1.1. Contact organisation
Ministry of Agriculture and Food
1.2. Contact organisation unit
Agriculture and Regional Policy Directorate
Agrostatistics Department
1.3. Contact name
Confidential because of GDPR
1.4. Contact person function
Confidential because of GDPR
1.5. Contact mail address
55 Hristo Botev Blvd., 1040 Sofia, Bulgaria
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
11 July 2025
2.2. Metadata last posted
22 July 2025
2.3. Metadata last update
11 July 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, nectarines area, apricots area, grapes for table use area, grapes for raisins 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) and “Bee-keeping and production of honey and beeswax” are included.
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 meet the criteria set in the Regulation (EU) 2018/1091 and 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
No3.6.2. 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 3.6.2 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 3.6.2 with arable land and with utilised agricultural area subject to drainage.
3.6.6. Population covered by the data sent to Eurostat for the module “Orchard”
The subset of agricultural holdings defined in item 3.6.2, 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 majority of the area of the holding
The most important parcel by physical size
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
A mixed approach has been chosen to determine the location of the holdings.
Criteria for determining the location of holdings:
- If the holding has an agricultural building (livestock building, granary, machinery, etc.), the location of the holding is determined by:
1. The location of the main agricultural building, which is the main building for production and the location where the main part of agricultural activities are situated.
- If the holding does not have an agricultural building, the location of the holding shall be determined by:
2. The location of the majority of the area of the holding;
3. The location of the most important parcel chosen by physical size;
4. The location of the most important parcel chosen by economic size (arable land, perennials, pastures);
5. The location of the farmer's residence if it is not further than 5 km straight from the farm.
Farm locations were determined using spatial data of declared areas in IACS, specifically by plots.
3.8. Coverage - Time
Farm structure statistics in our country cover the period from 2003 onwards. Older time series are described in the previous quality reports (national methodological reports).
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, places for animal housing etc.) and
- the number of agricultural holdings having these characteristics.
See sub-categories below.
5.1. Reference period for land variables
The use of land refers to the reference year 2022/2023. In the case of successive crops from the same piece of land, the land use refers to a crop that is harvested during the reference year, regardless of when the crop in question is sown.
The 12-month period ending on 31 August within the reference year 2023.
5.2. Reference period for variables on irrigation and soil management practices
The 12-month period ending on 31 August within the reference year 2023.
5.3. Reference day for variables on livestock and animal housing
The reference day for livestock variables is 31 August within the reference year 2023.
The animal housing variables are 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 31 August within the reference year 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
The reference day 31 August within the reference year 2023.
6.1. Institutional Mandate - legal acts and other agreements
See sub-categories below.
6.1.1. National legal acts and other agreements
Other formal agreement6.1.2. Name of national legal acts and other agreements
National statistical programme for 2023
6.1.3. Link to national legal acts and other agreements
National Statistical Programme for 2023 (in Bulgarian)
National Statistical Programme for 2023 (in English)
6.1.4. Year of entry into force of national legal acts and other agreements
2023
6.1.5. Legal obligations for respondents
Yes6.2. Institutional Mandate - data sharing
Within the framework of agreements, the IFS data is provided by the Ministry of Agriculture to Eurostat and the NSI of Bulgaria. The agreements are not publicly available.
7.1. Confidentiality - policy
Individual data are confidential and are not disseminated as such (affidavits and Statistics Act).
- The protection of statistical confidentiality is guaranteed by the Statistics Act.
- The IFS participants, comprising experts from the Ministry and Regional offices as well as the surveyors, fill in affidavits.
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)Dominance rule (The n largest contributions make up for more than k% of the cell total)
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
Aggregated data is published, except when fewer than 3 holdings contribute, or one holding exceeds 85% of the index. Confidential data is marked 'c'. Variables with low national distribution are aggregated.
To prevent confidential data disclosure, additional data is marked 'c'. Secondary confidentiality is also monitored for linked tables. There are no written rules for secondary confidentiality, but some of the rules are hiding the smallest values and crosscheck with the linked tables.
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
For Eurostat data release the methodology is described in the dedicated section of Eurostat's website.
There are rules for releasing confidential data for the purpose of research and the provision of confidential data is performed according to these rules.
According to Article 26a of the Statistics Act, individual and anonymous data may be provided for the purposes of scientific work in higher education institutions or to legal entities whose main activity is scientific research, with the permission of the Chairman of the National Statistical Institute.
Article 2. The following users may apply for access to anonymised individual data:
- universities and other higher education institutions;
- research organisations and institutions;
- other agencies, organisations or institutions in which there are separate structural units carrying out research activities;
- other legal entities using data for research and analytical purposes;
- natural persons who, for the purposes of scientific and research work, need anonymised data.
