About

Overview from FADN home page:

The Farm Accountancy Data Network (FADN) is an instrument for evaluating the income of agricultural holdings and the impacts of the Common Agricultural Policy.

Downloading

No bulk downloading. Material is available through a search interface:

• http://ec.europa.eu/agriculture/rica/database/database.cfm

There are also various PDF, DOC and other files available at:

• http://circa.europa.eu/Public/irc/agri/rica/library

Data

Information on data can be found at:

• http://ec.europa.eu/agriculture/rica/diffusion_en.cfm
• http://ec.europa.eu/agriculture/rica/reference_en.cfm

Openness

Free to be re-used as long as attribution is given. The Copyright notice on the European Commission website states:

Reproduction is authorised, provided the source is acknowledged, save where otherwise stated.

Where prior permission must be obtained for the reproduction or use of textual and multimedia information (sound, images, software, etc.), such permission shall cancel the above-mentioned general permission and shall clearly indicate any restrictions on use.

This dataset comprises spatial and temporal agricultural data compiled from the Farm Accountancy Data Network (FADN) Public Database available at the FADN-region level and further disaggregated using Corine Land Cover (CLC) information about agricultural area.

Processing to NUTS level:

CLC data layers were used to overlay what is defined as "Agricultural areas" in CLC level 1classification (#2**) with FADN and NUTS regions. The overlay allows to calculate area-weighted shares and further to allocate FADN farm weights to the NUTS level. This allows the application of weights at the NUTS granularity. Please keep in mind, this is only possible under the assumption of heterogeneous farms within the FADN region.

File description:

The dataset consists of eight files, corresponding to four different levels of NUTS coding (NUTS 0-3) according to the 2016 NUTS specification and each of those for the two different sampling periods.
FADN data from 2004 onwards, standard results calculated for farms grouped according to EU typology of agricultural holdings based on standard output (SO).
FADN data from 1989 to 2009, standard results calculated for farms grouped according to EU typology of agricultural holdings based on standard gross margin (SGM).

For each csv file, the following columns are included:

Identifier:

1. NUTS Code: The unique identifier for the NUTS (2016) region
2. Year: The year of the data point

Variables:

3. weighting: number of farms represented
4. and after: SE standard result variables, for detailed description, please have a look at the accompanied xlsx: variable_description_zenodo.xlsx

Source information:

The raw data for the public Farm Accountancy Data Network (FADN) can be accessed through the official platform using the following link: FADN Public Database.

The CLC layers for the weighting of the spatial disaggregation can be accessed via the Copernicus homepage undern the following link: https://land.copernicus.eu/en/products/corine-land-cover

The Farm Accountancy Data Network (FADN) is an instrument for evaluating the income of agricultural holdings and the impacts of the Common Agricultural Policy. The concept of the FADN was launched in 1965, when Council Regulation 79/65 established the legal basis for the organisation of the network. It consists of an annual survey carried out by the Member States of the European Union. The services responsible in the Union for the operation of the FADN collect every year accountancy data from a sample of the agricultural holdings in the European Union. Derived from national surveys, the FADN is the only source of microeconomic data that is harmonised, i.e. the bookkeeping principles are the same in all countries. Holdings are selected to take part in the survey on the basis of sampling plans established at the level of each region in the Union. The survey does not cover all the agricultural holdings in the Union but only those which due to their size could be considered commercial. FADN information is aggregated into a Standard Results database available for the following dimensions: Time (year), geographic (Country, Region), Typology (Type of Farming, TF8/TF14 and economic size SIZ6). The database can be consulted through a set of dynamic reports organized in themes or a set of data files for download only.

The dairy database comprises average values for a wide range of variables (110 or 119), available in 4 worksheets: BasicFarmType (18 rows), DetailedFarmType (10 rows), ClimateClass+BasicFarmType (100 rows), NUTS+DetailedFarmType (1452 rows). Data are omitted when the sample size (n) is below 15, as per the confidentiality agreement under FADN data use rules.

A combined farm characterisation database was constructed using two major data sources, the Farm Accountancy Data Network (FADN), and the Gridded Agro-Meteorological Data in Europe (AGRI4CAST). The database initially constructed was further enhanced through the addition of forage and crop yield data from the Food and Agriculture Organization of the United Nations (FAO) and the International Institute for Applied Systems Analysis (IIASA) developed Agro-Ecological Zones (AEZ) methodology database (FAO, 2012). The data was processed and is presented in D1.2 as two databases (dairy and beef), as averages for a wide range of variables at basic or detailed farm types, and at NUTS2 regional scale.

