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.

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

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

description:

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.

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

Abstract copyright UK Data Service and data collection copyright owner.The Farm Business Survey (FBS) is conducted annually to collect business information from c.2,400 farms in England and Wales. The FBS provides information on the financial position and physical and economic performance of farm businesses, to inform policy decisions on matters affecting farm businesses and to enable analysis of impacts of policy options. It is intended to serve the needs of farmers, farming and land management interest groups, government (both national and European), government partners, and researchers. The primary objective of survey results is to contrast the performance or other business characteristics of different groupings of farm, such as between regions or other geographical or environmental designations, farm types, farm size, age or education of farmer etc. Up to and including the 2001/02 survey, FBS estimates were based on matching of the sample between two adjacent years. Farm weights were still calculated to present a matched sample however. From the 2002/03 survey onwards, matching between adjacent years was dropped altogether, and weights are now calculated for the full sample. The typology used to determine the FBS farm type classification was revised for 2009 onwards. The FBS typology is now based on standard outputs expressed in euros, with a minimum threshold of 25,000 euro (irrespective of the SLR) for FBS eligibility. Between 2009 and 2011, FBS farm type classification has been based on 2007 standard output (SO) coefficients. From 2012 to 2016, FBS farm type classification was based on 2010 SO coefficients, and from 2017 the FBS farm type classifications are based on 2013 SO coefficients. The change in typology has had an effect on the distribution of farms by farm type and income averages. Further information regarding the change in typology is available from the 'FBS Documents' section on the gov.uk Farm Business Survey – technical notes and guidance webpage. The Farm Business Survey is available under Special Licence access conditions. For further details on how to apply for access to the data, see the Access section below. Latest edition informationFor the second edition (July 2021) a new version of the database was deposited, with previously unavailable sections F3, K, N and R added. The documentation has also been updated. Main Topics: Variables cover general and physical farm characteristics, labour, crops (previous and current harvest year, set-aside, by-products, forage and cultivations); miscellaneous receipts, livestock (dairy and beef cattle, sheep, pigs, poultry, miscellaneous livestock), variable and fixed costs, assets, investment, liabilities, income from diversified activities (integrated and semi-integrated into the farm business), farmer and spouse off-farm hours and incomes, subsidies. One-stage stratified or systematic random sample Telephone interview Transcription Face-to-face interview

Abstract copyright UK Data Service and data collection copyright owner.The Farm Business Survey (FBS) is conducted annually to collect business information from c.2,400 farms in England and Wales. The FBS provides information on the financial position and physical and economic performance of farm businesses, to inform policy decisions on matters affecting farm businesses and to enable analysis of impacts of policy options. It is intended to serve the needs of farmers, farming and land management interest groups, government (both national and European), government partners, and researchers. The primary objective of survey results is to contrast the performance or other business characteristics of different groupings of farm, such as between regions or other geographical or environmental designations, farm types, farm size, age or education of farmer etc. Up to and including the 2001/02 survey, FBS estimates were based on matching of the sample between two adjacent years. Farm weights were still calculated to present a matched sample however. From the 2002/03 survey onwards, matching between adjacent years was dropped altogether, and weights are now calculated for the full sample. The typology used to determine the FBS farm type classification was revised for 2009 onwards. The FBS typology is now based on standard outputs expressed in euros, with a minimum threshold of 25,000 euro (irrespective of the SLR) for FBS eligibility. Between 2009 and 2011, FBS farm type classification has been based on 2007 standard output (SO) coefficients. From 2012 to 2016, FBS farm type classification was based on 2010 SO coefficients, and from 2017 the FBS farm type classifications are based on 2013 SO coefficients. The change in typology has had an effect on the distribution of farms by farm type and income averages. Further information regarding the change in typology is available from the 'FBS Documents' section on the gov.uk Farm Business Survey – technical notes and guidance webpage. The Farm Business Survey is available under Special Licence access conditions. For further details on how to apply for access to the data, see the Access section below. For the second edition (February 2010), the data and documentation have been updated. This study was previously held in the form of SPSS files generated from a SIR database, which covered two survey years. The depositors have recently undergone a program to compile older data in the FBS series into Access databases, both to improve usability of the older data and compatibility with the later FBS waves, that are already available in Access format. Documentation has also been provided in similar format to current FBS surveys. Main Topics: Variables cover general and physical farm characteristics, labour, crops (previous and current harvest year, set-aside, by-products, forage and cultivations); miscellaneous receipts, livestock (dairy and beef cattle, sheep, pigs, poultry, miscellaneous livestock), variable and fixed costs, assets, investment, liabilities, income from diversified activities (integrated and semi-integrated into the farm business), farmer and spouse off-farm hours and incomes, subsidies.