Article 3. The User and eServices section within the Multisectoral statistics and user services Department maintains and updates a list of users that includes the user name, type of data provided, and date of submission.
Article 4. The data shall be provided on a technical medium in csv, sav, xls or any other format specified in the application.
Article 17. When anonymising the data, the following requirements are met:
- anonymised individual data cannot be provided if they disobey the provisions of Article 25;
- the individual records in the database of the requested survey are processed by competent officers by deleting all identifying individual signs from the data (name, address, publicly accessible identification number, etc.), which directly or indirectly identify a particular statistical unit;
- in the case of records where these rules are not abided by for certain characteristics, the directorates in whose portfolio the data is prepared shall assess whether they should entirely erase the data, delete the data only in the cells of those characteristics, or take other appropriate solutions to protect individual data.
Article 18. The following procedures shall be followed when providing data from studies for consecutive periods (years, quarters, etc.):
- where, in the case of comparative analyses, it is necessary to provide anonymised data from several consecutive observations, the individual data shall be coded with the same numbers for each unit in the database for the individual periods;
- under item 1, the information database can be processed with special software products, which prevent the eventual disclosure of the data for the statistical units by means of methods for comparison of the used identification numbers.
Article 19. In order to assess the risk of revelation of data sets prepared by the user by matching anonymised individual data in combination with external data, they are subject to re-control by the experts in the concerned domain.
8.1. Release calendar
There is a release calendar for Integrated Farm Statistics.
Preliminary data was uploaded on 1 April 2025 on the Ministry of Agriculture and Food website.
8.2. Release calendar access
National Statistical Programme for 2024 (in Bulgarian)
National Statistical Programme for 2024 (in English)
8.3. Release policy - user access
The information is disseminated to all users through a website publication by the Ministry of Agriculture and Food at 3 p.m. There is no access under embargo. A bilingual edition (Bulgarian and English) of the 2023 survey results is published.
8.3.1. Use of quality rating system
Yes, the EU quality rating system8.3.1.1. Description of the quality rating system
The methodology is described in the EU handbook.
For censuses: every 10 years
For sample surveys: every 3 years between censuses
Data for IFS 2023 were disseminated in 2025.
10.1. Dissemination format - News release
See sub-categories below.
10.1.1. Publication of news releases
No10.1.2. Link to news releases
Not applicable.
10.2. Dissemination format - Publications
See sub-categories below.
10.2.1. Production of paper publications
Yes, in English also10.2.2. Production of on-line publications
Yes, in English also10.2.3. Title, publisher, year and link
Integrated farm structure statistics in 2023 – Preliminary Results, Ministry of Agriculture and Food, 2025
10.3. Dissemination format - online database
See sub-categories below.
10.3.1. Data tables - consultations
Not applicable.
10.3.2. Accessibility of online database
No10.3.3. Link to online database
Not applicable.
10.4. Dissemination format - microdata access
See sub-category below.
10.4.1. Accessibility of microdata
Yes10.5. Dissemination format - other
Information is provided to users upon their specific requests, either via mail or post. There is a general mail option, but no online request system. The information provided consists of aggregated IFS data, including all specific modules, but no microdata.
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
Integrated Farm Statistics 2023 - metadata
Annexes:
10.6.3. IFS metadata
10.6.4. Availability of national handbook on methodology
Yes10.6.5. Title, publisher, year and link to handbook
Interviewer instructions
Manual of controls for statistical questionnaire completion
Data entry guide
Annexes:
10.6.5. Interviewer instructions
10.6.5. Manual of controls for statistical questionnaire completion
10.6.5. Data entry guide
10.6.6. Availability of national methodological papers
Yes10.6.7. Title, publisher, year and link to methodological papers
Integrated Farm Statistics 2023 - methodology
Annexes:
10.6.7. IFS methodology
10.7. Quality management - documentation
No available documentation on quality.
11.1. Quality assurance
See sub-categories below.
11.1.1. Quality management system
Yes11.1.2. Quality assurance and assessment procedures
Training coursesUse of best practices
Quality guidelines
Benchmarking
Compliance monitoring
11.1.3. Description of the quality management system and procedures
Data control at multiple levels: regional and national. We comply with the documents prepared by NSI.
