The average size of farms in the United States has seen a steady increase over the last decade. In 2024, the average farm size reached 466 acres, up from 418 acres in 2007. Between 2006 and 2007 there was a sudden drop in average farm size, but in recent years it has recovered and once again reached the same levels as its peak in 2006. Agriculture in the United States In 2023, there were about 1.9 million farms in the United States, down from 2.2 million in 2007. It appears that as average farm size has increased, the number of individual farms in the United States has decreased. Texas is home to the highest number of farms of any U.S. state, with 231,000 farms as of 2023. Major crops in the United States The United States produces a wide variety of crops. Though the production volumes of some major crops, such as wheat, have fluctuated considerably since 2000, the production of vegetables for processing has been on the rise in recent years. Grapes, apples, and oranges are the most produced fruits in the United States, with the majority of grapes being grown in California.

This statistic depicts the number and average size of farms in the United States from 2012 to 2024. The total number of farms in the U.S. in 1900 was *********, as compared to1,880,000 in 2024.

This statistic shows the average size of U.S. farms from 2019 to 2024, sorted by economic sales class. In 2024, farms belonging to the sales class between 100,000 to 249,999 U.S. dollars had an average size of 799 acres of land.

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.

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.

State fact sheets provide information on population, income, education, employment, federal funds, organic agriculture, farm characteristics, farm financial indicators, top commodities, and exports, for each State in the United States. Links to county-level data are included when available.This record was taken from the USDA Enterprise Data Inventory that feeds into the https://data.gov catalog. Data for this record includes the following resources: Query tool For complete information, please visit https://data.gov.

Graph and download economic data for Net farm income, USDA (B1448C1A027NBEA) from 1967 to 2023 about USDA, agriculture, Net, income, GDP, and USA.

What? A dataset containing 315 total variables from 33 secondary sources. There are 262 unique variables, and 53 variables that have the same measurement but are reported for a different year; e.g. average farm size in 2017 (CapitalID: N27a) and 2022 (N27b). Variables were grouped by the community capital framework's seven capitals—Natural (96 total variables), Cultural (38), Human (39), Social (40), Political (18), Financial (67), & Built (15)—and temporally and thematically ordered. The geographic boundary is NOAA NCEI's corn and soybean belt (figure below), which stretches across 18 states and includes N=860 counties/observations. Cover crop data for the 80 Crop Reporting Districts in the boundary are also included for 2015-2021. Why? Comprehensively assessing how community capital clustered variables, for both farmers and nonfarmers, impact conservation practices (and perennial groundcover) over time helps to examine county-level farm conservation agriculture practices in the context of community development. We contribute to the robust U.S. cover crop literature a better understanding of how overarching cultural, social, and human factors influence conservation agriculture practices to encourage better farm management practices. Analyses of this Dataverse will be presented as recomendations for farmers, nonfarmers, ag-adjacent stakeholders, and community leaders. How? Variables used in this dataset range 20 years, from 2004-2023, though primary analyses focus on data collected between 2017-2024, primarily 2017 and 2022 (NASS Ag Census years). First, JAM-K requested, accessed, and downloaded data, most of which was already publically available. Next, JAM-K cleaned the data and aggregated into one dataset, and made it publically available on Google Drive and Zenodo. What is 'new' or corrected in version 2.2? Edited/amended: Carroll, KY is now spelled correctly (two 'l's, not one); variable names, full and abbreviated, were updated to include the data year; Pike County's (IL) FIPS has been corrected from its wrong 17153 (same as Pulaski County) to 17149 (correct fips), and all Pike County (IL) data has been correctly amended; Farming dependent (ERS) updated for all variables; Data for built capital variables irrCorn17, irrSoy17, irrHcrp17, tractor17, and combine17 were incorrect for v.1, but were corrected for v.2; Several variable labels aggregated by Wisconsin University's Population Health Institute's County Health Rankings and Roadmaps were corrected to have the data's original source and years included, rather than citing CHR&R as the source (except for CHR&R's originally-produced values such as quartiles or rank scores); variables were reorganized by hypothesized community capital clusters (Natural -> Built), and temporally within each cluster. Added: 55 variables, mostly from the 2022 Ag Census, and v 2.2 added a .pdf file with descriptives of data sources and years, and a .sav file. Omitted: Four variables deemed irrelevant to the study; V1 codebook's "years internally available" column. Variable herbac22 for 55079, Milwaukee, WI, incorrectly had the value 2,049.612. That value was correctly changed to missing, with no data in the cell. CRediT: conceptualization, CBF, JAM-K; methodology, JAM-K; data aggregation and curation, JAM-K; formal analysis, JAM-K; visualization, JAM-K; supervision, CBF; funding acquisition, CBF; project administration, CBF; resources, CBF, JAM-K Acknowledgements: This research was funded by the Agriculture and Food Research Initiative Competitive Grant No. 2021-68012-35923 from the United States Department of Agriculture National Institute for Food and Agriculture. Any opinions, findings, conclusions, or recommendations expressed in this presentation are those of the authors and do not necessarily reflect the view of the U.S. Department of Agriculture. Much thanks to Corteva for granting data access of OpTIS 2.0 (2005-2019), and Austin Landini for STATA code and visualization assistance.

