Texas was by far the leading U.S. state in terms of total number of farms, with about 231 thousand farms by the end of 2024. Iowa was ranked second, among the leading ten states, with 86.7 thousand farms as of 2023. Farms classification In the United States farms are classified based on the farm income and government payments into six sales classes. According to the USDA, about half of all farms in the U.S. were classified in the 1,000 to 9,999 U.S. dollars sales class in 2023. Farming sector in the U.S. The total number of farms in the United States has decreased steadily since 2007. As of 2022, there were about 1.89 million farms in the U.S., down slightly from 1.9 million in the previous year. Contrastingly, the average farm acreage in the United States has increased in the past few years. The number of employees, including both part-time and full-time workers, in this sector was over 965 thousand as of 2019.

In 2024, there were about 1.88 million farms in the United States. However, the number of farms has been steadily dropping since the year 2007, when there were about 2.2 million farms in the United States. U.S. farms In 2007, the average size of farms in the United States was the smallest it had been since the year 2000. As the number of farms in the United States decrease, the average size of farms increases. Texas, the largest state in the contiguous United States, also contains the highest number of farms, at 231 thousand in 2023. Organic farming in the United States The United States has over 2.3 million hectares of organic agricultural land as of 2021. In 2022, organic food sales in the United States amounted to almost 59 billion euros, making it the largest market for organic food worldwide. In 2021, the number of certified organic farms in the United States reached 17,445, up from about 14,185 farms in 2016.

This EnviroAtlas data set depicts estimates for mean cash rent paid for land by farmers, sorted by county for irrigated cropland, non-irrigated cropland, and pasture by for most of the conterminous US. This data comes from national surveys which includes approximately 240,000 farms and applies to all crops. According to the USDA (U.S. Department of Agriculture) National Agricultural Statistics Service (NASS), these surveys do not include land rented for a share of the crop, on a fee per head, per pound of gain, by animal unit month (AUM), rented free of charge, or land that includes buildings such as barns. For each land use category with positive acres, respondents are given the option of reporting rent per acre or total dollars paid. 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).

State comparisons data for agricultural output, number of farms, value of farms, etc. Data include a national ranking.

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 cattle 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 Cattle 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.

In nine of the last 10 years, the United States Department of Agriculture (USDA) has reported that the average funds generated on-farm for farm operators to meet living expenses and debt obligations have been negative. This paper pieces together disparate data to understand why farm operators in the most productive agricultural systems on the planet are systematically losing money. The data-driven narrative we present highlights some troubling trends in US farm operator livelihoods. Though US farms are more productive than ever before, rising input costs, volatile production values, and rising land rents have left farmers with unprecedented levels of farm debt, low on-farm incomes, and high reliance on federal programs. For many US farm operators, the indicators of a “good livelihood”—stability, security, equitable rewards for work—are largely absent. We conclude by proposing three axes of intervention that would help US agriculture better sustain all farmers' livelihoods, a crucial step toward improving overall agricultural sustainability: (1) increase the diversity of people, crops, and cropping systems, (2) improve equity in access to land, support, and capital, and (3) improve the quality, accessibility, and content of data to facilitate monitoring of multiple indicators of agricultural “success.”

The United States Vertical Farming Market Report is Segmented by Growth Mechanism (Aeroponics, Hydroponics, and Aquaponics), by Structure (Building-Based Vertical Farms, Shipping-Container Farms, and More), and by Crop Type (Leafy Greens, Herbs and Microgreens, and More). The Market Forecasts are Provided in Terms of Value (USD).

In 2021, the State of California had approximately 3,061 organic farms and ranches. This was the largest number of certified organic farms in the United States. The state of Pennsylvania had approximately 1,125 certified organic farms. Nearly 16,194 thousand certified organic farms in operation used cropland in 2021, while 6,749 thousand farms operated pastureland and rangeland. The sales value of crops, including those from nurseries and greenhouses, amounted to nearly 6.15 billion U.S. dollars.

