1.1. Contact organisation
Hungarian Central Statistical Office (HCSO)
1.2. Contact organisation unit
Agricultural Farm Structure Section
1.3. Contact name
Confidential because of GDPR
1.4. Contact person function
Confidential because of GDPR
1.5. Contact mail address
Keleti Károly utca 5-7. HU-1024 Budapest
1.6. Contact email address
Confidential because of GDPR
1.7. Contact phone number
Confidential because of GDPR
1.8. Contact fax number
Confidential because of GDPR
2.1. Metadata last certified
16 May 2025
2.2. Metadata last posted
21 May 2025
2.3. Metadata last update
16 May 2025
3.1. Data description
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 that look at the impact of agriculture on the environment.
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. 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 2019/2020 (the agricultural census), 2023 and 2026. The data are as comparable and coherent as possible with the other European countries.
3.2. Classification system
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) 2021/2286.
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.
3.3. Coverage - sector
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.
3.4. Statistical concepts and definitions
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 2023 are set in Commission Implementing Regulation (EU) 2021/2286.
The following groups of variables are collected in 2023:
- 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 “Irrigation”: availability of irrigation, irrigation methods, sources of irrigation water, technical parameters of the irrigation equipment, crops irrigated during a 12 months period;
- for the module “Soil management practices”: tillage methods, soil cover on arable land, crop rotation on arable land, ecological focus area;
- for the module “Machinery and equipment”: internet facilities, basic machinery, use of precision farming, machinery for livestock management, storage for agricultural products, equipment used for production of renewable energy on agricultural holdings;
- for the module “Orchards”: apples area, pears area, peaches area, apricots area, grapes for table use area, each one by age of plantation and density of trees.
3.5. Statistical unit
See sub-category below.
3.5.1. Definition of agricultural holding
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) are included. Bee-keeping and production of honey and beeswax are included only for those holdings that reached the threshold with other variables.
Based on their legal entity, the target population has two main groups in Hungary:
- Private holdings: households engaged in agricultural activity reaching or exceeding certain physical thresholds at the reference time of the survey (see the thresholds below – item 3.6.1.).
- Agricultural enterprises: legal entities engaged in any kind of agricultural activity or classified as agricultural producer by NACE exceeding the physical thresholds at the reference date of the survey (1 June 2023).
As thresholds criteria refers to productive land area, the definition of agricultural holding in Hungary covers:
- holdings with only forest, fish ponds, reeds;
- holdings providing agricultural services only.
3.6. Statistical population
See sub-categories below.
3.6.1. Population covered by the core data sent to Eurostat (main frame and if applicable frame extension)
The thresholds of agricultural holdings are available in the annex.
Annexes:
3.6.1. Thresholds of agricultural holdings
3.6.1.1. Raised thresholds compared to Regulation (EU) 2018/1091
No3.6.1.2. Lowered and/or additional thresholds compared to Regulation (EU) 2018/1091
Yes3.6.2. Population covered by the data sent to Eurostat for the modules “Labour force and other gainful activities”, “Rural development” and “Machinery and equipment”
The same population of agricultural holdings defined in item 3.6.1.
3.6.3. Population covered by the data sent to Eurostat for the module “Animal housing and manure management”
Restricted from publication
3.6.4. Population covered by the data sent to Eurostat for the module “Irrigation”
The subset of agricultural holdings defined in item 3.6.1 with irrigable area.
3.6.5. Population covered by the data sent to Eurostat for the module “Soil management practices”
The subset of agricultural holdings defined in item 3.6.1 with arable land.
3.6.6. Population covered by the data sent to Eurostat for the module “Orchard”
The subset of agricultural holdings defined in item 3.6.1, with any of the individual orchard variables that meet the threshold specified in Article 7(5) of Regulation (EU) 2018/1091.
3.6.7. Population covered by the data sent to Eurostat for the module “Vineyard”
Restricted from publication
3.7. Reference area
See sub-categories below.
3.7.1. Geographical area covered
The entire territory of the country.
3.7.2. Inclusion of special territories
Not applicable.
3.7.3. Criteria used to establish the geographical location of the holding
The main building for productionThe location where all agricultural activities are situated
The most important parcel by economic size
The residence of the farmer (manager) not further than 5 km straight from the farm
3.7.4. Additional information reference area
Not available.
3.8. Coverage - Time
The first agricultural census was implemented in Hungary in 1895 and covered all characteristics of agriculture (land, livestock, labour force).
The second census of 1935 was also a comprehensive survey and had a unique feature: it observed farm indebtedness. The international recommendations (issued by the predecessor of the FAO, the International Agricultural Institute in Rome) have been taken into account during the implementation of this census.
In 1972, Hungary joined the FAO World Census of 1970 and fulfilled also the international data requirements.
The census of 1981 was also linked with the recommendations of the FAO World Census.
In 1991, HCSO conducted the first census after the change of political system in 1990. Following this census, in 1994, a farm structure survey was implemented, but this survey had an incomplete coverage and included only a narrow range of characteristics. The main deficiency of this survey was not covering the farmers living in the urban areas.
The Agricultural Census 2000 (AC 2000) is a historical milestone in the chronicle of Hungarian censuses. This was the first comprehensive survey that, apart from meeting the data needs of FAO, was also compliant with the relevant EU regulations. Based on the results of AC 2000, the microdata set was compiled and provided to Eurostat.
During the negotiations talks linked to EU accession, Hungary has committed itself to carry out the Farm Structure Survey 2003 (FSS 2003) according to EU relevant regulations. The FSS 2005 was the first survey carried out after the accession of Hungary to the EU. After FSS 2005 and also FSS 2007, the microdata of agricultural holdings were sent.
The AC 2010 was the seventh of its kind and it was the first one implemented by Hungary as an EU Member State. According to the relevant Regulations, FSSs were carried out in 2013 and 2016 also.
AC 2020 was also a milestone for the Hungarian Central Statistical Office since a new methodology and new thresholds were introduced, and the use of administrative sources became more focused.
IFS 2023 conducted with the same methodology as AC 2020. The sample districts were chosen from the AC 2020 survey districts.
3.9. Base period
The 2023 data are processed (by Eurostat) with 2020 standard output coefficients (calculated as a 5-year average of the period 2018-2022). 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), and
- the number of agricultural holdings having these characteristics.
See sub-categories below.
5.1. Reference period for land variables
For land variables, land use refers to the reference year of 2023, however, the reference date of the survey is 1st of June. Thus, data providers report the land on which the agricultural activity is carried out on 1st of June 2023 and the utilisation refers to the crop harvested in the reference year, irrespective of when between 1st of June 2022 and 1st of June 2023 the crop in question was sown. In case of successive crops, the main crop’s area is collected.
For national purposes, the prices of the rented land area were asked for the calendar year 2023.
5.2. Reference period for variables on irrigation and soil management practices
For variables on irrigation and soil management practices, the reference period is a 12-month period ending on 1 June 2023.
The main irrigation season typically takes place in autumn. For lands with crops sown in spring (April–May 2023), we collected detailed irrigation data based on the crop (or crop group) that was present for the majority of the reference period. Therefore, if a different crop was sown in spring 2023, the irrigated area refers to the crop previous to the crop sown in spring, and not to the one present in the field on the reference day (1 June 2023). Additionally, we also recorded irrigation information for the full reference period even in cases where the given land parcel was no longer in use of the same holder’s ownership on the reference day.
5.3. Reference day for variables on livestock and animal housing
The reference day was 1 June 2023 for livestock variables; animal housing variables were not applicable for 2023.
5.4. Reference period for variables on manure management
The manure management variables are not applicable for 2023.
5.5. Reference period for variables on labour force
The 12-month period ending on 01 June 2023.
5.6. Reference period for variables on rural development measures
The three-year period ending on 31 December 2023.
5.7. Reference day for all other variables
For all other variables, the reference date is 1 June 2023.
Beyond the variables mentioned under items 5.1-5.6, the following variables were collected for national purposes:
- Slaughtering outside slaughterhouses, monthly data (Reference period: 1 January 2023 - 30 June 2023; each month)
- Future plans on farming activities - Reference date: 1 June 2023
- Agricultural digitalisation - Reference date: 1 June 2023
6.1. Institutional Mandate - legal acts and other agreements
See sub-categories below.
6.1.1. National legal acts and other agreements
Legal act6.1.2. Name of national legal acts and other agreements
All the necessary legal issues were covered by the Act No. CLV of 2016 on official statistics, including using administrative data sources for statistical purposes.
Provision of data was based on the authorisation of Statistical Act regarding National Programme for Statistical Data Collection, which is a Government Decree, taking into consideration Regulations 2018/1091/EU, 1165/2008/EC, 543/2009/EC and 138/2004/EC. In accordance with Sections 24 and 26 of Act No. CLV of 2016 on official statistics, the provision of data was mandatory for those selected for the purpose of the data collections.
6.1.3. Link to national legal acts and other agreements
Act No. CLV of 2016 on official statistics (English version)
National Programme for Statistical Data Collection (Hungarian version) - under codes 2242, 2243, 2374
6.1.4. Year of entry into force of national legal acts and other agreements
Act No. CLV of 2016 on official statistics: 2017
National Programme for Statistical Data Collection: 2023
6.1.5. Legal obligations for respondents
Yes6.2. Institutional Mandate - data sharing
The Hungarian Central Statistical Office signed or renewed several cooperation agreements for the purposes of IFS 2023.
