Quick Stats is the National Agricultural Statistics Service's (NASS) online, self-service tool to access complete results from the 1997, 2002, 2007, and 2012 Censuses of Agriculture as well as the best source of NASS survey published estimates. The census collects data on all commodities produced on U.S. farms and ranches, as well as detailed information on expenses, income, and operator characteristics. The surveys that NASS conducts collect information on virtually every facet of U.S. agricultural production.

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.

This dataset contains estimates of proportional area of 18 major crops for each county in the United States at roughly decadal time steps between 1840 and 2017, and was used for analyses of historical changes in crop area, diversity, and distribution published in:Crossley, MS, KD Burke, SD Schoville, VC Radeloff. (2020). Recent collapse of crop belts and declining diversity of US agriculture since 1840. Global Change Biology (in press).The original data used to curate this dataset was derived by Haines et al. (ICPSR 35206) from USDA Agricultural Census archives (https://www.nass.usda.gov/AgCensus/). This dataset builds upon previous work in that crop values are georeferenced and rectified to match 2012 county boundaries, and several inconsistencies in the tabular-formatted data have been smoothed-over. In particular, smoothing included conversion of values of production (e.g. bushels, lbs, typical of 1840-1880 censuses) into values of area (using USDA NASS yield data), imputation of missing values for certain crop x county x year combinations, and correcting values for counties whose crop totals exceeded the possible land area.Please contact the PI, Mike Crossley, with any questions or requests: mcrossley3@gmail.com

Success.ai’s Agricultural Data provides unparalleled access to verified profiles of agriculture and farming leaders worldwide. Sourced from over 700 million LinkedIn profiles, this dataset includes actionable insights and contact details for professionals shaping the global agricultural landscape. Whether your objective is to market agricultural products, establish partnerships, or analyze industry trends, Success.ai ensures your outreach is powered by accurate, enriched, and continuously updated data.

Why Choose Success.ai’s Agricultural Data? Comprehensive Professional Profiles

Access verified LinkedIn profiles of farm owners, agricultural consultants, supply chain managers, agribusiness executives, and industry leaders. AI-validated data ensures 99% accuracy, minimizing wasted outreach and improving communication efficiency. Global Coverage Across Agricultural Sectors

Includes professionals from crop farming, livestock production, agricultural technology, and sustainable farming practices. Covers key regions such as North America, Europe, APAC, South America, and Africa. Continuously Updated Dataset

Real-time updates reflect role changes, organizational shifts, and emerging trends in agriculture and farming. Tailored for Agricultural Insights

Enriched profiles include professional histories, areas of specialization, and industry affiliations for deeper audience understanding. Data Highlights: 700M+ Verified LinkedIn Profiles: Gain access to a global network of agricultural and farming professionals. 100M+ Work Emails: Communicate directly with decision-makers in agribusiness and farming. Enriched Professional Histories: Understand career trajectories, expertise, and organizational affiliations. Industry-Specific Segmentation: Target professionals in crop farming, agtech, and sustainable agriculture with precision filters. Key Features of the Dataset: Agriculture and Farming Professional Profiles

Identify and connect with farm operators, agricultural consultants, supply chain managers, and agribusiness leaders. Engage with professionals responsible for farm management, equipment procurement, and sustainable farming initiatives. Detailed Firmographic Data

Leverage insights into farm sizes, crop or livestock focus, geographic distribution, and operational scales. Customize outreach to align with specific farming practices or market needs. Advanced Filters for Precision Targeting

Refine searches by region, type of agriculture (crop farming, livestock, horticulture), or years of experience. Customize campaigns to address unique challenges such as climate adaptation or supply chain optimization. AI-Driven Enrichment

Enhanced datasets deliver actionable data for personalized campaigns, highlighting certifications, achievements, and key projects. Strategic Use Cases: Marketing Agricultural Products and Services

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Target HR professionals and agricultural firms seeking skilled farm managers, agronomists, or agtech specialists. Support hiring for roles requiring agricultural expertise and leadership. Why Choose Success.ai? Best Price Guarantee

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Automate lead generation for a consistent pipeline of qualified professionals in the agriculture sector, scaling your outreach efficiently. Success.ai’s Agricultural Data empowers you to connect with the leaders and innovators transforming global agriculture. With verified contact details, enriched professional profiles, and global reach, your marketing, partn...

A collection of over 75 charts and maps presenting key statistics on the farm sector, food spending and prices, food security, rural communities, the interaction of agriculture and natural resources, and more.