The available statistics meet the needs of users. Statistical data meet European quality standards and serve the needs of European institutions, governments, research institutes, businesses and the general public. The quality of statistical products is measured by the extent to which statistical information is relevant, accurate and reliable, up-to-date, coherent and comparable across regions and countries, and easily accessible to all users, i.e. in accordance with the principles for statistical products. The common quality framework consists of the European Statistics Code of Practice, the European Statistical System Quality Assurance Framework and the common principles for quality management (e.g. continuous interaction with users, management involvement, partnership, staff satisfaction, continuous improvement, integration and harmonisation). This common self-regulatory quality framework complements the extensive regulatory framework within the European Statistical System, based on Regulation (EC) No 223/2009 on European statistics, which in turn derives from the Treaty on the Functioning of the European Union. High-quality European statistics and related services are therefore developed, produced and disseminated in accordance with a robust regulatory framework and quality framework with the aim of developing, producing and disseminating high-quality European statistics and services in order to sustainably provide added value for their users.
Guidelines on quality criteria in the National Statistical System of Bulgaria (in Bulgarian)
Part II: Guidelines for quality assessment in the National Statistical Institute (in Bulgarian)
Guidelines on quality criteria in the National Statistical System of Bulgaria (in English)
Part II: Guidelines for quality assessment in the National Statistical Institute (in English)
11.1.4. Improvements in quality procedures
Bulgaria plans to develop a new information system or upgrade the existing one, aiming to improve the statistical information quality - ensuring cybersecurity, integration with other systems and improving the possibilities for data validation and dissemination of results.
Bulgaria will take measures to develop documentation related to the preparation of the statistical information at all stages of the process.
11.2. Quality management - assessment
Good.
In 2023, an integrated farm statistics survey was conducted for core structural and module data. The list of survey holdings was updated with administrative data, and operating holdings above the specified thresholds were included in the survey. The large number of variables required extensive validation, which was performed at multiple levels: by regional and central-level experts, through validations in the information system during data entry, and by mandatory Eurostat validations.
12.1. Relevance - User Needs
The data users are individuals and legal entities.
The data is used for policy development, management purposes, trend analysis, research, and business and entrepreneurial activities.
The data primarily used relates to basic information, such as area, animal populations, and labour force.
12.1.1. Main groups of variables collected only for national purposes
For national needs not covered by Regulation (EU) 2018/1091, we collect the characteristics/variables listed in the first column, which fall under the topics/questions in the second column.
| Characteristics | Topic/question |
|---|---|
| Type of farm accountancy | Administrative data on holdings |
| Cereal processing (animal feed and flour for human consumption) | Do you convert on-farm produce into products, mainly for your own consumption? |
| Vegetables processing (frozen and canned) | |
| Fruits processing (frozen and canned - sweet, fruit juices, etc.) | |
| Milk processing into dairy products (yoghurt, cheese, etc.) | |
| Meat processing (frozen meat and homemade sausages) |
12.1.2. Unmet user needs
There were requests for more detailed data, but this would have increased the number of variables, adding burden for respondents and budget.
12.1.3. Plans for satisfying unmet user needs
No specific plans.
12.2. Relevance - User Satisfaction
No satisfaction survey.
12.2.1. User satisfaction survey
No12.2.2. Year of user satisfaction survey
Not applicable.
12.2.3. Satisfaction level
Not applicable12.3. 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 link: CircaBC website.
13.2.2. Reasons for non-compliant precision requirements in relation to Regulation (EU) 2018/1091
There are no cases where the estimated relative standard errors are above thresholds, for the applicable cases.
13.2.3. Reference on method of estimation
See in 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 link: CircaBC.
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
Not available.
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
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
Many controls have been carried out; for example, farms not part of the 2020 population were identified and included in the survey from administrative sources. All established holdings with activity above the thresholds set in Annex II of Regulation (EU) 2018/1091 are included in the submitted data.
13.3.1.3.4. Additional information under-coverage error
Not available.
13.3.1.4. Misclassification error
No13.3.1.4.1. Actions to minimise the misclassification error
Not applicable.
13.3.1.5. Contact error
Yes13.3.1.5.1. Actions to minimise the contact error
Cross-check with administrative registers or local authorities (mayors).
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
Not known.
13.3.2.2. Causes of measurement errors
Complexity of variablesRespondents’ inability to provide accurate answers
Other
13.3.2.3. Actions to minimise the measurement error
Pre-testing questionnairePre-filled questions
Explanatory notes or handbooks for enumerators or respondents
Training of enumerators
Other
13.3.2.4. Impact of measurement error on data quality
Moderate13.3.2.5. Additional information measurement error
The causes are commonly categorised as:
- Survey instrument: the form, questionnaire or measuring device used for data collection may lead to the recording of wrong values.
Data collection using paper questionnaires is susceptible to technical errors. To mitigate these errors, the computer system uses multiple controls to reduce these errors. This includes checks on logically related fields.
Example: The sum of the crop area cannot be greater than UAA, irrigated area and organic farming area cannot be larger than main area.
- Respondent: respondents may, consciously or unconsciously, give erroneous data.
