French ministry of agriculture and food  (Ministère de l'Agriculture et de l'Alimentation):

http://agriculture.gouv.fr/

Statistics and Prospective Department (Service de la Statistique et de la Prospective):

http://agreste.agriculture.gouv.fr/

Department of agricultural, forest and agrifood statistics (Sous-direction des statistiques agricoles, forestières et agroalimentaires: SDSAFA)

Unit of structural, environmental and forestry statistics (Bureau des Statistiques Structurelles, Environnementales et Forestières: BSSEF)

MAA- SG

SSP-BSSEF
Complexe agricole d'Auzeville
BP 32688
31326 CASTANET TOLOSAN

The data describe the structure of agricultural holdings providing the general characteristics of farms and farmers and information on their land, livestock and labour force. They also describe production methods, rural development measures and agro-environmental aspects.

The data are used by public, researchers, farmers and policy-makers to better understand the state of the farming sector and the impact of agriculture on the environment. The data follow up the changes in the agricultural sector and provide a basis for decision-making in the Common Agricultural Policy (CAP) and other European Union policies.

The statistical unit is the agricultural holding (farm). The aggregated results are disseminated through statistical tables and maps. The data are presented at different geographical levels and over periods.
The data collections are organised in line with Regulation (EU) 2018/1091 and have a new structure, consisting of a core data set and several modules. The regulation covers the data collections in 2020 (the agricultural census), 2023 and 2026. The data are as comparable and coherent as possible with the other European countries.

Data are arranged in tables using many classifications. Please find below information on most classifications.

The classifications of variables are available in Annex III of Regulation (EU) 2018/1091 and in Commission Implementing Regulation (EU) 2018/1874.

The farm typology means a uniform classification of the holdings based on their type of farming and their economic size. Both are determined on the basis of the standard gross margin (SGM) (until 2007) or standard output (SO) (from 2010 onward) which is calculated for each crop and animal. The farm type is determined by the relative contribution of the different productions to the total standard gross margin or the standard output of the holding.

The territorial classification uses the NUTS classification to break down the regional data. The regional data is available at NUTS level 2.

The statistics cover agricultural holdings undertaking agricultural activities as listed in item 3.5 below and meeting the minimum coverage requirements (thresholds) as listed in item 3.6 below.

The list of core variables is set in Annex III of Regulation (EU) 2018/1091.

The descriptions of the core variables as well as the lists and descriptions of the variables for the modules collected in 2020 are set in Commission Implementing Regulation (EU) 2018/1874.

The following groups of variables are collected in 2020:

• for core: location of the holding, legal personality of the holding, manager, type of tenure of the utilised agricultural area, variables of land, organic farming, irrigation on cultivated outdoor area, variables of livestock, organic production methods applied to animal production;
• for the module "Labour force and other gainful activities": farm management, family labour force, non-family labour force, other gainful activities directly and not directly related to the agricultural holding;
• for the module "Rural development": support received by agricultural holdings through various rural development measures;
• for the module "Animal housing and rural development module":  animal housing, nutrient use and manure on the farm, manure application techniques, facilities for manure.

See sub-category below.

The agricultural holding is a single unit, both technically and economically, that has a single management and that undertakes economic activities in agriculture in accordance with Regulation (EC) No 1893/2006 belonging to groups:

- A.01.1: Growing of non-perennial crops

- A.01.2: Growing of perennial crops

- A.01.3: Plant propagation

- A.01.4: Animal production

- A.01.5: Mixed farming or

- The “maintenance of agricultural land in good agricultural and environmental condition” of group A.01.6 within the economic territory of the Union, either as its primary or secondary activity.

Regarding activities of class A.01.49, only the activities “Raising and breeding of semi-domesticated or other live animals” (with the exception of raising of insects) and “Bee-keeping and production of honey and beeswax” are included.

See sub-categories below.

The thresholds of agricultural holdings are available in the annex.

The same population of agricultural holdings defined in item 3.6.1

The above answer holds for the modules ‘Labour force and other gainful activities’ and ‘Rural development’. The module ‘Machinery and equipment’ is not collected in 2020. It will be collected in 2023 Structure Survey.

The subset of the population of agricultural holdings defined in item 3.6.2 with at least one of the following: bovine animals, pigs, sheep, goats, poultry. It may happen that there are no animal present on the holding at the reference date (1st of November 2020) but there is a breeding activity at certain moments of the year. In such case, these holdings have been questioned about manure management.

See sub-categories below.

The entire territory of the country.

Overseas territories are included:

• French Guiana
• Guadeloupe
• Martinique
• Réunion
• Mayotte 

For farms without buildings: the most important parcel by physical size, from IACS if the farm is included in IACS.

Farm structure statistics in our country cover the period from 1966 onwards. Older time series are described in the previous quality reports (national methodological reports).

• National census: 1955-1970-1979-1988-2000-2010-2020
• National Farm Structure surveys: 1963-1967-1975-1981-1983-1985-1990-1993-1995-19997-2003-2005-2007-2013-2016

The 2020 data are processed (by Eurostat) with 2017 standard output coefficients (calculated as a 5-year average of the period 2015-2019). For more information, you can consult the definition of the standard output.