Quick Stats API is the programmatic interface to the National Agricultural Statistics Service's (NASS) online database containing 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.

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.

Geographic coverage

National coverage

Analysis unit

Households

Universe

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

Kind of data

Census/enumeration data [cen]

Sampling procedure

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

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

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

Mode of data collection

Mail Questionnaire [mail]

Research instrument

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

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

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

Cleaning operations

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

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

Data appraisal

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

Global Smart Agriculture Market size is set to expand from $ 14.86 Billion in 2023 to $ 61.78 Billion by 2032, a CAGR of around 15.3% from 2024 to 2032.

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.

The dataset contains information collected from 122 K-12 public school food service directors in Mississippi, USA, who completed an online survey designed for Mississippi school food service directors. The survey was created using Snap Surveys Desktop software. Information includes school size (number of enrolled students), percent of students participating in free or reduced-price lunch, foods sourced locally (defined as grown or produced in Mississippi), desire to purchase more or start purchasing locally sourced foods, fresh fruit and vegetable purchasing practices, experience purchasing fruits and vegetables from farmers, challenges purchasing from farmers, and interest in other farm to school (F2S) activities. School food service directors' demographic characteristics collected include gender, age, ethnicity/race, marital status, and education level. The data were collected from October 2021 to January 2022 using an online mobile and secure survey management system called Snap Online. The data were collected to obtain updated demographic and school purchasing characteristics from school food service directors in Mississippi and to determine their current abilities, experiences, and desires to engage in F2S activities. The dataset can be used to learn about K-12 public school food service directors in Mississippi but results should not be generalized to all school food service directors in Mississippi or elsewhere in the USA. Resources in this dataset:Resource Title: Mississippi Farm to School Food Service Director Dataset. File Name: MS F2S School Data Public.csvResource Description: The dataset contains information collected from 122 K-12 public school food service directors in Mississippi regarding their experience with and interest in farm to school, including purchasing local foods. It also contains demographic characteristics of the school food service directors and their fresh fruit and vegetable purchasing practices.Resource Software Recommended: Microsoft Excel,url: https://www.microsoft.com/en-us/microsoft-365/excel Resource Title: Mississippi Farm to School Food Service Director Data Dictionary. File Name: MS F2S School Data Dictionary Public.csvResource Description: The file contains information for variables contained in the associated dataset including names, brief descriptions, types, lengths, and values.Resource Software Recommended: Microsoft Excel,url: https://www.microsoft.com/en-us/microsoft-365/excel

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.

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.

(Link to Metadata) This datalayer contains Vermont agricultural data describing changes in farming activity (1860-1997), by county, extracted from U.S. Census of Agriculture. Initial 1969-1992 data was originally collected by Rick Wackernagel, UVM Extension Service, Department of Community Development & Applied Economics (CDAE), then distributed by VCGI as the data product 'FARMDATA' in 1995. VCGI has since updated and added additional years and items to the dataset, and released it as the geospatial database FarmStats_CNTYFARM. Funding for much of this data was provided by the 'Datashare' agreement between VCGI and The USDA-Natural Resources Consevation Service. Many thanks to Ray Godfrey, USDA-NRCS, for helping to make this datalayer available to the public. One may download Excel spreadsheets or comma-delimited ASCII text files of this data from the VGIS indicators webpage - [URL defunct]

The drone data collection service market is experiencing robust growth, driven by increasing demand across various sectors. The market's expansion is fueled by technological advancements leading to higher-resolution imagery, improved data processing capabilities, and more affordable drone technology. Industries like construction, agriculture, and infrastructure are increasingly adopting drone-based data collection for tasks such as site surveying, progress monitoring, crop health assessment, and pipeline inspection. This shift towards efficient and cost-effective data acquisition methods is a primary driver. The market is segmented by application (e.g., surveying, mapping, inspection), drone type (e.g., fixed-wing, rotary-wing), and end-user industry. While the initial investment in drones and specialized software can be a barrier for some, the long-term cost savings and efficiency gains are significant, overcoming this hurdle for many organizations. Competition is intensifying among established players and emerging companies, leading to innovation in data processing algorithms and service offerings. Looking ahead, the market is poised for continued expansion. Factors contributing to future growth include the increasing integration of AI and machine learning in data analysis, the development of more autonomous drone systems, and regulatory developments facilitating broader drone usage. The global adoption of 5G and improved communication infrastructure will further enhance real-time data transfer and processing capabilities. Although potential restraints such as stringent regulations in certain regions and concerns about data security and privacy could moderate growth, the overall market trajectory remains strongly positive. The presence of numerous companies, including both established players like Atkins and emerging specialists like Hivemapper, reflects the vibrant and competitive nature of this rapidly evolving market. The market is expected to see continued consolidation as larger companies acquire smaller, specialized firms to expand their service portfolios.