Detailed FADN data (anonymised individual farm data) was requested for all ruminant and mixed farm types, over 10 years and the most recent data available at request (2011-2013) was utilised for the analysis. Following receipt of the data (~250k farms) this has been compiled into two consistent datasets, one for dairy (141,961) farms and one for beef farms (54,417). Each dataset comprises some values directly from the FADN data, but also a large number of calculated variables, to identify dairy or beef enterprise performance at per animal, per output product unit or per hectare. These values were calculated according to the respective dairy and beef enterprise allocation methodologies described by FADN. Further economic and structural variables have been calculated as necessary, as described in GenTORE D1.1 (Quiédeville et al., 2019).

For each farm within the dataset, the structural, production and economic data from the FADN data is supplemented with the addition of meteorological data. The daily meteorological data was downloaded from the AGRI4STAT database web portal at a NUTS2 scale. For each NUTS2 region data was available for a number of weather stations. This large dataset was processed through scripts in STATA software to generate annual values for a wide range of climatic variables, including Temperature Humidity Index (THI), and indicators of drought and seasonality of weather. Furthermore, the altitude values per weather station allowed for a sub-grouping of weather station data by altitude zone (aligned with values available in the FADN dataset).

Using a Latent Class Analysis process, the meteorological data was analysed to identify consistent environmental regions in Europe. Selected climatic variables, together with altitude zone, were utilised to statistically identify differing zones, and to classify each NUTS2 region to a zone, resulting in 6 lowland zones and 3 upland zones (above 600m) The LCA process enhanced an earlier method of manually overlaying the Metzger et al. (20054) pedo-climatic zone allocation, but closely correlates. Therefore for each farm in the dairy and beef datasets, meteorological and environmental zone data was allocated on a NUTS2 by altitude zone basis and this dataset has been subsequently assessed and submitted as papers; Quiédeville et al., (submitted May 2020) and Grovermann et al. (submitted May 2020).

The GAEZ forage and crop yield data was downloaded from the GAEZ data portal as baseline and two future climate prediction periods: Baseline (1961-2000), 2020s (2011-2040), and 2050s (2041-2070), for the Hadley CM3 model and IPCC scenario A (the most extreme scenario). See: http://www.fao.org/nr/gaez/about-data-portal/agro-climatic-resources/en/#). A zonal statistics was applied to the GAEZ layers to aggregate the data to NUT2 region and altitude zone (0-300m, 300-600m, 600m+) with raster package in R. The result is an average yield[1] for varying forages and crops for each altitude zone in each nuts2, for both the baseline and the future climate scenario. This data allows further analysis of the future impacts on cattle farming at both a regional scale, but also by farm type or system, which may be affected differently (Moakes et al. in preparation).

All variable processing from FADN data is shown in the Annex, as performed in Stata software.

[1] The mean was performed on non-zero yield pixels in order to exclude non-suitable areas from average.

The purpose of this evaluation is to assess the representativeness of the means, or estimates, derived from the Farm Accountancy Data Network (FADN) by comparing them to the corresponding population averages and by providing associated precision measures.

This is achieved, first, by comparing FADN estimates to population averages for 2020 obtained from Integrated Farm Statistics (IFS). The population of interest for FADN includes farms that belong to IFS and have an economic size above a minimum threshold specified by each Member State. This Eurostat-FADN comparison focuses on three indicators: Utilised Agricultural Area (UAA) in hectares, Standard Output (SO) in euro, and Livestock Units (LU).

The dashboard also provides measures of precision of FADN estimates for a large number of FADN indicators. This precision assessment is based solely on FADN data and presents estimates from 2011 to 2020 at various levels of aggregation. Estimates account for the FADN sampling design, and standard errors are obtained using variance linearization techniques.

PPP usage estimates (kg) obtained by Farm Accountancy Data Network (FADN), industry (Phytofar) and Flemish Environment Agency (VMM) and PPP sales figures (kg) obtained by Federal Public Service (FPS) of the active substances in Belgium with the largest impact on results for the period 2009–2012.