Two kinds of units are generally used:

• the units of measurement for the variables (area in hectares, livestock in (1000) heads or LSU (livestock units), labour force in persons or AWU (annual working units), standard output in Euro, places for animal housing etc.) and
• the number of agricultural holdings having these characteristics.

See sub-categories below.

The use of land refers to the reference year 2020 : 

• IACS 2020 is used for IFS 2020, which registers the present crop the 15 of May 2020
• For farms not in IACS, we register the harvested crop in 2020, refering to crop year from 1/11/2019 to 31/10/2020
• In the case of successive crops from the same piece of land, the land use refers to a crop that is harvested during 2020

Reference crop year from 01/11/2019 to 31/10/2020

The reference day is 01/11/2020 for livestock.

For animal housing, we refer to 2020 year, not to a day, in order to avoid specific situations.

From 01/11/2019 to 31/10/2020

From 01/11/2019 to 31/10/2020.

The three-year period ending on 31 December 2020.

01/11/2020

See sub-categories below.

• Decree (05/06/2020) about 2020 agricultural census  : Order of June 4, 2020, supplementing the order of October 24, 2019, approving the national and regional statistical survey program for 2020 (business and farm surveys) among businesses and farms.
• Approval of the National council of statistical information, for 2020 agricultural census, as a mandatory survey of general interest.

• National decree: https://www.legifrance.gouv.fr/jorf/id/JORFTEXT000041983063
• National council of statistical information approval: https://www.cnis.fr/enquetes/recensement-agricole-2020-2020x032ag/
  

2020

• There is a general agreement with FR national payment agency (ASP) for IACS data access.
• There is a general agreement for bovine register data access.
• There is a specific agreement in 2021 with the national research agricultural institute (INRAE-ODR) for rural developpement module data processing.

• In accordance with the Statistical Confidentiality Act (No 51-711 of 7 June 1951), the data collected are professional, and the interviewers and statisticians are bound by professional secrecy. 
• Personal data are protected by the French data protection Act (No 78-17 of 6 January 1978) and Regulation on data protection (No 2016/679 if 27 April 2016).

See sub-categories below.

See sub-categories below.

The data circulates after encryption.
In the tables, data concerning fewer than three units and data where one unit represents more than 85% of the information are replaced by "s". 

See sub-categories below.

IFS 2020 National micro-data files can be accessed for research purposes.

• Researchers or institutes can ask for this access. If the request is approved, the data may be accessed only via a safe access center ("CASD") and the resulting tables are checked for the application of statistical confidentiality.
• There is an on-going project to produce an anonymated file for researchers ("FPR" for production and research file).

There is no online release calendar for IFS 2020 as it is a decennial operation. However, information have been given to users and respondents (see 8.3) for IFS2020.

Not applicable

There is a newsletter of the Statistical and Foresight Department of the French ministry of agriculture and food.

https://agreste.agriculture.gouv.fr/agreste-web/servicon/S.2/listeTypeServicon/

For IFS 2020

• calendar was presented to data users before data collection and after data collection.
• there was a press release on december 2021, for provisionnal results, and the date of availability of final results (April 2022) :

        https://agriculture.gouv.fr/dossier-de-presse-recensement-agricole-2020-premiers-resultats-provisoires

• respondents received a thank you letter on december 2021 with the first provisional results.

We calculate the sampling errors indicators (see 13.2.1. Sampling error – indicators) and publish if the RSE is considered low.

Data dissemination for farm structure survey is done after each survey :

• FSS 2013: data dissemination in 2014;
• FSS 2016: data dissemination in 2018;
• IFS 2020: first data dissemination in december 2021 (first results) and other dissemination are planed in 2022.

See sub-categories below.

• General newsletter is available for data dissemination of the Statistical and Foresight Department of the French ministry of agriculture and food: https://agreste.agriculture.gouv.fr/agreste-web/servicon/S.2/listeTypeServicon/

• For IFS 2020, there was a press release on december 2021, for provisionnal results: https://agriculture.gouv.fr/dossier-de-presse-recensement-agricole-2020-premiers-resultats-provisoires

See sub-categories below.

 Online publications, published by the Statistical and Foresight Department of the French ministry of agriculture and food:

• • Viz'Agreste : IFS 2020 data visualisation website, available since december 2021: https://vizagreste.agriculture.gouv.fr/
  • National and regional first results publications, december 2021: Recensement agricole 2020 - Surface moyenne des exploitations agricoles en 2020
  • Online maps, available since december 2021: https://stats.agriculture.gouv.fr/cartostat/#c=home
  • National and regional Infographics, december 2021: https://agreste.agriculture.gouv.fr/agreste-web/pages/infographies/
  • Online database, available since december 2021 for IFS 2020 results: https://agreste.agriculture.gouv.fr/agreste-web/disaron/RA2020_001/detail/
  • National and regional videos, december 2021: https://agreste.agriculture.gouv.fr/agreste-web/pages/videos/
   
• Paper publications : national map of agricultural production; infographics for respondents

Other publications are planed in 2022.