Organic Farming in Europe

The number of organic farms in the Netherlands has increased by over 40 percent since 2010. In 2021, there were approximately 2,063 certified organic farms in the Netherlands. Organic farms typically use ecologically based pest controls and biological fertilizers derived largely from animal and plant wastes. During the last six years, the number has continuously risen. The area of agricultural surface used for organic farming in France, the agricultural surface area used for organic farming, certified and in conversion, has also seen steady growth. In the United Kingdom, the land area in conversion for organic farming has decreased from its peak of 158 thousand hectares in 2007 to 42 thousand hectares in 2021.

Organic Farming in Asia

The area of certified organic farmland in Japan amounted to approximately 14.14 thousand hectares in 2021. In South Korea, it amounted to almost 39 thousand hectares, after several years of constant increases in organic farmland. In 2014, the acreage of organically farmed land in China amounted to nearly 23 million hectares. In 2021, this figure had reduced by more than half, as approximately 9.84 million hectares of farmland in China were certified as organic.

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.

In the two files available, File A presents data for number and size of all farms and for farms having gross sales of $2,500 or more. Data also include farm acreage, farm operations, land in farms, land use practices, income and sales, expenditure, machinery, and equipment. In addition, information is presented for livestock, poultry, livestock and poultry products, crops harvested, nursery and greenhouse products, and forest products. File B presents selected crops and livestock not availabl e in File A, and also shows race and ethnicity of farm operators. Comparable data from the 1969 census of agriculture are shown where applicable. "On File A, summaries are provided for states and for each county with 10 farms or more. File B presents county and state data for miscellaneous crops and livestock that are grown primarily in localized areas or in relatively few counties. The data are presented by product for counties having sufficient farms reporting the product to avoid disclosure of information for any individual operator. Counties not reporting the product in sufficient cases to be published separately are combined i nto an ""all other"" category when disclosure rules permit."

Abstract (en): This collection includes county-level data from the United States Censuses of Agriculture for the years 1840 to 2012. The files provide data about the number, types, output, and prices of various agricultural products, as well as information on the amount, expenses, sales, values, and production of machinery. Most of the basic crop output data apply to the previous harvest year. Data collected also included the population and value of livestock, the number of animals slaughtered, and the size, type, and value of farms. Part 46 of this collection contains data from 1980 through 2010. Variables in part 46 include information such as the average value of farmland, number and value of buildings per acre, food services, resident population, composition of households, and unemployment rates. ICPSR data undergo a confidentiality review and are altered when necessary to limit the risk of disclosure. ICPSR also routinely creates ready-to-go data files along with setups in the major statistical software formats as well as standard codebooks to accompany the data. In addition to these procedures, ICPSR performed the following processing steps for this data collection: Checked for undocumented or out-of-range codes.. Response Rates: Not applicable. Datasets:DS0: Study-Level FilesDS1: Farm Land Value Data Set (County and State) 1850-1959DS2: 1840 County and StateDS3: 1850 County and StateDS4: 1860 County and StateDS5: 1870 County and StateDS6: 1880 County and StateDS7: 1890 County and StateDS8: 1900 County and StateDS9: 1910 County and StateDS10: 1920 County and State, Dataset 1DS11: 1920 County and State, Dataset 2DS12: 1925 County and StateDS13: 1930 County and State, Dataset 1DS14: 1930 County and State, Dataset 2DS15: 1935 County and StateDS16: 1940 County and State, Dataset 1DS17: 1940 County and State, Dataset 2DS18: 1940 County and State, Dataset 3DS19: 1940 County and State, Dataset 4 (Water)DS20: 1945 County and StateDS21: 1950 County and State, Dataset 1DS22: 1950 Crops, County and State, Dataset 2DS23: 1950 County, Dataset 3DS24: 1950 County and State, Dataset 4DS25: 1954 County and State, Dataset 1DS26: 1954 Crops, County and State, Dataset 2DS27: 1959 County and State, Dataset 1DS28: 1959 Crops, County and State, Dataset 2DS29: 1959 County, Dataset 3DS30: 1964 Dataset 1DS31: 1964 Crops, County and State, Dataset 2DS32: 1964 County, Dataset 3DS33: 1969 All Farms, County and State, Dataset 1DS34: 1969 Farms 2500, County and State, Dataset 2DS35: 1969 Crops, County and State, Dataset 3DS36: 1974 All Farms, County and State, Dataset 1DS37: 1974 Farms 2500, County and State, Dataset 2DS38: 1974 Crops, County and State, Dataset 3DS39: 1978 County and StateDS40: 1982 County and StateDS41: 1987 County and StateDS42: 1992 County and StateDS43: 1997 County and StateDS44: 2002 County and StateDS45: 2007 County and StateDS46: State and County Data, United States, 1980-2010DS47: 2012 County and State Farms within United States counties and states. Smallest Geographic Unit: FIPS code The sample was the universe of agricultural operating units. For 1969-2007, data were taken from computer files from the Census Bureau and the United States Department of Agriculture. 2018-08-20 The P.I. resupplied data and documentation for 1935 County and State (dataset 15) and 1997 County and State (dataset 43). Additionally, documentation updates and variable label revisions have been incorporated in datasets 22, 26, 28, 31, 35, and 38 at the request of the P.I.2016-06-29 The data and documentation for 2012 County and State (data set 47) have been added to this collection. The collection and documentation titles have been updated to reflect the new year.2015-08-05 The data, setup files, and documentation for 1964 Dataset 1 have been updated to reflect changes from the producer. Funding insitution(s): National Science Foundation (NSF-SES-0921732; 0648045). United States Department of Health and Human Services. National Institutes of Health (R01 HD057929).

