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

Promote farm equipment, crop protection solutions, or livestock management tools to decision-makers in agriculture. Engage with professionals seeking innovative solutions to enhance productivity and sustainability. Collaboration and Partnerships

Identify agricultural leaders for collaborations on sustainability programs, research projects, or community initiatives. Build partnerships with agribusinesses, cooperatives, or government bodies driving agricultural development. Market Research and Industry Analysis

Analyze trends in crop yields, livestock production, and agricultural technology adoption. Use insights to refine product development and marketing strategies tailored to evolving industry needs. Recruitment and Talent Acquisition

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

Access industry-leading Agricultural Data at the most competitive pricing, ensuring cost-effective campaigns and strategies. Seamless Integration

Easily integrate verified agricultural data into CRMs, recruitment platforms, or marketing systems using APIs or downloadable formats. AI-Validated Accuracy

Depend on 99% accurate data to minimize wasted outreach and maximize engagement outcomes. Customizable Solutions

Tailor datasets to specific agricultural segments, regions, or areas of focus to meet your strategic objectives. Strategic APIs for Enhanced Campaigns: Data Enrichment API

Enhance existing records with verified agricultural profiles to refine targeting and engagement. Lead Generation API

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...

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.

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 (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 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.

This dataset includes data from the 2012 and 2017 USDA Census of Agriculture for American Indian Reservations combined into a single flatfile spreadsheet. The 2012 data was obtained through a special tabulation request from the USDA NASS and the 2017 data was tabulated by hand and double-checked for errors. The 2012 Census included data for only 76 reservations and the 2017 census includes data for only 75. The Census for American Indian Reservations includes all farms and ranches within the boundaries of the Reservation but does not not distinguish between farmers and ranchers operating on Trust land with those operating in fee or deeded lands within Reservation boundaries. Furthermore, the published Census reports only quantified each variable for “Native” and “Reservation Total” and failed to report statistics for “Non-native” which conceals the extreme disparity that exists on Native American Reservations. While we have submitted a special tabulation request to the USDA NASS for the data on non-native operators, in the mean time, we have included a provisional calculation for “Non-native” producers, making it possible to analyze the racial disparity in agriculture on Native Lands. Additionally, the way the data is presented by the USDA it makes it difficult to aggregate the data for one or all reservations (e.g. the big picture). This data and dashboard, while only representing a fraction of tribal lands, represents the most complete source of data for agriculture on native lands. Additionally, we have added a GEOID column so each reservation's data can be joined with US Census Tiger spatial boundary for American Indian Areas.

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.

Inventory of Online Public Databases and Repositories Holding Agricultural Data in 2017

Metadata Updated: March 30, 2024

United States agricultural researchers have many options for making their data available online. This dataset aggregates the primary sources of ag-related data and determines where researchers are likely to deposit their agricultural data. These data serve as both a current landscape analysis and a baseline for future studies of ag research data.

Purpose As sources of agricultural data become more numerous and disparate, and collaboration and open data become more expected if not required, this research provides a landscape inventory of online sources of open agricultural data. An inventory of current agricultural data-sharing options will help assess how the Ag Data Commons, a platform for USDA-funded data cataloging and publication, can best support data-intensive and multidisciplinary research. It will also help agricultural librarians assist their researchers in data management and publication. The goals of this study were to:

• Establish where agricultural researchers in the United States—land grant and USDA researchers, primarily ARS, NRCS, USFS, and other agencies—currently publish their data, including general research data repositories, domain-specific databases, and the top journals.
• Compare how much data is in institutional vs. domain-specific vs. federal platforms.
• Determine which repositories are recommended by top journals that require or recommend the publication of supporting data.
• Ascertain where researchers not affiliated with funding or initiatives possessing a designated open data repository can publish data.

Approach The National Agricultural Library team focused on Agricultural Research Service (ARS), Natural Resources Conservation Service (NRCS), and United States Forest Service (USFS) style research data, rather than ag economics, statistics, and social sciences data. To find domain-specific, general, institutional, and federal agency repositories and databases that are open to US research submissions and have some amount of ag data, resources including re3data, libguides, and ARS lists were analyzed. Primarily environmental or public health databases were not included, but places where ag grantees would publish data were considered.