See sub-categories below.

In march 2022:

• Viz'Agreste : IFS 2020 data visualisation website, available since december 2021 : https://vizagreste.agriculture.gouv.fr/ : 25 000 views.
• National first results publications, december 2021 : https://agreste.agriculture.gouv.fr/agreste-web/disaron/Pri2105/detail/ : 15 000 downloads, 27 384 views
• Online database :  available since december 2021 for IFS 2020 results : https://agreste.agriculture.gouv.fr/agreste-web/disaron/RA2020_001/detail/ : 8 705 views
• Online maps, available since december 2021 : https://stats.agriculture.gouv.fr/cartostat/#c=home : 4 329 views
• National Infographics,  december 2021 : https://agreste.agriculture.gouv.fr/agreste-web/pages/infographies/ : 2 911 views
• National video, december 2021 : https://agreste.agriculture.gouv.fr/agreste-web/pages/videos/ : 638 views

https://agreste.agriculture.gouv.fr/agreste-web/disaron/RA2020_001/detail/

The first table is available with provisional results (december 2021):

• number of farms,
• UAA
• AWU
• by type of farming, economical size, NUTS 1, NUTS2, NUTS 3 and municipalities.

Other tables are planed with final results in 2022.

See sub-category below.

Regional statistical services have their own websites with IFS 2020 results.

Example:

https://draaf.auvergne-rhone-alpes.agriculture.gouv.fr/IMG/html/fts_ra2020_auvergne_rhone_alpes_cle4a1a45-1.html

Not requested.

See sub-categories below.

Not requested.

Recensement agricole 2020, published by the Statistical and Foresight Department of the French ministry of agriculture and food: 

https://agreste.agriculture.gouv.fr/agreste-web/methodon/S-RA%202020/methodon/

IFS 2020 national handbook is not online, but it is available for researchers having an access to microdata.

• Methodological document explaining standard outputs coefficients and permanent pasture definitions, published by the Statistical and Foresight Department of the French ministry of agriculture and food "Recensement Agricole 2020, Note méthodologique": https://agreste.agriculture.gouv.fr/agreste-web/download/publication/publie/Pri2105/Note_methodologique_primeur_RA2020.pdf
• Methodological document for researchers (with microdata): not yet available.

Not available.

See sub-categories below.

There is a quality committee of the Statistical and Foresight Department of the French ministry of agriculture and food.

Improvements are in reflexion within our quality committee.

A report was published in August 2020 about quality of statistical data published by the Statistical and Foresight Department of the French ministry of agriculture and food  "Qualité des données statistiques produites par le SSP"

https://agriculture.gouv.fr/audit-qualistats

An user needs consultation was conducted with structure surveys data users in 2019, including:

• researchers of French National Institute for Agriculture, Food and Environment.
• national agencies working agricultural field (FranceAgriMer, Agence Bio, INAO, ASP, INSEE),
• professional organisations (agricultural unions : FNSEA, Confédération paysanne, Coordination Rurale, Modef),
• technical agricultural institutes (Arvalis, IDELE, IFIP ...)

After a written consultation (e-mail), a final meeting was organised on may 2019. The main groups of variables needed are : 1) General characteristics of farms 2) Variables on livestock 3) Variables on land 4) Labour force.

Questions were added for national purposes :

• Specific questions for overseas departments,
• General variables :
  • administrative identification numbers,
  • links to other firms,
  • quality and environmental certification schemes,
  • marketing via low food-mile systems, marketing channels,
  • tax system,
  • commitment in working groups,
  • risk management,
  • precision farming,
  • future of the farm for older farmers (>60 years old)
• Variables on land :
  
  • detailed categories, mainly for fruits, vegetables, flowers, ornamental plants and nurseries areas,
  • production for flowers, ornamental plants and nurseries
  • agroforestry area,
  • irrigation methods and irrigated areas,
  • tillage methods, intermediate crops,
  • storage capacity for grains, apple and pears,
• Variable on livestock :
  
  • detailed categories for livestock,
  • detailed categories for animal housing,
  • poultry production,
  • animal feeding autonomy,
• Other gainful activities :
  • data collection for all kind of holdings,
  • detailed categories mainly for processing of farm products,
  • OGA realised in other firs, legally separated from the farm.
• Labour force :
  
  • detailed information on family farming indicators,
  • detailed information on family workers, for all kind of holdings (non only sole holder holdings)
  • detailed information on contractual work
  • working condition indicators

User needs have not been included in our census survey for:

• not structural data (circumstantial informations),
• data covered by other national surveys (FADN, SAIO...),
• data available in administrative sources,
• data too complicated to collect

For each survey, there is a committee to share user needs and design the questionnaires. And there is a general committee agricultural statistics users to discuss about new data needs.

Not for IFS 2020

A satisfaction survey was conducted for agricultural statistics website (Agreste), in 2021 (April-June).

Information on low- and zero prevalence variables is available on Eurostat's website.