This coverage contains estimates of land in agricultural production in counties in the conterminous United States as reported in the 1987 Census of Agriculture (U.S. Department of Commerce, 1989a). Land in agriculture data are reported as either a number (for example, number of Farms), acres, or as a percentage of county area. Land in agriculture estimates were generated from surveys of all farms where $1,000 or more of agricultural products were sold, or normally would have been sold, during the census year. Most of the attributes summarized represent 1987 data, but some information for the 1982 Census of Agriculture also was included. The polygons representing county boundaries in the conterminous United States, as well as lakes, estuaries, and other nonland-area features were derived from the Digital Line Graph (DLG) files representing the 1:2,000,000-scale map in the National Atlas of the United States (1970). Agricultural land Census of Agriculture Counties United States

This metadata report documents tabular data sets consisting of items from the Census of Agriculture. These data are a subset of items from county-level data (including state totals) for the conterminous United States covering the census reporting years (every five years, with adjustments for 1978 and 1982) beginning with the 1950 Census of Agriculture and ending with the 2012 Census of Agriculture. Historical (1950-1997) data were extracted from digital files obtained through the Intra-university Consortium on Political and Social Research (ICPSR). More current (1997-2012) data were extracted from the National Agriculture Statistical Service (NASS) Census Query Tool for the census years of 1997, 2002, 2007, and 2012. Most census reports contain item values from the prior census for comparison. At times these values are updated or reweighted by the reporting agency; the Census Bureau prior to 1997 or NASS from 1997 on. Where available, the updated or reweighted data were used; otherwise, the original reported values were used. Changes in census item definitions and reporting as well as changes to county areas and names over the time span required a degree of manipulation on the data and county codes to make the data as comparable as possible over time. Not all of the census items are present for the entire 1950-2012 time span as certain items have been added since 1950 and when possible the items were derived from other items by subtracting or combining sub items. Specific changes and calculations are documented in the processing steps sections of this report. Other missing data occurs at the state and (or) county level due to census non-disclosure rules where small numbers of farms reporting an item have acres and (or) production values withheld to prevent identification of individual farms. In general, caution should be exercised when comparing current (2012) data with values reported in earlier censuses. While the 1974-2012 data are comparable, data prior to 1974 will have inflated farm counts and slightly inflated production amounts due to the differences in collection methods, primarily, the definition of a farm. Further discussion on comparability can be found the comparability section of the Supplemental Information element of this metadata report. Excluded from the tabular data are the District of Columbia, Menominee County, Wisconsin, and the independent cities of Virginia with the exception of the three county-equivalent cities of Chesapeake City, Suffolk, and Virginia Beach. Data for independent cities of Virginia prior to 1959 have been included with their surrounding or adjacent county. Please refer to the Supplemental Information element for information on terminology, the Census of Agriculture, the Inter-university Consortium for Political and Social Research (ICPSR), table and variable structure, data comparability, all farms and economic class 1-5 farms, item calculations, increase of farms from 1974 to 1978, missing data and exclusion explanations, 1978 crop irregularities, pastureland irregularities, county alignment, definitions, and references. In addition to the metadata is an excel workbook (VariableKey.xlsx) with spreadsheets containing key spreadsheets for items and variables by category and a spreadsheet noting the presence or absence of entire variable data by year. Note: this dataset was updated on 2016-02-10 to populate omitted irrigation values for Miami-Dade County, Florida in 1997.