With this database, we examine the impact of taking feeding into account in the calculation of enteric methane emissions of a panel of French dairy farms and estimate the extra-costs of adding more grasslands in fodder systems. A balanced panel of 735 dairy farms from the farm accountancy data network (FADN) for the years 2016 to 2018 was selected (otexe 45), downloaded online from Agreste. This database is representative for socio-economic and accountancy information of French medium and large farms and is relevant for assessing the financial needs of dairy farms to join a national programme such as Eco-Methane. We compare an indicator constructed using the Eco-Methane methodology with an indicator that only takes into account productivity. The values of the Eco-Methane indicator were collected from the association Bleu-Blanc-Coeur, and allocated to FADN observations based on their location and share of maize in the fodder area. The enteric methane emissions indicator which only accounts for productivity was calculated with FADN variables based on the Tier 2 methodology implemented in the annual French GHG inventories conducted by the Citepa. We use FADN data on production quantities, the factors of production, fodder crop rotation systems and fuel and feed expenses to estimate a variable cost function of milk production to assess marginal costs and evaluate the extra-cost associated with adding more grass in fodder crop rotation systems.

Economic reports on EU farming, based on sample data from the Farm Accountancy Data Network (FADN).

The Farm Accountancy Data Network (FADN) is an instrument for evaluating the income of agricultural holdings and the impacts of the Common Agricultural Policy. The concept of the FADN was launched in 1965, when Council Regulation 79/65 established the legal basis for the organisation of the network. It consists of an annual survey carried out by the Member States of the European Union. The services responsible in the Union for the operation of the FADN collect every year accountancy data from a sample of the agricultural holdings in the European Union. Derived from national surveys, the FADN is the only source of microeconomic data that is harmonised, i.e. the bookkeeping principles are the same in all countries. Holdings are selected to take part in the survey on the basis of sampling plans established at the level of each region in the Union. The survey does not cover all the agricultural holdings in the Union but only those which due to their size could be considered commercial.

This database is built from open data as described in the paper entitled ‘French wine: Combination of multiple open data sources to mapping the expected harvest value’ (2024).

Find here the relative research paper :

https://univ-lemans.hal.science/hal-04627672

Please find below the list of the sites where used data could be found (lasted view the June 26, 2024).

https://agreste.agriculture.gouv.fr/agreste-web/methodon/Z.1/!searchurl/listeTypeMethodon/

https://www.casd.eu/source/reseau-dinformation-comptable-agricole/?tab=16

https://www.douane.gouv.fr/la-douane/opendata?f%5B0%5D=categorie_opendata_facet%3A467

www.inao.gouv.fr

https://www.data.gouv.fr/fr/datasets/?q=inao

https://maisons-champagne.com/fr/appellation/aire-geographique/

https://info.agriculture.gouv.fr/boagri/document_administratif-4b9ef75e-29a7-449d-9e40-7e5253bfd642/telechargement

https://agreste.agriculture.gouv.fr/agreste-web/download/publication/publie/Dos2203/2Pages%20de%20Dossier2022-3_CCAN_ChapitreII.pdf

This study aims to quantify the level of environmental sustainability in EU farms using a synthetic measure of the environmental burden in agriculture and to assess the overall effectiveness of the “green” transformation of the Common Agricultural Policy (CAP) in enhancing farm sustainability. The Environmental Burden Index (EBI) was calculated for the average farm in a region using the TOPSIS method. The research hypothesis under investigation posits that environmental subsidies have contributed to a reduction in the environmental burden of agriculture. A panel regression model was estimated based on regional data from the EU FADN database covering the period 2004–2022 to identify the extent to which the “green” CAP transformation has impacted the environmental sustainability of farms. The research identified the EU regions that impose the lowest environmental burden in agriculture, with the leading regions primarily located in Italy. Furthermore, the estimated models revealed which types of subsidies benefit and hinder the environmental sustainability of farms. Notably, environmental subsidies were found to have a particularly positive impact on the EBI.

This dataset contains information on 20 FADN (Farm Accountancy Data Network) subsidy payment categories, expressed in €/ha, at the NUTS3 level for every second year over a 20-year period. It covers the following countries: Austria, Belgium, Bulgaria, Cyprus, Czech Republic, Germany, Denmark, Estonia, Greece, Spain, Finland, France, Hungary, Ireland, Italy, Lithuania, Luxembourg, Latvia, Malta, Netherlands, Poland, Portugal, Romania, Sweden, Slovenia, Slovakia, and the United Kingdom.

