1.1. Contact organisation
Statistical Office of the Republic of Slovenia
1.2. Contact organisation unit
Department for Agriculture, Forestry, Fishery and Hunting
1.3. Contact name
Confidential because of GDPR
1.4. Contact person function
Confidential because of GDPR
1.5. Contact mail address
Litostrojska cesta 54
1000 Ljubljana
Slovenia
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
27 February 2026
2.2. Metadata last posted
13 March 2026
2.3. Metadata last update
27 February 2026
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 “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, olives 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
In accordance with Regulation (EU) 2018/1091, the ‘farm’ or ‘agricultural holding’ means 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 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”
Not applicable for our country, according to Article 7(7) of Regulation (EU) 2018/1091.
3.6.5. Population covered by the data sent to Eurostat for the module “Soil management practices”
The same population of agricultural holdings defined in item 3.6.2.
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 production3.7.4. Additional information reference area
Not available.
3.8. Coverage - Time
Farm structure statistics in our country cover the period from 2000 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 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 reference period was from 01 January 2023 till 31 December 2023.
5.2. Reference period for variables on irrigation and soil management practices
The variables on irrigation are not applicable for 2023.
For the variables on soil management practices, the reference period was from 01 June 2022 till 31 May 2023.
5.3. Reference day for variables on livestock and animal housing
The reference day 01 June 2023 for livestock variables.
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 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
The reference day 01 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
The National Statistics Act (OJ RS, No. 45/95 and 9/01) and the Annual Programme of statistical surveys for 2023 (applicable between 01 January 2023 and 31 December 2023) (OJ RS, No. 146/2022).
6.1.3. Link to national legal acts and other agreements
- National Statistics Act (in Slovenian)
- National Statistics Act (in English)
- Annual Programme of statistical surveys for 2023 (in Slovenian)
6.1.4. Year of entry into force of national legal acts and other agreements
The National Statistics Act entered into force in 1995.
The Annual Programme of statistical surveys for 2023 entered into force in 2022.
6.1.5. Legal obligations for respondents
No6.2. Institutional Mandate - data sharing
The Statistical Office of the Republic of Slovenia is the sole institution responsible for integrated farm statistics and does not share data with data-producing agencies (administrative bodies).
7.1. Confidentiality - policy
The National Statistics Act respects statistical confidentiality.
The National Statistics Act (hereinafter the ZDSta) stipulates in Article 2 that “national statistics shall be implemented on the principles of neutrality, objectivity, professional independence, rationality, statistical confidentiality and transparency”.
This principle is further detailed in the provisions of the ZDSta, and its interpretation is supported by several international documents. The United Nations Resolution on Fundamental Principles of Official Statistics (adopted by the UN Statistical Commission in 1994 and confirmed by the UN General Assembly on 29 January 2014) establishes in Principle 6 that “individual data collected by statistical agencies for statistical compilation, whether they refer to natural or legal persons, are to be strictly confidential and used exclusively for statistical purposes”. The resolution explains that reliable official statistics are based on public trust and the goodwill of respondents to provide timely and accurate data that are requested. Such cooperation is only possible if statistical confidentiality is respected.
Similarly, this principle is elaborated in the European Statistics Code of Practice (adopted by the European Statistical System Committee on 28 September 2011), which determines that “the privacy of data providers (households, enterprises, administrations and other respondents), the confidentiality of the information they provide and its use only for statistical purposes must be absolutely guaranteed”.
Last but not least, item (e) of Article 2 of Regulation (EC) No 223/2009 on European Statistics determines statistical confidentiality as: “the protection of confidential data related to single statistical units which are obtained directly for statistical purposes or indirectly from administrative or other sources and implying the prohibition of use for non-statistical purposes of the data obtained and of their unlawful disclosure”.
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)p% rule (A contributor is able to derive an estimate of some other contributor within p% of its true value)
Secondary confidentiality rules
7.2.1.2. Methods to protect data in confidential cells
Cell suppression (Completely suppress the value of some cells)Rounding: controlled, deterministic or random (Round each cell value to a pre-specified rounding base)
7.2.1.3. Description of rules and methods
Regarding the protection of final output tables, the following confidentiality rules were applied:
- "Threshold rule" - the individual cell in the table is protected if there are fewer than "t" reporting units.