Graph and download economic data for Real farm output (A2000X1A020NBEA) from 2007 to 2023 about output, agriculture, real, GDP, and USA.

In recent years, the US corn farming industry has experienced volatile revenue largely driven by changes in corn prices, production levels and crop yields. Early in the current period, up through 2022, corn prices saw a significant increase, which resulted in considerable revenue growth for farmers. This increase was fueled by high demand for biofuels and animal feeds and limited global supplies. However, as production ramps up both domestically and internationally, the industry has begun to feel downward pressure on corn prices. Record yields have led to oversupply, driving prices downward and increasing market competition. While sectors like livestock agriculture and industrial production have supported demand, the surplus has outpaced consumption and kept prices lower, causing challenges for farmers trying to maintain profit as fertilizer and seed prices stay high. Industry revenue has grown at a CAGR of 1.9% to reach an estimated $66.9 billion after a decrease of 5.6% in 2025. On the international front, the US corn export market has demonstrated resilience despite reduced demand from China, driven by geopolitical tensions and China's shift toward agricultural self-sufficiency. Retaliatory tariffs have further strained this trade relationship, while increased corn production from South America has intensified global competition, impacting US market share. However, the Russia-Ukraine conflict has disrupted Ukrainian corn supplies, leading many countries to turn to the US as an alternative source. This shift has bolstered exports to Asian and Latin American markets, mitigating the decline from China. Reduced domestic demand for products like high fructose corn syrup due to health concerns has made international trade increasingly vital for balancing the US corn market. Corn prices will remain volatile in the coming years, influenced by fluctuating crude oil prices that impact ethanol production and growing international competition. Policy changes, such as ethanol mandates and evolving trade agreements, will reshape international demand, offering growth opportunities abroad if US farmers can adapt to new biofuel and market needs. Climate change is a significant concern, threatening yields due to heat stress and shifting precipitation and insect population patterns. To protect productivity, farmers will have to invest more in fertilizers, pesticides, irrigation technologies and drought-tolerant crops. Industry revenue is forecast to grow at a CAGR of 0.7% to reach $69.1 billion in 2030.

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.

Abstract

The main target of the FSS 2007 was to obtain information about structure and typology of the agricultural farms and their agricultural activities in Latvia in accordance with EU and national requirements.

Geographic coverage

National

Analysis unit

Farms

Universe

All economically active farms - farms, which produce agricultural production, were involved in the target population for the FSS 2007. The definition of a holding is in line with the EU Farm Structure Survey definition. Agricultural holding is a single unit both technically and economically, which has a single management and the output of which is agricultural production. The holding may also provide other supplementary (non-agricultural) products and services.