The dataset is part of the LAMASUS project deliverable D3.2 “Database on EU Policies and Payments for Agriculture, Forest, and Other Land Use Management (LUM) Related Drivers”. The methodology for the spatial downscaling approach, which was applied to derive FADN subsidy payments, is explained within this documentation. The outcome is probabilities how likely farm information is allocated to very small regional units. These probabilities are then used to calculate probability weighted farm information from at least 15 observations at these regional units. This information has been aggregated to the NUTS3 level.

Since the regional units prior to aggregation to the NUTS3 level are already expressed as ratios (€/ha), the aggregation value is calculated as an average of these ratios. This is because the numerator (payments) and denominator (hectares) are not an outcome of the downscaling approach.

A detailed description and discussion of the spatial downscaling method will be provided in a scientific article to be submitted later this year.

The dataset consists of one file and it has the following columns:

1. NUTS_ID: NUTS3 identification code

2. year: years of observation – 2000, 2002, 2004, 2006, 2008, 2010, 2012, 2014, 2016, 2018, 2020

3. Remaining columns are the FADN codes for subsidy payments – see table below

Empty cells must be treated as NA (not available).

Important note: The database of subsidy payments provided is subject to revision as data and methodologies continue to be refined and improved!

Current status of results: In the data processing used to generate the results, a variable was applied that became invalid from 2010 onwards. As a result, the current version of the database is not fully reliable and will be updated once the downscaling approach has been revised.

Abstract

In the second half of 2010 the Ministry of Agriculture and Food (MAF) carried out the farm structure survey (FSS) and the survey on agricultural production methods (SAPM) on the entire country’s territory in accordance with the Law on Agricultural Census 2010 in Bulgaria. This was the first census carried out in Bulgaria being a member of the European Union (EU) and the second one, in compliance with the legislation of the EU. The census was conducted using a methodology consistent with the requirements of Regulation (EC) No 1166/2008 of the European Parliament and of the Council of 19 November 2008 on farm structure surveys and the survey on agricultural production methods and repealing Council Regulation (EEC) No 571/88 and Regulation (EC) No 1200/2009 of 30 November 2009 implementing Regulation (EC) No 1166/2008 of the European Parliament and of the Council on farm structure surveys and the survey on agricultural production methods, as regards to livestock unit coefficients and definitions of the characteristics. This ensured comparability of the results on the structure of agricultural holdings in Bulgaria and agricultural production methods with those of the EU Member States (MS). The Agricultural Census is the main source of information on the status and trends in agriculture. It has to provide a current economic, social and environmental overview of the agrarian sector needed for the decision making in the Common Agricultural Policy (CAP). The census data will be taken as a basis for sampling of the annual production surveys, to determine the framework of the Rural Development Program for the programming period after 2013, to define the field of observation of the Farm Accountancy Data Network (FADN) and to start the creation a statistical farm register.

Geographic coverage

National coverage

Analysis unit

Households

Universe

In compliance with the EU Regulations Bulgaria applied the following national threshold:

0.5 ha of utilised agricultural area; or 0.3 ha of arable land; or 0.5 ha of natural meadows; or 0.1 ha of orchard (compact plantation), vineyard, vegetables, hops, tobacco, spices, medical and essential oil crops, flowers, ornamental plants; or 0.05 ha of greenhouses; or 1 cow/ buffalo-cow; or 2 cattle/ buffaloes; 1 female for reproduction (equidae); or 2 working animals (equidae); or 5 pigs; or 1 breeding-sow; or 5 breeding-ewes; or 2 breeding she-goats; or 50 laying hens; or 100 chicken for fattening; or 1 reproductive male animal used for natural mating - bull, stud, boar, etc.

Kind of data

Census/enumeration data [cen]

Sampling procedure

(a) Frame All agricultural holdings throughout the country on the list of agricultural holdings prepared by the Agrostatistics Department of the Ministry of Agriculture and Food. The list contained 750 733 agricultural holdings and was based on data from the previous census, agricultural administrative records and the annual updates from twelve major sources.

(b) Complete or Sample Enumeration Methods There was no sampling as the Census was an enumeration of all agricultural holdings for both the Farm Structure Survey, and the Survey on Agricultural Production Methods.

