are also available via Quick Stats. The data include the total crops and cropping practices for each county, and breakouts for irrigated and non-irrigated practices for many crops, for selected States. The database allows custom extracts based on commodity, year, and selected counties within a State, or all counties in one or more States. The county data includes totals for the Agricultural Statistics Districts (county groupings) and the State. The download data files contain planted and harvested area, yield per acre and production. NASS develops these estimates from data collected through:

hundreds of sample surveys conducted each year covering virtually every aspect of U.S. agriculture

the Census of Agriculture conducted every five years providing state- and county-level aggregates Resources in this dataset:Resource Title: Quick Stats database. File Name: Web Page, url: https://quickstats.nass.usda.gov/ Dynamic drill-down filtered search by Commodity, Location, and Date range, beginning with Census or Survey data. Filter lists are refreshed based upon user choice allowing the user to fine-tune the search.

Live Cattle increased 15.12 USd/Lbs or 7.89% since the beginning of 2025, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Live Cattle - values, historical data, forecasts and news - updated on March of 2025.

The Census of Agriculture is a complete count of U.S. farms and ranches and the people who operate them. Even small plots of land - whether rural or urban - growing fruit, vegetables or some food animals count if $1,000 or more of such products were raised and sold, or normally would have been sold, during the Census year. The Census of Agriculture, taken only once every five years, looks at land use and ownership, operator characteristics, production practices, income and expenditures. For America's farmers and ranchers, the Census of Agriculture is their voice, their future, and their opportunity. The Census Data Query Tool (CDQT) is a web-based tool that is available to access and download table level data from the Census of Agriculture Volume 1 publication. The data found via the CDQT may also be accessed in the NASS Quick Stats database. The CDQT is unique in that it automatically displays data from the past five Census of Agriculture publications. The CDQT is presented as a "2017 centric" view of the Census of Agriculture data. All data series that are present in the 2017 dataset are available within the CDQT, and any matching data series from prior Census years will also display (back to 1997). If a data series is not included in the 2017 dataset, then data cells will remain blank in the tool. For example, one of the data series had a label change from "Operator" to "Producer." This means that data from prior Census years labelled "Operator" will not show up where the label has changed to “Producer” for 2017. The new Census Data Query Tool application can be used to query Census data from 1997 through 2017. Data are searchable by Census table and are downloadable as CSV or PDF files. 2017 Census Ag Atlas Maps are also available for download. Resources in this dataset:Resource Title: 2017 Census of Agriculture - Census Data Query Tool (CDQT). File Name: Web Page, url: https://www.nass.usda.gov/Quick_Stats/CDQT/chapter/1/table/1 The Census Data Query Tool (CDQT) is a web based tool that is available to access and download table level data from the Census of Agriculture Volume 1 publication. The data found via the CDQT may also be accessed in the NASS Quick Stats database. The CDQT is unique in that it automatically displays data from the past five Census of Agriculture publications. The CDQT is presented as a "2017 centric" view of the Census of Agriculture data. All data series that are present in the 2017 dataset are available within the CDQT, and any matching data series from prior Census years will also display (back to 1997). If a data series is not included in the 2017 dataset, then data cells will remain blank in the tool. For example, one of the data series had a label change from "Operator" to "Producer." This means that data from prior Census years labelled "Operator" will not show up where the label has changed to "Producer" for 2017. Using CDQT:

Upon entering the CDQT, a data table is present. Changing the parameters at the top of the data table will retrieve different combinations of Census Chapter, Table, State, or County (when selecting Chapter 2). For the U.S., Volume 1, US/State Chapter 1 will include only U.S. data; Chapter 2 will include U.S. and State level data. For a State, Volume 1 US/State Level Data Chapter 1 will include only the State level data; Chapter 2 will include the State and county level data. Once a selection is made, press the “Update Grid” button to retrieve the new data table. Comma-separated values (CSV) download, compatible with most spreadsheet and database applications: to download a CSV file of the data as it is currently presented in the data grid, press the "CSV" button in the "Export Data" section of the toolbar. When CSV is chosen, data will be downloaded as numeric. To view the source PDF file for the data table, press the "View PDF" button in the toolbar.

