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

The Agri-Environmental Spatial Data (AESD) product from the Census of Agriculture provides a large selection of farm-level variables from the Census of Agriculture and uses alternative data sources to improve the spatial distribution of the production activities. Therefore, the AESD database offers clients the possibility to better analyze the impact of agriculture activities on the environment and produce key indicators, or for any applications where accurate location of activities matters. Variables are offered using two types of physical boundaries: by Soil Landscape of Canada polygons and by Sub-sub-drainage areas (watersheds). The focus of the redistribution of the data is on the field crops and land use variables, but the database includes all census variables related to crops, livestock and management practices. This frame can also be used to extract Census of Agriculture data by custom geographic areas. Also, users interested in this version of the Census of Agriculture database using administrative types of regions can request it. In both cases, please contact Statistics Canada. This file was produced by Statistics Canada, Agriculture Division, Remote Sensing and Geospatial Analysis section, 2022, Ottawa.

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

Database created for identifying mixed agriculture landscape in the framework of workpackage 3, task 3.3 Mapping mixed landscapes in Europe using existing data (top-down approach) of the MIXED project (Multi-actor and transdisciplinary development of efficient and resilient MIXED farming and agroforestry-systems) This database is presented and used in deliverable 3.3 Identifying the potential for expansion of mixed farming in European regions. This data record contains the final database used for delivererable 3.3 (databaseD3.3.csv) a description of the data in the database (datadescription.xlsx) the shapfiles to create maps from the database (map.xxx) Note that the data description files mentions the source of the raw data. Please consult the deliverable to understand how the data was processed and how the map was created.

The Census of Agriculture provides a detailed picture every five years of U.S. farms and ranches and the people who operate them. Conducted by USDA's National Agricultural Statistics Service, the 2012 Census of Agriculture collected more than six million data items directly from farmers. The Ag Census Web Maps application makes this information available at the county level through a few clicks. The maps and accompanying data help users visualize, download, and analyze Census of Agriculture data in a geospatial context. Resources in this dataset:Resource Title: Ag Census Web Maps. File Name: Web Page, url: https://www.nass.usda.gov/Publications/AgCensus/2012/Online_Resources/Ag_Census_Web_Maps/Overview/index.php/ The interactive map application assembles maps and statistics from the 2012 Census of Agriculture in five broad categories:

Crops and Plants – Data on harvested acreage for major field crops, hay, and other forage crops, as well as acreage data for vegetables, fruits, tree nuts, and berries. Economics – Data on agriculture sales, farm income, government payments from conservation and farm programs, amounts received from loans, a broad range of production expenses, and value of buildings and equipment. Farms – Information on farm size, ownership, and Internet access, as well as data on total land in farms, land use, irrigation, fertilized cropland, and enrollment in crop insurance programs. Livestock and Animals – Statistics on cattle and calves, cows and heifers, milk cows, and other cattle, as well as hogs, sheep, goats, horses, and broilers. Operators – Statistics on hired farm labor, tenure, land rented or leased, primary occupation of farm operator, and demographic characteristics such as age, sex, race/ethnicity, and residence location.

The Ag Census Web Maps application allows you to:

Select a map to display from a the above five general categories and associated subcategories. Zoom and pan to a specific area; use the inset buttons to center the map on the continental United States; zoom to a specific state; and show the state mask to fade areas surrounding the state. Create and print maps showing the variation in a single data item across the United States (for example, average value of agricultural products sold per farm). Select a county and view and download the county’s data for a general category. Download the U.S. county-level dataset of mapped values for all categories in Microsoft ® Excel format.

This table contains data on land use, arable farming, horticulture, grassland, grazing livestock and housed animals, at regional level, by general farm type.

The figures in this table are derived from the agricultural census. Data collection for the agricultural census is part of a combined data collection for a.o. agricultural policy use and enforcement of the manure law.

Regional breakdown is based on the main location of the holding. Due to this the region where activities (crops, animals) are allocated may differ from the location where these activities actually occur.

