This EnviroAtlas dataset summarizes by county the number of farm operations with dairy cows 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). Only data regarding dairy cows 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 dairy cows, 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).

United States Cattle Inventory: Cattle & Calves: Cows & Heifers That Have Calved: At the Beginning of the Yr: Milk Cows data was reported at 9,349.300 Head th in 2025. This records an increase from the previous number of 9,346.800 Head th for 2024. United States Cattle Inventory: Cattle & Calves: Cows & Heifers That Have Calved: At the Beginning of the Yr: Milk Cows data is updated yearly, averaging 9,349.300 Head th from Dec 1926 (Median) to 2025, with 17 observations. The data reached an all-time high of 9,450.400 Head th in 2021 and a record low of 9,208.600 Head th in 2014. United States Cattle Inventory: Cattle & Calves: Cows & Heifers That Have Calved: At the Beginning of the Yr: Milk Cows data remains active status in CEIC and is reported by Economic Research Service. The data is categorized under Global Database’s United States – Table US.RI018: Cattle Inventory.

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: Cattle productionGeographic Extent: 48 contiguous United States, Alaska, Hawaii, and Puerto RicoProjection: Web Mercator Auxiliary SphereSource: USDA National Agricultural Statistics ServiceUpdate Frequency: 5 yearsData Vintage: 2022Publication Date: April 2024AttributesNote 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. You should account for these values when symbolizing or doing any calculations.Many cattle production commodity fields are broken out into 6 or 7 ranges based on the number of head of cattle. For space reasons, a general sample of the fields is listed here.Commodities included in this layer: Cattle, (Excl Cows) - Inventory - Inventory of Cattle, (Excl Cows): (By number of head)Cattle, (Excl Cows) - InventoryCattle, (Excl Cows) - Operations with Inventory - Inventory of Cattle, (Excl Cows): (By number of head)Cattle, (Excl Cows) - Operations with InventoryCattle, Calves - Operations with Sales - Sales of Calves: (By number of head)Cattle, Calves - Operations with SalesCattle, Calves - Sales, Measured in Head - Sales of Calves: (By number of head)Cattle, Calves - Sales, Measured in HeadCattle, Calves, Veal, Raised or Sold - Number of OperationsCattle, Cows - Inventory; Cattle, Cows - Operations with InventoryCattle, Cows, Beef - Inventory - Inventory of Beef Cows: (By number of head)Cattle, Cows, Beef - InventoryCattle, Cows, Beef - Operations with Inventory - Inventory of Beef Cows: (By number of head)Cattle, Cows, Beef - Operations with InventoryCattle, Cows, Milk - Inventory - Inventory of Milk Cows: (By number of head)Cattle, Cows, Milk - InventoryCattle, Cows, Milk - Operations with Inventory - Inventory of Milk Cows: (By number of head)Cattle, Cows, Milk - Operations with InventoryCattle, >= 500 lbs - Operations with Sales - Sales of Cattle >= 500 lbs: (By number of head)Cattle, >= 500 lbs - Operations with SalesCattle, >= 500 lbs - Sales, Measured in Head - Sales of Cattle >= 500 lbs: (By number of 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: (By number of head)Cattle, Incl Calves - InventoryCattle, Incl Calves - Operations with Inventory - Inventory of Cattle, Incl Calves: (By number of head)Cattle, Incl Calves - Operations with InventoryCattle, Incl Calves - Operations with Sales - Sales of Cattle, Incl Calves: (By number of 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: (By number of head)Cattle, Incl Calves - Sales, Measured in HeadCattle, On Feed - Inventory - Inventory of Cattle On Feed: (By number of head)Cattle, On Feed - InventoryCattle, On Feed - Operations with Inventory - Inventory of Cattle On Feed: (By number of head)Cattle, On Feed - Operations with InventoryCattle, On Feed - Operations with Sales For Slaughter - Sales of Cattle On Feed: (By number of head)Cattle, On Feed - Operations with Sales For SlaughterCattle, On Feed - Sales For Slaughter, Measured in Head - Sales of Cattle On Feed: (By number of head)Cattle, On Feed - Sales For Slaughter, Measured in HeadCattle, Production Contract, On Feed - Operations with ProductionCattle, Production Contract, On Feed - Production, Measured in HeadGeography NoteIn Alaska, one or more county-equivalent entities (borough, census area, city, municipality) are included in an agriculture census area.What can you do with this layer?This layer is designed for data visualization. Identify features by clicking on the map to reveal the pre-configured pop-up. You may change the field(s) being symbolized. When symbolizing other fields, you will need to update the popup accordingly. Simple summary statistics are supported by this data.Questions?Please leave a comment below if you have a question about this layer, and we will get back to you as soon as possible.