Individual data is compared and clarified through a second interview when necessary. These interviews are conducted by phone if significant differences are found between interview data, administrative cross-checks, and previous surveys. Sum and logical consistency checks are also performed on related fields.
- Interviewer: interviewers may influence the answers given by respondents.
The possibility of such an error is low.
Difficult questions, unclear definitions, sensitive questions, and other similar factors can contribute to measurement errors.
Errors can be due to unit of measurement discrepancies (ha/dka, sq.m./ha).
The questionnaires include detailed explanations of definitions and units of measurement.
The questions are formulated in the most accessible language possible for the respondents.
The interviewers take notes from the interview with the respondents.
Additional interviews were conducted when necessary to clarify the data.
Administrative data are used as input during data entry into the system in order to avoid missing area and animals or errors in the measurement units.
In order to eliminate the measurement errors, logical consistency checks and comparison with administrative data have been made.
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 or re-weighted for (therefore considered eligible).
The unit non-response rate is calculated over the holdings in the main frame and if applicable frame extension, for which core data are sent to Eurostat.
13.3.3.1.1. Reasons for unit non-response
Refusal to participateInability to participate (e.g. illness, absence)
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
No analysis.
13.3.3.2. Item non-response - rate
Item non-response rates are not calculated as they cannot be calculated by variables.
13.3.3.2.1. Variables with the highest item non-response rate
The system does not support the ability to analyse the variables with the highest item non-response rate.
However, the variables from the Irrigation module posed difficulties for respondents, as they were confused about when a response was expected due to the inclusion or exclusion of certain areas.
13.3.3.2.2. Reasons for item non-response
Skip of due questionFarmers do not know the answer
13.3.3.2.3. Actions to minimise or address item non-response
Follow-up interviewsReminders
13.3.3.3. Impact of non-response error on data quality
Low13.3.3.4. Additional information non-response error
Some variables are missing from small farm responses. This does not affect the overall variable value.
13.3.4. Processing error
See sub-categories below.
13.3.4.1. Sources of processing errors
Data entry13.3.4.2. Imputation methods
Deductive imputationPrevious data for the same unit
13.3.4.3. Actions to correct or minimise processing errors
Comparison with administrative sources, logical consistency between variables.
13.3.4.4. Tools and staff authorised to make corrections
Tools: ISAS (information system of the Agrostatistics department) and Excel
Staff: Agrostatistics experts from the Agrostatistics Department within the Agriculture and Regional Policy Directorate of the Ministry of Agriculture and Food
13.3.4.5. Impact of processing error on data quality
Low13.3.4.6. Additional information processing error
Not available.
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 released on 1 April 2025: 15 months.
14.1.2. Time lag - final result
Not available yet.
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 is no delay for first results. However, for final results, the punctuality cannot be assessed.
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
No differences.
The common land is recorded in the data published nationally.
There is no difference in the way in which data on common land are disseminated at national level and the way in which they are transmitted to Eurostat.
Common land is proportionally distributed among herbivore holdings depending on the livestock units of herbivores on the holding.
15.1.2.2. Reasons for deviations
Not applicable.
There is no change in the common land publication method.
There is no difference in the way in which data on common land are disseminated at national level and the way in which they are transmitted to Eurostat.
Common land is proportionally distributed among herbivore holdings depending on the livestock units of herbivores on the holding.
15.1.3. Thresholds of agricultural holdings
See sub-categories below.
15.1.3.1. Proofs that the EU coverage requirements are met
Bulgaria surveys the holdings within the core framework according to the requirements (physical thresholds) set in Annex II of Regulation (EU) 2018/1091.
15.1.3.2. Differences between the national thresholds and the thresholds used for the data sent to Eurostat
No differences.
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
We collected, sent to Eurostat, and published data with the same definitions and classification of variables compared to Regulation (EU) 2018/1091, Commission Implementing Regulation (EU) 2021/2286, and 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 link: CircaBC.
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
The livestock coefficients are consistent with those fixed in Regulation (EU) 2018/1091. For equidae, LSU is equal to 0.8 for common land distribution.
15.1.4.1.5. Livestock included in “Other livestock n.e.c.”
No differences between the types of livestock that we include 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
The LSU coefficient of 0.8 for equidae serves to allocate the common land to agricultural holdings with grazing animals.
15.1.5. Reference periods/days
See sub-categories below.
15.1.5.1. Deviations from Regulation (EU) 2018/1091
No
15.1.5.2. Reasons for deviations
Not applicable.
15.1.6. Common land
The concept of common land exists15.1.6.1. Collection of common land data
Yes15.1.6.2. Reasons if common land exists and data are not collected
Not applicable.