Not applicable for Integrated Farm Statistics as the not collected variables, not-significant variables and not-existent variables are completed with 0.

See categories below.

See sub-categories below.

Please find the relative standard errors for the main variables in the annex.

Cases for which estimated RSE are above the thresholds of applicable RSE:

Concerned variables include heterogeneous categories, and farms in concerned regions can be very scattered.

Moreover, it is possible that we do not well know real values for these variables of these farms in our farm register (BALSA).

The estimation of RSEs, expressed as a percentage, is equivalent to the coefficient of variation. It is estimated by the formulae:

The stratification accounts for the calculation of the double inclusion probabilities that will be used for the calculation of the variance.

 A calibration on margins is carried out. We proceed as follows:

• In each calibration post-stratum, the variable Y is regressed on the variables used for the margin calibration, with weighting as the weight before calibration, after with the residuals  e_i of the regression are retrieved ;
• the variable g_i is created : where w_i is the weight at the output of the calibration on margins;
• this variable is used as input for classical variance calculation formulae, instead of Y (i.e. replace y_i with g_i).

To estimate the double inclusion probabilities of units, the following formulae is used:

 The variance is then calculated as follows:

 Tool: R function ("CALVA") developed within our agricultural statistical service

See sub-categories below.

See sub-categories below.

The over-coverage rate is available in the annex. The over-coverage rate is unweighted.
The over-coverage rate is calculated as the share of ineligible holdings to the holdings designated for the core data collection. The ineligible holdings include those holdings with unknown eligibility status that are not imputed nor re-weighted for (therefore considered ineligible).
The over-coverage rate is calculated over the holdings in the main frame and if applicable frame extension, for which core data are sent to Eurostat.

Over-coverage occurs when units are included erroneously. To minimise the over-coverage error, the department has been using a sampling frame of farms, which is updated with results of thematic surveys and administrative data.

To reduce quality risks in terms of coverage (and before beginning the census), quality operations were realized for the sampling universe for some uncertain units (undetermined between ceased or active, belonging or not to the agricultural sector): regional operators of the ministry were asked to check and, if necessary, correct the information in the sampling frame.

Not requested.

See sub-categories below.

To indicate the degree of under-coverage, a numeric rate is not available.

However, it was possible to respond for units not present in the sampling frame. The number of added survey was low : 1 247 units, mainly in the overseas departments: 1 213 units.

These 1 247 holdings, belongging to the relevant population of the core but not included in the frame, are:

• very small farms : 1 011 units,
• small farms : 201 units,
• medium farms : 25 units,
• big farms : 10 units

These farms are mainly (1133/1247) farms with both crops and livestock production (545 units), cereals (377 units) and fruits (209 units).

The frame used for the census was built with the maximum of available administrative data sources :

• Sirene (French business register): all active units with an agricultural activity code in the economic nomenclature,
• Common Agricultural Policy subsidies dataset: information on land and livestock (bovine, goat and sheep heads in subsidised farming) integrated,
• CVI (customs administration vineyard) dataset,
• BDNI (bovine identification national database) and other animal species databases: integration of information on bovine cattle sizes, goat and sheep cattle sizes, coupled with information on cattle moves, porcine cattle sizes, coupled with cattle moves
• Mutuelle Sociale Agricole (mandatory social security system for paid and independent agricultural workers): integration of annual information on contributors and paid workers;
• Fiscal information (benefits and micro-benefits): integration of the dataset,
• Other datasets for poorly covered activities with above administrative formalities, such as market gardening, horticulture, organic farming, horse farming and beekeeping.

The agricultural census is a mandatory survey.

The data collection protocol was made to minimise unit non-response rate.

Because of the registration in the business register of all units having an economical activity, the obligation to fill in the census survey and the possibility of fines, the under-coverage is estimated to be very low. Furthermore, there are regular checks on completeness of the frame: the annual coverage rate of the frame by activity was verified.

Misclassification errors cannot totally be ruled out but are estimated to be minimal.

Initially holdings are selected from the administrative farm register (BALSA) which is constantly updated with both results of thematic surveys and administrative data. Theses information can be incomplete.

Some holdings are affected by classification to inadequate strata in the sampling design. However, the region of units is well-known. For type of farms and standard outputs, knowing exactly the “true” value is complicated. It is particularly due to the fact of using classes (for example very small, small, medium and big for standard outputs).

For standard outputs, the rate of misclassified units is estimated at about 10 % and lower for classes without continuities (for example “big farms” in the frame and in reality “very small or small“). Concerning type of farms, the rate of misclassified units is also estimated at about 10 % (if we consider that Cereals, oil seed and other field crops are to be in the same stratum).

Contact information is constantly updated.

Information comes from administrative data or direct information from respondents in thematic surveys.

For units with no phone numbers and email addresses, sampling frame was updated with externals data, subcontracting with companies specialized on businesses contact details. 

See sub-categories below.

To prevent measurement errors, we pre-tested our questionnaire before data collection, we pre-filled it with IACS data,and surveyors had a complete on line training before data collection.

They also had an handbook during data collection, and definitions were included in the on-line or electronic survey for face to face interviews.

