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 also as 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 multi-disciplinary 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 analysed. 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 that are 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 then used 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 web site 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 compiled, in which USDA published in 2012 and 2016, combining search results in ARIS, Scopus, and the Forest Service's TreeSearch, plus the USDA web sites Economic Research Service (ERS), National Agricultural Statistics Service (NASS), Natural Resources and Conservation Service (NRCS), Food and Nutrition Service (FNS), Rural Development (RD), and Agricultural Marketing Service (AMS). The top 50 journals' author instructions were consulted to see if they (a) ask or require submitters to provide supplemental data, or (b) require submitters to submit data to open repositories. Data are provided for Journals based on a 2012 and 2016 study of where USDA employees publish their research studies, ranked by number of articles, including 2015/2016 Impact Factor, Author guidelines, Supplemental Data?, Supplemental Data reviewed?, Open Data (Supplemental or in Repository) Required? and Recommended data repositories, as provided in the online author guidelines for each the top 50 journals. Evaluation We ran a series of searches on all resulting general subject databases with the designated search terms. From the results, we noted the total number of datasets in the repository, type of resource searched (datasets, data, images, components, etc.), percentage of the total database that each term comprised, any dataset with a search term that comprised at least 1% and 5% of the total collection, and any search term that returned greater than 100 and greater than 500 results. We compared domain-specific databases and repositories based on parent organization, type of institution, and whether data submissions were dependent on conditions such as funding or affiliation of some kind. Results A summary of the major findings from our data review: Over half of the top 50 ag-related journals from our profile require or encourage open data for their published authors. There are few general repositories that are both large AND contain a significant portion of ag data in their collection. GBIF (Global Biodiversity Information Facility), ICPSR, and ORNL DAAC were among those that had over 500 datasets returned with at least one ag search term and had that result comprise at least 5% of the total collection. Not even one quarter of the domain-specific repositories and datasets reviewed allow open submission by any researcher regardless of funding or affiliation. See included README file for descriptions of each individual data file in this dataset. Resources in this dataset:Resource Title: Journals. File Name: Journals.csvResource Title: Journals - Recommended repositories. File Name: Repos_from_journals.csvResource Title: TDWG presentation. File Name: TDWG_Presentation.pptxResource Title: Domain Specific ag data sources. File Name: domain_specific_ag_databases.csvResource Title: Data Dictionary for Ag Data Repository Inventory. File Name: Ag_Data_Repo_DD.csvResource Title: General repositories containing ag data. File Name: general_repos_1.csvResource Title: README and file inventory. File Name: README_InventoryPublicDBandREepAgData.txt

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.

Abstract

For more than 150 years, the U.S. Department of Commerce, Bureau of the Census, conducted the census of agriculture. However, the 2002 Appropriations Act transferred the responsibility from the Bureau of the Census to the U.S. Department of Agriculture (USDA), National Agricultural Statistics Service (NASS). The 2007 Census of Agriculture for the U.S. Virgin Islands is the second census in the U.S. Virgin Islands conducted by NASS. The census of agriculture is taken to obtain agricultural statistics for each county, State (including territories and protectorates), and the Nation. The first U.S. agricultural census data were collected in 1840 as a part of the sixth decennial census. From 1840 to 1920, an agricultural census was taken as a part of each decennial census. Since 1920, a separate national agricultural census has been taken every 5 years. The 2007 census is the 14th census of agriculture of the U.S. Virgin Islands. The first, taken in 1920, was a special census authorized by the Secretary of Commerce. The next agriculture census was taken in 1930 in conjunction with the decennial census, a practice that continued every 10 years through 1960. The 1964 Census of Agriculture was the first quinquennial (5-year) census to be taken in the U.S. Virgin Islands. In 1976, Congress authorized the census of agriculture to be taken for 1978 and 1982 to adjust the data-reference year to coincide with the 1982 Economic Censuses covering manufacturing, mining, construction, retail trade, wholesale trade, service industries, and selected transportation activities. After 1982, the agriculture census reverted to a 5-year cycle. Data in this publication are for the calendar year 2007, and inventory data reflect what was on hand on December 31, 2007. This is the same reference period used in the 2002 census. Prior to the 2002 census, data was collected in the summer for the previous 12 months, with inventory items counted as what was on hand as of July 1 of the year the data collection was done.

Objectives: The census of agriculture is the leading source of statistics about the U.S. Virgin Islands’s agricultural production and the only source of consistent, comparable data at the island level. Census statistics are used to measure agricultural production and to identify trends in an ever changing agricultural sector. Many local programs use census data as a benchmark for designing and evaluating surveys. Private industry uses census statistics to provide a more effective production and distribution system for the agricultural community.

Geographic coverage

National coverage

Analysis unit

Households

Universe

The statistical unit was a farm, defined as "any place from which USD 500 or more of agricultural products were produced and sold, or normally would had been sold, during the calendar year 2007". According to the census definition, a farm is essentially an operating unit, not an ownership tract. All land operated or managed by one person or partnership represents one farm. In the case of tenants, the land assigned to each tenant is considered a separate farm, even though the landlord may consider the entire landholding to be one unit rather than several separate units.