Search Methods - We first compiled a list of known domain-specific USDA/ARS datasets/databases represented in the Ag Data Commons, including ARS Image Gallery, ARS Nutrition Databases (sub-components), SoyBase, PeanutBase, National Fungus Collection, i5K Workspace @ NAL, and GRIN. - We then searched using search engines such as Bing and Google for non-USDA/federal ag databases, using Boolean variations of “agricultural data” /“ag data” / “scientific data” + NOT + USDA (to filter out the federal/USDA results). Most of these results were domain-specific, though some contained a mix of data subjects. - We searched using search engines such as Bing and Google to find top agricultural university repositories using variations of “agriculture”, “ag data” and “university” to find schools with agriculture programs. Using that list of universities, we searched each university website to see if their institution had a repository for their unique, independent research data if not apparent in the initial web browser search. - We found both ag-specific university repositories and general university repositories that housed a portion of agricultural data. Ag-specific university repositories are included in the list of domain-specific repositories. Results included Columbia University – International Research Institute for Climate and Society, UC Davis – Cover Crops Database, etc. If a general university repository existed, we determined whether that repository could filter to include only data results after our chosen ag search terms were applied. General university databases that contain ag data included Colorado State University Digital Collections, University of Michigan ICPSR (Inter-university Consortium for Political and Social Research), and University of Minnesota DRUM (Digital Repository of the University of Minnesota). - We then split out NCBI (National Center for Biotechnology Information) repositories. - Next, we searched the internet for open general data repositories using a variety of search engines, and repositories containing a mix of data, journals, books, and other types of records were tested to determine whether that repository could filter for data results after search terms were applied. General subject data repositories include Figshare, Open Science Framework, PANGEA, Protein Data Bank, and Zenodo. - Finally, we compared scholarly journal suggestions for data repositories against our list to fill in any missing repositories that might contain agricultural data. Extensive lists of journals were compi...

GDP from Agriculture in the United States decreased to 196 USD Billion in the second quarter of 2025 from 196.40 USD Billion in the first quarter of 2025. This dataset provides - United States Gdp From Agriculture- actual values, historical data, forecast, chart, statistics, economic calendar and news.

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 0-4 years with a population of 20 (28.99%), according to the 2021 American Community Survey. At the same time, the smallest age group in Farmer, SD was the 15-19 years with a population of 0 (0.00%). Source: U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Content

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

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.

Context

This is a simple dataset regarding the agriculture of United States, which can be used for implementation of various machine learning algorithms.

Content

The dataset contains various states and their codes, the total exports, amount of dairy products, fruits, vegetables, corn, cotton, wheat, and various non vegan products as well.

Acknowledgements

We wouldn't be here without the help of others. If you owe any attributions or thanks, include them here along with any citations of past research.

Inspiration

Your data will be in front of the world's largest data science community. What questions do you want to see answered?

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.

Hokkaido Agriculture Image Dataset

  Introduction

In the face of declining numbers and aging of agricultural workers, image AI undoubtedly represents a crucial technology for smart agriculture. For instance, it enables accurate monitoring of farmland and crop conditions through counting crops and detecting pests, while precise shape recognition allows robots to reduce heavy labor, with many such applications anticipated. Image AI algorithms and models continue to advance… See the full description on the dataset page: https://huggingface.co/datasets/Sony/Hokkaido_Agriculture_Image_Dataset.

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 ...

Agri-LLM Raw Dataset

  Dataset Description

The Agri-LLM Raw Dataset is a collection of processed text extracted from various PDF documents related to agriculture. This dataset is intended for use in language modeling and other NLP tasks focused on agricultural content.

  Dataset Structure





  Data Fields

sentence_chunks: A list of sentence chunks, where each chunk contains up to 10 sentences.

  Example Data

Below is an example of a single entry in… See the full description on the dataset page: https://huggingface.co/datasets/dippatel2506/agri-llm-raw-dataset.