Data on rural development, organic farming and vineyards (variables W1110T, W1120T, W1190T) were not collected directly by the farmers.
The sources of the data were the following:
- Bovine Register (National Food Chain Safety Office)
- IACS Data: National Paying Agency (Hungarian State Treasury)
- Variables of the rural development: National Paying Agency (Hungarian State Treasury)
- Variables of organic farming: Certification bodies (Biokontroll Hungária, Hungária Öko Garancia)
- Some variables on vineyards: Vineyard register
7.1. Confidentiality - policy
The protection of personal data and the publicity of data of public interest are regulated by the following Acts in Hungary:
- Act No. CLV of 2016 on official statistics;
- Act CXII of 2011 on Informational Self-Determination and on Freedom of Information.
Besides the above-mentioned legal acts, internal regulations on confidentiality exist within the HCSO. The access to statistical data is regulated in a separate internal regulation (Regulation 18/2014 on the rules of data access) which contains the rules on the six data access channels of the HCSO.
In virtue of the Act CXII of 2011 on Informational Self-Determination and on Freedom of Information and the Act No. CLV of 2016 on Official statistics, all individual data are qualified as confidential and are treated as such. Survey data are validated and checked exclusively by the staff of HCSO, and enumerators are responsible for preventing unauthorised access to the completed questionnaires.
7.2. Confidentiality - data treatment
See sub-categories below.
7.2.1. Aggregated data
See sub-categories below.
7.2.1.1. Rules used to identify confidential cells
Threshold rule (The number of contributors is less than a pre-specified threshold)Secondary confidentiality rules
7.2.1.2. Methods to protect data in confidential cells
Cell suppression (Completely suppress the value of some cells)7.2.1.3. Description of rules and methods
In general, all data disseminated by HCSO goes through obligatory statistical disclosure control (SDC) procedures. The most commonly SDC method used for protecting sensitive cells is the cell suppression. Under the HCSO internal data protection regulation, all datasets are checked for secondary cell suppression where primary cell suppression is applied. Direct identifiers are removed from all datasets (except in cases regulated by law).
All research outputs produced in the safe environment (Safe Centre, remote access, remote execution) also go through obligatory output checking procedures.
Apart from these obligatory provisions, the typical SDC methods applied to IFS data are the following:
- global recoding (removing a dimension (e.g. column)),
- sub-sampling based on microdata,
- local suppression.
7.2.2. Microdata
See sub-categories below.
7.2.2.1. Use of EU methodology for microdata dissemination
Yes7.2.2.2. Methods of perturbation
Recoding of variablesRemoval of variables
Reduction of information
Merging categories
Rounding
Micro-aggregation
7.2.2.3. Description of methodology
As regards access to microdata for scientific purposes, researchers may have access to data through six channels upon filling in the request form published on the HCSO’s website. The six data access channels are: release of tabular data, public use files, release of anonymised microdata sets, Safe Centre, remote access and remote execution. The first 2 channels are open to all users; the last four channels are available exclusively for scientific purposes.
Anonymised microdata may be accessed by research institutions within the framework of a contract with the HCSO. Anonymised microdata are microdata which have been modified in order to reduce to an acceptable level, in accordance with current best practice, the disclosure risk of statistical units to which they relate.
The researcher may only use the data file for the scientific research purpose indicated in the request form and the data file has to be destroyed upon fulfilment of that purpose. The detailed conditions for the use of anonymised microdata are regulated in the contract between the research institution and the HCSO.
Researchers may also have access to microdata in a secure environment such as the HCSO’s Safe Centre, another remote access point and through remote execution. Farm statistics data of 2000, 2003, 2005, 2007, 2010, 2013, 2016, 2020 and 2023 are accessible for researchers.
The HCSO facility in Szeged is providing a remote access service to researchers under the same conditions as for the Safe Centre environment (available on HCSO premises in Budapest).
In the form of remote execution, researchers can also apply for research outputs based on microdata sets. Using this access channel, the researchers are requested to send detailed specifications and descriptions to HCSO. The data is then prepared by HCSO experts within HCSO. Outputs produced are released following an obligatory output checking procedure which is common for Safe Centre, remote access, and remote execution.
Data made available in the Safe Centre, remote access and remote execution does not contain direct identifiers. The access environment is strictly monitored and the research outputs are checked for statistical disclosure before they may be taken from the safe environment by the researcher.
The process of the evaluation of the data request for the safe environment data access channels covers checking information both on the research purpose and the researchers. Access is granted based on a contract which stipulates the conditions of access.
Researchers can use anonymised microdata at a specially prepared computer station. Reports with aggregated data generated by users are checked by the HCSO methodological staff, who are responsible for enforcing statistical confidentiality according to the rules mentioned in item 7.2.1.1.
8.1. Release calendar
There is no separate release calendar for IFS but there is a release calendar of the HCSO which covers the releases of the IFS.
8.2. Release calendar access
Release calendar for the English version of the publications can be found at this website.
Release calendar for the Hungarian version of the publications can be found at this website.
8.3. Release policy - user access
In general, the Dissemination Policy of the Hungarian Central Statistical Office was followed.
For the purpose of predictability, users are preliminarily informed about the publishing dates in the Release Calendar. If we have to change a publishing date indicated preliminarily, we immediately specify the new release date on our website. For the purpose of transparency, we provide clear information about the quality of data. For the purpose of wide-ranging dissemination, HCSO informs the audience through as many channels as possible, taking into account the demands of the user group. In order to ensure concurrent access, we keep pre-release access to data – the aim of which is to preliminarily inform the highest-level decision-makers and the press – to a minimum. We provide our data to certain highlighted international organisations according to a defined order, who also publish and analyse these data. The data transmitted to international organisations are published by organisations with a statistical profile, especially Eurostat, UN and its specialised agencies, IMF and OECD in various databases and publications according to their own release calendars and practices, together with the methodologically comparable data of other countries. During the co-operation, we note that the publishing of our data on our website precedes their publishing on the websites of international organisations.
There were no deviations of this policy in IFS.
8.3.1. Use of quality rating system
No8.3.1.1. Description of the quality rating system
Not applicable.
The national data are disseminated in every 3-4 years.
10.1. Dissemination format - News release
See sub-categories below.
10.1.1. Publication of news releases
Yes10.1.2. Link to news releases
At the time of publishing the preliminary data together with a press conference the following news release (available in Hungarian) was published.
10.2. Dissemination format - Publications
See sub-categories below.
10.2.1. Production of paper publications
Yes, but not in English10.2.2. Production of on-line publications
Yes, in English also10.2.3. Title, publisher, year and link
Preliminary data (analysis):
- in Hungarian: Agrárium, 2023, előzetes adatok
- in English: Integrated Farm Statistics data collection – IFS 2023, preliminary data
Final data (analysis):
- in Hungarian: Agrárium, 2023, végleges adatok
- in English: Integrated farm statistics data collection, 2023, finalised data
Brochure:
- in Hungarian: KSH webiste - Agrarium2023.
10.3. Dissemination format - online database
See sub-categories below.
10.3.1. Data tables - consultations
Not available.
10.3.2. Accessibility of online database
Yes10.3.3. Link to online database
Dissemination database (in English) and Dissemination database (in Hungarian)
IFS 2023 data will be available in Q2/2025
10.4. Dissemination format - microdata access
See sub-category below.
10.4.1. Accessibility of microdata
Yes10.5. Dissemination format - other
- Dedicated pages to IFS 2023
- Dashboard
- A letter with dissemination information was sent to data providers who requested feedback on the results.
10.5.1. Metadata - consultations
Not requested.
10.6. Documentation on methodology
See sub-categories below.
10.6.1. Metadata completeness - rate
Not requested.
10.6.2. Availability of national reference metadata
Yes10.6.3. Title, publisher, year and link to national reference metadata
Metainformation - Farm structure - agriculture
10.6.4. Availability of national handbook on methodology
No10.6.5. Title, publisher, year and link to handbook
Not applicable.
10.6.6. Availability of national methodological papers
No10.6.7. Title, publisher, year and link to methodological papers
Not applicable.
10.7. Quality management - documentation
Not available.
11.1. Quality assurance
See sub-categories below.
11.1.1. Quality management system
Yes11.1.2. Quality assurance and assessment procedures
Training coursesCompliance monitoring
Other
11.1.3. Description of the quality management system and procedures
A guide and video training material have been produced for the surveyors. After learning the educational material, they had to participate in an online consultation held by experts from the HCSO. To start the work, it was necessary to successfully complete and submit an exam and test questionnaires.
The progress of the enumerators' work was continuously monitored through the online monitoring system. Milestones were set in advance as to what percentage of the questionnaires had to be completed by when. We also checked the work of the enumerators and the completed questionnaires, especially at the beginning of the survey phase. Surveyors who did not perform well were replaced.
Certain quality criteria were also set before the project was launched. On this basis, the project was successful if the following points were met:
- the core and module variables listed in Regulation (EU) 2018/1091 and Commission Implementing Regulation (EU) 2021/2286 were successfully collected.