Moreover, we had cheks during and after data collection.

However, we can highlight some difficulties for some variables :

• Seeds and seedlings (E0000T) as there is a lot of included / not included items ;
• Fresh vegetables, melons, strawberries - OPEN FIELD or MARKET GARDENING : V0000_S0000TK / V0000_S0000TO, we made checks and corrections after data collection.
  
• Other farmland (FA_OTH):  errors of units by respondents, we made checks and corrections after data collection.

• Farm safety plan (FARM_SPLAN) : often unknown by respondents.
• Manure storage capacities (STCAP_), in month, (ST_), in %  : difficult to understand for respondants, but we made efforts to choose pertinent units in our questionnaire.

IACS used to prefill questionnaire for variables of land.

See sub-categories below.

The unit non-response rate is in the annex of item 13.3.1.1. The unit non-response rate is unweighted.
The unit non-response rate is calculated as the share of eligible non-respondent holdings to the eligible holdings. The eligible holdings include those holdings with unknown eligibility status which are imputed or re-weighted for (therefore considered eligible).
The unit non-response rate is calculated over the holdings in the main frame and if applicable frame extension, for which core data are sent to Eurostat.

• Unit non-response analysis was realised during data collection to estimate the size of non response units (big farms or small farms); and their probability to be in activity, to minimise non-response unit rate.
• Unit-non response analysis was realised after data collection by comparing variables of respondents and non respondents available in the sampling frame; and available in administrative sources.

Item non-response was low : unweighted rate <1 %

Not applicable

Live checks (in data collection tools) to minimise item non response: mandatory fields.

See sub-categories below.

To minimise processing errors the information system is extensively tested and manual actions are minimised as much as possible. All corrections are made using R scripts (no manual adjustments) and before data is released, extensive checks and analyses are performed.

Standard software tools are used (R mainly, Excel...). Only staff involved in the processing of the agricultural census is authorised to make corrections. 

Not applicable

We studied the outliers. For the outliers, a model is used to compare the values of outliers (for example, livestock numbers for cattle, sheep, goats and poultry or culture surfaces) with values of Balsa frame and administrative data. We correct values if it is necessary.

See sub-categories below.

National First results released on december 2021: 12 months after 31 December 2020

Final national file: April 2022.

From 31 December 2020: 16 months

See sub-categories below.

See sub-categories below.

Not requested

Before data collection, the aim was to publish first results on december 2021. They were finally released on 10 december 2021.

Before data collection, the aim was to have final dataset on march 2022. Eurostat file was delivered to Eurostat on march 2022.

See sub-categories below.

Not applicable, because there are no mirror flows in Integrated Farm Statistics.

See sub-categories below.

 No deviations. 

Not applicable

See sub-categories below.

• UAA from FR IFS 2020 (IFS thresholds) covers more than 99 % of FR Census 2020 results, with national thresholds, lower than Eurostat ones. 
• LSU from FR IFS 2020 (IFS thresholds) covers more than 99 % of FR Census 2020 results, with national thresholds, lower than Eurostat ones.

• UAA from FR IFS 2020 (IFS thresholds) represents more than 98 % of 2010 census results for UAA.
• LSU from FR IFS 2020 (IFS thresholds) represents more than 83 % but there is a decrease of farms with animals in France.

• FR IFS 2020 results for UAA covers more than 100 % of area declared in IACS in 2020.

For the thresholds for the data sent to Eurostat, see item 3.6.1.

National thresholds: a holding is covered if it meets at least one of the following thresholds:

For land variables :

• UAA >= 1 ha 
• specialised crops (tobacco, hops, aromatic or medicinal plants, seeds, fresh vegetables, flowers and ornemental plants, permanent crops, nurseries) >= 0.2 ha
• potatoes : >= 0.5 ha
• cabage for sauerkraut >= 0.20 ha
• asparagus >= 0.20 ha
• strawberies >= 0.15 ha
• fresh vegetables >= 0.50 ha
• flowers and ornemental plants >= 0.05 ha
• vineyards >= 0.10 ha
• vineyards for champagne >= 0.05 ha
• nurseries >= 0.05 ha
• crops under glass or high accessible cover >= 0.01 ha

For livestok :

• 1 male breeder
• 4 births of equidae animals
• 1 cow
• 2 bovine animals over 2 years old
• 1 breeding sow
• 1 fattening activity (for bovine animals or pigs or sheeps or goats or rabbits)
• sheeps : 6 breeding females
• goats : 6 beeding females
• rabbits : 10 breeding females
• 100 laying poultry
• incubation capacity : 1000 eggs
• 50 beehives
• 1 other animal production nec

Other thresholds regarding specific productions :

• 500 broiler poultry,
• 50 fat poultry
• 10 000 eggs
• 2 tons of endives
• 1 ton of mushroom

Thresholds are specific in overseas territories.

• Concerning labour force and OGA :
  • we collect and publish national data on familiy members for all legal forms (not only for sole holder holdings),
  • we collect and publish national data on OGA for all legal forms, and we have a wider definition of OGA,
• Concerning livestock :
  • we use national LSU coefficients,
• Concerning UAA : in general, UAA disseminated at national level does not include common land's areas.