The agricultural census is also used as the basis for the European Farm Structure Survey (FSS). Data from the agricultural census do not fully coincide with the FSS. In the FSS years (2000, 2003, 2005, 2007 and 2010) additional information was collected to meet the requirements of the FSS.

Reference date for livestock is 1 April and for crops 15 May.

Data available from: 2000

Status of the figures: the 2025 figures are provisional; the other figures are final.

Changes as of November 28, 2025: the provisional 2025 figures have been added.

When will new figures be published? According to regular planning provisional figures are published in November and the definite figures will follow in March of the following year.

Data collected from the Northern Farming Systems Project. The collection includes data describing crop production; agronomy; soil water, nitrogen and biology; herbicide, pesticide and fertiliser applications; and system economics for 8 farming systems experiments distributed throughout QLD (Emerald, Eastern Darling Downs, Western Darling Downs) and NSW (Spring Ridge, Trangie, Narrabri), collected from 2014 to present. Lineage: Data was collected from field experiments from the Northern Farming Systems project.

This database compiles secondary data originating from the National Statistical Offices of Kazakhstan, Kyrgyzstan, Tajikistan, Turkmenistan and Uzbekistan.
The database includes information about major agricultural statistics such as structure of the agricultural sector, land use, production of most important crops and livestock outputs as well as size of population at the level of provinces (oblasts). Most land use and production data are available for the three farm types, agricultural enterprises, individual farms, and households, and in aggregated form. Time coverage ranges from 1992 to 2014 (with few statistics additionally for 1991 and 2015), with some gaps in early years after independence.
The database is a product of the research project "AGRIWANET – Agricultural Restructuring, Water Scarcity and the Adaptation to Climate Change in Central Asia: A Five-Country Study".

Abstract

Syngenta is committed to increasing crop productivity and to using limited resources such as land, water and inputs more efficiently. Since 2014, Syngenta has been measuring trends in agricultural input efficiency on a global network of real farms. The Good Growth Plan dataset shows aggregated productivity and resource efficiency indicators by harvest year. The data has been collected from more than 4,000 farms and covers more than 20 different crops in 46 countries. The data (except USA data and for Barley in UK, Germany, Poland, Czech Republic, France and Spain) was collected, consolidated and reported by Kynetec (previously Market Probe), an independent market research agency. It can be used as benchmarks for crop yield and input efficiency.

Geographic coverage

National coverage

Analysis unit

Agricultural holdings

Kind of data

Sample survey data [ssd]

Sampling procedure

A. Sample design Farms are grouped in clusters, which represent a crop grown in an area with homogenous agro- ecological conditions and include comparable types of farms. The sample includes reference and benchmark farms. The reference farms were selected by Syngenta and the benchmark farms were randomly selected by Kynetec within the same cluster.

B. Sample size Sample sizes for each cluster are determined with the aim to measure statistically significant increases in crop efficiency over time. This is done by Kynetec based on target productivity increases and assumptions regarding the variability of farm metrics in each cluster. The smaller the expected increase, the larger the sample size needed to measure significant differences over time. Variability within clusters is assumed based on public research and expert opinion. In addition, growers are also grouped in clusters as a means of keeping variances under control, as well as distinguishing between growers in terms of crop size, region and technological level. A minimum sample size of 20 interviews per cluster is needed. The minimum number of reference farms is 5 of 20. The optimal number of reference farms is 10 of 20 (balanced sample).

C. Selection procedure The respondents were picked randomly using a “quota based random sampling” procedure. Growers were first randomly selected and then checked if they complied with the quotas for crops, region, farm size etc. To avoid clustering high number of interviews at one sampling point, interviewers were instructed to do a maximum of 5 interviews in one village.