Kind of data

Census/enumeration data [cen]

Sampling procedure

(a) Method of Enumeration As in the previous censuses of the U.S. Virgin Islands, a direct enumeration procedure was used in the 2007 Census of Agriculture. Enumeration was based on a list of farm operators compiled by the U.S. Virgin Islands Department of Agriculture. This list was compiled with the help of the USDA Farm Services Agency located in St. Croix. The statistics in this report were collected from farm operators beginning in January of 2003. Each enumerator was assigned a list of individuals or farm operations from a master enumeration list. The enumerators contacted persons or operations on their list and completed a census report form for all farm operations. If the person on the list was not operating a farm, the enumerator recorded whether the land had been sold or rented to someone else and was still being used for agriculture. If land was sold or rented out, the enumerator got the name of the new operator and contacted that person to ensure that he or she was included in the census.

(b) Frame The census frame consisted of a list of farm operators compiled by the U.S. Virgin Islands DA. This list was compiled with the help of the USDA Farm Services Agency, located in St. Croix.

(c) Complete and/or sample enumeration methods The census was a complete enumeration of all farm operators registered in the list compiled by the United States of America in the CA 2007.

Mode of data collection

Face-to-face [f2f]

Research instrument

The questionnaire (report form) for the CA 2007 was prepared by NASS, in cooperation with the DA of the U.S. Virgin Islands. Only one questionnaire was used for data collection covering topics on:

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

The questionnaire of the 2007 CA covered 12 of the 16 core items' recommended for the WCA 2010 round.

Cleaning operations

DATA PROCESSING The processing of the 2007 Census of Agriculture for the U.S. Virgin Islands was done in St. Croix. Each report form was reviewed and coded prior to data keying. Report forms not meeting the census farm definition were voided. The remaining report forms were examined for clarity and completeness. Reporting errors in units of measures, illegible entries, and misplaced entries were corrected. After all the report forms had been reviewed and coded, the data were keyed and subjected to a thorough computer edit. The edit performed comprehensive checks for consistency and reasonableness, corrected erroneous or inconsistent data, supplied missing data based on similar farms, and assigned farm classification codes necessary for tabulating the data. All substantial changes to the data generated by the computer edits were reviewed and verified by analysts. Inconsistencies identified, but not corrected by the computer, were reviewed, corrected, and keyed to a correction file. The corrected data were then tabulated by the computer and reviewed by analysts. Prior to publication, tabulated totals were reviewed by analysts to identify inconsistencies and potential coverage problems. Comparisons were made with previous census data, as well as other available data. The computer system provided the capability to review up-to-date tallies of all selected data items for various sets of criteria which included, but were not limited to, geographic levels, farm types, and sales levels. Data were examined for each set of criteria and any inconsistencies or potential problems were then researched by examining individual data records contributing to the tabulated total. W hen necessary, data inconsistencies were resolved by making corrections to individual data records.

Sampling error estimates

The accuracy of these tabulated data is determined by the joint effects of the various nonsampling errors. No direct measures of these effects have been obtained; however, precautionary steps were taken in all phases of data collection, processing, and tabulation of the data in an effort to minimize the effects of nonsampling errors.

Abstract

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

Geographic coverage

National

Analysis unit

Farms

Universe

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

Kind of data

Sample survey data [ssd]

Sampling procedure

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

Mode of data collection

Face-to-face [f2f]

Research instrument

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

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

Cleaning operations

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

Response rate

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

Sampling error estimates

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

Data appraisal

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

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

Drone Data Collection Service Market Outlook

The global market size for drone data collection services was valued at approximately USD 5.5 billion in 2023 and is projected to reach USD 21.4 billion by 2032, growing at a robust CAGR of 16.1% during the forecast period. This significant growth can be attributed to the increasing demand for advanced data analytics and the need for efficient data collection methods across various industries.

One of the major growth factors driving this market is the rapid advancement in drone technology. Innovations in drone hardware and software have significantly enhanced the capabilities of drones, making them more versatile and efficient in data collection tasks. Drones are now equipped with high-resolution cameras, LIDAR, and other advanced sensors that provide accurate and detailed data, which is invaluable for many industries. Additionally, improvements in battery life and flight stability have extended the operational range and endurance of drones, making them more practical for prolonged and large-scale data collection missions.

Another critical factor fueling the market's growth is the increasing adoption of drones in various applications such as agriculture, construction, mining, and oil & gas. In agriculture, drones are used for precision farming, crop monitoring, and soil analysis, which help in optimizing yields and reducing costs. Similarly, in construction, drones are utilized for site surveying, progress monitoring, and safety inspections, which enhance project efficiency and safety. The mining industry also benefits from drone data collection for exploration, mapping, and monitoring of mining operations, ensuring better resource management and operational safety.

The regulatory environment is another significant driver of market growth. Many countries are developing and implementing regulations that facilitate the integration of drones into commercial operations. These regulations are aimed at ensuring the safe and efficient use of drones while addressing privacy and security concerns. For instance, the Federal Aviation Administration (FAA) in the United States has established comprehensive guidelines for commercial drone operations, which have encouraged businesses to adopt drone technology for various data collection purposes.