This EnviroAtlas dataset summarizes by county the number of farm operations with beef cows 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). Only data regarding beef cows 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 beef cows, 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 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

How many cattle are in the world? The global live cattle population amounted to about 1.57 billion heads in 2023, up from approximately 1.51 million in 2021. Cows as livestock The domestication of cattle began as early as 10,000 to 5,000 years ago. From ancient times up to the present, cattle are bred to provide meat and dairy. Cattle are also employed as draft animals to plow the fields or transport heavy objects. Cattle hide is used for the production of leather, and dung for fuel and agricultural fertilizer. In 2022, India was home to the highest number of milk cows in the world. Cattle farming in the United States Cattle meat such as beef and veal is one of the most widely consumed types of meat across the globe, and is particularly popular in the United States. The United States is the top producer of beef and veal of any country worldwide. In 2021, beef production in the United States reached 12.6 million metric tons. Beef production appears to be following a positive trend in the United States. More than 33.07 million cattle were slaughtered both commercially and in farms annually in the United States in 2019, up from 33 million in the previous year.

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.

Feeder Cattle rose to 325.33 USd/Lbs on July 11, 2025, up 1.26% from the previous day. Over the past month, Feeder Cattle's price has risen 4.57%, and is up 25.78% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Feeder Cattle - values, historical data, forecasts and news - updated on July of 2025.

Live Cattle rose to 222.20 USd/Lbs on July 11, 2025, up 1.36% from the previous day. Over the past month, Live Cattle's price has fallen 2.65%, but it is still 21.79% higher than a year ago, 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 July of 2025.

To assess the magnitude of greenhouse gas (GHG) fluxes, nutrient runoff and leaching from dairy barnyards and to characterize factors controlling these fluxes, nine barnyards were built at the U.S. Dairy Forage Research Center Farm in Prairie du Sac, WI (latitude 43.33N, longitude 89.71W). The barnyards were designed to simulate outdoor cattle-holding areas on commercial dairy farms in Wisconsin. Each barnyard was approximately 7m x 7m; areas of barnyards 1-9 were 51.91, 47.29, 50.97, 46.32, 45.64, 46.30, 48.93, 48.78, 46.73 square meters, respectively. Factors investigated included three different surface materials (bark, sand, soil) and timing of cattle corralling. Each barnyard included a gravity drainage system that allowed leachate to be pumped out and analyzed. Each soil-covered barnyard also included a system to intercept runoff at the perimeter and drain to a pumping port, similar to the leachate systems. From October 2010 to October 2015, dairy heifers were placed onto experimental barnyards for approximately 7-day periods four times per year, generally in mid-spring, late-spring / early summer, mid-to-late summer and early-to-mid autumn. Heifers were fed once per day from total mixed rations consisting mostly of corn (maize) and alfalfa silages. Feed offered and feed refused were both weighed and analyzed for total nitrogen (N), carbon (C), phosphorus (P) and cell wall components (neutral detergent fiber, NDF). Leachate was pumped out of plots frequently enough to prevent saturation of surface materials and potential anaerobic conditions. Leachate was also pumped out the day before any gas flux measurements. Leachate total volume and nitrogen species were measured, and from “soil” barnyards the runoff was also measured. The starting bulk density, pH, total carbon (C) and total N of barnyard surface materials were analyzed. Decomposed bark in barnyards was replaced with new bark in 2013, before the spring flux measurements. Please note: the data presented here includes observations made in 2015; the original paper included observations through 2014 only. Gas fluxes (carbon dioxide, CO2; methane, CH4; ammonia, NH3; and nitrous oxide, N2O) were measured during the two days before heifers were corralled in barnyards, and during the two days after heifers were moved off the barnyards. During the first day of each two-day measurement period, gas fluxes were measured at two randomly selected locations within each barnyard. Each location was sampled once in the morning and once in the afternoon. During the second day, this procedure was repeated with two new randomly selected locations in each barnyard. This experiment was partially funded by a project called “Climate Change Mitigation and Adaptation in Dairy Production Systems of the Great Lakes Region,” also known as the Dairy Coordinated Agricultural Project (Dairy CAP). The Dairy CAP is funded by the United States Department of Agriculture - National Institute of Food and Agriculture (award number 2013-68002-20525). The main goal of the Dairy CAP is to improve understanding of the magnitudes and controlling factors over GHG emissions from dairy production in the Great Lakes region. Using this knowledge, the Dairy CAP is improving life cycle analysis (LCA) of GHG production by Great Lakes dairy farms, developing farm management tools, and conducting extension, education and outreach activities. Resources in this dataset:Resource Title: Data_dictionary_DairyCAP_Barnyards. File Name: BYD_Data_Dictionary.xlsxResource Description: This is the data dictionary for the data from the paper "Gas emissions from dairy barnyards" by Mark Powell and Peter Vadas. Resource Software Recommended: Microsoft Excel 2016,url: https://products.office.com/en-us/excel Resource Title: DairyCAP_Barnyards. File Name: BYD_Project_Data.xlsxResource Description: This is the complete data from the paper: Powell, J. M. & Vadas, P. A. (2016). Gas emissions from dairy barnyards. Animal Production Science, 56, 355-361. Data are separated into separate spreadsheet tabs.Resource Software Recommended: Microsoft Excel 2016,url: https://products.office.com/en-us/excel Resource Title: Data_dictionary_DairyCAP_Barnyards. File Name: Data_Dictionary_BYD.csvResource Description: This is the data dictionary for the data from the paper "Gas emissions from dairy barnyards" by Mark Powell and Peter Vadas. Resource Title: GHG Data. File Name: BYD_GHG.csvResource Description: Greenhouse gas flux dataResource Title: Intake Data. File Name: BYD_Intake.csvResource Title: Leachate Data. File Name: BYD_Leachate.csvResource Title: Runoff Data. File Name: BYD_Runoff.csvResource Title: Surface Data. File Name: BYD_Surface.csvResource Title: TMR Data. File Name: BYD_TMR.csvResource Description: Total mixed ration data