- "P%-rule" - the individual cell in the table is protected if the second largest contributor can recalculate the contribution of the largest contributor with a minimum precision.
- Secondary confidentiality - The cells that are not suppressed by the threshold rule or the p%-rule are considered safe from the primary confidentiality viewpoint. The linear relationships between the inner cells and the margins and overall totals can be exploited to recalculate the suppressed primary confidential cells. Some of the safe cells, according to the primary confidentiality rules, are further suppressed using appropriate methods to prevent the disclosure of primary suppressed cells.
- Rounding - All publishable values falling within the interval (0, 7.5) are rounded off to the nearest multiple of 5. The remaining publishable values are rounded to the nearest multiple of 10. When a cell is flagged as confidential (primary or secondary), the extrapolated aggregate of that cell is suppressed, but the extrapolated number of contributors is published rounded.
Primary and secondary confidentiality is applied using SAS. Secondary confidentiality is applied for standalone tables (and not also for linked tables) using a SAS-based procedure. Protected cells are marked with "z".
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
In the Statistical Office of the Republic of Slovenia, the dissemination of statistically protected microdata and sensitive tables (from the point of view of statistical confidentiality) to researchers is organised through the function of the Data Protection Committee, the advisory body of the Director General, in compliance with the system of rules and procedures related to the dissemination of statistically protected microdata to researchers, and the use of software for the statistical protection of data.
The microdata are not publicly available, but are made available according to special conditions to researchers for research purposes. Basic instructions concerning the access and the use of statistically protected microdata are available on the SURS website.
SURS decided that researchers could gain access to microdata sent to Eurostat ("Eurofarm data-set"). The data do not include precise locations of individual agricultural holdings. There will also be a possibility to gain other data on individual holdings that are not in the "Eurofarm data-set", but each request will be dealt with individually. Researchers must sign a contract with SURS, which includes confidentiality rules. Results intended for dissemination are later on reviewed by SURS to ensure statistical confidentiality.
8.1. Release calendar
The release calendar, which is typically published in November for the upcoming year, allows users to search for both past and future publication titles. This applies to all regular outputs, including Integrated Farm Statistics. To date, the only 2023 IFS publication released is the provisional data from 15 November 2023.
8.2. Release calendar access
The release calendar can be accessed via the website of the Statistical Office of the Republic of Slovenia. On the release calendar, publications can be searched by title, theme, sub-theme, publication date or producer.
8.3. Release policy - user access
The Dissemination and Communication Policy of the Statistical Office of the Republic of Slovenia (SURS) informs users about the diverse supply of data, products and services provided by SURS. It also presents the principles and standards considered in data publication and communication with its users. Together with the European Statistics Code of Practice, the policy - available on the SURS website - is the fundamental guideline for all SURS employees. SURS follows it in daily activities of publishing the data and communicating with users as well as in making long-term decisions.
Releases from the Statistical Office of the Republic of Slovenia are made available on working days at 10:30 am.
8.3.1. Use of quality rating system
Yes, another quality rating system8.3.1.1. Description of the quality rating system
The Statistical Office of the Republic of Slovenia draws attention to less reliable estimates by flagging them with a special sign. If the table contains estimated population totals of (continuous) variables, estimated averages of continuous variables or estimated ratios of population totals of (continuous) variables, publishing limitations are determined by the relative standard errors or the coefficients of variation (CV). In such cases it holds:
If the coefficient of variation is:
- 10% or below (CV <= 10%), the estimate is reliable enough and is published without limitations;
- between 10% and up to 30% (10% < CV <= 30%), the estimate is less reliable and is flagged for caution with the letter M;
- over 30% (CV > 30%), the estimate is too unreliable to be published and therefore suppressed for use by the letter N.
For more information, see the general methodological explanations - precision of statistical estimates.
Data on Integrated Farm Statistics (IFS) are disseminated at the national level following each IFS reference year, which occurs 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
From 2023 on, the structure of agricultural holdings will be monitored according to an altered methodology - 15 November 2023 (provisional data)
10.2. Dissemination format - Publications
See sub-categories below.
10.2.1. Production of paper publications
No10.2.2. Production of on-line publications
No10.2.3. Title, publisher, year and link
Not applicable.
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
The online database SiStat is accessible to users.
10.4. Dissemination format - microdata access
See sub-category below.