15.1.6.3. Methods to record data on common land
Common land is included in the land of agricultural holdings based on a statistical model.15.1.6.4. Source of collected data on common land
Administrative sources15.1.6.5. Description of methods to record data on common land
Administrative sources provided information on permanent grassland areas from the municipal land fund, which is used collectively by livestock farms, not distributed individually to agricultural farms. This area is proportionally distributed to grassland farms (including equidae) based on LSU unit area coefficients, at the municipal level.
15.1.6.6. Possible problems in relation to the collection of data on common land and proposals for future data collections
We do not experience problems to collect data on common land.
We experience problems after we distribute to farms the common land data (which we collect at municipal level). The precision of grassland distribution is insufficient. The proportional allocation using the LSU of farms and the inclusion of total land in the UAA of farms distort the classification of farms and their SO. There are no improvements or possible solutions to this problem since the last data collection.
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
No
15.1.7.2. Reasons for deviations
Not applicable.
15.1.8. Differences in methods across regions within the country
No
15.2. Comparability - over time
See sub-categories below.
15.2.1. Length of comparable time series
1
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 sufficient changes to warrant the designation of a break in series15.2.3.2. Description of changes
For IFS 2020, Bulgaria submitted data for the basic and extended framework. The holdings in the extended framework met the threshold rules specified in the file attached to concept 3.6.1 of the 2020 quality report. The holdings in the basic framework met the physical thresholds specified in Annex II to Regulation (EU) 2018/1091.
For IFS 2023, Bulgaria submitted data only for the holdings in the basic framework (without extended framework), which met the physical thresholds specified in Annex II to Regulation (EU) 2018/1091, therefore the thresholds applied and specified in the file attached to concept 3.6.1 of the 2023 quality report are different from the thresholds applied in 2020 and specified in concept 3.6.1 of the 2020 quality report. The change of thresholds between 2020 and 2023 significantly affects the number of holdings and the labour force.
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 no changes15.2.6.2. Description of changes
Not applicable.
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 no changes15.2.7.2. Description of changes
Not applicable.
15.2.8. Explanations for major trends of main variables compared to the last data transmission to Eurostat
The number of holdings between 2023 and 2020 decreased remarkably; in 2023 only the main frame holdings have been transmitted. 96% of the extended framework are farms of natural persons FARM_HLD, which leads to a substantial/significant reduction in this type of farms in 2023.
Comparing the farm data in the basic framework, the reduction is smaller and amounts to 27%. The values for HLD_GRP are very small, and the reason can also be a sampling error. FARM_HLD_SPOUFAM – the % of change also decreases when calculated relative to farms from the basic framework.
With regards to the evolution of variables over time, the following comments can be made:
- I3000T – tobacco: the area under tobacco is decreasing. Alternatives are being sought for those employed in tobacco growing, which is labour-intensive. In IACS, tobacco data shows the same trend of decrease 2023/2020 – 56%.
- K0000T – kitchen gardens: the area under kitchen gardens is decreasing. This decrease is confirmed by the annual territorial survey on land cover and land use, although at a slower pace. Farms are decreasing and larger and more specialised farms without kitchen gardens remain.
- Labour force: The workforce is decreasing mainly due to the reduction of farms and higher mechanisation. Farms are specialising.
There is a direct relation between the change in farm dimension and the labour force: the farm size is increasing and they become more specialised. Managers invest more time in agriculture and farm management. This has increased the share of managers working full time in the farms.
The advanced age of some managers affects their working capacity, reduces their productivity and the time to perform certain activities increases.
Farmers that had other gainful activities not directly related to farming are giving up farming causing a shrink of OGA_NRH (number of sole holders holding that have other gainful activities directly related to holding).
The evolution of number of farms broken down by the UAA reflects a reduction of weight of lower UAA classes (below 10 ha); the number of small farms is decreasing, this has been observed in previous years, the number of farms of 10 ha and more is more stable. Agriculture becomes more mechanised and farms with small UAA are not profitable and sustainable. The farms are mainly focused on growing cereals, and cereals are grown on larger area.
At the same time there was a solid increase of farms without livestock, which represent more than 6 holdings on 10 in Bulgaria in 2023.
In terms of distribution of holdings by SO_EURO reflects an increase of farms with higher standard outputs: farms with an UAA of less than 50 ha are significantly decreasing. Farms with field crops are increasing and their production on small area is not profitable.
With regards to the farm type, there has been an increase in the number of farms and the relative share of farms from FT15. All other types of farms are decreasing. The interest in growing cereals and oleaginous is increasing. The main part of the arable land is occupied by these crops, which require less labour and use more mechanisation. The production of cereals and oilseed crops is easier to be sold on the market compared to the production of fruits and vegetables. Farms specialising in dairy farming are decreasing, the number of animals is also decreasing over the years. This type of farming is associated with more labour input and more difficult market distribution of the production. Furthermore, the common land distribution in farms with herbivores affects their type of farming (there is no change in the methodology in 2020 and 2023).