BF Screened from Indonesia were selected based on the following criterion: (a) Corn growers in East Java - Location: East Java (Kediri and Probolinggo) and Aceh
- Innovative (early adopter); Progressive (keen to learn about agronomy and pests; willing to try new technology); Loyal (loyal to technology that can help them)
- making of technical drain (having irrigation system)
- marketing network for corn: post-harvest access to market (generally they sell 80% of their harvest)
- mid-tier (sub-optimal CP/SE use)
- influenced by fellow farmers and retailers
- may need longer credit

(b) Rice growers in West and East Java - Location: West Java (Tasikmalaya), East Java (Kediri), Central Java (Blora, Cilacap, Kebumen), South Lampung
- The growers are progressive (keen to learn about agronomy and pests; willing to try new technology)
- Accustomed in using farming equipment and pesticide. (keen to learn about agronomy and pests; willing to try new technology) - A long rice cultivating experience in his area (lots of experience in cultivating rice)
- willing to move forward in order to increase his productivity (same as progressive)
- have a soil that broad enough for the upcoming project
- have influence in his group (ability to influence others) - mid-tier (sub-optimal CP/SE use)
- may need longer credit

Mode of data collection

Face-to-face [f2f]

Research instrument

Data collection tool for 2019 covered the following information:

(A) PRE- HARVEST INFORMATION

PART I: Screening PART II: Contact Information PART III: Farm Characteristics a. Biodiversity conservation b. Soil conservation c. Soil erosion d. Description of growing area e. Training on crop cultivation and safety measures PART IV: Farming Practices - Before Harvest a. Planting and fruit development - Field crops b. Planting and fruit development - Tree crops c. Planting and fruit development - Sugarcane d. Planting and fruit development - Cauliflower e. Seed treatment

(B) HARVEST INFORMATION

PART V: Farming Practices - After Harvest a. Fertilizer usage b. Crop protection products c. Harvest timing & quality per crop - Field crops d. Harvest timing & quality per crop - Tree crops e. Harvest timing & quality per crop - Sugarcane f. Harvest timing & quality per crop - Banana g. After harvest PART VI - Other inputs - After Harvest a. Input costs b. Abiotic stress c. Irrigation

See all questionnaires in external materials tab

Cleaning operations

Data processing:

Kynetec uses SPSS (Statistical Package for the Social Sciences) for data entry, cleaning, analysis, and reporting. After collection, the farm data is entered into a local database, reviewed, and quality-checked by the local Kynetec agency. In the case of missing values or inconsistencies, farmers are re-contacted. In some cases, grower data is verified with local experts (e.g. retailers) to ensure data accuracy and validity. After country-level cleaning, the farm-level data is submitted to the global Kynetec headquarters for processing. In the case of missing values or inconsistences, the local Kynetec office was re-contacted to clarify and solve issues.

Quality assurance Various consistency checks and internal controls are implemented throughout the entire data collection and reporting process in order to ensure unbiased, high quality data.

• Screening: Each grower is screened and selected by Kynetec based on cluster-specific criteria to ensure a comparable group of growers within each cluster. This helps keeping variability low.

• Evaluation of the questionnaire: The questionnaire aligns with the global objective of the project and is adapted to the local context (e.g. interviewers and growers should understand what is asked). Each year the questionnaire is evaluated based on several criteria, and updated where needed.

• Briefing of interviewers: Each year, local interviewers - familiar with the local context of farming -are thoroughly briefed to fully comprehend the questionnaire to obtain unbiased, accurate answers from respondents.

• Cross-validation of the answers: o Kynetec captures all growers' responses through a digital data-entry tool. Various logical and consistency checks are automated in this tool (e.g. total crop size in hectares cannot be larger than farm size) o Kynetec cross validates the answers of the growers in three different ways: 1. Within the grower (check if growers respond consistently during the interview) 2. Across years (check if growers respond consistently throughout the years) 3. Within cluster (compare a grower's responses with those of others in the group) o All the above mentioned inconsistencies are followed up by contacting the growers and asking them to verify their answers. The data is updated after verification. All updates are tracked.

• Check and discuss evolutions and patterns: Global evolutions are calculated, discussed and reviewed on a monthly basis jointly by Kynetec and Syngenta.

• Sensitivity analysis: sensitivity analysis is conducted to evaluate the global results in terms of outliers, retention rates and overall statistical robustness. The results of the sensitivity analysis are discussed jointly by Kynetec and Syngenta.