Kind of data

Sample survey data [ssd]

Sampling procedure

The Latvian farm structure survey 2007 was made as combination of exhaustive enumeration and sample. All units were sampled in the part of sampling frame where exhaustive enumeration was done. Stratified simple random sampling was done in the sampling part of the frame. For more details see 3.3.2 of the Methodological Report available as external resources.. For each farm structure survey new sample is drawn. Procedure for sample selection is self-made using SPSS®. In 2007 total sample size comprised 58.0 thousand holdings.

Mode of data collection

Face-to-face [f2f]

Research instrument

The questionnaire form of FSS 2007 was developed in co-operation with the Ministry of Agriculture and other State institutions concerned. The list of characteristics included in the survey was compliant with EU requirements concerning the Farm Structure Survey 2007 (Commission Regulation (EC) No 204/2006 of February 6, 2006 adapting Council Regulation (EEC) No 571/88 and amending Commission Decision No 2000/115/EC with a view to the organization of Community surveys on the structure of agriculture holdings in 2007).

For all types of farms (private farms, state farms and statutory companies) Latvia has only one type of questionnaire form. The questionnaire form of FSS 2007 was developed in co-operation with the Ministry of Agriculture and other State institutions concerned.The questionnaire form was designed so that later it can easily be processed on scanners. The size of the questionnaire form is 8 pages. The following parts are included: · General description of the farm and holder (user) · Land use · Utilisation of arable land · Number of livestock and poultry · Stock of agricultural machines · Farm storage facilities of manure and irrigation devices · Farm labour force, permanent and temporary · Rural development

Cleaning operations

Data Control of the FSS 2007 was carried out as follows: Manual Control: The first visual control of questionnaire forms was done in regional offices. Regional supervisory stuff and other staff in regional offices carried out a preliminary verification to see if the forms were filled in correctly and completely. Verification and Logical Control: For data entering scanners were used. After scanning the verification of the logical and arithmetical control was done in the CSB in accordance with specially developed verification programme. There were approximately 200 different logical and arithmetical controls. After interviewers or farmers were contacted by phone the re-addressing of errors was done. Due to the error shown by logical control program, if necessary, land users were contacted by phone in, e.g., to find out volume of sown areas, number of livestock, etc. thus needed information was obtained, and there non-response in such cases does not exist. Comparison of the FSS with other data sources: After logical control was finished, the FSS data were compared with information from Statistical Farm Register (information on holder (user) of farm, land areas belonging to farm and other), with information from other statistical surveys (previous livestock survey), with Animal Register information (Agricultural Data Centre) on June 1, 2007, and with the list of Organic farms received from Ministry of Agriculture and Integrated Administration an Control System – IACS (Rural Support Service)

Response rate

Details on non-response are available in section 3.4.5 of the Methodological Report available as external resources.

Sampling error estimates

Please see section 3.5.2 of the Methodological Report (available as external resources) for a detailed explanation procedure used to estimate sampling errors.

Data appraisal

Comparison of the FSS with other data sources: After logical control was finished, the FSS data were compared with information from Statistical Farm Register (information on holder (user) of farm, land areas belonging to farm and other), with information from other statistical surveys (previous livestock survey), with Animal Register information (Agricultural Data Centre) on June 1, 2007, and with the list of Organic farms received from Ministry of Agriculture and Integrated Administration an Control System - IACS (Rural Support Service).

International comparability Eurostat Statistical Office of the European Union (Eurostat) on its homepage published information on agriculture on EU-27 and on each country separately. Main indicators are available in section: Main tables/ Agriculture, forestry and fisheries/ Agriculture/ Structure of agricultural holdings. More detailed Farm Structure Data: Database/ Agriculture, forestry and fisheries/ Agriculture/ Structure of agricultural holdings. Eurostat has published reports on agriculture in EU countries on its webpage: Publications/ Collections/ Statistics in focus.