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 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.

Beef traded flat at 299.70 BRL/15KG on July 11, 2025. Over the past month, Beef's price has fallen 5.11%, but it is still 31.97% higher than a year ago, 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 July of 2025.

This data and code archive provides all the files that are necessary to replicate the empirical analyses that are presented in the paper "Climate impacts and adaptation in US dairy systems 1981-2018" authored by Maria Gisbert-Queral, Arne Henningsen, Bo Markussen, Meredith T. Niles, Ermias Kebreab, Angela J. Rigden, and Nathaniel D. Mueller and published in 'Nature Food' (2021, DOI: 10.1038/s43016-021-00372-z). The empirical analyses are entirely conducted with the "R" statistical software using the add-on packages "car", "data.table", "dplyr", "ggplot2", "grid", "gridExtra", "lmtest", "lubridate", "magrittr", "nlme", "OneR", "plyr", "pracma", "quadprog", "readxl", "sandwich", "tidyr", "usfertilizer", and "usmap". The R code was written by Maria Gisbert-Queral and Arne Henningsen with assistance from Bo Markussen. Some parts of the data preparation and the analyses require substantial amounts of memory (RAM) and computational power (CPU). Running the entire analysis (all R scripts consecutively) on a laptop computer with 32 GB physical memory (RAM), 16 GB swap memory, an 8-core Intel Xeon CPU E3-1505M @ 3.00 GHz, and a GNU/Linux/Ubuntu operating system takes around 11 hours. Running some parts in parallel can speed up the computations but bears the risk that the computations terminate when two or more memory-demanding computations are executed at the same time.

This data and code archive contains the following files and folders:

* README
Description: text file with this description

* flowchart.pdf
Description: a PDF file with a flow chart that illustrates how R scripts transform the raw data files to files that contain generated data sets and intermediate results and, finally, to the tables and figures that are presented in the paper.