• It is recommended that users interested in using the administrative level 1 variable in the location dataset use this variable with care and crosscheck it with the postal code variable.

Data appraisal

Due to the above mentioned checks, irregularities in fertilizer usage data were discovered which had to be corrected:

For data collection wave 2014, respondents were asked to give a total estimate of the fertilizer NPK-rates that were applied in the fields. From 2015 onwards, the questionnaire was redesigned to be more precise and obtain data by individual fertilizer products. The new method of measuring fertilizer inputs leads to more accurate results, but also makes a year-on-year comparison difficult. After evaluating several solutions to this problems, 2014 fertilizer usage (NPK input) was re-estimated by calculating a weighted average of fertilizer usage in the following years.

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

This dataset provides comprehensive information on agricultural product sales, supply chain metrics, and pricing across various categories, including Vegetables, Fruits, Grains, Dairy, and Livestock, for the years 2022 and 2023. It contains detailed records for over 8,000 transactions, capturing essential data points such as product IDs, product names, categories, price per kilogram, units shipped, units sold, units on hand, supplier information, farm locations, and sale dates.

Brazil Agricultural Area: Planted: Permanent Crops: North: Dende data was reported at 102,685.000 ha in 2017. This records an increase from the previous number of 99,904.000 ha for 2016. Brazil Agricultural Area: Planted: Permanent Crops: North: Dende data is updated yearly, averaging 45,246.500 ha from Dec 1990 (Median) to 2017, with 28 observations. The data reached an all-time high of 102,685.000 ha in 2017 and a record low of 33,238.000 ha in 1990. Brazil Agricultural Area: Planted: Permanent Crops: North: Dende data remains active status in CEIC and is reported by Brazilian Institute of Geography and Statistics. The data is categorized under Brazil Premium Database’s Agriculture Sector – Table BR.RIA002: Agricultural Area: Farming: Permanent Crops: North.

Brazil Agricultural Area: Planted: Permanent Crops: South: Mate Herb data was reported at 75,542.000 ha in 2017. This records a decrease from the previous number of 77,323.000 ha for 2016. Brazil Agricultural Area: Planted: Permanent Crops: South: Mate Herb data is updated yearly, averaging 75,553.000 ha from Dec 1990 (Median) to 2017, with 28 observations. The data reached an all-time high of 106,019.000 ha in 2003 and a record low of 8,515.000 ha in 1990. Brazil Agricultural Area: Planted: Permanent Crops: South: Mate Herb data remains active status in CEIC and is reported by Brazilian Institute of Geography and Statistics. The data is categorized under Brazil Premium Database’s Agriculture Sector – Table BR.RIA005: Agricultural Area: Farming: Permanent Crops: South.

Data Monetization in Agriculture Market Outlook

According to our latest research, the global data monetization in agriculture market size reached USD 2.57 billion in 2024 and is exhibiting robust expansion, with a projected CAGR of 18.2% from 2025 to 2033. By the end of this forecast period, the market is anticipated to attain a value of USD 11.93 billion. The primary driver underpinning this growth is the rapid digital transformation of agriculture, where farms and agribusinesses are leveraging advanced data analytics, IoT, and AI to extract actionable insights, create new revenue streams, and optimize resource utilization. The integration of data monetization strategies is enabling stakeholders across the agricultural value chain to enhance productivity, improve sustainability, and drive profitability.

One of the most significant growth factors for the data monetization in agriculture market is the proliferation of smart farming technologies. With the adoption of IoT sensors, drones, and satellite imagery, farms are generating vast volumes of valuable data, including soil moisture levels, crop health indices, and machinery performance metrics. This influx of data, when aggregated and analyzed, provides unique opportunities for data monetization through subscription-based insights, targeted advisory services, and predictive analytics. The ability to commercialize such data is further supported by increasing investments from both public and private sectors in agri-tech infrastructure, encouraging farmers and agribusinesses to participate in data-sharing ecosystems and unlock new economic value from their operational data.