10.4.1. Accessibility of microdata
Yes10.5. Dissemination format - other
Not available.
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
Farm structure survey and agricultural census, SURS, 2023
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
Quality reports are prepared for national users and contain reference metadata and a detailed overview of quality components of the statistical survey (relevance, accuracy, timeliness and punctuality, accessibility and clarity, comparability, and coherence) together with the values of quality indicators. The national quality report for 2023 IFS data will be prepared in 2026 and published on the SURS website.
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
Compliance monitoring
11.1.3. Description of the quality management system and procedures
The Statistical Office of the Republic of Slovenia applies a quality management system in accordance with the European Statistics Code of Practice and the ESS Quality Assurance Framework. Quality assurance procedures are implemented throughout all phases of the statistical production process of Integrated Farm Statistics, including survey design, data collection, data validation, data processing and dissemination. Standardised validation rules, consistency checks and plausibility controls are applied, and responsibilities are clearly defined among staff involved in the process. Continuous monitoring and documentation of quality aspects support the reliability and consistency of the results.
11.1.4. Improvements in quality procedures
Minor improvements in quality procedures were implemented, mainly related to validation rules and data processing, contributing to improved data consistency and efficiency.
11.2. Quality management - assessment
Quality management is regularly assessed through internal evaluations in accordance with the ESS quality dimensions. The assessment confirms that the applied procedures are appropriate and ensure satisfactory data quality. Identified issues are addressed through standard validation processes and continuous improvements.
12.1. Relevance - User Needs
The purpose of publishing the data on the structure of agricultural holdings in Slovenia is to present the structure of agricultural production, machinery and equipment and labour input, and the data on agricultural holdings that are comparable with the data of other EU Member States.
Published key statistics cover the area and structure of land used, arable land, permanent grassland, and permanent crops. They also include the number and structure of livestock, labour force details, gainful activities, livestock units, economic size, and the production type of agricultural holdings.
The key users of data are the public sector, business entities, science, research and education, the general public, and foreign and internal users.
12.1.1. Needs at national level
National needs are discussed with main users represented in the Agricultural, Forestry and Fishery Statistics Committee, which is an advisory body of SURS.
Some of the characteristics were added to the questionnaire for national purposes only:
- the cutting timber on family farms was included in IFS to capture other gainful activities on agricultural holdings and to meet national needs (the quantity of wood cut is published).
- some livestock and crops categories are more detailed than Regulation (EU) 2018/1091 requires, facilitating better understanding by the farms.
Example (variables needed vs. variables collected):
-
- EU needs
- Piglets, live weight under 20 kg
- Breeding sows, live weight 50 kg or over
- Other pigs
- National (collected variables)
- Suckling piglets
- Piglets up to 20 kg
- Fattening pigs over 20 kg
- Breeding boars
- Breeding sows (including young)
- EU needs
12.1.2. Unmet user needs
User needs were thoroughly evaluated and integrated during the development of this system through extensive research and feedback analysis. In several meetings with key stakeholders, we ensured all primary requirements were addressed.
However, for national analytical purposes, there is a growing demand for more detailed data on smaller agricultural holdings. Stakeholders have indicated that returning to the lower reporting thresholds used prior to 2020 would better support national policy needs. Currently, the application of EU thresholds in the Integrated Farm Statistics limits data on smaller holdings, leaving some national requirements unfulfilled.
12.1.3. Plans for satisfying unmet user needs
The identified unmet national user needs will be addressed by exploring possibilities for complementary national data collections and by making greater use of available administrative data sources. In addition, methodological options for improving the coverage of smaller agricultural holdings for national analytical purposes will be assessed in future survey rounds, while remaining compliant with the requirements of Regulation (EU) 2018/1091.
12.2. Relevance - User Satisfaction
SURS last measured general user satisfaction in 2024. Overall satisfaction with SURS’s products and services reached an average score of 8.4, trust in the institution 8.8, and trust in the data 8.7 (on a scale from 1 – disagree completely to 10 – agree completely).
12.2.1. User satisfaction survey
Yes12.2.2. Year of user satisfaction survey
2024
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 link: CircaBC website.