National thresholds are different from Eurostat ones to keep comparability between time series, and to cover some specific productions in some regions.

See sub-categories below.

For national needs, national disseminated results differ from Eurostat ones (the national dataset is different from Eurostat one): see 15.1.3.

Concerning the data transmitted to Eurostat the only deviations regarding Regulation (EU) 2018/1091 concerns the animal housing module.  In order to record temporary empty places, and to have a clear question for the farmers, we asked for the maximum number of places in 2020, and not the average number of places occupied during the year. This was also the case in 2010 census (keeping time series).

The information is available in the annex. 
The number of working hours and days in a year for a full-time job correspond to one annual working unit (AWU) in the country. One annual work unit corresponds to the work performed by one person who is occupied on an agricultural holding on a full-time basis. Annual working units are used to calculate the farm work on the agricultural holdings.

The information is available in the annex of item 15.1.4.1.1. 

The information is available in the annex of item 15.1.4.1.1. 

FR uses national LSU coefficients, available in annex.

• Keeping comparability between national time series,
• Responding to national user needs

See sub-categories below.

The use of land refers to the reference year 2020: 

• IACS 2020 is used for IFS 2020, which registers the present crop the 15 of May 2020
• For farms not in IACS, we register the harvested crop in 2020, refering to crop year from 1/11/2019 to 31/10/2020 to include some specific productions, and to keep time series, knowing IACS cover the majority of UAA.
  
• In the case of successive crops from the same piece of land, the land use refers to a crop that is harvested during 2020.
  

For animal housing, we refer to 2020 year, not to a day, in order to avoid specific situations.

• Keeping comparability between national time series,
• Responding to national user needs

Not applicable

We used for IFS 2020 administrative data coming from CAP, in order to allocate common land's areas, proportionally on the basis of the grazing livestock of each farm using common land.

Not applicable

See sub-categories below.

No.

Not applicable

There were specific questions for overseas territories, adapted to the local context, for national needs.

See sub-categories below.

1 as we raised the thresholds of units sent to Eurostat.

See sub-categories below.

Regulation (EU) 2018/1091 newly considers agricultural holdings with only fur animals.

Only 31 farms declaring having fur animals are included in IFS 2020 dataset.

See sub-categories below.

For FSS 2016: data for Eurostat is over national thresholds.

For IFS 2020: data for Eurostat is over IFS thresholds.

See sub-categories below.

Mayotte is now included in data transmitted to Eurostat.

See sub-categories below.

Legal personality of the agricultural holding

In IFS, there is a new class (“shared ownership”) for the legal personality of the holding compared to FSS 2016, which trigger fluctuations of holdings in the classes of sole holder holdings and group holdings.

Other livestock n.e.c.

In FSS 2016, deer were included in this class, but in IFS they are classified separately.

Also in FSS 2016, there was a class for the collection of equidae. That has been dropped and equidae are included in IFS in "other livestock n.e.c."

Livestock units

In FSS 2016, turkeys, ducks, geese, ostriches and other poultry were considered each one in a separate class with a coefficient of 0.03 for all the classes except for ostriches (coefficient 0.035). In IFS 2020, the coefficients were adjusted accordingly, with turkeys remaining at 0.03, ostriches remaining at 0.35, ducks adjusted to 0.01, geese adjusted to 0.02 and other poultry fowls n.e.c. adjusted to 0.001.

Organic animals

While in FSS only fully compliant (certified converted) animals were included, in IFS both animals under conversion and fully converted are to be included.

Permanent pasture

CAP declarations have been used to fill in the questionnaires. In CAP declarations, farmers declare the total area of fields which is called "graphical area". Following instructions of the declarations, the "eligible area" is determined: all non used areas are deducted. For permanent pasture, the difference may be important in case of a lot of rocks, trees, etc. In such a case, we decided to split the graphical area in two classes: the eligible area is classified as permanent pasture and the non eligible area in other  farmland for rough grazing (including when grazing is in wooded areas).

See sub-categories below.

Not applicable

See sub-categories below.

Before IFS 2020, common land units were registered as farms.

For IFS 2020, there is an allocation of common land's areas, and there is no more records of common land as units.

- share % of holdings class bigger than 100ha increased in 2020 compared to 2016 as some holdings stopped their activities and have been annexed with another ones; common land have been included in the UAA. As consequence also the upper SO_EURO classes increased their share in 2020 as this is linked to the increase of the average surface per farm. Price effect could also explain such increase.

See sub-categories below.

Not applicable to Integrated Farm Statistics, because there are no sub annual data collections in agriculture.

Not applicable, because Integrated Farm Statistics have no relevance for national accounts.

See sub-categories below.

We do not make any comparisons with other agricultural microdata's sources.

In fact, the IFS use these information to prefill the questionnaire: CAP for crops, BDNI for bovine livestock, CVI for wine producers.

So, the information's within other sources are linked from the start and we don't need to realise further comparisons.

For other variables, no microdatasets are available.