Another critical factor fueling the expansion of the data monetization in agriculture market is the growing demand for transparency and traceability across the food supply chain. Consumers, retailers, and regulatory bodies are increasingly seeking assurances regarding food safety, origin, and sustainability practices. By monetizing data related to crop production, weather patterns, and supply chain logistics, stakeholders can provide verifiable records that enhance trust and compliance. This has led to the rise of blockchain-based platforms and digital marketplaces where agricultural data is traded, further incentivizing data owners to participate in monetization initiatives. The convergence of regulatory requirements and consumer preferences is expected to sustain the momentum for data-driven innovation in the sector.

Furthermore, the surge in global food demand, coupled with the challenges posed by climate change, is compelling the agriculture industry to adopt precision farming and risk management solutions powered by data monetization. By leveraging aggregated sensor data, geospatial analytics, and financial records, stakeholders can develop tailored insurance products, optimize input usage, and mitigate risks associated with weather variability and market fluctuations. This creates a virtuous cycle where data becomes a strategic asset, not only improving operational efficiency but also generating new revenue streams through data licensing, partnerships, and value-added services. As a result, data monetization is emerging as a cornerstone of sustainable and resilient agricultural practices worldwide.

From a regional perspective, North America and Europe are currently leading the data monetization in agriculture market, driven by high digital literacy, advanced infrastructure, and supportive regulatory frameworks. However, the Asia Pacific region is poised for the fastest growth over the forecast period, fueled by increasing adoption of smart farming technologies, government initiatives to promote digital agriculture, and the rising importance of data-driven decision-making among smallholder farmers. Latin America and the Middle East & Africa are also witnessing gradual uptake, supported by investments in agri-tech startups and cross-border collaborations aimed at enhancing food security and supply chain efficiency.

Component Analysis

The data monetization in agriculture market is segmented by component

While there is increasing evidence of the sustainability benefits of diversified systems in the organic cropping sector, this has been much less investigated with organic livestock farming. To fill this knowledge gap, we surveyed a sample of 128 European organic multi-species livestock farms located across seven countries – Austria, Belgium, France, Germany, Italy, Sweden and Switzerland – and covering a large range of livestock species combinations. We recorded 1574 variables as raw data out of which we calculated 107 indicators describing farm structure, management and several sustainability dimensions: resource use efficiency and conservation, animal, land and work productivities, animal and human welfare. After technical validation of the data, we withdrew 26 farms and the database covers 102 farms. This database is well suited to unveil relationships between various dimensions of organic multi-species livestock farm sustainability and their structure and management. It can help reveal sustainable strategies for organic multi-species livestock farming systems, and levers or barriers to their development. Scripts: https://forgemia.inra.fr/mix-enable/mix-enable_indicators/

This “Reference Manual” is the No. 3 of the FAO Pacific Farm Management and Marketing Series. Its content is based on material developed for FAO funded technical cooperation projects in Vanuatu, Palau, Fiji and Papua New Guinea as well as material prepared by the Agriculture Management, Marketing and Finance Service (AGSF) at FAO Headquarters, Rome. It is designed to help trainers and advisors guide small farmers in their efforts to grow and market their agriculture so as to earn more cash from their labor, their land and their capital