Note: This version supersedes version 1: https://doi.org/10.15482/USDA.ADC/1522654. In Fall of 2019 the USDA Food and Nutrition Service (FNS) conducted the third Farm to School Census. The 2019 Census was sent via email to 18,832 school food authorities (SFAs) including all public, private, and charter SFAs, as well as residential care institutions, participating in the National School Lunch Program. The questionnaire collected data on local food purchasing, edible school gardens, other farm to school activities and policies, and evidence of economic and nutritional impacts of participating in farm to school activities. A total of 12,634 SFAs completed usable responses to the 2019 Census. Version 2 adds the weight variable, “nrweight”, which is the Non-response weight. Processing methods and equipment used The 2019 Census was administered solely via the web. The study team cleaned the raw data to ensure the data were as correct, complete, and consistent as possible. This process involved examining the data for logical errors, contacting SFAs and consulting official records to update some implausible values, and setting the remaining implausible values to missing. The study team linked the 2019 Census data to information from the National Center of Education Statistics (NCES) Common Core of Data (CCD). Records from the CCD were used to construct a measure of urbanicity, which classifies the area in which schools are located. Study date(s) and duration Data collection occurred from September 9 to December 31, 2019. Questions asked about activities prior to, during and after SY 2018-19. The 2019 Census asked SFAs whether they currently participated in, had ever participated in or planned to participate in any of 30 farm to school activities. An SFA that participated in any of the defined activities in the 2018-19 school year received further questions. Study spatial scale (size of replicates and spatial scale of study area) Respondents to the survey included SFAs from all 50 States as well as American Samoa, Guam, the Northern Mariana Islands, Puerto Rico, the U.S. Virgin Islands, and Washington, DC. Level of true replication Unknown Sampling precision (within-replicate sampling or pseudoreplication) No sampling was involved in the collection of this data. Level of subsampling (number and repeat or within-replicate sampling) No sampling was involved in the collection of this data. Study design (before–after, control–impacts, time series, before–after-control–impacts) None – Non-experimental Description of any data manipulation, modeling, or statistical analysis undertaken Each entry in the dataset contains SFA-level responses to the Census questionnaire for SFAs that responded. This file includes information from only SFAs that clicked “Submit” on the questionnaire. (The dataset used to create the 2019 Farm to School Census Report includes additional SFAs that answered enough questions for their response to be considered usable.) In addition, the file contains constructed variables used for analytic purposes. The file does not include weights created to produce national estimates for the 2019 Farm to School Census Report. The dataset identified SFAs, but to protect individual privacy the file does not include any information for the individual who completed the questionnaire. Description of any gaps in the data or other limiting factors See the full 2019 Farm to School Census Report [https://www.fns.usda.gov/cfs/farm-school-census-and-comprehensive-review] for a detailed explanation of the study’s limitations. Outcome measurement methods and equipment used None Resources in this dataset:Resource Title: 2019 Farm to School Codebook with Weights. File Name: Codebook_Update_02SEP21.xlsxResource Description: 2019 Farm to School Codebook with WeightsResource Title: 2019 Farm to School Data with Weights CSV. File Name: census2019_public_use_with_weight.csvResource Description: 2019 Farm to School Data with Weights CSVResource Title: 2019 Farm to School Data with Weights SAS R Stata and SPSS Datasets. File Name: Farm_to_School_Data_AgDataCommons_SAS_SPSS_R_STATA_with_weight.zipResource Description: 2019 Farm to School Data with Weights SAS R Stata and SPSS Datasets

Livestock and Meat International Trade Data

The Livestock and Meat International Trade Data product includes monthly and annual data for imports of live cattle, hogs, sheep, goats, beef and veal, pork, lamb and mutton, chicken meat, turkey meat, eggs, and egg products. This product does not include any Dairy Data. Using official trade statistics reported by the U.S. Census, this data product provides data aggregated by commodity and converted to the same units used in the USDA’s World Agricultural Supply and Demand Estimates (WASDE). These units are carcass-weight-equivalent (CWE) pounds for meat products and dozen equivalents for eggs and egg products. Live animal numbers are not converted. With breakdowns by partner country and historical data back to 1989, these data can be used to analyze trends in livestock, meat, and poultry shipments alongside domestic production data and WASDE estimates. Timely analysis and discussion can be found in the monthly Livestock, Dairy, and Poultry Outlook report.

This includes all of the same monthly data as the Excel tables, as well as disaggregated, unconverted data. These files are machine-readable, providing a convenient format for Python users and programmers.

Details

The Livestock and Meat Trade Data Set contains monthly and annual data for imports of live cattle, hogs, sheep, and goats, as well as beef and veal, pork, lamb and mutton, chicken meat, turkey meat, and eggs. The tables report physical quantities, not dollar values or unit prices. Data on beef and veal, pork, lamb, and mutton are on a carcass-weight-equivalent basis. Breakdowns by country are included.

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.

This dataset measures food availability and access for 76 low- and middle-income countries. The dataset includes annual country-level data on area, yield, production, nonfood use, trade, and consumption for grains and root and tuber crops (combined as R&T in the documentation tables), food aid, total value of imports and exports, gross domestic product, and population compiled from a variety of sources. This dataset is the basis for the International Food Security Assessment 2015-2025 released in June 2015. This annual ERS report projects food availability and access for 76 low- and middle-income countries over a 10-year period. Countries (Spatial Description, continued): Democratic Republic of the Congo, Ecuador, Egypt, El Salvador, Eritrea, Ethiopia, Gambia, Georgia, Ghana, Guatemala, Guinea, Guinea-Bissau, Haiti, Honduras, India, Indonesia, Jamaica, Kenya, Kyrgyzstan, Laos, Lesotho, Liberia, Madagascar, Malawi, Mali, Mauritania, Moldova, Mongolia, Morocco, Mozambique, Namibia, Nepal, Nicaragua, Niger, Nigeria, North Korea, Pakistan, Peru, Philippines, Rwanda, Senegal, Sierra Leone, Somalia, Sri Lanka, Sudan, Swaziland, Tajikistan, Tanzania, Togo, Tunisia, Turkmenistan, Uganda, Uzbekistan, Vietnam, Yemen, Zambia, and Zimbabwe. Resources in this dataset:Resource Title: CSV File for all years and all countries. File Name: gfa25.csvResource Title: International Food Security country data. File Name: GrainDemandProduction.xlsxResource Description: Excel files of individual country data. Please note that these files provide the data in a different layout from the CSV file. This version of the data files was updated 9-2-2021

More up-to-date files may be found at: https://www.ers.usda.gov/data-products/international-food-security.aspx