Livestock Farm Management Software Market Outlook

The global market size for Livestock Farm Management Software is anticipated to grow significantly from USD 1.2 billion in 2023 to USD 3.8 billion by 2032, with a robust CAGR of 13.5% during the forecast period. The growth of this market is fueled by technological advancements in agriculture, increasing demand for livestock products, and the adoption of precision farming practices.

One of the primary growth factors for the Livestock Farm Management Software market is the technological advancement in the agricultural sector. Innovations such as the Internet of Things (IoT), artificial intelligence (AI), and big data analytics are transforming traditional farming practices. These technologies enable real-time monitoring and data collection, which help farmers make informed decisions, optimize resource utilization, and enhance productivity. The integration of these advanced technologies into farm management software solutions is significantly boosting market growth.

Another key driver for the market is the increasing demand for livestock products such as meat, dairy, and eggs. With the global population on the rise and an increasing awareness of the nutritional benefits of animal products, the demand for livestock products is expected to grow. This surge in demand necessitates efficient livestock management practices to ensure high-quality production, animal health, and sustainability. Livestock farm management software provides the tools needed to monitor and manage various aspects of livestock farming, thereby supporting the growing demand.

The adoption of precision farming practices is also contributing to the growth of the Livestock Farm Management Software market. Precision farming involves the use of advanced technologies and data-driven approaches to optimize agricultural practices. In the context of livestock farming, precision farming techniques help in monitoring animal health, managing feed, tracking breeding activities, and ensuring financial management. The implementation of precision farming practices through software solutions not only enhances farm productivity but also promotes sustainable farming practices.

Farm Accounting Software plays a crucial role in the financial management aspect of livestock farming. As farms grow and operations become more complex, the need for robust accounting solutions becomes evident. This software helps farmers manage their finances by tracking expenses, revenues, and cash flow, ensuring that they maintain a healthy financial status. With features such as budgeting, financial forecasting, and detailed reporting, farm accounting software provides the necessary tools for farmers to make informed financial decisions. The integration of this software with other farm management solutions allows for a seamless flow of information, enhancing overall farm efficiency and productivity. As the demand for transparency and accountability in farming operations increases, the adoption of farm accounting software is expected to rise significantly.

From a regional perspective, North America holds a significant share of the Livestock Farm Management Software market. The region's advanced technological infrastructure, high adoption rate of digital solutions in agriculture, and the presence of major market players contribute to its leading position. Additionally, the growing awareness about the benefits of precision farming and the increasing demand for livestock products further drive market growth in North America. Other regions such as Europe, Asia Pacific, and Latin America are also witnessing substantial growth, driven by similar factors and the increasing focus on sustainable agriculture.

Component Analysis

The Livestock Farm Management Software market is segmented by component into software and services. The software segment encompasses various types of farm management solutions that cater to different aspects of livestock farming. These solutions include herd management software, animal health monitoring software, and feed management software, among others. The software segment is expected to dominate the market due to the increasing adoption of digital solutions for efficient farm management. These software solutions help farmers in data collection, analysis, and decision-making processes, thereby enhancing overall farm productivity.

In the services segment, the market includes various support services such as in

This EnviroAtlas data set depicts estimates for mean cash rent paid for land by farmers, sorted by county for irrigated cropland, non-irrigated cropland, and pasture by for most of the conterminous US. This data comes from national surveys which includes approximately 240,000 farms and applies to all crops. According to the USDA (U.S. Department of Agriculture) National Agricultural Statistics Service (NASS), these surveys do not include land rented for a share of the crop, on a fee per head, per pound of gain, by animal unit month (AUM), rented free of charge, or land that includes buildings such as barns. For each land use category with positive acres, respondents are given the option of reporting rent per acre or total dollars paid. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).