13.2.2. Reasons for non-compliant precision requirements in relation to Regulation (EU) 2018/1091
In some specific domains and for certain variables, the precision requirements laid down in Regulation (EU) 2018/1091 were not fully met. This was mainly due to the limited effective sample size for small sub-populations and domains, as well as variability in the distribution of agricultural holdings. In addition, unit and item non-response in certain strata had an impact on the achieved precision. Despite these limitations, the overall quality of the results is considered adequate for analytical and policy purposes.
13.2.3. Reference on method of estimation
We used Eurostat's variance estimation method for the computation of the relative standard errors. The method is based on the ultimate cluster approximation. It accounts for the sampling design and for the presence of unequal weights within strata, however it does not account for the effect of calibration residuals on the estimated variance. For the description of the method, see the IFS 2023 Handbook, chapter “4.Data processing”, sub-chapter “4.6. Calculation of weights, variance estimation and quality rating system”, section "TOTALS OF CONTINUOUS VARIABLES", sub-section "Variance estimation in IFS".
13.2.4. Impact of sampling error on data quality
Moderate13.3. 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)
Temporarily out of production during the reference periodCeased activities
Merged to another unit
13.3.1.1.2. Actions to minimize the over-coverage error
Maintain of ineligible units in the records, recalculating weights of all units by considering the corrected population13.3.1.1.3. 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
Under-coverage is estimated to be negligible due to the high quality of the administrative sources.
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)
None13.3.1.3.3. Actions to minimise the under-coverage error
None.
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
Misclassification errors are minimised through the use of harmonised definitions, validation rules and consistency checks, supported by administrative data sources.
13.3.1.5. Contact error
Yes13.3.1.5.1. Actions to minimise the contact error
Actions to minimise contact errors include regular updating of contact information in the sampling frame using administrative sources, and repeated contact attempts. In addition, unclear or invalid contact details are verified and corrected during the data collection process.
13.3.1.6. Impact of coverage error on data quality
Moderate13.3.2. Measurement error
See sub-categories below.
13.3.2.1. List of variables mostly affected by measurement errors
We were aware of potential measurement errors and sought to avoid them through interviewer training, data checking, and validation processes. Where inconsistencies or extreme values were discovered, the data were cross-referenced with administrative records or verified via 'call-backs' to the farmers. Consequently, extreme values were checked and corrected where necessary. Because data were entered directly into a program with built-in controls, there were fewer errors caused by interviewers.
| Variable group | Description |
|---|---|
| Labour force variables (AWU, working time) | Variables related to labour input, in particular the number of working hours and the calculation of Annual Work Units (AWU), are prone to measurement errors due to recall difficulties and subjective estimation by respondents. |
| Other gainful activities | Variables related to other gainful activities may be affected by measurement errors because respondents may have difficulties distinguishing agricultural from non-agricultural activities or estimating their importance. |
| Soil management practices | Variables related to soil management practices may be affected by measurement errors due to recall problems and varying interpretations of specific practices. |
| Machinery and equipment | Information related to machinery and equipment may be subject to measurement errors due to misunderstandings regarding ownership, shared use or technical characteristics of the equipment. |
13.3.2.2. Causes of measurement errors
Complexity of variablesSensitivity of variables
Respondents’ 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
13.3.2.4. Impact of measurement error on data quality
Moderate13.3.2.5. Additional information measurement error
Not available.
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
Failure to make contact with the unitRefusal to participate
Inability to participate (e.g. illness, absence)
13.3.3.1.2. Actions to minimise or address unit non-response
Follow-up interviewsReminders
Weighting
13.3.3.1.3. Unit non-response analysis
By comparing the variables of respondents and non-respondents available in the sampling frame and with available administrative sources.
A non-response analysis was carried out using key variables available from administrative sources. The comparison between respondents and non-respondents was performed for the main variables.
The results show that there are no significant differences between the two groups. Non-response is not concentrated in any specific group of holdings, and there is no evidence that only a particular type of holdings did not respond.
Therefore, no systematic bias due to non-response was identified.
13.3.3.2. Item non-response - rate
No item non-response was detected, as core variables were collected primarily through administrative sources and via CATI. The CATI application was strictly configured to ensure all questions were answered.
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
Moderate13.3.3.4. Additional information non-response error
Not available.
13.3.4. Processing error
See sub-categories below.