See sub-categories below.

Coherence cross-domain: IFS vs MAIN AREA in relative terms

Coherence cross-domain: IFS vs MAIN AREA in absolute terms

 They are issued from the census (exhaustive collect) and based on CAP declarations which are controlled each year. Please note that some definitions have been modified in comparison with other inquiries (eligible area for pastures instead total area, permanent grassland is now 5 years old or more instead of 6 years for the last census,...).

Coherence cross-domain: IFS vs CULTIVATED AREA in relative terms

Concerning the C1200 variable, this item was not collected with this description during the previous inquiries. It may explain the increasing of the areas. Concerning the FRY3 region, the total UAA annually increase; the areas for a lot of crops are increasing too.

Coherence cross-domain: IFS vs CULTIVATED AREA in absolute terms

The increasing of G2000 and P0000 areas may be explain by the national plan concerning the development of the protein production (in order to replace imported soy). Concerning the C1200 variable, this item was not collected with this description during the previous inquiries (winter cerals mixtures – maslin were not included in the past). It may explain the increasing of the areas.

Concerning the C1500 variable, the difference between Eurobase and census is not significant It may be due to annual variations.

The data are internally consistent. This is ensured by the application of a wide range of validation rules.

See sub-categories below.

In 2020, we didn't realise our national livestock survey (autumn one) for farms with pigs, sheep and goats (regulation 1165/2008).

We adapted 202 census questionnaire to meet national livestock survey needs.

For the first time, for IFS 2020, we had:

• an on line survey, 
• e-training for surveyors,
• a database uptaded with administrative data,
• pre-filled questionnaire with CAP areas,
• a new sampling design,
• nudge methods to increase response rate

See sub-categories below.

Around 30 minutes

Around 50 minutes (including core data).

Administrative data only

Around 50 minutes (including core data).

For IFS 2020:

• Preliminary results published on december 2021 (only core data)
• Final results: march 2022

No revisions to report

Not requested.

See sub-categories below.

See sub-categories below.

BALSA: Database for agricultural statistics, our agricultural register (Base de Sondage pour la Statistique Agricole)

See sub-categories below.

Not applicable for 2019/2020.

Not applicable for 2019/2020.

Not applicable for 2019/2020.

See sub-categories below.

Not applicable

Not applicable

Not applicable

See sub-categories below.

Systematic sampling sorted on NUST3. The stratification is based on:

• NUTS2 and NUTS3
• Standard output :
  
  • Very small farms : Standard output   <= 25 000 €
  • Small farms: 25 000 € < Standard output   <= 50 000 €
  • Medium farms: 50 000 € < Standard output   <= 100 000 €
  • Big farms: Standard output   > 100 000 €
• Farm type :
  • Cereals, oilseed
  • Other field crops
  • vegetables and mushrooms
  • horticulture
  • vineyards
  • Fruits and other permanent crops
  • Milk cattle
  • Meat cattle
  • Cattle combination (milk and meat)
  • Sheep and goats
  • Other herbivores
  • Pigs
  • Poultry
  • Various granivores combined
  • Various crops and livestock combined
  • Non-classified farms

For the construction of strata, variables (NUTS2, farm type, standard output and NUTS3) are crossed (in this order). Each strata had to contain a minimum of 50 units. When it is not the case, strata are defined with a neighbour, by grouping in the following order: NUTS3, then if necessary standard output, then farm type. Furthermore, the units for which only farm type is known on the one hand and those for which farm type and standard output are unknown on the other hand, are defined in “special” NUTS2 strata.

In total, there are 1 986 strata (without full coverage strata).

Tool Palourde (Production d'allocations localement optimisées utilisables pour des résultats sur des domaines d'études - Methodology Department of INSEE) was used.This tool optimises the allocation of the overall stratified simple random sample size with multiple objectives of published data. First, the algorithm calculates the minimum number of units to select from each stratum in order to respect the RSE on the variables of interest with some published data domains (solution by J. Bethel of the problem of optimal multivariate distribution of the sample). A minimum number has been determined and an algorithm (proposed by M. Koubi and S. Mathern) finalise the optimisation on a key variable of interest in the survey, while ensuring that the minimum number of units is correctly selected in each stratum. The chosen key variable of interest is standard outputs because it is overall correlated with the different variables for which the European regulation requires RSE. A minimum of six units is necessary in each stratum and the minimum sampling rate per stratum is 1/50. The resulting sample for France, excluding geographic exhaustiveness, must ultimately include 55 334 units to meet local constraints. The allocations per stratum are calculated to optimise the RSE on “standard output” variable with the constraint of the total allocation of 70 000 surveys.

There are full coverage strata for:

• units with standard output above 500 000 € (or 250 000 € for vegetables and mushrooms, horticulture and poultry type farm)
• units with 10 permanent salaried people
• units  in some regions: NUTS2 FRMO / FRY1/ FRY2/ FRY3/ FRY4/ FRY6  and NUTS3 FR101-FR105-FR106-FR108
• units in the Farm Sustainability Data Network sample
• units with the same business register identifier (SIREN)

The sample size has been defined in order to meet several requirements:

- the cost of the survey: maximum 70 000 units in total

- RSE by NUTS 2 under 5 % for land and livestock variables in Annex V of 2018/1091 regulation;

See sub-categories below.