Regionally, the North American market is expected to dominate the global drone data collection service market, followed by Europe and Asia Pacific. North America’s dominance can be attributed to the presence of major drone technology companies, a favorable regulatory environment, and high adoption rates across various industries. The Asia Pacific region, with its rapidly growing economies and increasing investments in drone technology, is projected to witness the highest growth rate during the forecast period. Europe is also expected to see significant growth, driven by technological advancements and increasing demand for efficient data collection methods in industries such as agriculture and construction.

Service Type Analysis

The drone data collection service market can be segmented by service type into aerial photography, mapping & surveying, inspection & monitoring, and others. Aerial photography is one of the most commonly used services in this market. High-resolution aerial photographs captured by drones are utilized in various industries, including real estate, tourism, and media. These photographs provide detailed and accurate visual data that can be used for marketing, planning, and documentation purposes. The advancements in camera technology and drone stability have further enhanced the quality and reliability of aerial photography.

Mapping & surveying is another critical segment in the drone data collection service market. Drones equipped with LIDAR, photogrammetry, and other advanced sensors are used to create detailed and accurate maps and surveys of large areas. This service is particularly beneficial in industries such as construction, mining, and agriculture, where precise data is crucial for planning and operational efficiency. The use of drones in mapping & surveying reduces the time and cost associated with traditional ground-based survey methods while providing high-quality and comprehensive data.

Inspection & monitoring services provided by drones are increasingly being adopted in industries such as utilities, oil & gas, and infrastructure. Drones are used to inspect and monitor assets such as power lines, pipelines, and bridges, ensuring their integrity and safety. The ability of drones to acce

Abstract

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

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

American Samoa is one of the territories collectively referred as the "US Outlying areas". The 2008 American Samoa Census of Agriculture was conducted by personal interviews of all farm operations on the list of commercial farms, and supplemented by an area sample of the remaining households. The purpose of the area sample was to efficiently accountfor farms not on the commercialfarmlist and provide an accurate measure of the agricultural activity in American Samoa.

Geographic coverage

National coverage

Analysis unit

Households

Universe

The statistical unit for the CA 2008 was the farm, an operating unit defined as any place from which USD 1 000 or more of agricultural products were produced and sold, or normally would have been sold, during the census year.

Kind of data

Census/enumeration data [cen]

Sampling procedure

i. Methodological modality for conducting the census The classical approach was used in the CA 2008.

ii. sample design The design of the sample for the 2008 Census of Agriculture made use of materials and information available from the American Samoa Department of Commerce. These included detailed maps of all the islands in the territory, up-to-date map-spotting (location on a map) of all households in the territory, a system of numbering each household to provide it a unique identifier, and identification of householdswhich were on the list of commercial farms. The households that were on the list of commercial farms were excluded from the universe used to select the area sample. A random sample of the remaining households was selected, using the available maps with the household identification information. It was determined that a 20 percent sample would be optimal. A serpentine selection methodology, starting at a point determined by the generation of a random number, was used to select the area sample.

Mode of data collection

Face-to-face paper [f2f]

Research instrument

One questionnaire was used which collected information on:

• Land owned
• Field crops
• Fruit
• Root crops
• Cattle and calves
• Poultry
• Aquaculture
• Expenditure
• Production expenses
• Machinery, equipment and buildings
• Household characteristics

Cleaning operations

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

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

Data appraisal

The complete data series from the 2008 Census of Agriculture is available from the NASS website free of charge in multiple formats, including Quick Stats 2.0 - an online database to retrieve customized tables with Census data at the national, state and county levels. The 2012 Census of Agriculture provides information on a range of topics, including agricultural practices, conservation, organic production, as well as traditional and specialty crops.

Abstract

The Statistical and Forecasting Service has been entrusted with the production of the AC 2010. (SSP) which is the central statistical department of the Ministry in charge of agriculture, (MAAPRAT) the central department is in charge of the design of the operation, the drafting of the questionnaire and instructions, the training of regional services, the final quality control of the data collected and of the first publication of the results. The SSP has relied on its specialised decentralised levels, the services regional statistics (NUTS2) of statistical and economic information (SRISE). The threshold definition of agricultural holding applied has been the same since 1955, and corresponds exactly to the one proposed by the European regulation. The geographical area is the whole of France; for the DOM the territories of Saint-Martin and Saint-Barthélemy are now excluded, Mayotte is not yet included.

For statistical purposes, agricultural censuses in French territories (French Guyana, Guadeloupe, Reunion and Martinique) are recorded separately in the World Census of Agriculture Database. The census results are presented for all of France.

Geographic coverage

National coverage

Analysis unit

Households

Universe

The statistical unit in the AC 2010 was the agricultural holding, defined as an economic unit that participates in agricultural production and meets the following criteria: · it has an agricultural activity either of production, or of maintenance of the lands in good agricultural and environmental

Kind of data

Census/enumeration data [cen]

Sampling procedure

a. Frame The basic list of agricultural holdings was built using the SSP farm register, the SIRENE register (business register), the list of farmers who had applied for aid (area declarations),' and some additional sources for beekeeping, olive oil, aromatic plants. The holding lists were checked at local level by communal commissions.

b. Complete and/or sample enumeration method(s) The AC and SAPM were conducted using complete enumeration.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

Three questionnaires were used: one for France in Europe (including questions of regional interest) and two for France's overseas territories: one for Guadeloupe, Martinique and Reunion and another for Guyana. The census covered all 16 core items recommended in the WCA 2010. ie.