13.3.4.1. Sources of processing errors
Data entryCoding
Imputation methods
Data processing
13.3.4.2. Imputation methods
Mean imputationRandom hot deck imputation
Sequential hot deck imputation
Nearest neighbour imputation
Previous data for the same unit
13.3.4.3. Actions to correct or minimise processing errors
The internal application SOP (Statistical Data Editing Program), developed at SURS, was used for imputation. All formulas (metadata) are saved within the system, and the process is executed step by step, ensuring that every step is well-defined and repeatable.
13.3.4.4. Tools and staff authorised to make corrections
Data processing was conducted by a single authorised person (Methodologist, Disseminator, and Database Manager within the Agriculture, Forestry, Fishing and Hunting Statistics Section). Imputations were performed using a specialised program developed internally by SURS (SOP; Statistical Data Editing Program).
13.3.4.5. Impact of processing error on data quality
Moderate13.3.4.6. Additional information processing error
Not available.
13.3.5. Model assumption error
Model assumption errors are considered negligible and do not significantly affect data quality.
14.1. Timeliness
See sub-categories below.
14.1.1. Time lag - first result
First provisional data was published on 15 November 2023.
14.1.2. Time lag - final result
At the time of preparation of this quality report, the final results have not yet been published.
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
Final results have not yet been published at the time of preparation of this quality report.
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 collected, sent to Eurostat, and published have the same definition as in Regulation (EU) 2018/1091.
15.1.2.2. Reasons for deviations
Not applicable.
15.1.3. Thresholds of agricultural holdings
See sub-categories below.
15.1.3.1. Proofs that the EU coverage requirements are met
All holdings meeting at least one of the physical thresholds listed in Annex II of Regulation (EU) 2018/1091 were covered.
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
No deviations from the definitions and classifications laid down in Regulation (EU) 2018/1091, Commission Implementing Regulation (EU) 2021/2286, and the EU handbook were applied except for the following:
- In 2023, there are no holdings in the classes FARM_SPOU (farm managed by spouse of holder), FARM_HLD_SPOUFAM (farm co-managed by holder and spouse or family member), FARM_FAM (farm managed by a family member (not spouse) of holder) and FARM_NFAM (farm not managed by any family member of holder) as these are included in the class FARM_HLD (farm managed by sole holder).
- LSU coefficients for national dissemination are different from those used in Eurostat.
Please see item 15.1.4.2 for the reasons.
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
In Slovenia, different LSU coefficients are used for national data dissemination. The coefficients used are listed in the table 1 of the methodological document Farm structure survey and agricultural census.
15.1.4.1.5. Livestock included in “Other livestock n.e.c.”
Included are Equidae, male breeding rabbits, and fattening rabbits.
15.1.4.2. Reasons for deviations
Definitions are the same, but classifications are not, since some categories in IFS are grouped, and in national publications they are not.
In 2023, the classes FARM_HLD (farm managed by sole holder), FARM_SPOU (farm managed by spouse of holder), FARM_HLD_SPOUFAM (farm co-managed by holder and spouse or family member), FARM_FAM (farm managed by a family member (not spouse) of holder) and FARM_NFAM (farm not managed by any family member of holder) are all grouped under the class FARM_HLD. According to new administrative rules in Slovenia, the person registered as the farm holder is automatically designated as the farm manager.
The LSU coefficients used for national dissemination differ from those applied by Eurostat. The competent authority applies its own coefficients, and SURS follows these established standards to ensure consistency with internal statistical practices and reporting.
15.1.5. Reference periods/days
See sub-categories below.
15.1.5.1. Deviations from Regulation (EU) 2018/1091
No deviations.
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
We use administrative data from the Ministry of Agriculture, Forestry and Food. Common land is included in the land use data of the agricultural holdings making use of the common land, allocated 'in proportion to the use by each holding'.
Common land is allocated to holdings based on a statistical model using data from an administrative source. The source provides information on the number and type of animals grazing on common land, as well as the duration of grazing (number of grazing days).
The allocation is performed using livestock units (LSU), taking into account both the type and number of grazing animals (e.g. cattle, sheep and goats) and the length of the grazing period. Based on these factors, the total common land area is proportionally distributed among the holdings using the common pasture.
This approach ensures that the allocation reflects the actual intensity of use of common land. Double counting is avoided by distributing the total recorded area only once among all users of the common pasture.
The area of common land consists only of pastures (rough grazing).
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 differences detected.
15.1.7.2. Reasons for deviations
Not applicable.