How much do you know about food and agriculture? What about rural America or conservation? ERS has assembled more than 75 charts and maps covering key information about the farm and food sectors, including agricultural markets and trade, farm income, food prices and consumption, food security, rural economies, and the interaction of agriculture and natural resources.

How much, for example, do agriculture and related industries contribute to U.S. gross domestic product? Which commodities are the leading agricultural exports? How much of the food dollar goes to farmers? How do job earnings in rural areas compare with metro areas? How much of the Nation’s water is used by agriculture? These are among the statistics covered in this collection of charts and maps—with accompanying text—divided into the nine section titles.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: Ag and Food Sectors and the Economy Land and Natural Resources Farming and Farm Income Rural Economy Agricultural Production and Prices Agricultural Trade Food Availability and Consumption Food Prices and Spending Food Security and Nutrition Assistance For complete information, please visit https://data.gov.

The NASS Census of Agriculture is a comprehensive dataset produced by the U.S. Department of Agriculture’s (USDA) National Agricultural Statistics Service (NASS). Conducted every five years, the census gathers detailed data on America’s farming and ranching operations. It covers a wide range of topics, including land use and ownership, farm and operator characteristics, production practices, income, expenditures, and the types and quantities of crops and livestock produced. The primary purpose of the Census of Agriculture is to provide accurate, objective, and meaningful statistical information that supports agricultural policy-making, business decisions, research, and rural development. It serves as a key resource for government agencies, policymakers, researchers, agribusinesses, and farmers themselves, helping to track trends and inform decisions at national, state, and county levels. Key features of the dataset include its breadth and depth—data are collected from all U.S. farms and ranches, regardless of size—and its granularity, offering insights down to the county level. The census uniquely gives voice to all agricultural producers, ensuring even small and specialized operations are represented, making it an essential tool for understanding the evolving landscape of American agriculture.

Dataset Card for Dataset Name

This dataset contains question-answer pairs related to agriculture. The dataset can be used for tasks such as question answering, information retrieval, and natural language understanding in the agricultural domain. The questions cover various aspects of agriculture, including crop production, animal husbandry, soil management, and farming practices.

  Dataset Details

he dataset is structured as a collection of JSON files, with each file… See the full description on the dataset page: https://huggingface.co/datasets/KisanVaani/agriculture-qa-english-only.

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).

Longitude and latitude, state, address, name, and zip code of Farmers Markets in the United States

This product provides tabular data from the U.S. Department of Agriculture (USDA) Census of Agriculture for selected items for the period 1950-2017 for counties in the conterminous United States. Data from 1950-2012 are taken from LaMotte (2015) and 2017 data are retrieved from the USDA QuickStats online tool. Data which are withheld in the Census of Agriculture are filled with estimates. The data include crop production values for 12 commodities (for example, corn in bushels), land use values for 7 land use types (for example, acres of total cropland), and 9 values for livestock types (for example, number of hogs and pigs). The data are largely intended as a 2017 update to the LaMotte dataset for items of research interest. LaMotte, A.E., 2015, Selected items from the Census of Agriculture at the county level for the conterminous United States, 1950-2012: U.S. Geological Survey data release, http://dx.doi.org/10.5066/F7H13016.

NASS USDA estimates the irrigated croplands at county level every five years. But this estimation does not provide the geospatial information of the irrigated croplands. To provide a comprehensive, consistent, and timely geospatially detailed information about irrigated cropland conterminous U.S. (CONUS), the “Moderate Resolution Imaging Spectroradiometer (MODIS) Irrigated Agriculture Dataset for the United States (MIrAD-US)” product was produced by the U.S. Geological Survey (USGS) Earth Resources Observation and Science (EROS) Center with funding from several USGS programs (National Land Imaging and National Water-Quality Assessment). A primary objective was to identify, and map irrigated agricultural areas to factor into water quality studies and drought monitoring investigations. This product uses three primary data inputs, (a) USDA county-level irrigation area statistics for 2002, (b) annual peak eMODIS Normalized Difference Vegetation Index (NDVI), and (c) a land cover mask ...

A large-scale aerial farmland image dataset for semantic segmentation of agricultural patterns. Collects 94,986 high-quality aerial images from 3,432 farmlands across the US, where each image consists of RGB and Near-infrared (NIR) channels with resolution as high as 10 cm per pixel.