Mode of data collection

Face-to-face [f2f]

Research instrument

EU Regulations require information on holding location and geo-coordinates, legal status, ownership and tenancy, land use and crops grown, irrigation, livestock, organic farming, machinery (mandatory in 2013 FSS), renewable energy installations, other gainful activities, socio-economic circumstances (full and part-time farming), labour force (family, non-family, contractors), agricultural and vocational training of the manager, inclusion in rural development support programmes, soil tillage methods, crop rotation, erosion protection, livestock housing and livestock management, grazing of animals, manure application, manure storage and treatment facilities, maintenance and installation of landscape features.In addition, Bulgaria included more detailed breakdown on land ownership, area with aromatic crops – oil rose, coriander, lavender, spearmint, valeriana; questions on holding’s bookkeeping, mineral fertilizers and plant protection products application on open-field area; availability and types of milking facilities.There were three collection forms. The main statistical questionnaire (Form No.1) was a questionnaire collecting information on farm characteristics. The household-listing questionnaire (Form No.2) was used to determine whether the households in urban areas met the criteria for an agricultural holding. Form No.3 was used for temporary or permanently inactive holdings being part of the farm holdings list or the Farm Register.

Cleaning operations

(a) Data Entry, Edits and Imputations, Estimation and Tabulation Data processing, estimation and analysis were carried out on central level. The data file was prepared and sent to Eurostat for final validation. A special computer module was prepared for data entry. Data entry from the completed questionnaires in the computer module began in mid-September 2010 by operators in the regional offices of the Ministry of Agriculture and Food. Data regarding Rural Development Support was cross-checked with the administrative records of the Paying Agency. In the case of doubt, data from Paying Agency was imputed into the database.

(b) Census Data Quality The individual and aggregated data control on regional and central level started from mid-September 2010, together with the data entry of the questionnaires into the computer program. The 28 regional offices sent data to Headquarter’s database on a weekly basis. The Agrostatistics Department at the Ministry of Agriculture and Food conducted multiple checks of the logical links within each data record. Obvious erroneous questionnaires with incoherent data were compared with data from administrative sources. In case of significant differences holdings were revisited for follow-up interviews. The data was summarized and analyzed at central level for the 28 districts and the 6 statistical regions. The data from regular crop, livestock, poultry and beekeeping surveys proved to be comparable with the Census data. Some of the differences were attributable to the different survey reference periods. The difference in annual crop estimates was often due to non-harvested area and was normally within the published survey sampling errors.

Sampling error estimates

The primary effort to minimize non-sampling error was placed in the interviewer and supervisor training programs and the instruction and procedures manuals for the field collection operation. Processes were also put in place for correction of the anticipated under-coverage, duplicate records, non-response and no contacts. Measurement errors were mostly detected by control in the computer module or by the additional monitoring of the data at central level. When discovering errors the regional experts and the enumerators contacted the holder for data clarification and data correction.

Data appraisal

The preliminary results were published in May 2011 on the website of the Ministry of Agriculture and Food, seven months after the end of the reference period (crop year). Final detailed results were released in October 2012. The census results reflect the state of agriculture in Bulgaria in 2010 and are the basis for decisionmaking by state and local governments, as well as by the European Union and other European institutions in the implementation of the Common Agricultural Policy in the EU.

Estonia Land Use: Agricultural Holdings: Non Classifiable data was reported at 8,636.000 ha in 2016. This records a decrease from the previous number of 51,096.000 ha for 2013. Estonia Land Use: Agricultural Holdings: Non Classifiable data is updated yearly, averaging 1,003.000 ha from Dec 2001 (Median) to 2016, with 7 observations. The data reached an all-time high of 51,096.000 ha in 2013 and a record low of 129.000 ha in 2003. Estonia Land Use: Agricultural Holdings: Non Classifiable data remains active status in CEIC and is reported by Statistics Estonia. The data is categorized under Global Database’s Estonia – Table EE.B015: Agricultural Land Use. In 2007 the 1586 holdings whose agricultural area consists only of maintained grassland are classified as belonging to farming type Specialist Field Crops due to the methodology of Farm Accountancy Data Network (FADN). In 2010 these holdings are classified as Non Classifiable.

Maximum Allowable Concentration (MAC) values (mg/l) for the priority substances according to Fraunhofer institute different from EU authorization files and footprint database [54].