- the information available from the different administrative sources has been integrated into the Farm register so that the survey starts with an up-to-date list of addresses.
- the burden on respondents is significantly reduced.
- the response rate for the online data collection phase was at least 25% among the farms that were given the opportunity to do so.
- the microdata and the quality report have been compiled before the deadline laid down in the Regulation (EU) 2018/1091 and the grant agreement.
- the budget was within the allocated budget.
Overall, the pre-defined criteria were met and the project was considered successful.
11.1.4. Improvements in quality procedures
Data collection was continuously monitored during the CAWI and CAPI+CATI phases. By the start of the CAPI and CATI phases, power query tables were prepared to verify the data entered by the surveyors.
CAWI:
- completion rates were monitored continuously
- outliers were monitored, and measurement units were corrected as necessary
- responds relating administrative information (persons who carry out activities together) were monitored continuously
- data were compared to previous data collections and administrative information continuously
- number and rate of questionnaires below and above the thresholds were monitored continuously
- questionnaires were checked when a farm was below the thresholds but listed in several administrative databases
CAPI+CATI:
- questionnaires were checked when a farm was below the thresholds but listed in several administrative databases
- completion rates were monitored continuously
- outliers were monitored, and measurement units were corrected as necessary
- responds relating administrative information (persons who carry out activities together) were monitored continuously
- data were compared to previous data collections and administrative information continuously
- number of non-respondents
- surveyors' steady progress was monitored, with attention paid to the number of addresses remaining to be visited
- information monitored on surveyors' work:
- number and rate of questionnaires below and above the thresholds were monitored continuously
- duration of questionnaire completion
- notes written in the questionnaire
Monitoring the persons farming together: when we compiled the address list of the survey, we pre-combined the persons in the various administrative databases into one farm where possible. For example, a husband-wife, parent-child at one address, registered separately but in practice forming a single unit, together meet the definition of a holding. It was checked during the different phases of the survey that if they were indeed farming together, one questionnaire would be completed in relation to the farm.
11.2. Quality management - assessment
All of the mentioned goals under items 11.1.3 and 11.1.4 had been met at the end of the data collection.
12.1. Relevance - User Needs
The aim of the integrated farm statistics is to provide a realistic and objective picture of Hungarian agriculture and its changes, using high-quality statistical data, to farmers, the private sector, national and EU institutions, farmers' associations, and civil society organisations.
Accurate reporting is essential for farmers, their interest groups, and government decision-makers. It enables informed decisions and the design of effective domestic and EU subsidy policies, based on high-quality data and considering farmers' interests, as well as the assessment of their impact.
The main users are:
- policy makers: Ministry of Agriculture, Eurostat, DG AGRI;
- other organisations: FAO;
- reviewers, assessors, analysts;
- professional groups (unions, press);
- researchers; and
- farmers.
12.1.1. Main groups of variables collected only for national purposes
| Main groups | National characteristics surveyed | Users |
|---|---|---|
| Land use | Renting prices and land area by location | HCSO for EAA, government organisations, and private users |
| More detailed breakdown of crops by species | Agricultural government organisations, research institutions, and universities | |
| Livestock production | More detailed observation of livestock | Agricultural government organisations, research institutions, and universities |
| Slaughtering outside slaughterhouses | HCSO for the production of livestock supply balance sheets and for EAA, and research institutions | |
| Production methods | More detailed, not only percentage bands and yes/no questions asked | Government organisations |
| Other | Identification data | HCSO |
| Agricultural services provided | HCSO for EAA | |
| Future plans on farming activities | Ministry of Agriculture | |
| Agricultural digitalisation | Ministry of Agriculture |
12.1.2. Unmet user needs
All user needs are met at both the national and European levels.
12.1.3. Plans for satisfying unmet user needs
Not applicable.
12.2. Relevance - User Satisfaction
The last general user satisfaction survey was carried out in 2019 by the HCSO. In addition, publications can be rated or users can submit their own comments, which are continuously evaluated by the relevant department.
12.2.1. User satisfaction survey
Yes12.2.2. Year of user satisfaction survey
General user satisfaction survey: 2019.
The evaluation of publications by the users is continuous, including those of IFSs.
12.2.3. Satisfaction level
Satisfied12.3. Completeness
Information on not collected, not-significant and not-existent variables is available on Eurostat’s website, at the link: Additional data - Eurostat (europa.eu).
12.3.1. Data completeness - rate
Not applicable for Integrated Farm Statistics as the not collected variables, not-significant variables and not-existent variables are completed with 0.
13.1. Accuracy - overall
See categories below.
13.2. Sampling error
See sub-categories below.
13.2.1. Sampling error - indicators
Please find the relative standard errors on Eurostat’s website, at the CIRCABC website.
13.2.2. Reasons for non-compliant precision requirements in relation to Regulation (EU) 2018/1091
A3110_3130_LSU (piglets live weight of under 20 kg and other pigs) in one region (HU22) did not meet the expected conditions. This affected both the CORE and LAFO modules. Thus, there are a total of two rows where the conditions were slightly exceeded (6.1% instead of 5.0%). The sample size was determined based on 2020 data. For the modelling, 1 000 different sample realisations were selected, and none of the selected realisations exceeded the expected limit. When designing the sample for the IFS 2026 survey, we will increase the sample size in this region. In addition, we will further refine the methodology used for the preliminary size classification of farms.
A4000_LSU (sheep and goats) in region HU1 did not meet the expected conditions. This affected the CORE, LAFO and RDEV modules. The sample design and all related calculations (sample size, design weights, standard errors) were based on the location of the farm headquarters, as were available in our address list. Accordingly, prevalence was also assessed using this classification. The REGION variable in the microdata reflects the location of main agricultural activity, which may differ. Using the design-based regional classification, the calculated prevalence remained below 5%, while it slightly exceeded 7.5% when using the REGION variable. This problem is most significant for farms that are mainly livestock farms. We are in continuous consultation with the administrators of the relevant administrative databases to explore ways of obtaining higher-quality data that would allow us to incorporate information about the location where livestock are kept — at least at the county level — already during the sample design phase. We are committed to resolving this issue as soon as possible so that future surveys can ensure reliable geographic classification before the survey is carried out.
Regarding F1210 (peaches) at country level (RSE of 6.7%), as the area represented by this variable is extremely small (0.04% of the UAA), achieving the required RSE would necessitate a significantly larger sample.
13.2.3. Reference on method of estimation
Formulae applied for variance estimation methods are provided in the annex.
Annexes:
13.2.3 Methodology used to calculate relative standard errors
13.2.4. Impact of sampling error on data quality
Low13.3. Non-sampling error
See sub-categories below.
13.3.1. Coverage error
See sub-categories below.
13.3.1.1. Over-coverage - rate
The over-coverage rate is available on Eurostat’s website, at the CircABC website.
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.
13.3.1.1.1. Types of holdings included in the frame but not belonging to the population of the core (main frame and if applicable frame extension)
Below thresholds during the reference periodTemporarily out of production during the reference period
Ceased activities
Merged to another unit
Duplicate units
13.3.1.1.2. Actions to minimize the over-coverage error
Removal of ineligible units from the records, leaving unchanged the weights for the other units13.3.1.1.3. Additional information over-coverage error
New sampling frame was built before the Agricultural Census and was based on several different administrative sources and former FSS surveys. The maintenance of this sampling frame has been continuous since then.
Some of these sources contained information only about existence and did not contain quantitative production information, therefore, in many cases there was insufficient information for preliminary size classification. Some of these sources also contained outdated information about units which have already ceased farming.
Therefore, many units were visited during the survey which did not exceed the farm thresholds or even did not exist.
Units which do not belong to the target population were not measured (based on the thresholds test at the beginning of the questionnaire) or were excluded during processing.
13.3.1.2. Common units - proportion
Not requested.
13.3.1.3. Under-coverage error
See sub-categories below.
13.3.1.3.1. Under-coverage rate
Under-coverage is estimated to be very low.
13.3.1.3.2. Types of holdings belonging to the population of the core but not included in the frame (main frame and if applicable frame extension)
New birthsUnits with outdated information in the frame (variables below thresholds in the frame but above thresholds in the reference period)
13.3.1.3.3. Actions to minimise the under-coverage error
The statistical Farm Register is continuously updated on the basis of many sources.
The sources used for the update of the Farm register are:
- Hungarian Chamber of Agriculture
- Hungarian State Treasury
- SAPS/BISS and other income supports scheme
- National Food Chain Safety Office
- Animal registers
- Licensed traditional small-scale producers
- Family farms
- Organic farming registers
- National Council of Wine Communities
- Plantations in wine production areas
- Hungarian Central Statistical Office business register
13.3.1.3.4. Additional information under-coverage error
Not available.
13.3.1.4. Misclassification error
Yes13.3.1.4.1. Actions to minimise the misclassification error
Due to difficulties in preliminary unit size classification, we reclassified all units based on the results of the integrated farm statistics.
13.3.1.5. Contact error
Yes13.3.1.5.1. Actions to minimise the contact error
Imputation was performed in cases where contact was unsuccessful.
13.3.1.6. Impact of coverage error on data quality
Low13.3.2. Measurement error
See sub-categories below.