The Census of Agriculture provides a detailed picture every five years of U.S. farms and ranches and the people who operate them. Conducted by USDA's National Agricultural Statistics Service, the 2012 Census of Agriculture collected more than six million data items directly from farmers. The Ag Census Web Maps application makes this information available at the county level through a few clicks. The maps and accompanying data help users visualize, download, and analyze Census of Agriculture data in a geospatial context. Resources in this dataset:Resource Title: Ag Census Web Maps. File Name: Web Page, url: https://www.nass.usda.gov/Publications/AgCensus/2012/Online_Resources/Ag_Census_Web_Maps/Overview/index.php/ The interactive map application assembles maps and statistics from the 2012 Census of Agriculture in five broad categories:

Crops and Plants – Data on harvested acreage for major field crops, hay, and other forage crops, as well as acreage data for vegetables, fruits, tree nuts, and berries. Economics – Data on agriculture sales, farm income, government payments from conservation and farm programs, amounts received from loans, a broad range of production expenses, and value of buildings and equipment. Farms – Information on farm size, ownership, and Internet access, as well as data on total land in farms, land use, irrigation, fertilized cropland, and enrollment in crop insurance programs. Livestock and Animals – Statistics on cattle and calves, cows and heifers, milk cows, and other cattle, as well as hogs, sheep, goats, horses, and broilers. Operators – Statistics on hired farm labor, tenure, land rented or leased, primary occupation of farm operator, and demographic characteristics such as age, sex, race/ethnicity, and residence location.

The Ag Census Web Maps application allows you to:

Select a map to display from a the above five general categories and associated subcategories. Zoom and pan to a specific area; use the inset buttons to center the map on the continental United States; zoom to a specific state; and show the state mask to fade areas surrounding the state. Create and print maps showing the variation in a single data item across the United States (for example, average value of agricultural products sold per farm). Select a county and view and download the county’s data for a general category. Download the U.S. county-level dataset of mapped values for all categories in Microsoft ® Excel format.

The Census of Agriculture, produced by the USDA National Agricultural Statistics Service (USDA), provides a complete count of America's farms, ranches and the people who grow our food. The census is conducted every five years, most recently in 2017, and provides an in-depth look at the agricultural industry.This layer summarizes dairy production from the 2017 Census of Agriculture at the county level.This layer was produced from data downloaded using the USDA's QuickStats Application. The data was transformed using the Pivot Table tool in ArcGIS Pro and joined to the county boundary file provided by the USDA. The layer was published as feature layer in ArcGIS Online. Dataset SummaryPhenomenon Mapped: 2017 Dairy ProductionCoordinate System: Web Mercator Auxiliary SphereExtent: 48 Contiguous United States, Alaska, and HawaiiVisible Scale: All ScalesSource: USDA National Agricultural Statistics Service QuickStats ApplicationPublication Date: 2017AttributesThis layer provides values for the following attributes. Note that some values are not disclosed (coded as -1 in the layer) to protect the privacy of producers in areas with limited production.Cattle - Operations with SalesCattle - Sales in US DollarsCattle - Sales in HeadDairy - Operations with SalesDairy - Sales in US DollarsAdditionally attributes of State Name, State Code, County Name and County Code are included to facilitate cartography and use with other layers.What can you do with this layer?This layer can be used throughout the ArcGIS system. Feature layers can be used just like any other vector layer. You can use feature layers as an input to geoprocessing tools in ArcGIS Pro or in Analysis in ArcGIS Online. Combine the layer with others in a map and set custom symbology or create a pop-up tailored for your users. For the details of working with feature layers the help documentation for ArcGIS Pro or the help documentation for ArcGIS Online are great places to start. The ArcGIS Blog is a great source of ideas for things you can do with feature layers. This layer is part of ArcGIS Living Atlas of the World that provides an easy way to find and explore many other beautiful and authoritative layers, maps, and applications on hundreds of topics.