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).

The US farm equipment industry is a robust sector projected to experience significant growth in the coming years. With a 2025 market size of $39.56 billion and a Compound Annual Growth Rate (CAGR) of 6.30% from 2025 to 2033, the industry demonstrates consistent expansion. This growth is fueled by several key factors. Increased adoption of precision farming technologies, including GPS-guided machinery and automated systems, enhances efficiency and yields, driving demand for advanced equipment. Furthermore, government initiatives promoting sustainable agricultural practices and supporting farmers through subsidies contribute to market expansion. The rising global population and increasing demand for food security further bolster the industry's growth trajectory. However, challenges such as fluctuating commodity prices, potential supply chain disruptions, and the increasing cost of raw materials for equipment manufacturing present some headwinds. The leading players in this market, including Deere & Company, AGCO Corporation, and CNH Industrial, are actively investing in research and development to innovate and introduce technologically advanced equipment. This competition drives innovation and enhances the overall quality and capabilities of farm machinery available to farmers. Market segmentation, likely categorized by equipment type (tractors, harvesters, planters, etc.), and farm size, influences the specific growth dynamics within various niches. Regional variations in agricultural practices and government policies further shape the market's development. Analyzing these segments and regional differences is crucial for understanding the specific opportunities and challenges within the US farm equipment industry, and tailoring strategies for success. Key drivers for this market are: Low Availability of Skilled Labor, Technological Advancements. Potential restraints include: Increasing Farm Expenditure, Security Concerns in Modern Farming Machinery. Notable trends are: Increasing Average Farm Size.

This map shows the relationship between Federal payments toward conservation and wetlands and payments toward producers not including conservation/wetlands. The data is produced by the USDA National Agricultural Statistics Service (USDA).Areas in yellow show where there are high amounts of Federal payments toward Conservation in comparison to other types, whereas areas in light blue have a higher amount of Federal payments toward all other agriculture in comparison to Conservation. Areas in black have an overall high amount of both types of payments. The map uses size to emphasize which counties received the overall largest receipts in US dollars.In 2017, the average farm received an average of $13,906, and conservation/wetland programs received and average of $6,980. These are the central colors of the map in order to anchor the map around the national figure. Areas with a pattern above or below the national average are highlighted by the colors along the edges of the legend (mentioned in the previous paragraph). For more information about Federal payments in 2017, visit this summary table from the USDA.For more information about the relationship mapping style used in this map, visit this blog. About the data and source: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 a in-depth look at the agricultural industry.This layer summarizes payments made to producers by the Federal government 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: Payments made to producers by the Federal governmentCoordinate System: Web Mercator Auxiliary SphereExtent: United States including Hawaii and AlaskaVisible 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.Federal Payments - Operations with ReceiptsFederal Payments - Receipts in US DollarsFederal Payments - Receipts in US Dollars per OperationFederal Payments not Including Conservation and Wetland Programs - Operations with ReceiptsFederal Payments not Including Conservation and Wetland Programs - Receipts in US DollarsFederal Payments not Including Conservation and Wetland Programs - Receipts in US Dollars per OperationFederal Payments for Conservation and Wetland Programs - Operations with ReceiptsFederal Payments for Conservation and Wetland Programs - Receipts in US DollarsFederal Payments for Conservation and Wetland Programs - Receipts in US Dollars per OperationConservation and wetland programs include:Conservation Reserve Program (CRP)Wetlands Reserve Program (WRP)Farmable Wetlands Program (FWP)Conservation Reserve Enhancement Program (CREP)Other programs with payments to producers include:2014 Agricultural Act (Farm Bill)Agriculture Risk Coverage (ARC)Price Loss Coverage (PLC)Commodity Credit Corporation (CCC)Loan Deficiency PaymentsDisaster Assistance ProgramsState and local government agricultural program payments and Federal crop insurance payments are not included.Additionally, attributes of State Name, State Code, County Name and County Code are included to facilitate cartography and use with other layers.