Abstract

For 156 years (1840 - 1996), the U.S. Department of Commerce, Bureau of the Census was responsible for collecting census of agriculture data. The 1997 Appropriations Act contained a provision that transferred the responsibility for the census of agriculture from the Bureau of the Census to the U.S. Department of Agriculture (USDA), National Agricultural Statistics Service (NASS). The 2007 Census of Agriculture is the 27th Federal census of agriculture and the third conducted by NASS. The first agriculture census was taken in 1840 as part of the sixth decennial census of population. The agriculture census continued to be taken as part of the decennial census through 1950. A separate middecade census of agriculture was conducted in 1925, 1935, and 1945. From 1954 to 1974, the census was taken for the years ending in 4 and 9. In 1976, Congress authorized the census of agriculture to be taken for 1978 and 1982 to adjust the data reference year so that it coincided with other economic censuses. This adjustment in timing established the agriculture census on a 5-year cycle collecting data for years ending in 2 and 7. Agriculture census data are used to:

• Evaluate, change, promote, and formulate farm and rural policies and programs that help agricultural producers; • Study historical trends, assess current conditions, and plan for the future; • Formulate market strategies, provide more efficient production and distribution systems, and locate facilities for agricultural communities; • Make energy projections and forecast needs for agricultural producers and their communities; • Develop new and improved methods to increase agricultural production and profitability; • Allocate local and national funds for farm programs, e.g. extension service projects, agricultural research, soil conservation programs, and land-grant colleges and universities; • Plan for operations during drought and emergency outbreaks of diseases or infestations of pests. • Analyze and report on the current state of food, fuel, feed, and fiber production in the United States.

Geographic coverage

National coverage

Analysis unit

Households

Universe

The statistical unit for the CA 2012 was the farm, an operating unit defined as any place from which USD 1 000 or more of agricultural products were produced and sold, or normally would have been sold, during the census year.

Kind of data

Census/enumeration data [cen]

Sampling procedure

i. Methodological modality for conducting the census The classical approach was used in the CA 2012.

ii. Frame NASS maintains a list of farmers and ranchers from which the CML is compiled.

iii. Complete and/or sample enumeration methods The CA 2012 was an enumeration of all known agricultural holdings meeting the USDA definition of a farm.

Mode of data collection

Mail Questionnaire [mail]

Research instrument

Seven regionalized versions of the main report form (questionnaire) were used for the CA 2012. The report form versions were designed to facilitate reporting on the crops most commonly grown within each report form region. Additionally, an American Indian report form was developed to facilitate reporting for operations on reservations in Arizona, New Mexico and Utah. All of the forms allowed respondents to write in specific commodities that were not listed on their form.

• Land owned
• Land use
• Irrigation
• Conservation programs and crop insurance
• Field crops
• Bananas, coffee, pineapples and plantain crops
• Hay and forage crops
• Nursery, Greenhouse, Floriculture, Sod and tree seedlings
• Vegetables and melons
• Hydroponic crops
• Fruit
• Root crops
• Cattle and calves
• Poultry
• Hogs and pigs
• Aquaculture
• Other animals and livestock products
• Value of sales
• Organic agriculture
• Federal and commonwealth agricultural program payments
• Income from farm-related sources
• Production expenses
• Farm labour
• Fertilizer and chemicals applied
• Market value of land and buildings
• Machinery, equipment and buildings
• Practices
• Type of organization
• Operator characteristics

The CA 2012 covered all 16 core items recommended to be collected in the WCA 2010. See questionnaire in external materials.

Cleaning operations

1. DATA PROCESSING AND ARCHIVING The completed forms were scanned and Optical Mark Recognition (OMR) was used to retrieve categorical responses and to identify the other answer zones in which some type of mark was present. The edit system determined the best value to impute for reported responses that were deemed unreasonable and for required responses that were absent. The complex edit ensured the full internal consistency of the record. After tabulation and review of the aggregates, a comprehensive disclosure review was conducted. Cell suppression was used to protect the cells that were determined to be sensitive to a disclosure of information.