0001 Identification and location of agricultural holding 0002+ Legal status of agricultural holder 0003 Sex of agricultural holder 0004 Age of agricultural holder 0005 Household size 0006 Main purpose of production of the holding 0007 Area of holding according to land use types 0008 Total area of holding 0009 Land tenure types on the holding 0010 Presence of irrigation on the holding 0011 Types of temporary crops on the holding 0012 Types of permanent crops on the holding and whether in compact plantation 0013 Number of animals on the holding for each livestock type 0014 Presence of aquaculture on the holding 0015+ Presence of forest and other wooded land on the holding 0016 Other economic production activities of the holding's enterprise

Cleaning operations

a. DATA PROCESSING AND ARCHIVING The CAPI interface included controls to ensure that there were responses to all questions. In addition, interactive range and consistency checks were included for each variable so that corrections could be made by the enumerator during the interview. Further edits and imputations were completed at the central office where the census validation and tabulation was completed. To ensure that the list of holdings was complete, several tests were conducted at the end of collection. All available administrative sources were used to verify that existing holdings had been identified and included. The key databases and registers used included that for EU agriculture aid applications, the national database of bovine identification, the computerized vineyard register, organic producer records, and some local registers for small productions. The data, after validation, were archived on secured servers.

b. CENSUS DATA QUALITY To assess the quality of field data collection, completeness checks and feedback were performed at the end of field data collection operation, from March to June 2011. Data checking began during the collection phase on the farmer's premises. It then continued throughout the processing chain. A special effort was made to check the AC's coverage by using the administrative data available. The nonresponse rate was of only 0.96 percent, and the missing data were imputed using the hot deck method.

Data appraisal

The first provisional census results were disseminated in September 2011, ten months after the end of the reference period. The main final results were made available at the end of February 2012, 16 months after the end of the reference period. The AC 2010 results were disseminated online and are available on the SSP website.9 The "ADEL" tool allows web users to build their own tables.

The first table with main results shows the total number and area of holdings broken down by continental France, on one hand, and its overseas territories, on the other. See metadata review tables in external materials.

Ecosystem services can maintain or increase crop yield in agricultural systems, but data to support management decisions is expensive and time-consuming to collect. Furthermore, relationships derived from small-scale plot data may not apply to ecosystem services operating at larger spatial scales (fields, landscapes). Precision yield data can be used to improve the accuracy and geographic range of ecosystem service studies, but have been underused in previous studies: out of 370 literature records, we found that less than 2% of all records were used to study biotic or landscape effects on yield. We argue that this is likely due to low data accessibility and a lack of familiarity with spatial data analysis. We provide examples of analysis using simulated and real precision yield data and outline two case studies of ecosystem services using precision yield data. Ecologists and agronomists should consider using precision yield data more broadly, as it can be used to test hypotheses about ecosystem services across multiple spatial scales, and could be used to inform the design of multifunctional farming landscapes.

Methods

The combined yield monitor data for Supplemental 1 was donated by Trent Clark (the absolution location of the spatial data has been anonymized for privacy). Supplemental 2 uses entirely generated data (see script for details). Supplemental 3 uses a correlation matrix created from unpublished yield data collected by Hector Cárcamo.

The Quick Stats Database is the most comprehensive tool for accessing agricultural data published by the USDA National Agricultural Statistics Service (NASS). It allows you to customize your query by commodity, location, or time period. You can then visualize the data on a map, manipulate and export the results as an output file compatible for updating databases and spreadsheets, or save a link for future use. Quick Stats contains official published aggregate estimates related to U.S. agricultural production. County level data are also available via Quick Stats. The data include the total crops and cropping practices for each county, and breakouts for irrigated and non-irrigated practices for many crops, for selected States. The database allows custom extracts based on commodity, year, and selected counties within a State, or all counties in one or more States. The county data includes totals for the Agricultural Statistics Districts (county groupings) and the State. The download data files contain planted and harvested area, yield per acre and production. NASS develops these estimates from data collected through:

hundreds of sample surveys conducted each year covering virtually every aspect of U.S. agriculture

the Census of Agriculture conducted every five years providing state- and county-level aggregates Resources in this dataset:Resource Title: Quick Stats database. File Name: Web Page, url: https://quickstats.nass.usda.gov/ Dynamic drill-down filtered search by Commodity, Location, and Date range, beginning with Census or Survey data. Filter lists are refreshed based upon user choice allowing the user to fine-tune the search.

Comprehensive dataset of 17,024 Agricultural services in United States as of June, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.

Precision Farming Agriculture Service Market Outlook

The global precision farming agriculture service market is projected to experience substantial growth, with a market size estimated at approximately USD 9.1 billion in 2023, expected to soar to USD 23.2 billion by 2032, reflecting a compound annual growth rate (CAGR) of 11.2%. This growth trajectory underscores the increasing reliance on advanced agricultural technologies to enhance crop productivity and efficiency. The mounting pressure to feed a growing global population, coupled with the need for sustainable farming practices, is driving the adoption of precision farming solutions worldwide. Precision farming leverages technology to monitor and optimize agricultural inputs, enabling farmers to make data-driven decisions that improve yield, reduce input costs, and minimize environmental impact.