Informationsnettet for Landøkonomisk Bogføring (INLB) er et instrument til evaluering af landbrugsbedrifternes indkomstforhold og virkningerne af den fælles landbrugspolitik. INLB-konceptet blev lanceret i 1965, da Rådets forordning 79/65 fastlagde retsgrundlaget for netværkets organisation. Den består af en årlig undersøgelse foretaget af Den Europæiske Unions medlemsstater. De tjenester i Unionen, der er ansvarlige for INLB's drift, indsamler hvert år regnskabsmæssige data fra et udsnit af landbrugsbedrifterne i Den Europæiske Union. INLB er den eneste kilde til mikroøkonomiske data, der er harmoniseret, dvs. regnskabsprincipperne er de samme i alle lande. Bedrifterne udvælges til at deltage i undersøgelsen på grundlag af prøveudtagningsplaner, der er udarbejdet for hver region i Unionen. Undersøgelsen omfatter ikke alle landbrugsbedrifter i Unionen, men kun dem, der på grund af deres størrelse kan betragtes som kommercielle.
INLB-oplysninger aggregeres i en standardresultatdatabase, der er tilgængelig for følgende dimensioner: Tid (år), geografisk (land, region), Typologi (landbrugstype, TF8/TF14 og økonomisk størrelse SIZ6). Databasen kan konsulteres via et sæt dynamiske rapporter, der er organiseret i temaer eller et sæt datafiler, der kun kan downloades.

National estimated use (kg) based on data from Farm Accountancy Data Network (FADN), industry (Phytofar) and Flemish Environment Agency (VMM) and sales figures (kg) obtained by Federal Public Service (FPS), ΣSeq based on use estimates and sales figures of the active substances in Flanders with the largest impact on results for the period 2009–2012.

Das Datennetz landwirtschaftlicher Buchführungen (FADN) ist ein Instrument zur Bewertung der Einkommen landwirtschaftlicher Betriebe und der Auswirkungen der gemeinsamen Agrarpolitik. Das Konzept des INLB wurde 1965 ins Leben gerufen, als die Verordnung 79/65 des Rates die Rechtsgrundlage für die Organisation des Netzes bildete. Sie besteht aus einer jährlichen Erhebung, die von den Mitgliedstaaten der Europäischen Union durchgeführt wird. Die in der Union für den Betrieb des INLB zuständigen Dienststellen erheben jährlich Buchführungsdaten aus einer Stichprobe der landwirtschaftlichen Betriebe in der Europäischen Union. Aus nationalen Erhebungen abgeleitet, ist das INLB die einzige Quelle mikroökonomischer Daten, die harmonisiert sind, d. h. die Buchführungsgrundsätze sind in allen Ländern gleich. Die Betriebe werden für die Teilnahme an der Erhebung auf der Grundlage von Stichprobenplänen ausgewählt, die auf der Ebene jeder Region in der Union erstellt wurden. Die Erhebung umfasst nicht alle landwirtschaftlichen Betriebe in der Union, sondern nur diejenigen, die aufgrund ihrer Größe als kommerziell betrachtet werden konnten.

FADN-Informationen werden in eine Standard-Ergebnisdatenbank zusammengefasst, die für folgende Dimensionen verfügbar ist: Zeit (Jahr), geographische (Land, Region), Typologie (Art der Landwirtschaft, TF8/TF14 und wirtschaftliche Größe SIZ6). Die Datenbank kann über eine Reihe dynamischer Berichte, die in Themen organisiert sind, oder eine Reihe von Datendateien nur zum Download abgerufen werden.

Копия страниц и файлов сайта 2015-12-27 на fadn.gov.ru. Сделано в формате WARC с использованием wget, с сохранением поддоменов и веб-страниц

As a permanent service of Eurostat, GISCO: promotes and stimulates the use of GIS within the European Statistical System and the Commission; manages and disseminates the Geographical reference database of the Commission; acts as a reference centre concerning GIS; promotes geo-referencing of statistics and collaboration between national statistical institutes and mapping agencies; pursues and ensures standardisation and harmonisation in the exchange of Geographic Information; co-leads the INSPIRE initiative on the introduction of a European Spatial Data Infrastructure.