In 2023, the number of holdings that have benefitted of rural development measures increased by more than 10% compared to 2020 figures.
15.2.9. Maintain of statistical identifiers over time
Partially15.3. 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
Several data sources were used, namely crop statistics, animal statistics, IACS, animal register and organic farm register. The deviations observed are within the limits of the confidence intervals.
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 CROP PRODUCTION (main area in 1000 ha) in relative terms
The two surveys were conducted using different methodologies. The data on area was obtained from a sample territorial survey on land cover and land use in Bulgaria. No thresholds were applied and certain points of the land surface were observed. The survey has a similar methodology to LUCAS. While in the IFS the thresholds according to Annex II of Regulation (EU) 2018/1091 were applied.
The lack of an extended framework has an impact on the results. The 2020 data shows that BG41 and BG42 have the most small farms with the most UAA in the extended framework.
Regarding the comparison of data at regional level, there is a difference according to the location of the crop (territorial survey) and the location of the farm (IFS).
Regarding the reporting of permanent crops, one should bear in mind that they are often on small area and may be covered by the territorial sample but remain uncovered by the IFS. It is also possible that in the territorial survey abandoned permanent crops were included.
Regarding the data on fallow land, it is possible that the territorial survey includes area not belonging to active farms and thus not included in the IFS.
Regarding permanently grassland, it is possible that the territorial survey includes area visited by animals but not permanently for grazing. This area is not included in the IFS.
Coherence cross-domain: IFS vs ORGANIC CROP PRODUCTION (hectares) in relative terms
The difference in the reference period for the organic register and IFS data for land variables should be taken into account. The administrative data includes area registered after the IFS reference period. It should also be taken into account that the sample was not designed to stratify holdings by this criterion and it is possible that sampling error may also affect the results.
Coherence cross-domain: IFS vs ORGANIC ANIMAL PRODUCTION (heads) in relative terms
The difference in the reference period for the data from the organic register and IFS should be taken into account. Regarding the data on animals, the reporting difference related to organic production should also be taken into account. In IFS, animals in a control system are reported in total - converted and in-conversion animals, while in the administrative data for organic livestock farming, only converted organic animals are reported.
Fact is that the sample was not designed to stratify farms according to this criterion and it is possible that the results may also be affected by sampling error.
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
No coordination of data collections in agricultural statistics.
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
Not available.
16.3.2. Module ‘Labour force and other gainful activities‘
Not available.
16.3.3. Module ‘Rural development’
Not relevant, use of administrative data.
16.3.4. Module ‘Animal housing and manure management’
Restricted from publication
16.3.5. Module ‘Irrigation’
Not available.
16.3.6. Module ‘Soil management practices’
Not available.
16.3.7. Module ‘Machinery and equipment’
Not available.
16.3.8. Module ‘Orchard’
Not available.
16.3.9. Module ‘Vineyard’
Restricted from publication
17.1. Data revision - policy
Bulgaria uses a Code of Practice for Official Statistics applicable to all Official Statistics, which covers the first release of many IFS components (see Guidelines on quality criteria in the national statistical system of Bulgaria). The IFS dataset is subject to Eurostat validation, and revisions may be made until Eurostat approves the dataset. After Eurostat validation, we do not anticipate making further revisions unless a significant error is discovered. There have been no such cases so far. In case of a significant error and data changes, revised data will be published and users will be notified.
17.2. Data revision - practice
Data revision for IFS data was not performed.
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
Agricultural sample base.
18.1.1.3. Update frequency
Less frequent18.1.2. 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
Sampling at NUTS 3 level.
See sub-categories below.
18.1.2.2.1. Name of sampling design
Stratified one-stage random sampling18.1.2.2.2. Stratification criteria
Unit sizeUnit location
Unit specialization
Other
Annexes:
18.1.2.2.2. Stratification criteria
18.1.2.2.3. Use of systematic sampling
No18.1.2.2.4. Full coverage strata
Yes
18.1.2.2.5. Method of determination of the overall sample size
Optimal number of holdings to meet the precision requirements and ensure full coverage of the population of holdings relevant for the Orchard module.
18.1.2.2.6. Method of allocation of the overall sample size
Neymann allocation18.1.3. Core data collection on the frame extension
See sub-categories below.
18.1.3.1. Coverage of agricultural holdings
Not applicable18.1.3.2. Sampling design
Not applicable.