13.3.2.1. List of variables mostly affected by measurement errors
No specific variables are mostly affected by measurement errors.
13.3.2.2. Causes of measurement errors
Complexity of variablesRespondents’ inability to provide accurate answers
13.3.2.3. Actions to minimise the measurement error
Pre-testing questionnaireExplanatory notes or handbooks for enumerators or respondents
On-line FAQ or Hot-line support for enumerators or respondents
Training of enumerators
Other
13.3.2.4. Impact of measurement error on data quality
Low13.3.2.5. Additional information measurement error
Several validation rules were incorporated into the data entry application such as logical and arithmetical coherence checks within and between tables in order to minimise the risk of measurement errors.
In case of outliers and suspicious cases, follow-up interviews were carried out in order to check, correct or confirm the data.
13.3.3. Non response error
See sub-categories below.
13.3.3.1. Unit non-response - rate
See 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 (therefore considered eligible).
The unit non-response rate is calculated over the holdings in the main frame and the frame extension, for which core data are sent to Eurostat.
13.3.3.1.1. Reasons for unit non-response
Failure to make contact with the unitRefusal to participate
13.3.3.1.2. Actions to minimise or address unit non-response
Follow-up interviewsReminders
Imputation
13.3.3.1.3. Unit non-response analysis
We have not carried out a detailed non-response analysis.
13.3.3.2. Item non-response - rate
Data collection was conducted online or via face-to-face interviews using tablets. Validation rules were applied during fieldwork. The program did not allow saving questionnaires with missing required fields, therefore item non-response in mandatory fields did not occur, and item non-response in non-mandatory fields could not be detected.
Mandatory items could only be missed when the entire group of questions was lost.
13.3.3.2.1. Variables with the highest item non-response rate
Not applicable.
13.3.3.2.2. Reasons for item non-response
Not applicable13.3.3.2.3. Actions to minimise or address item non-response
None13.3.3.3. Impact of non-response error on data quality
Low13.3.3.4. Additional information non-response error
Not available.
13.3.4. Processing error
See sub-categories below.
13.3.4.1. Sources of processing errors
Imputation methods13.3.4.2. Imputation methods
Nearest neighbour imputation13.3.4.3. Actions to correct or minimise processing errors
Validation rules were incorporated into the data entry application such as logical and arithmetical coherence checks within and between tables in order to minimise the risk of processing errors.
13.3.4.4. Tools and staff authorised to make corrections
Corrections were run within HCSO's ADÉL applications (Uniform Data Entry and Validation System) by staff from local department.
13.3.4.5. Impact of processing error on data quality
Low13.3.4.6. Additional information processing error
The unweighted imputation rate was 3.9% in the frame extension and 4.0% in the main frame.
13.3.5. Model assumption error
Not applicable.
14.1. Timeliness
See sub-categories below.
14.1.1. Time lag - first result
First results were published: 14/09/23
14.1.2. Time lag - final result
Final results were published: 21/10/24 (t+10 months)
14.2. Punctuality
See sub-categories below.
14.2.1. Punctuality - delivery and publication
See sub-categories below.
14.2.1.1. Punctuality - delivery
Not requested.
14.2.1.2. Punctuality - publication
There was no difference in case of the publication of the preliminary results. For the final results, there were 3 weeks delay between the originally planned target date and the actual publication date.
15.1. Comparability - geographical
See sub-categories below.
15.1.1. Asymmetry for mirror flow statistics - coefficient
Not applicable, because there are no mirror flows in Integrated Farm Statistics.
15.1.2. Definition of agricultural holding
See sub-categories below.
15.1.2.1. Deviations from Regulation (EU) 2018/1091
Data on agricultural holdings were sent to Eurostat with the same definition as in Regulation (EU) 2018/1091. At national level, data were collected and published using an extended definition, including also holdings with only forest, fish ponds, reeds, or providing agricultural services only.
15.1.2.2. Reasons for deviations
As the data on agricultural holdings were sent to Eurostat with the same definition as in Regulation (EU) 2018/1091, there are no deviations in relation to the EU legislation. The reason data were collected and published using an extended definition at the national level is the national need for comprehensive statistics on all productive land and a more complete picture of the agricultural sector.
15.1.3. Thresholds of agricultural holdings
See sub-categories below.
15.1.3.1. Proofs that the EU coverage requirements are met
The national thresholds of agricultural holdings are lower than the thresholds listed in Regulation (EU) 2018/1091. It guarantees that the EU coverage requirements are met.
15.1.3.2. Differences between the national thresholds and the thresholds used for the data sent to Eurostat
No differences between the national thresholds and the thresholds for the data sent to Eurostat (frame extension data were also sent).
15.1.3.3. Reasons for differences
Not applicable.
15.1.4. Definitions and classifications of variables
See sub-categories below.
15.1.4.1. Deviations from Regulation (EU) 2018/1091 and EU handbook
The data were collected, sent to Eurostat and published with the same definitions and classification of variables as in Regulation (EU) 2018/1091, Commission Implementing Regulation (EU) 2021/2286 and the EU Handbook.
15.1.4.1.1. The number of working hours and days in a year corresponding to a full-time job
The information is available on Eurostat’s website, at the CIRCABC website.
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.
15.1.4.1.2. Point chosen in the Annual work unit (AWU) percentage band to calculate the AWU of holders, managers, family and non-family regular workers
See item 15.1.4.1.1.
15.1.4.1.3. AWU for workers of certain age groups
See item 15.1.4.1.1.
15.1.4.1.4. Livestock coefficients
We use the same LSU coefficients as the ones set in Regulation (EU) 2018/1091.
15.1.4.1.5. Livestock included in “Other livestock n.e.c.”
There are no differences between the types of livestock that we included under the heading “Other livestock n.e.c.” and the types of livestock that should be included according to the EU handbook.
15.1.4.2. Reasons for deviations
Not applicable.
15.1.5. Reference periods/days
See sub-categories below.
15.1.5.1. Deviations from Regulation (EU) 2018/1091
The data were collected, sent to Eurostat and published with the same reference periods/days as in Regulation (EU) 2018/1091.
15.1.5.2. Reasons for deviations
Not applicable.
15.1.6. Common land
The concept of common land does not exist15.1.6.1. Collection of common land data
Not applicable15.1.6.2. Reasons if common land exists and data are not collected
Not applicable.
15.1.6.3. Methods to record data on common land
Not applicable15.1.6.4. Source of collected data on common land
Not applicable15.1.6.5. Description of methods to record data on common land
Not applicable.
15.1.6.6. Possible problems in relation to the collection of data on common land and proposals for future data collections
Not applicable.
15.1.7. National standards and rules for certification of organic products
See sub-categories below.
15.1.7.1. Deviations from Council Regulation (EC) No 834/2007
There are no deviations in the national standards and rules for certification of organic products from Council Regulation (EC) No 834/2007.
15.1.7.2. Reasons for deviations
Not applicable.
15.1.8. Differences in methods across regions within the country
There are no differences in the methods used across regions within the country.
15.2. Comparability - over time
See sub-categories below.
15.2.1. Length of comparable time series
The introduction of new thresholds for the Agricultural Census 2020 caused a break in the long-term time series. To ensure comparability over time, all published data for the years 2010, 2013, and 2016 were recalculated using these new thresholds and made available on the HCSO website. Consequently, the time series from 2020 onwards (including the 2023 IFS data) is directly comparable, representing a continuous series of at least two years.
15.2.2. Definition of agricultural holding
See sub-categories below.
15.2.2.1. Changes since the last data transmission to Eurostat
There have been no changes15.2.2.2. Description of changes
There are no changes as both 2020 and 2023 are data collection years covered by the same Regulation (EU) 2018/1091.
15.2.3. Thresholds of agricultural holdings
See sub-categories below.
15.2.3.1. Changes in the thresholds of holdings for which core data are sent to Eurostat since the last data transmission
There have been no changes15.2.3.2. Description of changes
Not applicable.
15.2.4. Geographical coverage
See sub-categories below.
15.2.4.1. Change in the geographical coverage since the last data transmission to Eurostat
There have been no changes15.2.4.2. Description of changes
Not applicable.
15.2.5. Definitions and classifications of variables
See sub-categories below.
15.2.5.1. Changes since the last data transmission to Eurostat
There have been no changes15.2.5.2. Description of changes
There are no changes as both 2020 and 2023 are data collection years covered by the same Regulation (EU) 2018/1091.
15.2.6. Reference periods/days
See sub-categories below.
15.2.6.1. Changes since the last data transmission to Eurostat
There have been some changes but not enough to warrant the designation of a break in series15.2.6.2. Description of changes
There is a very minor change for labour force variables: the reference date changed from 31 May (2020) to 1 June (2023).
15.2.7. Common land
See sub-categories below.
15.2.7.1. Changes in the methods to record common land since the last data transmission to Eurostat
There have been some changes but not enough to warrant the designation of a break in series15.2.7.2. Description of changes
Hungary reported in the calendar year preceding the reference year 2023 that common land was a non-significant variable for 2023, as its value for 2020 was only 69 hectares (less than 0.01% of UAA).