The growth of the precision farming agriculture service market is significantly driven by technological advancements and innovations. The integration of Internet of Things (IoT), artificial intelligence (AI), and big data analytics in farming practices has transformed traditional agricultural methods, providing farmers with actionable insights. These technologies help in precise monitoring of crops, prediction of weather patterns, and optimization of resources such as water and fertilizers. The advent of drones and satellite imagery has further enhanced the ability to monitor large expanses of farmland, enabling real-time data collection and analysis. Additionally, the increasing affordability and accessibility of these technologies are encouraging even small and medium-sized farms to adopt precision farming techniques.

Another critical factor propelling the market is the growing awareness and concern about the environmental impact of traditional farming practices. Precision farming offers solutions that can significantly reduce the overuse of fertilizers and pesticides, thus lowering the carbon footprint and preventing soil degradation. The implementation of precision agriculture not only promotes sustainable farming by conserving resources but also ensures compliance with stringent environmental regulations. Governments and international bodies are increasingly supporting and incentivizing the adoption of precision agriculture technologies, recognizing their potential to enhance food security while preserving environmental integrity.

Moreover, the increasing demand for high-quality agricultural produce is encouraging farmers to adopt precision farming techniques. Consumer preferences are shifting towards organic and non-genetically modified products, which necessitates meticulous monitoring of crop health and soil conditions. Precision agriculture facilitates the production of such premium quality crops by enabling farmers to implement precise farming practices. Additionally, the global rise in farm labor costs and labor shortages is pushing farmers towards automation and advanced technological interventions to maintain and increase productivity without proportionately increasing labor input.

Precision Farming Software & Services play a pivotal role in the modern agricultural landscape by providing comprehensive solutions that integrate various technologies to enhance farm management. These services encompass a wide range of applications, from data collection and analysis to decision support systems that aid farmers in optimizing their operations. By leveraging advanced software platforms, farmers can access real-time data on crop conditions, weather forecasts, and resource availability, enabling them to make informed decisions that improve yield and efficiency. The integration of software and services in precision farming not only streamlines operations but also fosters sustainable practices by minimizing resource wastage and environmental impact. As the demand for precision farming solutions continues to grow, the role of software and services in facilitating seamless technology adoption and utilization becomes increasingly crucial.

Regionally, North America remains a frontrunner in the adoption of precision farming technologies, driven by the presence of key market players and a highly developed agricultural sector. The region is characterized by large-scale farms and a high level of technological adoption, which are pivotal in driving the growth of the precision farming market. Europe is also witnessing substantial growth due to stringent environmental regulations and a strong emphasis on sustainable farming practices. Meanwhile, the Asia Pacific region is expec

Farm Data Management System Market Outlook

The global farm data management system market size was valued at USD 3.2 billion in 2023 and is projected to reach USD 9.6 billion by 2032, growing at a compound annual growth rate (CAGR) of 12.5% during the forecast period. The market is driven by the increasing adoption of advanced technologies in agriculture to enhance productivity and efficiency, coupled with growing concerns over sustainable farming practices and food security.

The integration of sophisticated technologies such as IoT, AI, and satellite imagery in farm data management systems is significantly propelling market growth. These advanced technologies enable farmers to collect, analyze, and interpret vast amounts of data, leading to informed decision-making. For instance, IoT devices can monitor soil conditions, weather patterns, and crop health in real-time, providing valuable insights that help optimize resource utilization and crop yields. This technological shift not only enhances productivity but also contributes to sustainable farming practices by reducing waste and minimizing environmental impact.

Another major growth factor is the increasing need for efficient farm management due to the rising global population. With the world population expected to reach 9.7 billion by 2050, there is an escalating demand for food, which in turn requires farmers to maximize their output. Farm data management systems play a pivotal role in this scenario by enabling precision farming. Precision farming allows for the targeted application of inputs such as water, fertilizers, and pesticides, which ensures optimal plant growth and reduces the likelihood of overuse and wastage. Consequently, this contributes to higher crop productivity and better resource management.

Government initiatives and funding are also critical drivers of the farm data management system market. Governments worldwide are increasingly recognizing the importance of modernizing agricultural practices to ensure food security and environmental sustainability. Subsidies, grants, and policy support for the adoption of smart farming technologies are encouraging farmers to invest in farm data management systems. These government interventions not only provide financial support but also raise awareness about the benefits of advanced farming technologies, accelerating market growth.

Regionally, North America held the largest market share in 2023, attributed to the high adoption rate of advanced agricultural technologies and substantial investment in research and development. Europe follows closely, driven by stringent regulations on sustainable farming and strong government support. The Asia Pacific region is expected to witness the highest growth rate during the forecast period, fueled by rapid urbanization, increasing population, and a growing need for efficient agricultural practices. Countries like India and China are investing heavily in smart farming technologies to enhance agricultural productivity and meet the rising food demand.