The United States Precision Farming Software Market size was valued at USD 521.3 Million in 2023 and is projected to reach USD 1000.3 Million by 2032, exhibiting a CAGR of 11.10 % during the forecasts periods. The market is driven by various factors such as the increasing adoption of smart farming practices, technological advancements, government initiatives, and rising food security concerns. Key drivers for this market are: Increasing Legalization of Cannabis, Growing Focus on Health Benefits of Cannabis. Potential restraints include: Lack of Data on Dosages and Results, Lack of Access to Financial Assistance. Notable trends are: Farm Labor Shortage and Rise in Average Farm Size.

Data in acres on seeded and harvested areas, production, amount utilized, average price and value of sales of potatoes in Canadian dollars.

This table contains 60 series, with data for years 1908 - 1976 (not all combinations necessarily have data for all years), and is no longer being released. This table contains data described by the following dimensions (Not all combinations are available):Geography (12 items: Canada;Maritime provinces;Prince Edward Island;Nova Scotia; ...) ;Field roots (5 items: Seeded area;Average yield;Production;Average farm price; ...).

This dataset explores Farm census family by size, by province (2001 Censuses of Agriculture and Population) for 2001. 1. Only unincorporated farms are included because incorporated farms are legal entities. Source: Statistics Canada, Censuses of Agriculture and Population. Last modified: 2004-09-29.

North America Agricultural Robots Market size was valued at USD 5.3 Billion in 2024 and is projected to reach USD 13.5 Billion by 2031, growing at a CAGR of 23.5% from 2024 to 2031.

North America Agricultural Robots Market Drivers

Labor Lack and Workforce Aging: North America's agriculture sector is facing a substantial labor deficit, which is being driven by an aging workforce and diminishing interest in traditional farming vocations. According to the United States Department of Agriculture (USDA), farmers' average age has continuously climbed, reaching 57.5 years in 2017. This demographic transition has created an urgent demand for robotic solutions to cover labor shortfalls. According to the American Farm Bureau Federation, 56% of farms are having difficulty finding enough personnel, making robots and automation more appealing for sustaining agricultural output. Precision Agriculture and Resource Optimization: Technological breakthroughs are increasing the usage of agricultural robots as a tool for improving precision farming operations. According to the National Agricultural Statistics Service, precision agricultural technology can help farmers save 15-20% on input costs while increasing crop yields. Agricultural robots with powerful sensors and AI can offer extremely precise data on crop health, soil conditions, and resource allocation. Increasing Food Demand and Climate Change Adaptation: The growing world population and the problems posed by climate change are putting unprecedented strain on agricultural output. The United Nations estimates that global food production must rise by 70% by 2050 to feed the world's expanding population. Agricultural robots are developing as a vital tool for meeting this problem. According to a National Resources Conservation Service assessment, robotic technology can help farmers adapt to changing climate conditions by improving crop management, water conservation, and yield optimization.

Agriculture Sensors Market Outlook 2032

The global agriculture sensors market size was USD 1.80 Billion in 2023 and is likely to reach USD 4.79 Billion by 2032, expanding at a CAGR of 11.50% during 2024–2032. The market growth is attributed to the rising adoption of soil moisture sensor technology across the globe.



Increasing adoption of soil moisture sensor technology is projected to fuel the market during the forecast period. Farmers optimize their irrigation systems by measuring soil moisture and other parameters in real-time such as weather conditions, nutrient levels in soil, and crop health. In addition to water saving, there are several benefits including enhanced crop yield and quality as well as soil erosion, lower run-off, and nutrient loss.