2. CENSUS DATA QUALITY NASS conducted an extensive program to follow-up all non-response. NASS also used capture-recapture methodology to adjust for under-coverage, non-response, and misclassification. To implement capture-recapture methods, two independent surveys were required --the 2012 Census of Agriculture (based on the Census Mail List) and the 2012 June Agricultural Survey (based on the area frame). Historically, NASS has been careful to maintain the independence of these two surveys.

Data appraisal

The complete data series from the 2012 Census of Agriculture is available from the NASS website free of charge in multiple formats, including Quick Stats 2.0 - an online database to retrieve customized tables with Census data at the national, state and county levels. The 2012 Census of Agriculture provides information on a range of topics, including agricultural practices, conservation, organic production, as well as traditional and specialty crops.

2014 Swine CAFO Study SE for Agricultural Antibiotic Resistance in Mississippi State, Mississippi The environmental influence of farm management in concentrated animal feeding operations (CAFO) can yield vast changes to the microbial biota and ecological structure of both the pig and waste manure lagoon wastewater. While some of these changes may not be negative, it is possible that CAFOs can enrich antibiotic resistant bacteria or pathogens based on farm type, thereby influencing the impact imparted by the land application of its respective wastewater. The purpose of this study was to measure the microbial constituents of swine-sow, -nursery, and -finisher farm manure lagoon wastewater and determine the changes induced by farm management. A total of 37 farms were visited in the Mid-South USA and analyzed for the genes 16S rRNA, spaQ (Salmonella spp.), Camp-16S (Campylobacter spp.), tetA, tetB, ermF, ermA, mecA, and intI using quantitative PCR. Additionally, 16S rRNA sequence libraries were created. Overall, it appeared that finisher farms were significantly different from nursery and sow farms in nearly all genes measured and in 16S rRNA clone libraries. Nearly all antibiotic resistance genes were detected in all farms. Interestingly, the mecA resistance gene (e.g. methicillin resistant Staphylococcus aureus) was below detection limits on most farms, and decreased as the pigs aged. Finisher farms generally had fewer antibiotic resistance genes, which corroborated previous phenotypic data; additionally, finisher farms produced a less diverse 16S rRNA sequence library. Comparisons of Camp-16S and spaQ GU (genomic unit) values to previous culture data demonstrated ratios from 10 to 10,000:1 depending on farm type, indicating viable but not cultivatable bacteria were dominant. The current study indicated that swine farm management schemes positively and negatively affect microbial and antibiotic resistant populations in CAFO wastewater which has future “downstream” implications from both an environmental and public health perspective. 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/651220ea-a65e-43a1-9c28-433c27464cae

Hay and crop farmers have experienced a healthy five years of growth since 2019. Industry revenue has been bolstered in the current period primarily by a rising agricultural price index, making industry products more expensive. This growth has also been built up by heightened demand for livestock feed and steady population growth. Industry revenue has increased at a CAGR of 2.0% and is expected to reach $44.7 billion in 2024 as revenue rises an estimated 0.8%. Growth in prices for industry products and the rising demand from beef cattle production have provided the foundation for the industry's expansion in the current period. As agricultural prices have grown, fueled by bottlenecked supply chains and lower yield for core industry crops, industry farmers have charged more for their products and generated more revenue. A higher demand for beef has translated to increased beef cattle production and a growing need for livestock feed, of which hay is a major input. However, a decrease in exports has limited industry growth because a pricey US dollar makes domestic crops relatively expensive for international buyers. Industry profit hasn't grown in line with revenue and dropped from 11.6% to 9.6% due to inflationary pressures in the agricultural industry at large and significant spikes in essential resources for farm operations like crude oil. The coming years don't look as promising for the industry, as revenue is set to decline. The strong agricultural price index that has kept revenue for industry farmers so strong in the current period will start to drop in 2025 as supply chain issues of previous years abate and demand for agricultural products from China decreases. The depreciation of the US dollar will help prevent imports and encourage exports of US-grown crops, but not enough to offset larger negative revenue pressures. Revenue is forecast to drop at a CAGR of 1.4% to reach $41.7 billion in 2029.