Component Analysis

The farm data management system market is segmented by component into software, hardware, and services. The software segment is anticipated to hold the largest share owing to its crucial role in data collection, analysis, and interpretation. Advanced software solutions facilitate real-time monitoring and decision-making, which are integral to modern farming practices. These software solutions often integrate with IoT devices and other sensors to gather data on various parameters such as soil moisture, weather conditions, and crop health. This data is then processed using algorithms and analytics to provide actionable insights, helping farmers optimize their operations.

Hardware is another critical component, encompassing devices such as sensors, GPS units, drones, and other IoT devices. These hardware components are essential for the effective collection of data from the farm. Sensors, for instance, can measure soil moisture levels, temperature, and nutrient content, while drones offer aerial imaging and monitoring capabilities. The data collected by these devices is indispensable for precision farming, as it allows for accurate assessment and management of farming activities. The hardware segment is expected to grow steadily, driven by the increasing adoption of IoT and automation technologies in agriculture.

The services segment includes consulting, installation, maintenance, and support services. As farm data management systems become more sophisticated, the demand for professional services to support these sys

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.

To help enhance USA soil health, and ensure a robust living soil component that sustains essential functions for healthy plants, animals, and environment, and ultimately provides food for a healthy society, the GRACEnet Soil Biology group are working together with the larger USDA-ARS GRACEnet community to provide soil biology component measurements across regions and to eliminate data gaps for GRACEnet and REAP efforts. The Soil Biology group is focused on efforts that foster method comparison and meta-analyses to allow researchers to better assess soil biology and soil health indicators that are most responsive to agricultural management and that reflect the ecosystems services associated with a healthy, functioning soil. The GRACEnet Soil Biology mission is to produce the soil biology data, including methods of identifying and quantifying specific organisms and processes they govern, that are needed to evaluate impacts on agroecosystems and sustainable agricultural practices. This data collection effort is being accomplished in a highly structured manner to support current and future soil health and antimicrobial resistance research initiatives. The outcomes of the efforts of this team will provide a common biological data platform for several ARS databases, including: GRACEnet/REAP, Nutrient Use and Outcome Network (NUOnet), Long-Term Agroecosystem Research (LTAR) network, soil biology (e.g., MyPhyloDB) databases, and others. Resources in this dataset:Resource Title: Soil Biology Data Search. File Name: Web Page, url: https://agcros-usdaars.opendata.arcgis.com/datasets?group_ids=091b86e9e44a4e948ef2aeae3c916ca5

Abstract

The Annual Agricultural Sample Survey (AASS) for the year 2022/23 aimed to enhance the understanding of agricultural activities across the United Republic of Tanzania by collecting comprehensive data on various aspects of the agricultural sector. This survey is crucial for policy formulation, development planning, and service delivery, providing reliable data to monitor and evaluate national and international development frameworks.

The 2022/23 survey is particularly significant as it informs the monitoring and evaluation of key agricultural development strategies and frameworks. The collected data will contribute to the Tanzania Development Vision 2025, Zanzibar Development Vision 2020, the Five-Year Development Plan 2021/22–2025/26, the National Strategy for Growth and Reduction of Poverty (NSGRP) known as MKUKUTA, and the Zanzibar Strategy for Growth and Reduction of Poverty (ZSGRP) known as MKUZA. The survey data also supports the evaluation of Sustainable Development Goals (SDGs) and Comprehensive Africa Agriculture Development Programme (CAADP). Key indicators for agricultural performance and poverty monitoring are directly measured from the survey data.

The 2022/23 AASS provides a detailed descriptive analysis and related tables on the main thematic areas. These areas include household members and holder identification, field roster, seasonal plot and crop rosters (Vuli, Masika, and Dry Season), permanent crop production, crop harvest use, seed and seedling acquisition, input use and acquisition (fertilizers and pesticides), livestock inventory and changes, livestock production costs, milk and eggs production, other livestock products, aquaculture production, and labor dynamics. The 2022/23 AASS offers an extensive dataset essential for understanding the current state of agriculture in Tanzania. The insights gained will support the development of policies and interventions aimed at enhancing agricultural productivity, sustainability, and the livelihoods of farming communities. This data is indispensable for stakeholders addressing challenges in the agricultural sector and promoting sustainable agricultural development.

Statistical Disclosure Control (SDC) methods have been applied to the microdata, to protect the confidentiality of the individual data collected. Users must be aware that these anonymization or SDC methods modify the data, including suppression of some data points. This affects the aggregated values derived from the anonymized microdata, and may have other unwanted consequences, such as sampling error and bias. Additional details about the SDC methods and data access conditions are provided in the data processing and data access conditions below.

Geographic coverage

National, Mainland Tanzania and Zanzibar, Regions

Analysis unit

Households for Smallholder Farmers and Farm for Large Scale Farms

Universe

The survey covered agricultural households and large-scale farms.

Agricultural households are those that meet one or more of the following two conditions: a) Have or operate at least 25 square meters of arable land, b) Own or keep at least one head of cattle or five goats/sheep/pigs or fifty chicken/ducks/turkeys during the agriculture year.