In 2020 data was collected under a special question of rented or being allotted land based on written or oral agreements, while in 2023 common land is summarised under being allotted land and is an NS/NE variable on its own. The method slightly changed, but in both cases the aggregates (utilised land) contain these areas so there is no break in time series.
15.2.8. Explanations for major trends of main variables compared to the last data transmission to Eurostat
Between 2020 and 2023 the number of Hungarian holdings having natural person as legal personality has remarkably decreased. The number of holdings comprised in the UAA class 0-2 HA has significantly decreased, as well as the number of holdings with livestock.
As of 2023 data, the vast majority of HU holdings has no livestock; the proportion of farms without livestock increased significantly, with many livestock farmers abandoning this activity.
If we look at the typology of farms, we can see that between 2013 and 2023 the share of crop and livestock farms has changed significantly. The number of crop specialist farms remained relatively stable in the past years: though it decreased by 5.8% compared to 2020, it showed the same rate of growth compared to 2013. By contrast, the number of specialist livestock farms suffered a dramatic fall: it shrank to nearly the half in 3 years and to a quarter in 10 years.
The EU’s subsidy policy favoured primarily crop production, in addition, animal diseases, recurring from time to time, and increased animal feed and energy costs also hit the sector, which made the situation of animal keepers more difficult.
Overall, the reduction in the number of holdings with livestock has also contributed greatly to the reduction in the number of farms.
A consequence of the reduction of the number of farms is that, when comparing 2023 vs 2020 over the distribution of number of holdings by SO_EURO, holdings with higher SO_EURO increased their weight compared to 2020.
By observing the holdings distribution by farm type evolution, the typologies FT15 and FT90 sharply increased, at the expenses of FT51 and FT52.
In the same time frame they can be noticed other sharp variations, such as:
Crops variables:
- C1120T-I1120T: The share of some cereals, mainly maize, is reduced in 2023 due to the extreme drought and low average yields in 2022. At the same time, other cereals and industrial crops were growing at a higher rate.
Climate change has led to the spread of alternative cereals such as sorghum, millet and buckwheat.
- I1140T: The use of linseed (oil flax) has shifted significantly towards animal feed and human nutrition and has become an important dietary supplement and pharmaceutical raw material.
- I5000T: In 2020 all varieties of fennel were considered as vegetables, in 2023 fennel for seed or foliar use was classified as I5000T.
- Q0000T: The CAP support schemes for 2023 have had a significant impact (decrease) on the area of fallow land.
- W1200T: The decrease of the area is continuous since 2020.
- UAAT_IB: The size of the irrigable area has increased steadily in recent years thanks to various forms of support and government measures.
Variables on animals:
For the highlighted variables, livestock numbers have decreased, in many cases in line with EU trends. For most indicators, the percentage decrease is spectacular, but they are not considered important in terms of quantity.
Labour force variables:
The number of family and non-family workers employed on family farms is continuously decreasing, due to the continuous decrease in the number of family farms.
Rural development measures:
New rural development measures were announced between 2021 and 2023, with a larger number of holdings having benefited than in the 2018-2020 period. The increase in the number of beneficiary farms is mainly due to the following measures:
- Agricultural environmental management payments 2021
- Support to organic farming 2021
- Development of small farms 2020
- Subsidised agricultural insurance
- Supporting the digital transition of agricultural holdings 2021
The population also decreased significantly between 2020 and 2023 (-15%), which also resulted in the increase of the share of holdings having benefited of rural development measures, because beneficiary holdings finished farming typically at a lower rate than farms that did not receive rural development support.
15.2.9. Maintain of statistical identifiers over time
No15.3. Coherence - cross domain
See sub-categories below.
15.3.1. Coherence - sub annual and annual statistics
Not applicable to Integrated Farm Statistics, because there are no sub annual data collections in agriculture.
15.3.2. Coherence - National Accounts
Not applicable, because Integrated Farm Statistics have no relevance for national accounts.
15.3.3. Coherence at micro level with data collections in other domains in agriculture
See sub-categories below.
15.3.3.1. Analysis of coherence at micro level
Yes15.3.3.2. Results of analysis at micro level
Livestock: all animal number data were checked and compared to the individual-level data from the December 2022 survey (which covered issues related primarily to crop production, livestock farming, agricultural labour force, agricultural services and investments, as well as inputs) at individual level. In addition, bovine animal data were compared to data from the Unique Identification System of Animals (ENAR) handled by the National Food Chain Office (NFCO). The availability of sheep and goat data could be compared with the data in the ENAR register. In several cases, some species were not initially entered into the questionnaire during the survey. These species were subsequently asked about and recorded in the questionnaire. Discrepancies also arose at the micro level due to differing reference dates. Bovine animal data are continuously reported to the NFCO, while the data were asked for 1 June 2023 during the IFS.
Crop production: The data were compared to the administrative data sources (IACS, organic data, vineyard register) and the previous years' data collections of the HCSO. The outliers were detected and removed.
15.3.4. Coherence at macro level with data collections in other domains in agriculture
See sub-categories below.
15.3.4.1. Analysis of coherence at macro level
Yes15.3.4.2. Results of analysis at macro level
Coherence cross-domain: IFS vs ANIMAL PRODUCTION
Annual data in Eurobase are referring to the reference date 1 December. Reference date for IFS data is 1 June.
Coherence cross-domain: IFS vs ORGANIC ANIMAL PRODUCTION
In the case of organic farming, the organic animal production data contained not only the data on holdings but also the data on units below the threshold for livestock.
In addition, the IFS data refer to the reference day (1st of June 2023), the organic livestock register data refer to the livestock of the on-spot inspections of the control body.
15.4. Coherence - internal
The data are internally consistent. This is ensured by the application of a wide range of validation rules.
See sub-categories below.
16.1. Coordination of data collections in agricultural statistics
The regular June agricultural surveys on sown area and livestock were not carried out separately in 2023, questions related to those surveys were incorporated into the IFS 2023 questionnaire.
16.2. Efficiency gains since the last data transmission to Eurostat
On-line surveysFurther automation
Increased use of administrative data
16.2.1. Additional information efficiency gains
Not available.
16.3. Average duration of farm interview (in minutes)
See sub-categories below.
16.3.1. Core
A part of interviewed farms were administered only the questions for core and the module "Labour force and other gainful activities".
The below table presents the average duration of farm interview for these farms. The average duration of farm interview covers the questions for core and the module "Labour force and other gainful activities".
| CAPI |
CATI |
CAWI |
Average duration of farm interview over all data collection modes |
|---|---|---|---|
| 18.0 minutes | 16.8 minutes | 47.6 minutes | 28.1 minutes |
16.3.2. Module ‘Labour force and other gainful activities‘
A part of interviewed farms were administered only the questions for core and the module "Labour force and other gainful activities".
The below table presents the average duration of farm interview for these farms. The average duration of farm interview covers the questions for core and the module "Labour force and other gainful activities".
| CAPI |
CATI |
CAWI |
Average duration of farm interview over all data collection modes |
|---|---|---|---|
| 18.0 minutes | 16.8 minutes | 47.6 minutes | 28.1 minutes |
16.3.3. Module ‘Rural development’
Not relevant (data were taken from administrative source).
16.3.4. Module ‘Animal housing and manure management’
Restricted from publication
16.3.5. Module ‘Irrigation’
Only a subset of the interviewed farms were administered all questions for core and the modules "Labour force and other gainful activities", "Irrigation", "Soil management practices", "Machinery and equipment" and "Orchard".
The below table presents the average duration of farm interview for these farms. The average duration of farm interview covers all questions for core and the modules "Labour force and other gainful activities", "Irrigation", "Soil management practices", "Machinery and equipment" and "Orchard".
| CAPI |
CATI |
CAWI |
Average duration of farm interview over all data collection modes |
|---|---|---|---|
| 21.9 minutes | 20.3 minutes | 54.7 minutes | 36.7 minutes |
16.3.6. Module ‘Soil management practices’
Only a subset of the interviewed farms were administered all questions for core and the modules "Labour force and other gainful activities", "Irrigation", "Soil management practices", "Machinery and equipment" and "Orchard".
The below table presents the average duration of farm interview for these farms. The average duration of farm interview covers all questions for core and the modules "Labour force and other gainful activities", "Irrigation", "Soil management practices", "Machinery and equipment" and "Orchard".
| CAPI |
CATI |
CAWI |
Average duration of farm interview over all data collection modes |
|---|---|---|---|
| 21.9 minutes | 20.3 minutes | 54.7 minutes | 36.7 minutes |
16.3.7. Module ‘Machinery and equipment’
Only a subset of the interviewed farms were administered all questions for core and the modules "Labour force and other gainful activities", "Irrigation", "Soil management practices", "Machinery and equipment" and "Orchard".
The below table presents the average duration of farm interview for these farms. The average duration of farm interview covers all questions for core and the modules "Labour force and other gainful activities", "Irrigation", "Soil management practices", "Machinery and equipment" and "Orchard".
| CAPI |
CATI |
CAWI |
Average duration of farm interview over all data collection modes |
|---|---|---|---|
| 21.9 minutes | 20.3 minutes | 54.7 minutes | 36.7 minutes |
16.3.8. Module ‘Orchard’
Only a subset of the interviewed farms were administered all questions for core and the modules "Labour force and other gainful activities", "Irrigation", "Soil management practices", "Machinery and equipment" and "Orchard".