15.1.8. Differences in methods across regions within the country
No differences.
15.2. Comparability - over time
See sub-categories below.
15.2.1. Length of comparable time series
1 year
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
The thresholds for agricultural holdings applied in 2023 differ from those used in previous survey rounds. In 2023, the EU thresholds defined in Regulation (EU) 2018/1091 were adopted, replacing the lower national thresholds used in earlier years. This change reduces the coverage of smaller holdings and, as a result, the 2023 data are not fully comparable to previous years. Time series comparisons should therefore be interpreted with caution.
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
In 2023, data collection was carried out in June, with reference periods and reference days applied in accordance with Regulation (EU) 2018/1091. The reference day for livestock was 01 June. By comparison, for the 2020 Agricultural Census, livestock data referred to the reference day of 01 February.
For the labour force module, the 2020 data was collected for a 12-month period ending on 01 December 2020, whereas for 2023, it covered a 12-month period ending on 01 June 2023.
These differences in reference timing may slightly affect the comparability of livestock data and labour force data between 2020 and 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 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
Between 2020 and 2023, the aggregates of the CORE variables are not comparable as they cover both main frame and frame extension in 2020, and only main frame in 2023.
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
The 2023 results were checked and compared with all the available administrative data. Indeed, a substantial amount of data was used directly from administrative sources. The results were also partially checked with other statistical data collections, such as Annual Crop Statistics and Animal Production Statistics, to make sure the data fell within the same farm size class (e.g., large or small farms). The analysis showed no significant differences in microdata.
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 ORGANIC ANIMAL PRODUCTION (heads) in relative terms
Compared to organic animal production, in IFS, there is a different reference day, and both animals under conversion and fully converted are included.
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
All agricultural statistics are produced within the same department, ensuring effective coordination across surveys. This prevents overlapping data requests and reduces respondent burden.
16.2. Efficiency gains since the last data transmission to Eurostat
Further automationIncreased 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 applicable (use of administrative data source).
16.3.4. Module ‘Animal housing and manure management’
Restricted from publication
16.3.5. Module ‘Irrigation’
Not applicable (not collected).
16.3.6. Module ‘Soil management practices’
Not available.
16.3.7. Module ‘Machinery and equipment’
Not available.
16.3.8. Module ‘Orchard’
Not applicable (use of administrative data source).
16.3.9. Module ‘Vineyard’
Restricted from publication
17.1. Data revision - policy
SURS has a data revision policy and a scheduled revision program of the preliminary to final data which is published on its website.
17.2. Data revision - practice
In practice, data revisions are rare. For Integrated Farm Statistics, data are considered final once they have been validated and transmitted to Eurostat. Revisions are made only in exceptional cases, such as the identification of significant errors, and revised data are clearly identified and communicated to users.
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
The Statistical 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
Core data were collected using a sample survey based on a stratified random sampling design. The sampling frame was stratified according to relevant structural characteristics of agricultural holdings in order to improve the efficiency and precision of the estimates.
Strata were defined by unit location at the NUTS 2 level and by four size classes of family farms. Agricultural businesses formed an additional separate stratum due to their relatively small number. Within each stratum, agricultural holdings were selected using random sampling.
The same sample was used for the collection of core variables and all mandatory modules. This integrated approach ensured consistency across data domains and reduced the response burden on agricultural holdings.
Sampling weights were calculated to reflect the sampling design and were further adjusted to account for non-response.
18.1.2.2.1. Name of sampling design
Stratified one-stage random 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
Full coverage strata were applied to the largest agricultural holdings, as these units significantly impact key structural variables. Including them with certainty improves estimate precision and reduces sampling variability.
Agricultural businesses formed an additional separate full coverage stratum due to their relatively small number.
18.1.2.2.5. Method of determination of the overall sample size
The overall sample size was determined based on precision requirements laid down in Regulation (EU) 2018/1091, taking into account the expected variability of key variables, the structure of the agricultural population and available resources. The sample size was designed to ensure an adequate level of precision for the main domains while balancing statistical quality and response burden.
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
The same sample as for CORE (see item 18.1.2.2).
18.1.4.2.1. Name of sampling design
Stratified one-stage random 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
Full coverage strata were applied to the largest agricultural holdings, as these units significantly impact key structural variables. Including them with certainty improves estimate precision and reduces sampling variability.