Within the framework of the GISCO project, an extensive geo-referenced database has been developed. One of the main topics of the GISCO mandate is to extend, maintain and update this database. List of data sets offered by GISCO per ISO 19115 topic category (short name in []): a) Farming: farm accountancy data network [FADN] b) Biota: Natural Vegetation [VEGT], Biogeographical Zones [BIOG], Biotopes [BIOT] c) Boundaries: Territorial Units for Statistics (NUTS + Statistical Regions) [NUTS], Communes [COMM], Subcommunes [SCOM], Administrative regions [ADRG], Countries [CNTR] d) Climatology / Meteorology / Atmosphere: Climate [CLIM] e) Economy: Fishing Areas [FISH] f) Elevation: Digital Elevation Model [DEM], Bathimetry [BATH] g) Environment: Land Quality [LNQU], Designated Areas [DSIG] h) Geo-scientific information: Soil Erosion Risk [SOER], Geology Geomorphology ErosionTrend [ERTR], Soil [SOIL], Sediments Discharges [SDDS], Coastal Erosion [COER] i) Imagery/Base maps/Earth cover: Land Cover [LCOV] j) Inland waters: Water Patterns [WTPT], Lakes [LAKE], Watersheds [WTSH] k) Locations: Geographical Grid [GGGR], LUCAS [LUCA], Settlements [STTL], Gazetteer [GAZZ] l) Oceans: Coastline boundaries [COAS], Sea Level rise [SELV] m) Planning/Cadastre: Inter Regional [IREG], Leader Zones [LEAD], Less Favoured Areas [LFAV], National Support [NTSU], Structural Funds Zones [STFU], Urban Audit [URAU] n) Society: Population [POPU], Degree of urbanisation [DGUR] o) Transportation: Airports [AIRP], Ferry links [FERR], Ports [PORT], Road infrastructure [ROAD], Railway infrastructure [RAIL] p) Utilities/Communication: Nuclear Power [NUPW], Energy Production [ENPR], Energy Transport [ENTR]