* runAll.sh
Description: a (bash) shell script that runs all R scripts in this data and code archive sequentially and in a suitable order (on computers with a "bash" shell such as most computers with MacOS, GNU/Linux, or Unix operating systems)

* Folder "DataRaw"
Description: folder for raw data files
This folder contains the following files:

- DataRaw/COWS.xlsx
Description: MS-Excel file with the number of cows per county
Source: USDA NASS Quickstats
Observations: All available counties and years from 2002 to 2012

- DataRaw/milk_state.xlsx
Description: MS-Excel file with average monthly milk yields per cow
Source: USDA NASS Quickstats
Observations: All available states from 1981 to 2018

- DataRaw/TMAX.csv
Description: CSV file with daily maximum temperatures
Source: PRISM Climate Group (spatially averaged)
Observations: All counties from 1981 to 2018

- DataRaw/VPD.csv
Description: CSV file with daily maximum vapor pressure deficits
Source: PRISM Climate Group (spatially averaged)
Observations: All counties from 1981 to 2018

- DataRaw/countynamesandID.csv
Description: CSV file with county names, state FIPS codes, and county FIPS codes
Source: US Census Bureau
Observations: All counties

- DataRaw/statecentroids.csv
Descriptions: CSV file with latitudes and longitudes of state centroids
Source: Generated by Nathan Mueller from Matlab state shapefiles using the Matlab "centroid" function
Observations: All states

* Folder "DataGenerated"
Description: folder for data sets that are generated by the R scripts in this data and code archive. In order to reproduce our entire analysis 'from scratch', the files in this folder should be deleted. We provide these generated data files so that parts of the analysis can be replicated (e.g., on computers with insufficient memory to run all parts of the analysis).

* Folder "Results"
Description: folder for intermediate results that are generated by the R scripts in this data and code archive. In order to reproduce our entire analysis 'from scratch', the files in this folder should be deleted. We provide these intermediate results so that parts of the analysis can be replicated (e.g., on computers with insufficient memory to run all parts of the analysis).

* Folder "Figures"
Description: folder for the figures that are generated by the R scripts in this data and code archive and that are presented in our paper. In order to reproduce our entire analysis 'from scratch', the files in this folder should be deleted. We provide these figures so that people who replicate our analysis can more easily compare the figures that they get with the figures that are presented in our paper. Additionally, this folder contains CSV files with the data that are required to reproduce the figures.

* Folder "Tables"
Description: folder for the tables that are generated by the R scripts in this data and code archive and that are presented in our paper. In order to reproduce our entire analysis 'from scratch', the files in this folder should be deleted. We provide these tables so that people who replicate our analysis can more easily compare the tables that they get with the tables that are presented in our paper.

* Folder "logFiles"
Description: the shell script runAll.sh writes the output of each R script that it runs into this folder. We provide these log files so that people who replicate our analysis can more easily compare the R output that they get with the R output that we got.

* PrepareCowsData.R
Description: R script that imports the raw data set COWS.xlsx and prepares it for the further analyses

* PrepareWeatherData.R
Description: R script that imports the raw data sets TMAX.csv, VPD.csv, and countynamesandID.csv, merges these three data sets, and prepares the data for the further analyses

* PrepareMilkData.R
Description: R script that imports the raw data set milk_state.xlsx and prepares it for the further analyses

* CalcFrequenciesTHI_Temp.R
Description: R script that calculates the frequencies of days with the different THI bins and the different temperature bins in each month for each state

* CalcAvgTHI.R
Description: R script that calculates the average THI in each state

* PreparePanelTHI.R
Description: R script that creates a state-month panel/longitudinal data set with exposure to the different THI bins

* PreparePanelTemp.R
Description: R script that creates a state-month panel/longitudinal data set with exposure to the different temperature bins

* PreparePanelFinal.R
Description: R script that creates the state-month panel/longitudinal data set with all variables (e.g., THI bins, temperature bins, milk yield) that are used in our statistical analyses

* EstimateTrendsTHI.R
Description: R script that estimates the trends of the frequencies of the different THI bins within our sampling period for each state in our data set

* EstimateModels.R
Description: R script that estimates all model specifications that are used for generating results that are presented in the paper or for comparing or testing different model specifications

* CalcCoefStateYear.R
Description: R script that calculates the effects of each THI bin on the milk yield for all combinations of states and years based on our 'final' model specification

* SearchWeightMonths.R
Description: R script that estimates our 'final' model specification with different values of the weight of the temporal component relative to the weight of the spatial component in the temporally and spatially correlated error term

* TestModelSpec.R
Description: R script that applies Wald tests and Likelihood-Ratio tests to compare different model specifications and creates Table S10