The LDMI experiment (Low-Disturbance Manure Incorporation) was designed to evaluate nutrient losses with conventional and improved liquid dairy manure management practices in a corn silage (Zea mays) / rye cover-crop (Secale cereale) system. The improved manure management treatments were designed to incorporate manure while maintaining crop residue for erosion control. Field observations included greenhouse gas (GHG) fluxes from soil, soil nutrient concentrations, crop growth and harvest biomass and nutrient content, as well as monitoring of soil physical and chemical properties. Observations from LDMI have been used for parameterization and validation of computer simulation models of GHG emissions from dairy farms (Gaillard et al., submitted). The LDMI experiment was performed as part of the Dairy CAP, described below. The experiment included ten different treatments: (1) broadcast manure with disk-harrow incorporation, (2) broadcast manure with no tillage incorporation, (3) manure application with “strip-tillage” which was sweep injection ridged with paired disks, (4) aerator band manure application, (5) low-disturbance sweep injection of manure, (6) Coulter injection of manure with sweep tillage, (7) no manure with urea to supply 60 lb N/acre (67 kg N/ha), (8) no manure with urea to supply 120 lb N/acre (135 kg N/ha), (9) no manure with urea to supply 180 lb N/acre (202 kg N/ha), (10) no manure / no fertilizer control. Manure was applied in the fall; fertilizer was applied in the spring. These ten treatments were replicated four times in a randomized complete block design. The LDMI experiment was conducted at the Marshfield Research Station of the University of Wisconsin and the USDA Agricultural Research Service (ARS) in Stratford, WI (Marathon County, Latitude 44.7627, Longitude -90.0938). Soils at the research station are from the Withee soil series, fine-loamy, mixed, superactive, frigid Aquic Glossudalf. Each experimental plot was approximately 70 square meters. A weather station was located at the south edge of field site. A secondary weather station (MARS South), for snow and snow water equivalence data and for backup of the main weather station, was located at Latitude 44.641445 and Longitude -90.133526 (16,093 meters southwest of the field site). The experiment was initiated on November 28, 2011 with fall tillage and manure application in each plot according to its treatment type. Each spring, corn silage was planted in rows at a rate of 87500 plants per hectare. The cultivar was Pioneer P8906HR. The LDMI experiment ended on November 30, 2015. The manure applied in this experiment was from the dairy herd at the Marshfield Research Station. Cows were fed a diet of 48% dry matter, 17.45% protein, and 72.8% total digestible nutrients. Liquid slurry manure, including feces, urine, and rain, was collected and stored in a lagoon on the site. Manure was withdrawn from the lagoon, spread on the plots and sampled for analysis all on the same day, once per year. Manure samples were analyzed at the University of Wisconsin Soil and Forage Lab in Marshfield (NH4-N, total P and total K) and at the Marshfield ARS (pH, dry matter, volatile solids, total N and total C). GHG fluxes from soil (CO2, CH4, N2O) were measured using static chambers as described in Parkin and Venterea (2010). Measurements were made with the chambers placed across the rows of corn. I Additional soil chemical and physical characteristics were measured as noted in the data dictionary and other metadata of the LDMI data set, included here. This experiment was part of “Climate Change Mitigation and Adaptation in Dairy Production Systems of the Great Lakes Region,” also known as the Dairy Coordinated Agricultural Project (Dairy CAP), funded by the United States Department of Agriculture - National Institute of Food and Agriculture (award number 2013-68002-20525). The main goal of the Dairy CAP was to improve understanding of the magnitudes and controlling factors over GHG emissions from dairy production in the Great Lakes region. Using this knowledge, the Dairy CAP has improved life cycle analysis (LCA) of GHG production by Great Lakes dairy farms, developing farm management tools, and conducting extension, education and outreach activities. Resources in this dataset:Resource Title: Data_dictionary_DairyCAP_LDMI. File Name: Data_dictionary_DairyCAP_LDMI.xlsxResource Description: This is the data dictionary for the Low-Disturbance Manure Incorporation (LDMI) experiment, conducted at the USDA-ARS research station in Marshfield, WI. (Separate spreadsheet tabs)Resource Software Recommended: Microsoft Excel 2016,url: https://products.office.com/en-us/excel Resource Title: DairyCAP_LDMI. File Name: DairyCAP_LDMI.xlsxResource Description: This is the data from the Low-Disturbance Manure Incorporation (LDMI) experiment, conducted at the USDA-ARS research station in Marshfield, WI.Resource Software Recommended: Microsoft Excel 2016,url: https://products.office.com/en-us/excel Resource Title: Data Dictionary DairyCAP LDMI. File Name: Data_dictionary_DairyCAP_LDMI.csvResource Description: This is the data dictionary for the Low-Disturbance Manure Incorporation (LDMI) experiment, conducted at the USDA-ARS research station in Marshfield, WI.

Resource Title: Biomass Data. File Name: LDMI_Biomass.csvResource Title: Experimental Set-up Data. File Name: LDMI_Exp_setup.csvResource Title: Gas Flux Data. File Name: LDMI_Gas_Fluxes.csvResource Title: Management History Data. File Name: LDMI_Management_History.csvResource Title: Manure Analysis Data. File Name: LDMI_Manure_Analysis.csvResource Title: Soil Chemical Data. File Name: LDMI_Soil_Chem.csvResource Title: Soil Physical Data. File Name: LDMI_Soil_Phys.csvResource Title: Weather Data. File Name: LDMI_Weather.csv