Annual crop data from 1972 to 1998 are now available on EOS-WEBSTER. These data are county-based acreage, production, and yield estimates published by the National Agricultural Statistics Service. We also provide county level livestock, geography, agricultural management, and soil properties derived from datasets from the early 1990s.

 The National Agricultural Statistics Service (NASS), the statistical
 arm of the U.S. Department of Agriculture, publishes U.S., state, and
 county level agricultural statistics for many commodities and data
 series. In response to our users requests, EOS-WEBSTER now provides 27
 years of crop statistics, which can be subset temporally and/or
 spatially. All data are at the county scale, and are only for the
 conterminous US (48 states + DC). There are 3111 counties in the
 database. The list includes 43 cities that are classified as
 counties: Baltimore City, MD; St. Louis City, MO; and 41 cities in
 Virginia.

 In addition, a collection of livestock, geography, agricultural
 practices, and soil properties variables for 1992 is available through
 EOS-WEBSTER. These datasets were assembled during the mid-1990's to
 provide driving variables for an assessment of greenhouse gas
 production from US agriculture using the DNDC agro-ecosystem model
 [see, for example, Li et al. (1992), J. Geophys. Res., 97:9759-9776;
 Li et al. (1996) Global Biogeochem. Cycles, 10:297-306]. The data
 (except nitrogen fertilizer use) were all derived from publicly
 available, national databases. Each dataset has a separate DIF.

 The US County data has been divided into seven datasets.

 US County Data Datasets:

 1) Agricultural Management
 2) Crop Data (NASS Crop data)
 3) Crop Summary (NASS Crop data)
 4) Geography and Population
 5) Land Use
 6) Livestock Populations
 7) Soil Properties

Beef decreased 4.45 BRL/15Kg or 1.40% since the beginning of 2025, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Beef - values, historical data, forecasts and news - updated on March of 2025.

Livestock manure, as recyclable sources for nitrogen (N) and phosphorus (P) in the Soil-Plant-Animal system, plays an important role in nutrient cycling. Given the agricultural benefits and environmental pollutions brought by manure, it is of great importance to estimate the spatial variations and temporal trajectories of manure production and its application in croplands of the continental United States (U.S.). Here, we developed datasets of annual animal manure N and P production and application in the continental U.S. at a 30 arc-second resolution over the period of 1860-2017. The total production of manure N and P increased from 1.4 Tg N yr-1 and 0.3 Tg P yr-1 in 1860 to 7.4 Tg N yr-1 and 2.3 Tg P yr-1 in 2017. The increasing manure nutrient production was associated with increased livestock numbers before the 1980s and enhanced livestock weights after the 1980s. The high-nutrient region mainly enlarged from the Midwest toward the Southern U.S., and became more concentrated in numerous hot spots after the 1980s. The South Atlantic-Gulf and Mid-Atlantic basins were the two critical coastal regions with high environmental risks due to the enrichment of manure nutrient production and application from the 1970s to 2010s. Our long-term manure N and P datasets provide critical information for national and regional assessments of nutrient budgets and can also serve as the input data for ecosystem and hydrological models to examine biogeochemical cycles in terrestrial and aquatic ecosystems.

The Organic INTEGRITY Database is a certified organic operations database that contains up-to-date and accurate information about operations that may and may not sell as organic, deterring fraud, increases supply chain transparency for buyers and sellers, and promotes market visibility for organic operations. Only certified operations can sell, label, or represent products as organic, unless exempt or excluded from certification. The INTEGRITY database improves access to certified organic operation information by giving industry and public users an easier way to search for data with greater precision than the formerly posted Annual Lists of Certified Operations. You can find a certified organic farm or business, or search for an operation with specific characteristics such as:

The status of an operation: Certified, Surrendered, Revoked, or Suspended The scopes for which an operation is certified: Crops, Livestock, Wild Crops, or Handling

The organic commodities and services that operations offer. A new, more structured classification system (sample provided) will help you find more of what you’re looking for and details about the flexible taxonomy can be found in the INTEGRITY Categories and Items list. Resources in this dataset:Resource Title: Organic INTEGRITY Database. File Name: Web Page, url: https://organic.ams.usda.gov/integrity/Default.aspx Find a specific certified organic farm or business, or search for an operation with specific characteristics. Listings come from USDA-Accredited Certifying Agents. Historical Annual Lists of Certified Organic Operations and monthly snapshots of the full data set are available for download on the Data History page. Only certified operations can sell, label or represent products as organic, unless exempt or excluded from certification.