Large-scale farms are those farms with at least 20 hectares of cultivated land, or 50 herds of cattle, or 100 goats/sheep/pigs, or 1,000 chickens. In addition to this, they should fulfill all of the following four conditions: i) The greater part of the produce should go to the market, ii) Operation of farm should be continuous, iii) There should be application of machinery / implements on the farm, and iv) There should be at least one permanent employee.

Kind of data

Sample survey data [ssd]

Sampling procedure

The frame used to extract the sample for the Annual Agricultural Sample Survey (AASS-2022/23) in Tanzania was derived from the 2022 Population and Housing Census (PHC-2022) Frame that lists all the Enumeration Areas (EAs/Hamlets) of the country. The AASS 2022/23 used a stratified two-stage sampling design which allows to produce reliable estimates at regional level for both Mainland Tanzania and Zanzibar.

In the first stage, the EAs (primary sampling units) were stratified into 2-3 strata within each region and then selected by using a systematic sampling procedure with probability proportional to size (PPS), where the measure of size is the number of agricultural households in the EA. Before the selection, within each stratum and domain (region), the Enumeration Areas (EAs) were ordered according to the codes of District and Council which reflect the geographical proximity, and then ordered according to the codes of Constituency, Division, Wards, and Village. An implicit stratification was also performed, ordering by Urban/Rural type at Ward level.

In the second stage, a simple random sampling selection was conducted . In hamlets with more than 200 households, twelve (12) agricultural households were drawn from the PHC 2022 list with a simple random sampling without replacement procedure in each sampled hamlet. In hamlets with 200 households or less, a listing exercise was carried out in each sampled hamlet, and twelve (12) agricultural households were selected with a simple random sampling without replacement procedure. A total of 1,352 PSUs were selected from the 2022 Population and Housing Census frame, of which 1,234 PSUs were from Mainland Tanzania and 118 from Zanzibar. A total number of 16,224 agricultural households were sampled (14,808 households from Mainland Tanzania and 1,416 from Zanzibar).

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The 2022/23 Annual Agricultural Survey used two main questionnaires consolidated into a single questionnaire within the CAPIthe CAPI System, Smallholder Farmers and Large-Scale Farms Questionnaire. Smallholder Farmers questionnaire captured information at household level while Large Scale Farms questionnaire captured information at establishment/holding level. These questionnaires were used for data collection that covered core agricultural activities (crops, livestock, and fish farming) in both short and long rainy seasons. The 2022/23 AASS questionnaire covered 23 sections which are:

1. COVER; The cover page included the title of the survey, survey year (2022/23), general instructions for both the interviewers and respondents. It sets the context for the survey and also it shows the survey covers the United Republic of Tanzania.
2. SCREENING: Included preliminary questions designed to determine if the respondent or household is eligible to participate in the survey. It checks for core criteria such as involvement in agricultural activities.
3. START INTERVIEW: The introductory section where basic details about the interview are recorded, such as the date, location, and interviewer’s information. This helped in the identification and tracking of the interview process.
4. HOUSEHOLD MEMBERS AND HOLDER IDENTIFICATION: Collected information about all household members, including age, gender, relationship to the household head, and the identification of the main agricultural holder. This section helped in understanding the demographic composition of the agriculture household.
5. FIELD ROSTER: Provided the details of the various agricultural fields operated by the agriculture household. Information includes the size, location, and identification of each field. This section provided a comprehensive overview of the land resources available to the household.
6. VULI PLOT ROSTER: Focused on plots used during the Vuli season (short rainy season). It includes details on the crops planted, plot sizes, and any specific characteristics of these plots. This helps in assessing seasonal agricultural activities.
7. VULI CROP ROSTER: Provided detailed information on the types of crops grown during the Vuli season, including quantities produced and intended use (e.g., consumption, sale, storage). This section captures the output of short rainy season farming.
8. MASIKA PLOT ROSTER: Similar to Section 4 but focuses on the Masika season (long rainy season). It collects data on plot usage, crop types, and sizes. This helps in understanding the agricultural practices during the primary growing season.
9. MASIKA CROP ROSTER: Provided detailed information on crops grown during the Masika season, including production quantities and uses. This section captures the output from the main agricultural season.
10. PERMANENT CROP PRODUCTION: Focuses on perennial or permanent crops (e.g., fruit trees, tea, coffee). It includes data on the types of permanent crops, area under cultivation, production volumes, and uses. This section tracks long-term agricultural investments.
11. CROP HARVEST USE: In this, provided the details how harvested crops are utilized within the household. Categories included consumption, sale, storage, and other uses. This section helps in understanding food security and market engagement.
12. SEED AND SEEDLINGS ACQUISITION: Collected information on how the agriculture household acquires seeds and seedlings, including sources (e.g., purchased, saved, gifted) and types (local, improved, etc). This section provided insights into input supply chains and planting decisions based on the households, or head.
13. INPUT USE AND ACQUISITION (FERTILIZERS AND PESTICIDES): It provided the details of the use and acquisition of agricultural inputs such as fertilizers and pesticides. It included information on quantities used, sources, and types of inputs. This section assessed the input dependency and agricultural practices.
14. LIVESTOCK IN STOCK AND CHANGE IN STOCK: The questionnaire