18.1.3.2.1. Name of sampling design
Not applicable18.1.3.2.2. Stratification criteria
Not applicable18.1.3.2.3. Use of systematic sampling
Not applicable18.1.3.2.4. Full coverage strata
Not applicable.
18.1.3.2.5. Method of determination of the overall sample size
Not applicable.
18.1.3.2.6. Method of allocation of the overall sample size
Not applicable18.1.4. 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
Sampling at NUTS 3 level.
See sub-categories below.
18.1.4.2.1. Name of sampling design
Stratified one-stage random sampling18.1.4.2.2. Stratification criteria
Unit sizeUnit location
Unit specialization
Other
Annexes:
18.1.4.2.2. Stratification criteria
18.1.4.2.3. Use of systematic sampling
No18.1.4.2.4. Full coverage strata
Yes
18.1.4.2.5. Method of determination of the overall sample size
Optimal number of holdings to meet the precision requirements and ensure full coverage of the population of holdings relevant for the Orchard module.
18.1.4.2.6. Method of allocation of the overall sample size
Neymann allocation18.1.4.2.7. 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
Sampling at NUTS 3 level.
See sub-categories below.
18.1.5.2.1. Name of sampling design
Stratified one-stage random sampling18.1.5.2.2. Stratification criteria
Unit sizeUnit location
Unit specialization
Other
Annexes:
18.1.5.2.2. Stratification criteria
18.1.5.2.3. Use of systematic sampling
No18.1.5.2.4. Full coverage strata
Yes
18.1.5.2.5. Method of determination of the overall sample size
Optimal number of holdings to meet the precision requirements and ensure full coverage of the population of holdings relevant for the Orchard module.
18.1.5.2.6. Method of allocation of the overall sample size
Neymann allocation18.1.5.2.7. 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
Sampling at NUTS 3 level.
See sub-categories below.
18.1.7.2.1. Name of sampling design
Stratified one-stage random sampling18.1.7.2.2. Stratification criteria
Unit sizeUnit location
Unit specialization
Other
Annexes:
18.1.7.2.2. Stratification criteria
18.1.7.2.3. Use of systematic sampling
No18.1.7.2.4. Full coverage strata
Yes
18.1.7.2.5. Method of determination of the overall sample size
Optimal number of holdings to meet the precision requirements and ensure full coverage of the population of holdings relevant for the Orchard module.
18.1.7.2.6. Method of allocation of the overall sample size
Neymann allocation18.1.7.2.7. If sampled from the core sample, the sampling strategy and calibration strategy
Not applicable18.1.8. Module ‘Soil management practices’
See sub-categories below.
18.1.8.1. Coverage of agricultural holdings
Sample18.1.8.2. Sampling design
Sampling at NUTS 3 level.
See sub-categories below.
18.1.8.2.1. Name of sampling design
Stratified one-stage random sampling18.1.8.2.2. Stratification criteria
Unit sizeUnit location
Unit specialization
Other
Annexes:
18.1.8.2.2. Stratification criteria
18.1.8.2.3. Use of systematic sampling
No18.1.8.2.4. Full coverage strata
Yes
18.1.8.2.5. Method of determination of the overall sample size
Optimal number of holdings to meet the precision requirements and ensure full coverage of the population of holdings relevant for the Orchard module.
18.1.8.2.6. Method of allocation of the overall sample size
Neymann allocation18.1.8.2.7. If sampled from the core sample, the sampling strategy and calibration strategy
Not applicable18.1.9. Module ‘Machinery and equipment’
See sub-categories below.
18.1.9.1. Coverage of agricultural holdings
Sample18.1.9.2. Sampling design
Sampling at NUTS 3 level.
See sub-categories below.
18.1.9.2.1. Name of sampling design
Stratified one-stage random sampling18.1.9.2.2. Stratification criteria
Unit sizeUnit location
Unit specialization
Other
Annexes:
18.1.9.2.2. Stratification criteria
18.1.9.2.3. Use of systematic sampling
No18.1.9.2.4. Full coverage strata
Yes
18.1.9.2.5. Method of determination of the overall sample size
Optimal number of holdings to meet the precision requirements and ensure full coverage of the population of holdings relevant for the Orchard module.
18.1.9.2.6. Method of allocation of the overall sample size
Neymann allocation18.1.9.2.7. If sampled from the core sample, the sampling strategy and calibration strategy
Not applicable18.1.10. Module ‘Orchard’
See sub-categories below.
18.1.10.1. Coverage of agricultural holdings
Census18.1.10.2. Sampling design
Not applicable.
18.1.10.2.1. Name of sampling design
Not applicable18.1.10.2.2. Stratification criteria
Not applicable18.1.10.2.3. Use of systematic sampling
Not applicable18.1.10.2.4. Full coverage strata
Not applicable.