Elasticities of indicator variables of the four capitals and monetized average value of their marginal contributions per county.

Landscape characteristics associated with livestock farms in the United StatesThis data describes the environmental or anthropogenic features occurring at locations where livestock farms are either present or absent in the conterminous United States. The spatial coordinates of locations have been removed so they do not contain any personally identifiable information. Please see accompanying ReadMe file for definitions of field names.InputData_GeographicModel.csv

Anthropogenic discharge of excess phosphorus (P) to water bodies and increasingly stringent discharge limits have fostered interest in quantifying opportunities for P recovery and reuse. To date, geospatial estimates of P recovery potential in the United States (US) have used human and livestock population data, which do not capture the engineering constraints of P removal from centralized water resource recovery facilities (WRRFs) and corn ethanol biorefineries where P is concentrated in coproduct animal feeds. Here, renewable P (rP) estimates from plant-wide process models were used to create a geospatial inventory of recovery potential for centralized WRRFs and biorefineries, revealing that individual corn ethanol biorefineries can generate on average 3 orders of magnitude more rP than WRRFs per site, and all corn ethanol biorefineries can generate nearly double the total rP of WRRFs across the US. The Midwestern states that make up the Corn Belt have the largest potential for P recovery and reuse from both corn biorefineries and WRRFs with a high degree of co-location with agricultural P consumption, indicating the untapped potential for a circular P economy in this globally significant grain-producing region.

This EnviroAtlas dataset contains data on the mean livestock manure application to cultivated crop and hay/pasture lands by 12-digit Hydrologic Unit (HUC) in 2006. Livestock manure inputs to cultivated crop and hay/pasture lands were estimated using county-level estimates of recoverable animal manure from confined feeding operations compiled for 2007. Recoverable manure is defined as manure that is collected, stored, and available for land application from confined feeding operations. County-scale data on livestock populations -- needed to calculate manure inputs -- were only available for the year 2007 from the USDA Census of Agriculture (http://www.agcensus.usda.gov/index.php). We acquired county-level data describing total farm-level inputs (kg N/yr) of recoverable manure to individual counties in 2007 from the International Plant Nutrition Institute (IPNI) Nutrient Geographic Information System (NuGIS; http://www.ipni.net/nugis). These data were converted to per area rates (kg N/ha/yr) of manure N inputs by dividing the total N input by the land area (ha) of combined cultivated crop and hay/pasture (agricultural) lands within a county as determined from county-level summarization of the 2006 NLCD. We distributed county-specific, per area N inputs rates to cultivated crop and hay/pasture lands (30 x 30 m pixels) within the corresponding county. Manure data described here represent an average input to a typical agricultural land type within a county, i.e., they are not specific to individual crop types. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).