The below table presents the average duration of farm interview for these farms. The average duration of farm interview covers all questions for core and the modules "Labour force and other gainful activities", "Irrigation", "Soil management practices", "Machinery and equipment" and "Orchard".
| CAPI |
CATI |
CAWI |
Average duration of farm interview over all data collection modes |
|---|---|---|---|
| 21.9 minutes | 20.3 minutes | 54.7 minutes | 36.7 minutes |
16.3.9. Module ‘Vineyard’
Restricted from publication
17.1. Data revision - policy
The data revision policy of HCSO contains the general principles of data review, promotes transparency of revision methods, facilitates their comprehension, thus making data easier to understand and use.
Unplanned revision can occur at any time to correct a mistake.
17.2. Data revision - practice
For IFS 2023, preliminary data covered only the main trends, most important figures. After data publication, we carried out further checks on the database, comparing the individual-level data with data from other HCSO data collections and administrative databases.
There were no huge differences between the published preliminary (and not too detailed) and final data, but rather they concerned internal correlations and corrections had to be made at individual level.
There were no conceptual or methodological changes that would cause changes in the data values that would require revisions to previous data or breaks in the series.
17.2.1. Data revision - average size
Not requested.
18.1. Source data
See sub-categories below.
18.1.1. Sampling design & Procedure frame
See sub-categories below.
18.1.1.1. Type of frame
List frame18.1.1.2. Name of frame
Farm Register
18.1.1.3. Update frequency
Continuous18.1.2. Core data collection on the main frame
See sub-categories below.
18.1.2.1. Coverage of agricultural holdings
Sample18.1.2.2. Sampling design
A census was carried out for the agricultural enterprises.
A census was also carried out for the key-private farms (exceeding certain physical thresholds - see item 18.1.2.2.4. Full coverage strata). Based on the preliminary classification of the units of Farm Register, 4 387 private farms belonged to this category.
For non-key private farms, a stratified one-stage cluster sample survey was carried out. Stratification was based on unit location, at the NUTS 3 (county) level.
The territory of Hungary is divided into 4 043 enumeration districts. 979 enumeration districts were selected from them. All (non-key) private farms and households with potential agricultural activity in the sample district were visited.
The selected sample districts contained 42 266 addresses in the main frame. These addresses were taken from the Farm Register.
The extrapolation factors were calculated at the NUTS 3 (county) level.
18.1.2.2.1. Name of sampling design
Stratified one-stage cluster sampling18.1.2.2.2. Stratification criteria
Unit sizeUnit location
Unit legal status
18.1.2.2.3. Use of systematic sampling
No18.1.2.2.4. Full coverage strata
- Agricultural enterprises (units with legal entity)
- Key-private farms (exceeding certain physical thresholds)
Key-private farms thresholds:
-
- Land use variables:
- Arable land; 250 ha
- Permanent pastures and meadows; 150 ha
- Fruit trees, berries, nut trees, citrus fruit trees and other permanent crops excluding nurseries, vineyards and olive trees; 15 ha
- Vineyards; 15 ha
- Livestock variables:
- Cattle; 100 heads
- Pigs; 300 heads
- Sheep; 500 heads
- Goats; 50 heads
- Broiler chickens; 15 000 heads
- Laying hens; 5 000 heads
- Turkeys; 2 500 heads
- Ducks; 2 500 heads
- Geese; 2 500 heads
- Ostriches; 50 heads
- Breeding rabbits; 500 heads
- Land use variables:
18.1.2.2.5. Method of determination of the overall sample size
The sample size was determined based on the precision requirements specified in the Regulation (EU) 2018/1091.
The sample size was calculated on the basis of AC 2020 data.
18.1.2.2.6. Method of allocation of the overall sample size
Optimal allocation considering costs18.1.3. Core data collection on the frame extension
See sub-categories below.
18.1.3.1. Coverage of agricultural holdings
Sample18.1.3.2. Sampling design
Considering that we used a cluster sample, the design of the frame extension and the main frame sample was done together.
A census was carried out for the agricultural enterprises.
For private farms in frame extension, a stratified one-stage cluster sample survey was carried out. Stratification was based on unit location, at the NUTS 3 (county) level.
The territory of Hungary is divided into 4 043 enumeration districts. 979 enumeration districts were selected from them. All (non-key) private farms and households with potential agricultural activity in the sample district were visited.
The selected sample districts contained 27 659 addresses in the frame extension. These addresses were taken from the Farm Register.
The extrapolation factors were calculated at the NUTS 3 (county) level.
18.1.3.2.1. Name of sampling design
Stratified one-stage cluster sampling18.1.3.2.2. Stratification criteria
Unit sizeUnit location
Unit legal status
18.1.3.2.3. Use of systematic sampling
No18.1.3.2.4. Full coverage strata
Agricultural enterprises (units with legal entity)
18.1.3.2.5. Method of determination of the overall sample size
The sample size was determined based on the precision requirements specified in the Regulation (EU) 2018/1091.
The sample size was calculated on the basis of AC 2020 data.
18.1.3.2.6. Method of allocation of the overall sample size
Optimal allocation considering costs18.1.4. Module “Labour force and other gainful activities”
See sub-categories below.
18.1.4.1. Coverage of agricultural holdings
Sample18.1.4.2. Sampling design
The LAFO sample was the same as the CORE sample.
A census was carried out for the agricultural enterprises.
A census was also carried out for the key-private farms (exceeding certain physical thresholds - see item 18.1.4.2.4. Full coverage strata). Based on the preliminary classification of the units of Farm Register, 4 387 private farms belonged to this category.
For non-key private farms, a stratified one-stage cluster sample survey was carried out. Stratification was based on unit location, at the NUTS 3 (county) level.
The territory of Hungary is divided into 4 043 enumeration districts. 979 enumeration districts were selected from them. All (non-key) private farms and households with potential agricultural activity in the sample district were visited.
The selected sample districts contained 69 925 addresses (42 266 in the main frame and 27 659 in the frame extension). These addresses were taken from the Farm Register.
18.1.4.2.1. Name of sampling design
Stratified one-stage cluster sampling18.1.4.2.2. Stratification criteria
Unit sizeUnit location
Unit legal status
18.1.4.2.3. Use of systematic sampling
No18.1.4.2.4. Full coverage strata
- Agricultural enterprises (units with legal entity)
- Key-private farms (exceeding certain physical thresholds)
Key-private farms thresholds:
-
- Land use variables:
- Arable land; 250 ha
- Permanent pastures and meadows; 150 ha
- Fruit trees, berries, nut trees, citrus fruit trees and other permanent crops excluding nurseries, vineyards and olive trees; 15 ha
- Vineyards; 15 ha
- Livestock variables:
- Cattle; 100 heads
- Pigs; 300 heads
- Sheep; 500 heads
- Goats; 50 heads
- Broiler chickens; 15 000 heads
- Laying hens; 5 000 heads
- Turkeys; 2 500 heads
- Ducks; 2 500 heads
- Geese; 2 500 heads
- Ostriches; 50 heads
- Breeding rabbits; 500 heads
- Land use variables:
18.1.4.2.5. Method of determination of the overall sample size
The sample size was determined based on the precision requirements specified in the Regulation (EU) 2018/1091.
The sample size was calculated on the basis of AC 2020 data.
18.1.4.2.6. Method of allocation of the overall sample size
Optimal allocation considering costs18.1.4.2.7. If sampled from the core sample, the sampling and calibration strategy
Not applicable18.1.5. Module “Rural development”
See sub-categories below.
18.1.5.1. Coverage of agricultural holdings
Sample18.1.5.2. Sampling design
The characteristics of the rural development module have been completed by reception of administrative data. Although the information was available for all farms in the country, it was only possible to link the information from the administrative databases to the farms in the CORE sample.
Therefore, the sample of the rural development module is the same as the CORE sample.
A census was carried out for the agricultural enterprises.
A census was also carried out for the key-private farms (exceeding certain physical thresholds - see item 18.1.5.2.4. Full coverage strata). Based on the preliminary classification of the units of Farm Register, 4 387 private farms belonged to this category.
For non-key private farms, a stratified one-stage cluster sample survey was carried out. Stratification was based on unit location, at the NUTS 3 (county) level.
The territory of Hungary is divided into 4 043 enumeration districts. 979 enumeration districts were selected from them. All (non-key) private farms and households with potential agricultural activity in the sample district were visited.
The selected sample districts contained 69 925 addresses (42 266 in the main frame and 27 659 in the frame extension). These addresses were taken from the Farm Register.
18.1.5.2.1. Name of sampling design
Stratified one-stage cluster sampling18.1.5.2.2. Stratification criteria
Unit sizeUnit location
Unit legal status
18.1.5.2.3. Use of systematic sampling
No18.1.5.2.4. Full coverage strata
- Agricultural enterprises (units with legal entity)
- Key-private farms (exceeding certain physical thresholds)
Key-private farms thresholds:
-
- Land use variables:
- Arable land; 250 ha
- Permanent pastures and meadows; 150 ha
- Fruit trees, berries, nut trees, citrus fruit trees and other permanent crops excluding nurseries, vineyards and olive trees; 15 ha
- Vineyards; 15 ha
- Livestock variables:
- Cattle; 100 heads
- Pigs; 300 heads
- Sheep; 500 heads
- Goats; 50 heads
- Broiler chickens; 15 000 heads
- Laying hens; 5 000 heads
- Turkeys; 2 500 heads
- Ducks; 2 500 heads
- Geese; 2 500 heads
- Ostriches; 50 heads
- Breeding rabbits; 500 heads
- Land use variables:
18.1.5.2.5. Method of determination of the overall sample size
The sample size was determined based on the precision requirements specified in the Regulation (EU) 2018/1091.