The ARS Culture Collection is one of the largest public collections of microorganisms in the world, containing approximately 93,000 strains of bacteria and fungi. The collection is split into subcollections of molds, prokaryotes, and yeasts. In addition, the online catalog is searchable by genus, species, subvar type, and subspecies.The collection is housed within the Mycotoxin Prevention and Applied Microbiology Research Unit at the National Center for Agricultural Utilization Research in Peoria, Illinois. The scientists and staff of the ARS Culture Collection conduct and facilitate microbiological research that advances agricultural production, food safety, public health, and economic development. These goals are pursued through in-house research that improves understanding and utilization of microbiological diversity and through efforts to enhance the value and accessibility of microbial accessions in the Agricultural Research Service Culture Collection.Resources in this dataset:Resource Title: The ARS Culture (NRRL) Collection Online Catalog.File Name: Web Page, url: https://nrrl.ncaur.usda.gov/ Online catalog and database server for the ARS Culture Collection (NRRL).

Agricultural Mapping Services Market Outlook

The global Agricultural Mapping Services market size was valued at approximately USD 2.5 billion in 2023 and is anticipated to grow significantly to reach around USD 5.8 billion by 2032, reflecting a Compound Annual Growth Rate (CAGR) of approximately 9.8%. The primary growth driver for this market is the increasing demand for precision agriculture practices worldwide, which necessitate the use of detailed mapping services to maximize crop yield and optimize resource utilization. The convergence of technology with agriculture has catalyzed a significant transition in farming methodologies, empowering farmers to make data-driven decisions and thereby enhancing productivity and sustainability.

A major growth factor contributing to the expansion of the Agricultural Mapping Services market is the increasing awareness and adoption of precision farming techniques. Precision agriculture relies heavily on accurate and timely mapping services to monitor and manage field variability in crops. Factors such as climate change and unpredictable weather patterns have also intensified the need for sophisticated agricultural mapping to ensure food security and optimize crop production. Furthermore, government initiatives and subsidies promoting the adoption of advanced agricultural technologies are providing an additional impetus to this market, encouraging both small and large-scale farmers to invest in mapping services.

Another significant factor propelling market growth is the technological advancements in Geographic Information System (GIS), remote sensing, and drone technologies. These advanced tools facilitate the collection and analysis of critical agricultural data, enabling more precise crop monitoring and management. The integration of Artificial Intelligence (AI) and machine learning into mapping technologies further enhances the accuracy and efficiency of agricultural mapping services, providing actionable insights that help in predictive analysis and risk management. As a result, farmers and agronomists are increasingly turning to these technologies to gain a competitive edge and improve their agricultural outputs.

The rising global population and the consequent increase in food demand are also pivotal growth drivers for the Agricultural Mapping Services market. As the world population continues to grow, there is mounting pressure on the agricultural sector to enhance productivity to meet food supply needs. Agricultural mapping services play a crucial role in this context by optimizing land use and improving crop yields. Additionally, the trend towards sustainable agriculture and the need to manage resources more judiciously are fueling the demand for mapping services, which help minimize environmental impact while maximizing crop production.

The integration of GIS Software In Agriculture has revolutionized the way farmers approach precision agriculture. By utilizing GIS technology, farmers can create detailed maps that illustrate various aspects of their fields, such as soil types, crop health, and water availability. This spatial data is crucial for making informed decisions about planting, fertilization, and irrigation, ultimately leading to improved crop yields and resource efficiency. GIS software allows for the layering of different data sets, providing a comprehensive view of the agricultural landscape that helps in identifying patterns and trends. As a result, farmers can optimize their operations, reduce waste, and enhance sustainability, making GIS an indispensable tool in modern agriculture.

Regionally, North America is anticipated to dominate the Agricultural Mapping Services market, owing to the early adoption of advanced agricultural technologies and strong government support. Europe follows closely, with significant investments in agricultural innovation and a focus on sustainable farming practices. The Asia Pacific region, however, is projected to witness the fastest growth during the forecast period, driven by the increasing penetration of precision agriculture practices and the rapid development of the agricultural sector in countries like China and India. Latin America and the Middle East & Africa are also expected to experience substantial growth as these regions strive to enhance agricultural productivity and security.

Service Type Analysis

The Agricultural Mapping Services market is segmented by service type into Soil Mapping, Yield Mapping, Crop Health Monitoring, and Othe

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.

Comprehensive dataset of 46 Agricultural services in West Virginia, United States as of July, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.

The United States Department of Agriculture-Agricultural Research Service (USDA-ARS) North Central Soil Conservation Research Laboratory - Soil Management Unit established a weather data collection system at the Swan Lake Research Farm in 1997. Weather data collected include wind speed and direction, barometric pressure, relative humidity, air temperature, soil temperatures, soil heat flux, solar radiation, photosynthetic active radiation, and precipitation. In 2015 the site became part of the Long Term Agroecosystem Research (LTAR) project. The Swan Lake Research Farm is located in Stevens County Minnesota, in the Upper Mississippi River Basin (UMRB) watershed. Resources in this dataset:Resource Title: GeoData catalog record. File Name: Web Page, url: https://geodata.nal.usda.gov/geonetwork/srv/eng/catalog.search#/metadata/ad80c14b-f4a0-41b2-8592-3a5b6bbebcc7