* CreateFigure1a.R
Description: R script that creates subfigure a of Figure 1

* CreateFigure1b.R
Description: R script that creates subfigure b of Figure 1

* CreateFigure2a.R
Description: R script that creates subfigure a of Figure 2

* CreateFigure2b.R
Description: R script that creates subfigure b of Figure 2

* CreateFigure2c.R
Description: R script that creates subfigure c of Figure 2

* CreateFigure3.R
Description: R script that creates the subfigures of Figure 3

* CreateFigure4.R
Description: R script that creates the subfigures of Figure 4

* CreateFigure5_TableS6.R
Description: R script that creates the subfigures of Figure 5 and Table S6

* CreateFigureS1.R
Description: R script that creates Figure S1

* CreateFigureS2.R
Description: R script that creates Figure S2

* CreateTableS2_S3_S7.R
Description: R script that creates Tables S2, S3, and S7

* CreateTableS4_S5.R
Description: R script that creates Tables S4 and S5

* CreateTableS8.R
Description: R script that creates Table S8

* CreateTableS9.R
Description: R script that creates Table S9

This study presents data from aerial surveys conducted in Yellowstone National Park spanning from 1970 to July 1997, aimed at monitoring the bison population. Surveys were initially conducted four times per year from 1970 to 1990, with a consistent pilot and observer, weather permitting. In 1991, the frequency increased to 9-10 surveys per year. Ideal flying conditions required clear-to-partly-cloudy skies and minimal wind, typically in the early morning to avoid stronger winds later in the day. The flight path commonly began in the northern range and proceeded sequentially southward, though weather conditions such as wind or ground fog sometimes necessitated route adjustments. In instances where surveys could not be completed in a single day, the remaining areas were surveyed the following day, and data were combined. During the early years, data from an elk researcher were sometimes used for the northern range when timing allowed, minimizing overlap in efforts. The objective of the su..., The study area encompassed most of Yellowstone National Park, USA. Aerial survey efforts concentrated on the locales used by the mixed groups (cows with young, usually some mature bulls). Historically, the winter locales used by bison occupied three areas designated the northern range (Lamar Valley), Pelican Valley, and Mary Mountain. The latter encompassed both the centrally located Hayden Valley and the Firehole to the west as one wintering unit because of the movements in both directions across Mary Mountain. Beginning in the 1980’s as bison use patterns began to change, the west side was considered as part of the Mary Mountain geographic unit. Because adult bulls may wander widely, extra efforts were not made to locate these scattered individuals. The data span 1970 through July 1997. There were four aerial surveys per year (Piper Supercub, same pilot and observer with few exceptions), weather, and desired timing permitting from 1970 through 1990. Thereafter surveys increased to 9-1..., , # Bison population surveys in Yellowstone National Park (USA): 1970-1997

Dataset DOI: 10.5061/dryad.2bvq83c25

Description of the data and file structure

Collection Metadata

The data span 1970 through July 1997. There were four aerial surveys per year (Piper supercub, same pilot and observer with rare exceptions), weather and desired timing permitting from 1970 through 1990. Thereafter surveys increased to 9-10 per year, circumstances permitting. Flying weather required reasonably wind-free, clear-to-partly-cloudy conditions park-wide, beginning 0800-0900 usually to be ahead of the strong winds that often developed later in the day. Surveys most commonly began with the northern range and proceeded sequentially toward the east boundary and upper Lamar, Mirror Plateau and Pelican Valley, Hayden Valley, the Firehole, and the west side. However wind and/or ground-fog over open valleys sometimes required a change of route. The same factors occasionally d...,