The sample size was calculated on the basis of AC 2020 data.
18.1.5.2.6. Method of allocation of the overall sample size
Optimal allocation considering costs18.1.5.2.7. If sampled from the core sample, the sampling strategy and calibration strategy
Not applicable18.1.6. Module “Animal housing and manure management module”
Restricted from publication
18.1.6.1. Coverage of agricultural holdings
Restricted from publication
18.1.6.2. Sampling design
Restricted from publication
18.1.6.2.1. Name of sampling design
Restricted from publication
18.1.6.2.2. Stratification criteria
Restricted from publication
18.1.6.2.3. Use of systematic sampling
Restricted from publication
18.1.6.2.4. Full coverage strata
Restricted from publication
18.1.6.2.5. Method of determination of the overall sample size
Restricted from publication
18.1.6.2.6. Method of allocation of the overall sample size
Restricted from publication
18.1.6.2.7. If sampled from the core sample, the sampling strategy and calibration strategy
Restricted from publication
18.1.7. Module ‘Irrigation’
See sub-categories below.
18.1.7.1. Coverage of agricultural holdings
Sample18.1.7.2. Sampling design
Stratified two-stage sampling method.
Module MIRR sample is a subset of the CORE sample.
A census was carried out for the agricultural enterprises.
A census was also carried out for the key-private farms (exceeding certain physical thresholds - see item 18.1.2.2.4. Full coverage strata). Based on the preliminary classification of the units of Farm Register, 4 387 private farms belonged to this category.
For non-key private farms, a stratified two-stage sample survey was carried out. At first stage, stratification was based on unit location, at the NUTS 3 (county) level.
The territory of Hungary is divided into 4 043 enumeration districts. 979 enumeration districts were selected from them.
At second stage, sample was selected from (non-key) private farms and households with potential agricultural activity in the sample districts. These addresses were taken from the Farm Register.
18.1.7.2.1. Name of sampling design
Stratified multi-stage sampling18.1.7.2.2. Stratification criteria
Unit sizeUnit location
Unit legal status
18.1.7.2.3. Use of systematic sampling
No18.1.7.2.4. Full coverage strata
- Agricultural enterprises (units with legal entity)
- Key-private farms (exceeding certain physical thresholds)
Key-private farms thresholds:
-
- Land use variables:
- Arable land; 250 ha
- Permanent pastures and meadows; 150 ha
- Fruit trees, berries, nut trees, citrus fruit trees and other permanent crops excluding nurseries, vineyards and olive trees; 15 ha
- Vineyards; 15 ha
- Livestock variables:
- Cattle; 100 heads
- Pigs; 300 heads
- Sheep; 500 heads
- Goats; 50 heads
- Broiler chickens; 15 000 heads
- Laying hens; 5 000 heads
- Turkeys; 2 500 heads
- Ducks; 2 500 heads
- Geese; 2 500 heads
- Ostriches; 50 heads
- Breeding rabbits; 500 heads
- Land use variables:
18.1.7.2.5. Method of determination of the overall sample size
The sample size was determined based on the precision requirements specified in the Regulation (EU) 2018/1091.
The sample size was calculated on the basis of AC 2020 data.
18.1.7.2.6. Method of allocation of the overall sample size
Optimal allocation considering costs18.1.7.2.7. If sampled from the core sample, the sampling strategy and calibration strategy
Multi-phase sampling where core and module data are collected at the same time18.1.8. Module ‘Soil management practices’
See sub-categories below.
18.1.8.1. Coverage of agricultural holdings
Sample18.1.8.2. Sampling design
Stratified two-stage sampling method.
Module MSMP sample is a subset of the CORE sample.
A census was carried out for the agricultural enterprises.
A census was also carried out for the key-private farms (exceeding certain physical thresholds - see item 18.1.2.2.4. Full coverage strata). Based on the preliminary classification of the units of Farm Register, 4 387 private farms belonged to this category.
For non-key private farms, a stratified two-stage sample survey was carried out. At first stage, stratification was based on unit location, at the NUTS 3 (county) level.
The territory of Hungary is divided into 4 043 enumeration districts. 979 enumeration districts were selected from them.
At second stage, sample was selected from (non-key) private farms and households with potential agricultural activity in the sample districts. These addresses were taken from the Farm Register.
18.1.8.2.1. Name of sampling design
Stratified multi-stage sampling18.1.8.2.2. Stratification criteria
Unit sizeUnit location
Unit legal status
18.1.8.2.3. Use of systematic sampling
No18.1.8.2.4. Full coverage strata
- Agricultural enterprises (units with legal entity)
- Key-private farms (exceeding certain physical thresholds)
Key-private farms thresholds:
-
- Land use variables:
- Arable land; 250 ha
- Permanent pastures and meadows; 150 ha
- Fruit trees, berries, nut trees, citrus fruit trees and other permanent crops excluding nurseries, vineyards and olive trees; 15 ha
- Vineyards; 15 ha
- Livestock variables:
- Cattle; 100 heads
- Pigs; 300 heads
- Sheep; 500 heads
- Goats; 50 heads
- Broiler chickens; 15 000 heads
- Laying hens; 5 000 heads
- Turkeys; 2 500 heads
- Ducks; 2 500 heads
- Geese; 2 500 heads
- Ostriches; 50 heads
- Breeding rabbits; 500 heads
- Land use variables:
18.1.8.2.5. Method of determination of the overall sample size
The sample size was determined based on the precision requirements specified in the Regulation (EU) 2018/1091.
The sample size was calculated on the basis of AC 2020 data.
18.1.8.2.6. Method of allocation of the overall sample size
Optimal allocation considering costs18.1.8.2.7. If sampled from the core sample, the sampling strategy and calibration strategy
Multi-phase sampling where core and module data are collected at the same time18.1.9. Module ‘Machinery and equipment’
See sub-categories below.
18.1.9.1. Coverage of agricultural holdings
Sample18.1.9.2. Sampling design
Stratified two-stage sampling method.
Module MMEQ sample is a subset of the CORE sample.
A census was carried out for the agricultural enterprises.
A census was also carried out for the key-private farms (exceeding certain physical thresholds - see item 18.1.2.2.4. Full coverage strata). Based on the preliminary classification of the units of Farm Register, 4 387 private farms belonged to this category.
For non-key private farms, a stratified two-stage sample survey was carried out. At first stage, stratification was based on unit location, at the NUTS 3 (county) level.
The territory of Hungary is divided into 4 043 enumeration districts. 979 enumeration districts were selected from them.
At second stage, sample was selected from (non-key) private farms and households with potential agricultural activity in the sample districts. These addresses were taken from the Farm Register.
18.1.9.2.1. Name of sampling design
Stratified multi-stage sampling18.1.9.2.2. Stratification criteria
Unit sizeUnit location
Unit legal status
18.1.9.2.3. Use of systematic sampling
No18.1.9.2.4. Full coverage strata
- Agricultural enterprises (units with legal entity)
- Key-private farms (exceeding certain physical thresholds)
Key-private farms thresholds:
-
- Land use variables:
- Arable land; 250 ha
- Permanent pastures and meadows; 150 ha
- Fruit trees, berries, nut trees, citrus fruit trees and other permanent crops excluding nurseries, vineyards and olive trees; 15 ha
- Vineyards; 15 ha
- Livestock variables:
- Cattle; 100 heads
- Pigs; 300 heads
- Sheep; 500 heads
- Goats; 50 heads
- Broiler chickens; 15 000 heads
- Laying hens; 5 000 heads
- Turkeys; 2 500 heads
- Ducks; 2 500 heads
- Geese; 2 500 heads
- Ostriches; 50 heads
- Breeding rabbits; 500 heads
- Land use variables:
18.1.9.2.5. Method of determination of the overall sample size
The sample size was determined based on the precision requirements specified in the Regulation (EU) 2018/1091.
The sample size was calculated on the basis of AC 2020 data.
18.1.9.2.6. Method of allocation of the overall sample size
Optimal allocation considering costs18.1.9.2.7. If sampled from the core sample, the sampling strategy and calibration strategy
Multi-phase sampling where core and module data are collected at the same time18.1.10. Module ‘Orchard’
See sub-categories below.
18.1.10.1. Coverage of agricultural holdings
Sample18.1.10.2. Sampling design
Stratified two-stage sampling method.
Module MORC sample is a subset of the CORE sample.
A census was carried out for the agricultural enterprises.
A census was also carried out for the key-private farms (exceeding certain physical thresholds - see item 18.1.2.2.4. Full coverage strata). Based on the preliminary classification of the units of Farm Register, 4 387 private farms belonged to this category.