The primary greenhouse gas (GHG) sources for agriculture are nitrous oxide (N2O) emissions from cropped and grazed soils, methane (CH4) emissions from ruminant livestock production and rice cultivation, and CH4 and N2O emissions from managed livestock waste. The management of cropped, grazed, and forestland has helped offset GHG emissions by promoting the biological uptake of carbon dioxide (CO2) through the incorporation of carbon into biomass, wood products, and soils, yielding a U.S. net emissions of 5,903 MMT CO2 eq (million metric tonnes of carbon dioxide equivalents). Net emissions equate to total greenhouse gas emissions minus CO2 sequestration in growing forests, wood products, and soils. The report 'U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990-2018' serves to estimate U.S. GHG emissions for the agricultural sector, to quantify uncertainty in emission estimates, and to estimate the potential of agriculture to mitigate U.S. GHG emissions. This dataset contains tabulated data from the figures and tables presented in Chapter 2, Livestock and Grazed Lands Emissions, of the report. This chapter covers carbon dioxide, methane, and nitrous oxide emissions and removals due to enteric fermentation, animal waste management, and land use for confined and grazed animals. Please refer to the report for full descriptions of and notes on the data. Resources in this dataset:Resource Title: Table 2-1. File Name: Table2_1.csvResource Description: Greenhouse Gas Emission Estimates and Uncertainty in the United States, 2018 for enteric fermentation, managed waste, grazed land, grazed land remaining grazed land, and land converted to grazed land, in MMT CO2 eq. Measured in Millions of Metric Tons, Carbon Dioxide Equivalent (MMT CO2 eq.) and also displayed in percentage units.Resource Title: Table 2-2. File Name: Table2_2.csvResource Description: Greenhouse Gas Emissions by Livestock Category and Source, 2018. For enteric fermentation, managed livestock waste, and grazed land, in MMT CO2 eq. (Millions of Metric Tons, Carbon Dioxide Equivalent)Resource Title: Table 2-3. File Name: Table2_3.csvResource Description: Descriptions of livestock waste deposition and storage pathways.Resource Title: Table 2-4. File Name: Table2_4.csvResource Description: Methane emissions from enteric fermentation, 1990-2018, from beef cattle, dairy cattle, sheep, poultry, swine, horses, goats, American bison, and mules and asses in MMT CO2 eq. (Millions of Metric Tons, Carbon Dioxide Equivalent)Resource Title: Table 2-5. File Name: Table2_5.csvResource Description: Greenhouse Gas Emissions from Managed Livestock Waste in 1990, 1995, 2000, 2005, 2010-2018. In MMT CO2 eq. (Millions of Metric Tons, Carbon Dioxide Equivalent).Resource Title: Table 2-6. File Name: Table2_6.csvResource Description: Greenhouse Gas Emissions from Grazed Lands in 1990, 1995, 2000, 2005, 2010-2018, for nitrous oxide and methane, presented in MMT CO2 eq. (Millions of Metric Tons, Carbon Dioxide Equivalent).Resource Title: Data for Figure 2-1. File Name: Figure2_1.csvResource Description: Greenhouse Gas Emissions from Livestock, 2018. MMT CO2 eq. emissions from beef cattle, dairy cattle, sheep, poultry, swine, horses, goats, bison, and mules. Measured in Millions of Metric Tons, Carbon Dioxide Equivalent (MMT CO2 eq.) and also displayed in percentage units.Resource Title: Data for Figure 2-2. File Name: Figure2_2.csvResource Description: Greenhouse Gas Emissions from Managed Livestock Waste by Livestock Type, 2018. MMT CO2 eq. emissions from beef cattle, dairy cattle, sheep, poultry, swine, horses, goats, bison, and mules. Measured in Millions of Metric Tons, Carbon Dioxide Equivalent (MMT CO2 eq.) and also displayed in percentage units.Resource Title: Data for Figure 2-3. File Name: Figure2_3.csvResource Description: Greenhouse Gas Emission from Managed Livestock Waste, 1990-2018. MMT CO2 eq. (Millions of Metric Tons, Carbon Dioxide Equivalent) for N2O and CH4.Resource Title: Data for Figure 2-4. File Name: Figure2_4.csvResource Description: Estimated Reductions in Methane Emissions from Anaerobic Digesters, 2000-2018 in MMT CO2 eq. (Millions of Metric Tons, Carbon Dioxide Equivalent).Resource Title: Data for Map 2-1. File Name: Map2_1.csvResource Description: GHG Emission from Livestock Production in 2018, by U.S. State, in MMT CO2 eq. (Millions of Metric Tons, Carbon Dioxide Equivalent)Resource Title: Data for Map 2-2. File Name: Map2_2.csvResource Description: Map 2-2 Methane Emissions from Enteric Fermentation in 2018, by U.S. State, in MMT CO2 eq. (Millions of Metric Tons, Carbon Dioxide Equivalent).Resource Title: Data for Map 2-3. File Name: Map2_3.csvResource Description: GHG Emission from Managed Livestock Waste in 2018, by U.S. State, in MMT CO2 eq. (Millions of Metric Tons, Carbon Dioxide Equivalent).Resource Title: Chapter 2 Appendix Tables. File Name: Chapter2_Appendix_Tables.xlsxResource Description: Chapter 2 includes 27 appendix tables, that include data on, inter alia, the population of animals by state, emission factors for livestock, state level GHG emissions from enteric fermentation, state level methane and nitrous oxide emissions from managed manure, and state volatile solids production rates for 2018.Resource Title: Figures, maps, tables and appendices from Chapter 2. File Name: Chapter 2 Data.zip

