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.

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).

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 325.77 USd/Lbs on July 11, 2025, up 1.40% from the previous day. Over the past month, Feeder Cattle's price has risen 4.71%, and is up 25.95% 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 222.38 USd/Lbs on July 11, 2025, up 1.44% from the previous day. Over the past month, Live Cattle's price has fallen 2.58%, but it is still 21.89% 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.

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.

Body conformation traits are directly associated with longevity, fertility, health, and workability in dairy cows and have been under direct genetic selection for many decades in various countries worldwide. The main objectives of this study were to perform genome-wide association studies and functional enrichment analyses for fourteen body conformation traits using imputed high-density single nucleotide polymorphism (SNP) genotypes. The traits analyzed include body condition score (BCS), body depth (BD), bone quality (BQ), chest width (CW), dairy capacity (DC), foot angle (FAN), front legs view (FLV), heel depth (HDe), height at front end (HFE), locomotion (LOC), rear legs rear view (RLRV), rear legs side view (RLSV), stature (ST), and a composite feet and legs score index (FL) of Holstein cows scored in Canada. De-regressed estimated breeding values from a dataset of 39,135 North American Holstein animals were used as pseudo-phenotypes in the genome-wide association analyses. A mixed linear model was used to estimate the SNP effects, which ranged from 239,533 to 242,747 markers depending on the trait analyzed. Genes and quantitative trait loci (QTL) located up to 100 Kb upstream or downstream of the significant SNPs previously cited in the Animal QTLdb were detected, and functional enrichment analyses were performed for the candidate genes identified for each trait. A total of 20, 60, 13, 17, 27, 8, 7, 19, 4, 10, 13, 15, 7, and 13 genome-wide statistically significant SNPs for Bonferroni correction based on independent chromosomal segments were identified for BCS, BD, BQ, CW, DC, FAN, FLV, HDe, HFE, LOC, RLRV, RLSV, ST, and FL, respectively. The significant SNPs were located across the whole genome, except on chromosomes BTA24, BTA27, and BTA29. Four markers (for BCS, BD, HDe, and RLRV) were statistically significant when considering a much stricter threshold for the Bonferroni correction for multiple tests. Moreover, the genomic regions identified overlap with various QTL previously reported for the trait groups of exterior, health, meat and carcass, milk, production, and reproduction. The functional enrichment analyses revealed 27 significant gene ontology terms. These enriched genomic regions harbor various candidate genes previously reported as linked to bone development, metabolism, as well as infectious and immunological diseases.

Beef traded flat at 299.70 BRL/15KG on July 11, 2025. Over the past month, Beef's price has fallen 4.61%, but it is still 32.29% 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.

Body conformation traits are directly associated with longevity, fertility, health, and workability in dairy cows and have been under direct genetic selection for many decades in various countries worldwide. The main objectives of this study were to perform genome-wide association studies and functional enrichment analyses for fourteen body conformation traits using imputed high-density single nucleotide polymorphism (SNP) genotypes. The traits analyzed include body condition score (BCS), body depth (BD), bone quality (BQ), chest width (CW), dairy capacity (DC), foot angle (FAN), front legs view (FLV), heel depth (HDe), height at front end (HFE), locomotion (LOC), rear legs rear view (RLRV), rear legs side view (RLSV), stature (ST), and a composite feet and legs score index (FL) of Holstein cows scored in Canada. De-regressed estimated breeding values from a dataset of 39,135 North American Holstein animals were used as pseudo-phenotypes in the genome-wide association analyses. A mixed linear model was used to estimate the SNP effects, which ranged from 239,533 to 242,747 markers depending on the trait analyzed. Genes and quantitative trait loci (QTL) located up to 100 Kb upstream or downstream of the significant SNPs previously cited in the Animal QTLdb were detected, and functional enrichment analyses were performed for the candidate genes identified for each trait. A total of 20, 60, 13, 17, 27, 8, 7, 19, 4, 10, 13, 15, 7, and 13 genome-wide statistically significant SNPs for Bonferroni correction based on independent chromosomal segments were identified for BCS, BD, BQ, CW, DC, FAN, FLV, HDe, HFE, LOC, RLRV, RLSV, ST, and FL, respectively. The significant SNPs were located across the whole genome, except on chromosomes BTA24, BTA27, and BTA29. Four markers (for BCS, BD, HDe, and RLRV) were statistically significant when considering a much stricter threshold for the Bonferroni correction for multiple tests. Moreover, the genomic regions identified overlap with various QTL previously reported for the trait groups of exterior, health, meat and carcass, milk, production, and reproduction. The functional enrichment analyses revealed 27 significant gene ontology terms. These enriched genomic regions harbor various candidate genes previously reported as linked to bone development, metabolism, as well as infectious and immunological diseases.

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...,