Not applicable

Not applicable.

Not applicable.

See sub-categories below.

Systematic sampling sorted on NUST3. The stratification is based on:

• NUTS2 and NUTS3
• Standard output :
  
  • Very small farms: Standard output   <= 25 000 €
  • Small farms: 25 000 € < Standard output   <= 50 000 €
  • Medium farms: 50 000 € < Standard output   <= 100 000 €
  • Big farms: Standard output   > 100 000 €
• Farm type :
  • Cereals, oilseed
  • Other field crops
  • vegetables and mushrooms
  • horticulture
  • vineyards
  • Fruits and other permanent crops
  • Milk cattle
  • Meat cattle
  • Cattle combination (milk and meat)
  • Sheep and goats
  • Other herbivores
  • Pigs
  • Poultry
  • Various granivores combined
  • Various crops and livestock combined
  • Non-classified farms

For the construction of strata, variables (NUTS2, farm type, standard output and NUTS3) are crossed (in this order). Each strata had to contain a minimum of 50 units. When it is not the case, strata are defined with a neighbour, by grouping in the following order: NUTS3, then if necessary standard output, then farm type. Furthermore, the units for which only farm type is known on the one hand and those for which farm type and standard output are unknown on the other hand, are defined in “special” NUTS2 strata.

In total, there are 1 986 strata (without full coverage strata).

Tool Palourde (Production d'allocations localement optimisées utilisables pour des résultats sur des domaines d'études - Methodology Department of INSEE) was used.This tool optimises the allocation of the overall stratified simple random sample size with multiple objectives of published data. First, the algorithm calculates the minimum number of units to select from each stratum in order to respect the RSE on the variables of interest with some published data domains (solution by J. Bethel of the problem of optimal multivariate distribution of the sample). A minimum number has been determined and an algorithm (proposed by M. Koubi and S. Mathern) finalise the optimisation on a key variable of interest in the survey, while ensuring that the minimum number of units is correctly selected in each stratum. The chosen key variable of interest is standard outputs because it is overall correlated with the different variables for which the European regulation requires RSE. A minimum of six units is necessary in each stratum and the minimum sampling rate per stratum is 1/50. The resulting sample for France, excluding geographic exhaustiveness, must ultimately include 55 334 units to meet local constraints. The allocations per stratum are calculated to optimise the RSE on “standard output” variable with the constraint of the total allocation of 70 000 surveys.

There are full coverage strata for:

• units with standard output above 500 000 € (or 250 000 € for poultry type farm)
• units with 10 permanent salaried people
• units  in some regions: NUTS2 FRMO / FRY1/ FRY2/ FRY3/ FRY4/ FRY6  and NUTS3 FR101-FR105-FR106-FR108
• units in the Farm Sustainability Data Network sample
• units with the same business register identifier (SIREN)

The sample size has been defined in order to meet several requirements:

- the cost of the survey: maximum 70 000 units in total

– RSE by NUTS 2 under 5 % for land and livestock variables in Annex V of 2018/1091 regulation;

R software was used (Palourde function - INSEE)

See sub-categories below.

The information is available on Eurostat's website.

See the attached Excel file in the Annex.

The information on innovative approaches and the quality methods applied is available on Eurostat's website.

The agricultural census is conducted every 10 years. The decennial agricultural census is complemented by sample or census-based data collections organised every 3-4 years in-between.

See sub-categories below.

Please find the questionnaire in annex.

See sub-categories below.

• Validation checks within data collection tools,
• E-lists of questionnaires to check, connected to electronic questionnaires (face to face data collection, for modules only), for regional staff,
• R-programs validation
• XLSX files with agregated results, comparing 2020 / 2010 results, for core variables, at the national, NUTS2 and NUTS3 levels

For sample data (LAFO, AHMM), we applied re-weighting.

First, new weights are calculated for respondents with the same NUTS3, farm type, standard outputs, legal status and first updating date in the frame Balsa. Then, we used a calibration method using, for each NUTS3, the number of units by standard outputs, the total utilised agricultural area, the total livestock units and the total annual working units.

The overall imputation rate is 3%.

Imputation is done for unit non-response, and includes all corresponding variables from the core data collection.

Covered under Data compilation.

Not applicable to Integrated Farm Statistics, because it collects structural data on agriculture.

See sub-categories below.

CAP – Common Agricultural Policy

CAPI –  Computer Assisted Personal Interview

CATI – Computer Assisted Telephone Interview

CAWI – Computer Assisted Web Interview

FSS – Farm Structure Survey

IACS – Integrated Administration and Control System

IFS – Integrated Farm Statistics

LSU – Livestock units

NACE – Nomenclature of Economic Activities

NUTS – Nomenclature of territorial units for statistics

PAPI – Paper and Pencil Interview

SO – Standard output

UAA – Utilised agricultural area

No additional comments.