The Pesticide Data Program (PDP) is a national pesticide residue database program. Through cooperation with State agriculture departments and other Federal agencies, PDP manages the collection, analysis, data entry, and reporting of pesticide residues on agricultural commodities in the U.S. food supply, with an emphasis on those commodities highly consumed by infants and children. This dataset provides information on where each tested sample was collected, where the product originated from, what type of product it was, and what residues were found on the product, for calendar years 1992 through 2020. The data can measure residues of individual compounds and classes of compounds, as well as provide information about the geographic distribution of the origin of samples, from growers, packers and distributors. The dataset also includes information on where the samples were taken, what laboratory was used to test them, and all testing procedures (by sample, so can be linked to the compound that is identified). The dataset also contains a reference variable for each compound that denotes the limit of detection for a pesticide/commodity pair (LOD variable). The metadata also includes EPA tolerance levels or action levels for each pesticide/commodity pair. The dataset will be updated on a continual basis, with a new resource data file added annually after the PDP calendar-year survey data is released. Resources in this dataset:Resource Title: CSV Data Dictionary for PDP. File Name: PDP_DataDictionary.csvResource Description: Machine-readable Comma Separated Values (CSV) format data dictionary for PDP Database Zip files. Defines variables for the sample identity and analytical results data tables/files. The ## characters in the Table and Text Data File name refer to the 2-digit year for the PDP survey, like 97 for 1997 or 01 for 2001. For details on table linking, see PDF. Resource Software Recommended: Microsoft Excel,url: https://www.microsoft.com/en-us/microsoft-365/excel Resource Title: Data dictionary for Pesticide Data Program. File Name: PDP DataDictionary.pdfResource Description: Data dictionary for PDP Database Zip files.Resource Software Recommended: Adobe Acrobat,url: https://www.adobe.com Resource Title: 2019 PDP Database Zip File. File Name: 2019PDPDatabase.zipResource Title: 2018 PDP Database Zip File. File Name: 2018PDPDatabase.zipResource Title: 2017 PDP Database Zip File. File Name: 2017PDPDatabase.zipResource Title: 2016 PDP Database Zip File. File Name: 2016PDPDatabase.zipResource Title: 2015 PDP Database Zip File. File Name: 2015PDPDatabase.zipResource Title: 2014 PDP Database Zip File. File Name: 2014PDPDatabase.zipResource Title: 2013 PDP Database Zip File. File Name: 2013PDPDatabase.zipResource Title: 2012 PDP Database Zip File. File Name: 2012PDPDatabase.zipResource Title: 2011 PDP Database Zip File. File Name: 2011PDPDatabase.zipResource Title: 2010 PDP Database Zip File. File Name: 2010PDPDatabase.zipResource Title: 2009 PDP Database Zip File. File Name: 2009PDPDatabase.zipResource Title: 2008 PDP Database Zip File. File Name: 2008PDPDatabase.zipResource Title: 2007 PDP Database Zip File. File Name: 2007PDPDatabase.zipResource Title: 2005 PDP Database Zip File. File Name: 2005PDPDatabase.zipResource Title: 2004 PDP Database Zip File. File Name: 2004PDPDatabase.zipResource Title: 2003 PDP Database Zip File. File Name: 2003PDPDatabase.zipResource Title: 2002 PDP Database Zip File. File Name: 2002PDPDatabase.zipResource Title: 2001 PDP Database Zip File. File Name: 2001PDPDatabase.zipResource Title: 2000 PDP Database Zip File. File Name: 2000PDPDatabase.zipResource Title: 1999 PDP Database Zip File. File Name: 1999PDPDatabase.zipResource Title: 1998 PDP Database Zip File. File Name: 1998PDPDatabase.zipResource Title: 1997 PDP Database Zip File. File Name: 1997PDPDatabase.zipResource Title: 1996 PDP Database Zip File. File Name: 1996PDPDatabase.zipResource Title: 1995 PDP Database Zip File. File Name: 1995PDPDatabase.zipResource Title: 1994 PDP Database Zip File. File Name: 1994PDPDatabase.zipResource Title: 1993 PDP Database Zip File. File Name: 1993PDPDatabase.zipResource Title: 1992 PDP Database Zip File. File Name: 1992PDPDatabase.zipResource Title: 2006 PDP Database Zip File. File Name: 2006PDPDatabase.zipResource Title: 2020 PDP Database Zip File. File Name: 2020PDPDatabase.zipResource Description: Data and supporting files for PDP 2020 surveyResource Software Recommended: Microsoft Access,url: https://products.office.com/en-us/access