For non-key private farms, a stratified two-stage sample survey was carried out. At first stage, stratification was based on unit location, at the NUTS 3 (county) level.
The territory of Hungary is divided into 4 043 enumeration districts. 979 enumeration districts were selected from them.
At second stage, sample was selected from (non-key) private farms and households with potential agricultural activity in the sample districts. These addresses were taken from the Farm Register.
18.1.10.2.1. Name of sampling design
Stratified multi-stage sampling18.1.10.2.2. Stratification criteria
Unit sizeUnit location
Unit legal status
18.1.10.2.3. Use of systematic sampling
No18.1.10.2.4. Full coverage strata
- Agricultural enterprises (units with legal entity)
- Key-private farms (exceeding certain physical thresholds)
Key-private farms thresholds:
-
- Land use variables:
- Arable land; 250 ha
- Permanent pastures and meadows; 150 ha
- Fruit trees, berries, nut trees, citrus fruit trees and other permanent crops excluding nurseries, vineyards and olive trees; 15 ha
- Vineyards; 15 ha
- Livestock variables:
- Cattle; 100 heads
- Pigs; 300 heads
- Sheep; 500 heads
- Goats; 50 heads
- Broiler chickens; 15 000 heads
- Laying hens; 5 000 heads
- Turkeys; 2 500 heads
- Ducks; 2 500 heads
- Geese; 2 500 heads
- Ostriches; 50 heads
- Breeding rabbits; 500 heads
- Land use variables:
18.1.10.2.5. Method of determination of the overall sample size
The sample size was determined based on the precision requirements specified in the Regulation (EU) 2018/1091.
The sample size was calculated on the basis of AC 2020 data.
18.1.10.2.6. Method of allocation of the overall sample size
Optimal allocation considering costs18.1.10.2.7. If sampled from the core sample, the sampling strategy and calibration strategy
Multi-phase sampling where core and module data are collected at the same time18.1.11. Module ‘Vineyard’
Restricted from publication
18.1.11.1. Coverage of agricultural holdings
Restricted from publication
18.1.11.2. Sampling design
Restricted from publication
18.1.11.2.1. Name of sampling design
Restricted from publication
18.1.11.2.2. Stratification criteria
Restricted from publication
18.1.11.2.3. Use of systematic sampling
Restricted from publication
18.1.11.2.4. Full coverage strata
Restricted from publication
18.1.11.2.5. Method of determination of the overall sample size
Restricted from publication
18.1.11.2.6. Method of allocation of the overall sample size
Restricted from publication
18.1.11.2.7. If sampled from the core sample, the sampling strategy and calibration strategy
Restricted from publication
18.1.12. Software tool used for sample selection
R
18.1.13. Administrative sources
See sub-categories below.
18.1.13.1. Administrative sources used and the purposes of using them
The information is available on Eurostat’s website, at the link: Additional data - Eurostat (europa.eu).
18.1.13.2. Description and quality of the administrative sources
See the Excel file in the annex.
Annexes:
18.1.13.2 Description and quality of administrative sources
18.1.13.3. Difficulties using additional administrative sources not currently used
Problems related to data quality of the source18.1.14. Innovative approaches
The information on the innovative approaches and the quality methods applied is available on Eurostat’s website, at the link: Additional data - Eurostat (europa.eu).
18.2. Frequency of data collection
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.
18.3. Data collection
See sub-categories below.
18.3.1. Methods of data collection
Face-to-face, electronic versionTelephone, electronic version
Use of Internet
18.3.2. Data entry method, if paper questionnaires
Not applicable18.3.3. Questionnaire
Please find attached the English and Hungarian versions of the questionnaire in annex.
Annexes:
18.3.3 Questionnaire (private holdings) in English
18.3.3 Questionnaire (agricultural enterprises) in English
18.3.3 Questionnaire (key private holdings) in English
18.3.3 Questionnaire (private holdings) in Hungarian
18.3.3 Questionnaire (agricultural enterprises) in Hungarian
18.3.3 Questionnaire (key private holdings) in Hungarian
18.4. Data validation
See sub-categories below.
18.4.1. Type of validation checks
Data format checksCompleteness checks
Range checks
Relational checks
Comparisons with previous rounds of the data collection
Comparisons with other domains in agricultural statistics
18.4.2. Staff involved in data validation
InterviewersSupervisors
Staff from local departments
Staff from central department
18.4.3. Tools used for data validation
First logical and arithmetical checks within and between the tables were implemented into the HCSO data collection application (MAJA/Integrated Data Gathering System for private farms, ELEKTRA for legal entities).
Batch checks (arithmetical and logical) were run within HCSO's ADÉL applications (Uniform Data Entry and Validation System).
After data collection, SQL, R, and Excel were used for data processing and validation.
HCSO's Unified Data Processing System was used for imputation, processing, and aggregation.
18.5. Data compilation
Holdings outside the scope (over-coverage) were excluded.
Non-response, presumably eligible holdings were imputed.
The weights of the module's sample were adjusted after unit-size reclassification.
18.5.1. Imputation - rate
| Unweighted | Weighted | |||||
|---|---|---|---|---|---|---|
| The ratio of the number of farms for which all values are imputed to the total number of farms |
The ratio of the sum of the UAA (utilised agricultural area) of farms for which farms are imputed to the sum of the UAA of all farms |
The ratio of the sum of the LSU (livestock units) of farms for which farms are imputed to the sum of the LSU of all farms |
The ratio of the extrapolated number of farms for which all values are imputed to the total extrapolated number of farms |
The ratio of the extrapolated sum of the UAA (utilised agricultural area) of farms for which farms are imputed to the extrapolated sum of the UAA of all farms |
The ratio of the extrapolated sum of the LSU (livestock units) of farms for which farms are imputed to the extrapolated sum of the LSU of all farms |
|
| Main Frame | 4.0% | 2.5% | 2.8% | 3.9% | 2.5% | 2.8% |
| Frame Extension | 3.9% | 4.3% | 3.2% | 4.0% | 4.2% | 3.7% |
| Main Frame + Frame Extension | 4.0% | 2.5% | 2.8% | 3.9% | 2.5% | 2.8% |
18.5.2. Methods used to derive the extrapolation factor
Design weight18.6. Adjustment
Covered under Data compilation.
18.6.1. Seasonal adjustment
Not applicable to Integrated Farm Statistics, because it collects structural data on agriculture.
See sub-categories below.
19.1. List of abbreviations
AC – Agricultural Census
AWU – Annual working unit
BISS – Basic income support for sustainability
CAP – Common Agricultural Policy
CAPI – Computer Assisted Personal Interview
CATI – Computer Assisted Telephone Interview
CAWI – Computer Assisted Web Interview
CORE – General, crops and livestock variables of Annex III of Regulation (EU) 2018/1091
DG AGRI – Directorate-General for Agriculture and Rural Development
EAA – Economic accounts for Agriculture
ENAR – Unique Identification System of Animals
EU – European Union
Eurostat – Statistical Office of the European Union
FAO – Food and Agriculture Organization
FAQ – Frequently asked question
FSS – Farm Structure Survey
HCSO – Hungarian Central Statistical Office
IACS – Integrated Administration and Control System
IFS – Integrated Farm Statistics
IMF – International Monetary Fund
LAFO – Labour force and other gainful activities
LSU – Livestock unit
MIRR – Irrigation module
MMEQ – Machinery and equipment module
MORC – Orchards module
MSMP – Soil management practices module
NACE – Nomenclature of Economic Activities
NFCO – National Food Chain Office
NS/NE – Non-significant/non-existent
NUTS – Nomenclature of territorial units for statistics
OECD – Organisation for Economic Co-operation and Development
RDEV – Rural development
RSE – Relative standard error
SAPS – Single Area Payment Scheme
SDC – Statistical disclosure control
SGM – Standard gross margin
SO – Standard output
UAA – Utilised agricultural area
UN – United Nations
19.2. Additional comments
No additional comments.
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 that look at the impact of agriculture on the environment.
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. 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 2019/2020 (the agricultural census), 2023 and 2026. The data are as comparable and coherent as possible with the other European countries.
16 May 2025
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 2023 are set in Commission Implementing Regulation (EU) 2021/2286.
The following groups of variables are collected in 2023:
- 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 “Irrigation”: availability of irrigation, irrigation methods, sources of irrigation water, technical parameters of the irrigation equipment, crops irrigated during a 12 months period;
- for the module “Soil management practices”: tillage methods, soil cover on arable land, crop rotation on arable land, ecological focus area;
- for the module “Machinery and equipment”: internet facilities, basic machinery, use of precision farming, machinery for livestock management, storage for agricultural products, equipment used for production of renewable energy on agricultural holdings;
- for the module “Orchards”: apples area, pears area, peaches area, apricots area, grapes for table use area, each one by age of plantation and density of trees.
See sub-category below.
See sub-categories below.
See sub-categories below.
See sub-categories below.
See categories below.
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), and
- the number of agricultural holdings having these characteristics.
Holdings outside the scope (over-coverage) were excluded.
Non-response, presumably eligible holdings were imputed.
The weights of the module's sample were adjusted after unit-size reclassification.
See sub-categories below.
The national data are disseminated in every 3-4 years.
See sub-categories below.
See sub-categories below.
See sub-categories below.