This dataset consists of soil data for 64 field sites on paired farm sites, with 29 variables measured for soil texture and structural condition, aggregate stability, organic matter content, soil shear strength, fuel consumption, work rate, infiltration rate, water quality and hydrological condition (HOST) data. The study is part of the NERC Rural Economy and Land Use (RELU) programme. A move to organic farming can have significant effects on wildlife, soil and water quality, as well as changing the ways in which food is supplied, the economics of farm business and indeed the attitudes of farmers themselves. Two key questions were addressed in the SCALE project: what causes organic farms to be arranged in clusters at local, regional and national scales, rather than be spread more evenly throughout the landscape; and how do the ecological, hydrological, socio-economic and cultural impacts of organic farming vary due to neighbourhood effects at a variety of scales. The research was undertaken in 2006-2007 in two study sites: one in the English Midlands, and one in southern England. Both are sites in which organic farming has a 'strong' local presence, which we defined as 10 per cent or more organically managed land within a 10 km radius. Potential organic farms were identified through membership lists of organic farmers provided by two certification bodies (the Soil Association and the Organic Farmers and Growers). Most who were currently farming (i.e. their listing was not out of date) agreed to participate. Conventional farms were identified through telephone listings. Respondents' farms ranged in size from 40 to 3000 acres, with the majority farming between 100 and 1000 acres. Most were mixed crop-livestock farmers, with dairy most common in the southern site, and beef and/or sheep mixed with arable in the Midlands. In total, 48 farms were studied, of which 21 were organic farmers. No respondent had converted from organic to conventional production, whereas 17 had converted from conventional to organic farming. Twelve of the conventional farmers defined themselves as practicing low input agriculture. Farmer interview data from this study are available at the UK Data Archive under study number 6761 (see online resources). Further documentation for this study may be found through the RELU Knowledge Portal and the project's ESRC funding award web page (see online resources).

• Entries for English-Spanish: Scientific research & mathematical sciences (906 entries), Geosciences (10,215), Computer science, electronics & telecommunications (70,580), Industry (47,578), Transport & Maintenance (12,291), Economy (145,572), Biological sciences (38,989), Communication & media (8,143), Chemical & physical sciences (27,467). * Entries for English-French-German-Spanish: Environment (36,658), Health (66,727), Agriculture & food (25,975), Construction & public works (8,429), Law & policy (56,578), Sports & Leisure (17,312) * Two specialized lexicons: Spanish-English and English-French-German without domain codes: electronics, telematics, law, taxes, customs, etc. (550,000 entries). * Two general lexicons: Spanish-English-French-German and Spanish-English-French-German-Portuguese-Italian (83,000 entries).This terminological database contains, for each domain, a sub-domain indication is given (from 2 sub-domains for Scientific research to 39 for Sports & leisure). Each entry consists of a definition, phraseological unit, abbreviation, usage information, grammatical labels. Format: ASCII

Statistics Canada conducts the Census of Agriculture every five years at the same time as the Census of Population. The most recent Census of Agriculture was on May 15, 2001.The Census of Agriculture collects and disseminates a wide range of data on the agriculture industry such as number and type of farms, farm operator characteristics, business operating arrangements, land management practices, crop areas, numbers of livestock and poultry, farm capital, operating expenses and receipts, and farm machinery and equipment. These data provide a comprehensive picture of the agriculture industry across Canada every five years at the national and provincial levels as well as at lower levels of geography. The Census of Agriculture is the cornerstone of Canada's Agriculture Statistics Program. Census of Agriculture data are an indispensable public and private sector tool for analysing important changes in the agriculture and food industries;developing, implementing and evaluating agricultural policies and programs such as farm income safety nets and environmental sustainability; and making production, marketing and investment decisions. Statistics Canada uses the data as benchmarks for its regular surveys on crops, livestock and farm finances between census years. In addition, data extracted from the unique Agriculture Population Linkage Database, which links data from both the Census of Population and Census of Agriculture databases, paint a socio-economic portrait not only of farm operators but also of their families and households. This release contains all farm data and farm operations data plus selected historical files. In 2001, a census farm was defined as an agricultural operation that produces at least one of the following products intended for sale: crops (hay, field crops, tree fruits or nuts, berries or grapes, vegetables, seed); livestock (cattle, pigs, sheep, horses, game animals, other livestock); poultry (hens, chickens, turkeys, chicks, game birds, other poultry); animal products (milk or cream, eggs, wool, furs, meat); or other agricultural products (Christmas trees, greenhouse or nursery products, mushrooms, sod, honey, maple syrup products). For 2001, a new farm type classification based on the North American Industrial Classification System (NAICS) has been added to the historical classification used in previous censuses. All tabulated data are subject to confidentiality restrictions prior to release. Due to confidentiality constraints, data for those geographic areas with very few agricultural operations are not released separately, but rather merged with a geographically adjacent area.