"The Farm Service Agency (FSA) makes farm ownership loans to farmers and ranchers who are temporarily unable to obtain private, commercial credit at reasonable rates and terms. Farm ownership loans are used to purchase farmland, construct and repair buildings, and make farm improvements. Both guaranteed and direct loans are available through this program. FSA guaranteed loans provide lenders (e.g., banks, Farm Credit System institutions, credit unions) with a guarantee of up to 95 percent of the loss of principal and interest on a loan. The maximum FSA guaranteed farm ownership loan is $1,302 ,000 (adjusted annually based on inflation). Your lender can tell you if a guarantee is the right loan for you. Applicants who are unable to qualify for a guaranteed loan may be eligible for a direct loan from FSA. Direct loans are made and serviced by FSA officials using government funds. FSA provides direct loan customers with supervision and credit counseling so that they have a greater chance to be successful. The maximum direct farm ownership loan is $300,000."

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.

About this dataset

Context

The dataset tabulates the Farmer population distribution across 18 age groups. It lists the population in each age group along with the percentage population relative of the total population for Farmer. The dataset can be utilized to understand the population distribution of Farmer by age. For example, using this dataset, we can identify the largest age group in Farmer.

Key observations

The largest age group in Farmer, SD was for the group of age Under 5 years years with a population of 22 (34.38%), according to the ACS 2019-2023 5-Year Estimates. At the same time, the smallest age group in Farmer, SD was the 15 to 19 years years with a population of 0 (0%). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Variables / Data Columns

• Age Group: This column displays the age group in consideration
• Population: The population for the specific age group in the Farmer is shown in this column.
• % of Total Population: This column displays the population of each age group as a proportion of Farmer total population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Farmer Population by Age. You can refer the same here

About this dataset

Context

The dataset presents median household incomes for various household sizes in Farmers Branch, TX, as reported by the U.S. Census Bureau. The dataset highlights the variation in median household income with the size of the family unit, offering valuable insights into economic trends and disparities within different household sizes, aiding in data analysis and decision-making.

Key observations

• Of the 7 household sizes (1 person to 7-or-more person households) reported by the census bureau, all of the household sizes were found in Farmers Branch. Across the different household sizes in Farmers Branch the mean income is $97,455, and the standard deviation is $24,965. The coefficient of variation (CV) is 25.62%. This high CV indicates high relative variability, suggesting that the incomes vary significantly across different sizes of households.
• In the most recent year, 2021, The smallest household size for which the bureau reported a median household income was 1-person households, with an income of $66,154. It then further increased to $140,731 for 7-person households, the largest household size for which the bureau reported a median household income.

https://i.neilsberg.com/ch/farmers-branch-tx-median-household-income-by-household-size.jpeg" alt="Farmers Branch, TX median household income, by household size (in 2022 inflation-adjusted dollars)">

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Household Sizes:

• 1-person households
• 2-person households
• 3-person households
• 4-person households
• 5-person households
• 6-person households
• 7-or-more-person households

Variables / Data Columns

• Household Size: This column showcases 7 household sizes ranging from 1-person households to 7-or-more-person households (As mentioned above).
• Median Household Income: Median household income, in 2022 inflation-adjusted dollars for the specific household size.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

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Recommended for further research

This dataset is a part of the main dataset for Farmers Branch median household income. You can refer the same here

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.

Increased productivity is the main contributor to growth in U.S. agriculture. This data set provides estimates of productivity growth in the U.S. farm sector for the 1948-2011 period, and estimates of the growth and relative levels of productivity across the States for the period 1960-2004.