Groundwater is an often overlooked freshwater resource compared to surface water, but groundwater is used widely across the United States, especially during periods of drought. If groundwater models can successfully simulate past conditions, they may be used to evaluate potential future pumping scenarios or climate conditions, thus providing a valuable planning tool for water-resource managers. Quantifying the groundwater-use component for a groundwater model is a vital but often challenging endeavor. This dataset includes groundwater withdrawal rates modeled for the Ozark Plateaus aquifer system (Ozark system) from 1900 to 2010 by groundwater model cell (2.6 square kilometers) for five water-use divisions: agriculture (including irrigation and aquaculture), livestock, public supply (including municipal and rural water districts), and non-agriculture (including thermoelectric power generation, mining, commercial, and industrial). Domestic groundwater withdrawal rates are available in the complementary dataset “Domestic groundwater withdrawal rates from the Ozark Plateaus aquifer system, 1900 to 2010”. The Ozark system is located in the central United States and is composed of interbedded Cambrian to Pennsylvanian clastic and carbonate lithologies. In stratigraphic order, the Ozark system includes the Basement confining unit, St. Francois aquifer, St. Francois confining unit, Ozark aquifer, Ozark confining unit, Springfield Plateau aquifer, and Western Interior Plains confining system. Generally, the lower portion of the Ozark aquifer is the primary source of groundwater across much of Missouri and the Springfield Plateau aquifer is used across northern Arkansas. A full description of the methods used to model groundwater withdrawal rates from the Ozark system are available in Knierim et al. (2017) (see larger work citation). Briefly, groundwater use was modeled by 1) acquiring site-specific and county-level groundwater withdrawal rates and well locations (with and without pumping information) from state agencies and the U.S. Geological Survey, 2) linearly interpolating groundwater withdrawal rates to create a yearly time-step for the period of observations (generally 1962 to 2010), 3) extrapolating county-level groundwater withdrawal rates to 1900 for non-agriculture, agriculture, and livestock groundwater use by assuming a linear decrease from the oldest, recorded groundwater withdrawal rate (generally between 1962 and 1985) to 0 million liters per day (ML/d) in 1900, 4) extrapolating site-specific groundwater withdrawal rates to 1900 for public supply assuming use was linearly related to population change, then constraining groundwater withdrawal rate to 0 ML/d in 1900 using a multiplier that incrementally ranged from zero in 1900 to one in 2010, 5) attributing groundwater withdrawal rates to well locations using a hierarchical process where observed site-specific groundwater withdrawal rates were used first, followed by county-level groundwater withdrawal rates disaggregated to wells where pumping was known to occur at any time, and lastly county-level groundwater withdrawal rates disaggregated to well locations with a potential groundwater-use type based on land use, and 6) aggregation into model cells (row, column, layer) by summing modeled site-specific groundwater withdrawal rates using well location and depth. The large dataset (148,836 well locations) and long period (110 years) necessitated modeling groundwater use programmatically using Python 2.7.

Cattle temperament has been considered by farmers as a key breeding goal due to its relevance for cattlemen’s safety, animal welfare, resilience, and longevity and its association with many economically important traits (e.g., production and meat quality). The definition of proper statistical models, accurate variance component estimates, and knowledge on the genetic background of the indicator trait evaluated are of great importance for accurately predicting the genetic merit of breeding animals. Therefore, 266,029 American Angus cattle with yearling temperament records (1–6 score) were used to evaluate statistical models and estimate variance components; investigate the association of sex and farm management with temperament; assess the weighted correlation of estimated breeding values for temperament and productive, reproductive efficiency and resilience traits; and perform a weighted single-step genome-wide association analysis using 69,559 animals genotyped for 54,609 single-nucleotide polymorphisms. Sex and extrinsic factors were significantly associated with temperament, including conception type, age of dam, birth season, and additional animal–human interactions. Similar results were observed among models including only the direct additive genetic effect and when adding other maternal effects. Estimated heritability of temperament was equal to 0.39 on the liability scale. Favorable genetic correlations were observed between temperament and other relevant traits, including growth, feed efficiency, meat quality, and reproductive traits. The highest approximated genetic correlations were observed between temperament and growth traits (weaning weight, 0.28; yearling weight, 0.28). Altogether, we identified 11 genomic regions, located across nine chromosomes including BTAX, explaining 3.33% of the total additive genetic variance. The candidate genes identified were enriched in pathways related to vision, which could be associated with reception of stimulus and/or cognitive abilities. This study encompasses large and diverse phenotypic, genomic, and pedigree datasets of US Angus cattle. Yearling temperament is a highly heritable and polygenic trait that can be improved through genetic selection. Direct selection for temperament is not expected to result in unfavorable responses on other relevant traits due to the favorable or low genetic correlations observed. In summary, this study contributes to a better understanding of the impact of maternal effects, extrinsic factors, and various genomic regions associated with yearling temperament in North American Angus cattle.