18.1.10.2.5. Method of determination of the overall sample size
Not applicable.
18.1.10.2.6. Method of allocation of the overall sample size
Not applicable18.1.10.2.7. If sampled from the core sample, the sampling strategy and calibration strategy
Not applicable18.1.11. 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
SPSS
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
Other18.1.14. 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 decennial agricultural census is complemented by sample or census-based data collections organised every 3-4 years in-between. The agricultural census is conducted every 10 years.
18.3. Data collection
See sub-categories below.
18.3.1. Methods of data collection
Face-to-face, non-electronic versionFace-to-face, electronic version
Use of Internet
18.3.2. Data entry method, if paper questionnaires
Manual18.3.3. Questionnaire
Common land data comes from administrative sources. Subsequently, it was distributed proportionally (according to the LSU number) among the farms with herbivores in the IT system. There is no questionnaire for the common land data.
Please find the questionnaire in annex.
Annexes:
18.3.3. Questionnaire (core and modules) in Bulgarian
18.3.3. Questionnaire (core and modules) in English
18.3.3. Questionnaire (short version) in Bulgarian
18.3.3. Questionnaire (short version) in English
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
Data comparison and logical consistency checks within the information system (including validations from the manual of controls for statistical questionnaire completion), and Eurostat's data testing system.
An algorithm has been created for processing the collected data in the information system for bringing them into a form suitable for transmission to Eurostat (microdata file).
ISAS is the information system of the Agrostatistics department.
The data is checked and controlled at data entry in the electronic questionnaire in a specialised module in the agricultural statistics information system (ISAS). The system includes multiple validations for logical connections between variables, thresholds for entered values, automatic sums, visualisation of administrative data for control, etc. The data entry is performed by respondents, interviewers, and agrostatistics experts from the 28 regional directorates of agriculture (RO). The experts from the Ministry of Agriculture and Food prepare various queries and run the entered individual data through them, performing additional checks. If necessary, the data is then sent to the RO experts, who, after rechecking the farm data, can correct the electronic questionnaire in ISAS. ISAS allows for the exact time record during which the different participants in the survey had access to the electronic questionnaire.
Annexes:
18.4.3. Manual of controls for statistical questionnaire completion
18.5. Data compilation
Design weights (w(h)) - The weights in the sample modelling are defined as the ratio of the number of farms in the population of the respective stratum (N(h)) divided by the number of farms in the respective stratum determined in the sample (n(h)).
No adjustment of the weights was made due to non-response.
A correction of the weights was made due to changed farm location; grown up farms moving up in the upper stratum because their activity does not correspond to the stratum affecting the RSE, farms with crops defined as exhaustive, inherited and divided farms.
These farms are removed from the respective stratum of the population (N(h)) and are allocated to a new stratum.
The number of farms in the sample (n(h)) is updated according to the new stratification.
18.5.1. Imputation - rate
The imputation rate for partially missing variables cannot be calculated because the IT system does not account for every missing variable.
Data for all variables were imputed for 1 non-response unit in the interview.
This type of error has a negligible impact on the data quality.
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
AWU – Annual Working Unit
CAP – Common Agricultural Policy
dka – Decare
EU – European Union
ha – Hectare
IACS – Integrated Administration and Control System
IFS – Integrated Farm Statistics
ISAS – Information system of the Agrostatistics department
LSU – Livestock unit
LUCAS – Land Use and Cover Area frame Survey
NSI – National Statistical Institute
NUTS – Nomenclature of territorial units for statistics
RSE – Relative standard error
SGM – Standard Gross Margin
SO – Standard output
sq.m. – Square metres
UAA – Utilised agricultural area
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.
11 July 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, nectarines area, apricots area, grapes for table use area, grapes for raisins 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, places for animal housing etc.) and
- the number of agricultural holdings having these characteristics.
Design weights (w(h)) - The weights in the sample modelling are defined as the ratio of the number of farms in the population of the respective stratum (N(h)) divided by the number of farms in the respective stratum determined in the sample (n(h)).
No adjustment of the weights was made due to non-response.
A correction of the weights was made due to changed farm location; grown up farms moving up in the upper stratum because their activity does not correspond to the stratum affecting the RSE, farms with crops defined as exhaustive, inherited and divided farms.
These farms are removed from the respective stratum of the population (N(h)) and are allocated to a new stratum.
The number of farms in the sample (n(h)) is updated according to the new stratification.
See sub-categories below.
For censuses: every 10 years
For sample surveys: every 3 years between censuses
Data for IFS 2023 were disseminated in 2025.
See sub-categories below.
See sub-categories below.
See sub-categories below.