Further details can be found in gisco_naming_conventions_20090831.pdf

Information on farm household income and farm household composition. Source agency: Environment, Food and Rural Affairs Designation: National Statistics Language: English Alternative title: Farm Household Income and Household Composition, England If you require the datasets in a more accessible format, please contact fbs.queries@defra.gsi.gov.uk Background and guidance on the statistics Information on farm household income and farm household composition was collected in the Farm Business Survey (FBS) for England for the first time in 2004/05. Collection of household income data is restricted to the household of the principal farmer from each farm business. For practical reasons, data is not collected for the households of any other farmers and partners. Two-thirds of farm businesses have an input only from the principal farmer’s household (see table 5). However, details of household composition are collected for the households of all farmers and partners in the business, but not employed farm workers. Data on the income of farm households is used in conjunction with other economic information for the agricultural sector (e.g. farm business income) to help inform policy decisions and to help monitor and evaluate current policies relating to agriculture in the United Kingdom by Government. It also informs wider research into the economic performance of the agricultural industry. This release gives the main results from the income and composition of farm households and the off-farm activities of the farmer and their spouse (Including common law partners) sections of the FBS. These sections include information on the household income of the principal farmer’s household, off-farm income sources for the farmer and spouse and incomes of other members of their household and the number of working age and pensionable adults and children in each of the households on the farm (the information on household composition can be found in Appendix B). This release provides the main results from the 2013/14 FBS. The results are presented together with confidence intervals. Survey content and methodology The Farm Business Survey (FBS) is an annual survey providing information on the financial position and physical and economic performance of farm businesses in England. The sample of around 1,900 farm businesses covers all regions of England and all types of farming with the data being collected by face to face interview with the farmer. Results are weighted to represent the whole population of farm businesses that have at least 25 thousand Euros of standard output as recorded in the annual June Survey of Agriculture and Horticulture. In 2013 there were just over 58 thousand farm businesses meeting this criteria. Since 2009/10 a sub-sample of around 1,000 farms in the FBS has taken part in both the additional surveys on the income and composition of farm households and the off-farm activities of the farmer and their spouse. In previous years, the sub-sample had included over 1,600 farms. As such, caution should be taken when comparing to earlier years. The farms that responded to the additional survey on household incomes and off-farm activities of the farmer and spouse had similar characteristics to those farms in the main FBS in terms of farm type and geographical location. However, there is a smaller proportion of very large farms in the additional survey than in the main FBS. Full details of the characteristic of responding farms can be found at Appendix A of the notice. For further information about the Farm Business Survey please see: https://www.gov.uk/government/organisations/department-for-environment-food-rural-affairs/series/farm-business-survey Data analysis The results from the FBS relate to farms which have a standard output of at least 25,000 Euros. Initial weights are applied to the FBS records based on the inverse sampling fraction for each design stratum (farm type by farm size). These weights are then adjusted (calibration weighting) so that they can produce unbiased estimators of a number of different target variables. Completion of the additional survey on household incomes and off-farm activities of the farmer and spouse was voluntary and a sample of around 1,000 farms was achieved. In order to take account of non-response, the results have been reweighted using a method that preserves marginal totals for populations according to farm type and farm size groups. As such, farm population totals for other classifications (e.g. regions) will not be in-line with results using the main FBS weights, nor will any results produced for variables derived from the rest of the FBS (e.g. farm business income). Accuracy and reliability of the results We show 95% confidence intervals against the results. These show the range of values that may apply to the figures. They mean that we are 95% confident that this range contains the true value. They are calculated as the standard errors (se) multiplied by 1.96 to give the 95% confidence interval. The standard errors only give an indication of the sampling error. They do not reflect any other sources of survey errors, such as non-response bias. For the Farm Business Survey, the confidence limits shown are appropriate for comparing groups within the same year only; they should not be used for comparing with previous years since they do not allow for the fact that many of the same farms will have contributed to the Farm Business Survey in both years. Availability of results This release contains headline results for each section. The full set of results can be found at: https://www.gov.uk/government/organisations/department-for-environment-food-rural-affairs/series/farm-business-survey#publications Defra statistical notices can be viewed on the on the statistics pages of the Defra website at https://www.gov.uk/government/organisations/department-for-environment-food-rural-affairs/about/statistics. This site also shows details of future publications, with pre-announced dates. Data Uses Data from the Farm Business Survey (FBS) are provided to the EU as part of the Farm Accountancy Data Network (FADN). The data have been used to help inform policy decisions (e.g. Reform of Pillar 1 and Pillar 2 of Common Agricultural Policy) and to help monitor and evaluate current policies relating to agriculture in England (and the EU). It is also widely used by the industry for benchmarking and informs wider research into the economic performance of the agricultural industry. User engagement As part of our ongoing commitment to compliance with the Code of Practice for Official Statistics http://www.statisticsauthority.gov.uk/assessment/code-of-practice/index.html, we wish to strengthen our engagement with users of these statistics and better understand the use made of them and the types of decisions that they inform. Consequently, we invite users to make themselves known, to advise us of the use they do, or might, make of these statistics, and what their wishes are in terms of engagement. Feedback on this notice and enquiries about these statistics are also welcome. Definitions Household income of the principal farmer Principal farmer’s household income has the following components: (1) The share of farm business income (FBI) (including income from farm diversification) attributable to the principal farmer and their spouse. (2) Principal farmer’s and spouse’s off farm income from employment and self-employment, investment income, pensions and social payments. (3) Income of other household members. The share of farm business income and all employment and self-employment incomes, investment income and pension income are recorded as gross of income tax payments and National Insurance contributions, but after pension contributions. In addition, no deduction is made for council tax. Household A household is defined as a single person or group of people living at the same address as their only or main residence, who either share one meal a day together or share the living accommodation. A household must contain at least one person who received drawings from the farm business or who took a share of the profit from the business. Drawings Drawings represent the monies which the farmer takes from the business for their own personal use. The percentage of total drawings going to each household is collected and is used to calculate the total share of farm business income for the principal farmer’s household. Mean Mean household income of individuals is the ”average”, found by adding up the weighted household incomes for each individual farm in the population for analysis and dividing the result by the corresponding weighted number of farms. In this report average is usually taken to refer to the mean. Percentiles These are the values which divide the population for analysis, when ranked by an output variable (e.g. household income or net worth), into 100 equal-sized groups. E.g. twenty five per cent of the population would have incomes below the 25th percentile. Median Median household income divides the population, when ranked by an output variable, into two equal sized groups. The median of the whole population is the same as the 50th percentile. The term is also used for the midpoint of the subsets of the income distribution Quartiles Quartiles are values which divide the population, when ranked by an output variable, into four equal-sized groups. The lowest quartile is the same as the 25th percentile. The divisions of a population split by quartiles are referred to as quarters in this publication. Quintiles Quintiles are values which divide the population, when ranked by an output variable, into five equal-sized groups. The divisions of a population split by quintiles are referred to as fifths in this publication. Assets Assets include milk and livestock quotas, as well as land, buildings (including the farm house), breeding livestock, and machinery and equipment. For tenanted farmers,