The market report finds that the COVID-19 pandemic hampered the agriculture sensors market, due to the stringent regulations imposed by the government. Due to the lockdown, the disruption of the supply chain has resulted in a shortage of equipment. However, the pandemic has disrupted the supply chain in the market and several companies are exploring new ways to interact with farmers through technology, which fuels the market. The increasing adoption of agriculture sensors owing to the well-developed information technology infrastructure, networks, and increased farm mechanization boosts the market.

Impact of Artificial Intelligence on the Agriculture Sensors Market

Artificial intelligence (AI) technologies are expected to fuel the agriculture market during the forecast period. AI in agriculture is transforming the farming industry by utilizing technologies such as sensors, the Internet of Things (IoT), location systems, robots, and AI on farms. Smart agriculture aims to achieve greater food self-sufficiency and revenue by offsetting environmental risks of crop failure and growing overall crop quality. IoT devices and AI solutions increase efficiency and output. Machine learning (ML) models with new and existing information increase yield predictions into reality.

Agriculture Sensors Market Dynamics

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In 2024, the average value of U.S. farm real estate was 4,170 U.S. dollars per acre. Compared to one decade earlier, the value has increased by almost 40 percent. Generally, the value of U.S. farm real estate has had an upward trend since 1970. U.S. farms The number of farms in the United States has conversely been decreasing each year, reaching about two million farms as of 2022. That year, Texas had the most farms out of any other U.S. state by far, with about 246,000 farms. Missouri and Iowa had the second and third most farms, though neither state exceeded 100,000 farms. Agricultural trade Agricultural products encompass any products from agricultural origin that are meant for human consumption or animal feed. Agricultural products can include livestock products or crops. In 2022, the U.S. exported about 196.4 billion U.S. dollars’ worth of agricultural goods worldwide, increasing from the previous several years. Mexico is a key destination for U.S. agricultural products and imported just over 28 billion dollars’ worth in 2022, more than Europe and Eurasia combined.

This dataset was created from the USDA Farmers Market database listing. The point data is usually the exact location of the market but may sometimes be the general area. Attributes include contact information, hours, and if the market takes food stamps.

Agriculture Energy-Saving Cooling and Freezing (Cold Chain) Market Outlook 2032

The global agriculture energy-saving cooling and freezing (cold chain) market size was USD XX Billion in 2023 and is likely to reach USD XX Billion by 2032, expanding at a CAGR of XX% during 2024–2032. The market is propelled by the expansion of agriculture sector.



Increasing global demand for fresh produce and the need for sustainable practices are expected to drive the market during the forecast period. As consumers worldwide become health-conscious and seek fresh, quality food, the importance of efficient cold chain solutions that extend the shelf life of perishable goods while minimizing energy consumption has become paramount. These systems are crucial for preventing food spoilage and reducing waste from farm to table, aligning with global efforts to enhance food security and sustainability. Innovations in refrigeration technologies that focus on energy efficiency are particularly in demand, as they help reduce operational costs and lower the environmental impact of cooling processes.

Growing international trade in agricultural products is expanding the applications of energy-saving cold chain solutions. Efficient and reliable cooling and freezing systems are essential for maintaining the quality and safety of perishable goods during long transit periods. Advanced cold chain logistics not only ensure that agricultural products meet safety standards but also help in accessing new markets, thus boosting trade opportunities. Furthermore, these systems are increasingly incorporating renewable energy sources and smart monitoring technologies that optimize energy use and provide real-time data on the condition of goods, enhancing transparency and trust in food supply chains.



Rising awareness of the environmental impact of traditional cooling methods is prompting the adoption of sustainable cold chain technologies in agriculture. Energy-saving systems that utilize alternative refrigerants and innovative insulation materials are being developed to reduce greenhouse gas emissions and energy consumption. Additionally, the integration of Internet of Things (IoT) devices allows for the precise control of temperature and humidity levels, ensuring that products are stored in optimal conditions while conserving energy. As the agricultural sector continues to evolve, these advanced cold chain solutions play a critical role in meeting both consumer expectations and environmental standards.

Impact of Artificial Intelligence (AI) in Agriculture Energy-Saving Cooling and Freezing (Cold Chain) Market