Nitrogen Source Study for Greenhouse gas Reduction through Agricultural Carbon Enhancement network in Mandan, North Dakota Use of dietary amendments to reduce nitrogen (N) in excreta represents a possible strategy to decrease greenhouse gas (GHG) emissions from livestock. In this regard, ingestion of small amounts of condensed quebracho tannin has been found to reduce N concentration in livestock urine. In this study, we sought to quantify the effects of tannin-affected cattle urine, normal cattle urine, and NH4NO3 in solution on greenhouse gas flux. Carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O) flux was measured using static chamber methodology from the three N treatments and a no application control over a six-week period in a mixed grass prairie in west-central North Dakota, USA. Over the course of the study, average CO2 emission was greatest from normal urine (335 ± 8 mg C m-2 hr-1) and least from the control (229 ± 19 mg C m-2 hr-1), with intermediate fluxes for the tannin urine and NH4NO3 treatments (290 ± 27 and 286 ± 54 mg C m-2 hr-1, respectively). Methane uptake was prevalent throughout the study, as soil conditions were predominantly warm and dry. Uptake of CH4 was greatest within the control (-30 ± 2 µg C m-2 hr-1) and least in the tannin urine treatment (-12 ± 4 µg C m-2 hr-1). Uptake of CH4 was over 40% less within the tannin urine treatment as compared to normal urine, and may have been repressed by the capacity of tannin to bind monooxygenases responsible for CH4 oxidation. Average N2O emission from NH4NO3 solution was more than twice that of all other treatments. Though the tannin urine treatment possessed 34% less N than normal cattle urine, cumulative N2O emission between the treatments did not differ. Results from this study suggest the use of condensed quebracho tannin as a dietary amendment for livestock does not yield GHG mitigation benefits in the short-term. Resources in this dataset:Resource Title: GeoData catalog record. File Name: Web Page, url: https://geodata.nal.usda.gov/geonetwork/srv/eng/catalog.search#/metadata/40cfe233-a757-4049-b1e8-eb37b1c017e0

This dataset was used in the generation of the manuscript titled: Implied climate warming contributions of enteric methane emissions are dependent on the estimate source and accounting methodology (DOI: 10.15232/aas.2022-02344). The objectives of this manuscript were to (1) demonstrate the differences in enteric methane emission estimates between 2 reporting entities (FAO and EPA) and to (2) demonstrate how the implied contribution to climate warming caused by enteric methane is dependent on accounting methodologies. The accounting methodologies explored were the conventional global warming potential (GWP) based on a 100-year time horizon (GWP100) or the newer GWP. The GWP methodology was developed because GWP100 fails to capture the short atmospheric lifespan of CH4, which is only 12-years (EPA, 2021a). The equations to calculate GWP100 (Equation 1; IPCC, 2013) and GWP* (Equation 2; Smith et al., 2021) are: CH4, Mt CO2 equivalence = 28 × CH4(t) [Equation 1]; CH4, Mt CO2-we = (4.53 × CH4(t) – 4.25 × CH4(t–20)) × 28 [Equation 2]. Where CH4(t) is the CH4 emitted in million metric tons (Mt) at year t and CH4(t-20) is the amount of CH4 emitted in Mt 20-years prior. Data was obtained from publicly available sources — the USDA-NASS biannual reports on cow and calf inventory in January and June (USDA-NASS, 2022), the EPA website (EPA, 2021b), and FAO from the FAOSTAT website (FAO, 2022). The data provided herein is an excel spreadsheet (.xlsx format) and contains 5 different sheets. Sheet titled "Descriptions" describe what each column names contain for the data-containing sheets. References: EPA (US Environmental Protection Agency). 2021a. Inventory of US Greenhouse Gas Emissions and Sinks 1990–2019. US Environmental Protection Agency. EPA (US Environmental Protection Agency). 2021b. Inventory of US Greenhouse Gas Emissions and Sinks: 1990–2019. Accessed May 18, 2022. https://www.epa.gov/ghgemissions/inventory-us-greenhouse-gas-emissions-and-sinks-1990-2019. FAO (Food and Agriculture Organization). 2022. FAOSTAT Emissions Totals. Accessed: May 18, 2022. https://www.fao.org/faostat/en/#data/GT. IPCC (Intergovernmental Panel on Climate Change). 2013. Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. T. F. Stocker, D. Qin, G. K. Plattner, M. Tignor, S. K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, and P. M. Midgley, ed. Cambridge University Press. NASS (National Agricultural Statistics Service). 2022. Cattle NASS. Accessed May 18, 2022. https://usda.library.cornell.edu/concern/publications/h702q636h?locale=en. Smith, M. A., M. Cain, and M. R. Allen. 2021. Further improvement of warming-equivalent emissions calculation. npj Clim. Atmos. Sci. 4:1–3. https://doi.org/10.1038/s41612-021-00169-8. Resources in this dataset:Resource Title: Data from: Implied climate warming contributions of enteric methane emissions are dependent on the estimate source and accounting methodology. File Name: Enteric_CH4_emission_estimates_scenarios.xlsx

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