Agricultural businesses formed an additional separate full coverage stratum due to their relatively small number.
18.1.4.2.5. Method of determination of the overall sample size
The overall sample size was determined based on precision requirements laid down in Regulation (EU) 2018/1091, taking into account the expected variability of key variables, the structure of the agricultural population and available resources. The sample size was designed to ensure an adequate level of precision for the main domains while balancing statistical quality and response burden.
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
The same sample as for CORE (see item 18.1.2.2).
18.1.5.2.1. Name of sampling design
Stratified one-stage random 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
Full coverage strata were applied to the largest agricultural holdings, as these units significantly impact key structural variables. Including them with certainty improves estimate precision and reduces sampling variability.
Agricultural businesses formed an additional separate full coverage stratum due to their relatively small number.
18.1.5.2.5. Method of determination of the overall sample size
The overall sample size was determined based on precision requirements laid down in Regulation (EU) 2018/1091, taking into account the expected variability of key variables, the structure of the agricultural population and available resources. The sample size was designed to ensure an adequate level of precision for the main domains while balancing statistical quality and response burden.
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
Not applicable18.1.7.2. Sampling design
Not applicable.
18.1.7.2.1. Name of sampling design
Not applicable18.1.7.2.2. Stratification criteria
Not applicable18.1.7.2.3. Use of systematic sampling
Not applicable18.1.7.2.4. Full coverage strata
Not applicable.
18.1.7.2.5. Method of determination of the overall sample size
Not applicable.
18.1.7.2.6. Method of allocation of the overall sample size
Not applicable18.1.7.2.7. 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
The same sample as for CORE (see item 18.1.2.2).
18.1.8.2.1. Name of sampling design
Stratified one-stage random 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
Full coverage strata were applied to the largest agricultural holdings, as these units significantly impact key structural variables. Including them with certainty improves estimate precision and reduces sampling variability.
Agricultural businesses formed an additional separate full coverage stratum due to their relatively small number.
18.1.8.2.5. Method of determination of the overall sample size
The overall sample size was determined based on precision requirements laid down in Regulation (EU) 2018/1091, taking into account the expected variability of key variables, the structure of the agricultural population and available resources. The sample size was designed to ensure an adequate level of precision for the main domains while balancing statistical quality and response burden.
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
The same sample as for CORE (see item 18.1.2.2).
18.1.9.2.1. Name of sampling design
Stratified one-stage random 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
Full coverage strata were applied to the largest agricultural holdings, as these units significantly impact key structural variables. Including them with certainty improves estimate precision and reduces sampling variability.
Agricultural businesses formed an additional separate full coverage stratum due to their relatively small number.
18.1.9.2.5. Method of determination of the overall sample size
The overall sample size was determined based on precision requirements laid down in Regulation (EU) 2018/1091, taking into account the expected variability of key variables, the structure of the agricultural population and available resources. The sample size was designed to ensure an adequate level of precision for the main domains while balancing statistical quality and response burden.
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
Sample18.1.10.2. Sampling design
The same sample as for CORE (see item 18.1.2.2).
18.1.10.2.1. Name of sampling design
Stratified one-stage random 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
Full coverage strata were applied to the largest agricultural holdings, as these units significantly impact key structural variables. Including them with certainty improves estimate precision and reduces sampling variability.
Agricultural businesses formed an additional separate full coverage stratum due to their relatively small number.
18.1.10.2.5. Method of determination of the overall sample size
The overall sample size was determined based on precision requirements laid down in Regulation (EU) 2018/1091, taking into account the expected variability of key variables, the structure of the agricultural population and available resources. The sample size was designed to ensure an adequate level of precision for the main domains while balancing statistical quality and response burden.
18.1.10.2.6. Method of allocation of the overall sample size
Neymann allocation18.1.10.2.7. 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
SAS
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 sourceThe final validated data in the source would not be in time to meet statistical deadlines or would relate to a period which does not coincide with the reference period
18.1.14. 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
Postal, non-electronic versionTelephone, electronic version
18.3.2. Data entry method, if paper questionnaires
Manual18.3.3. Questionnaire
Please find the questionnaire in annex.
Annexes:
18.3.3 Questionnaire - family farm - in English
18.3.3 Questionnaire - legal unit - in English
18.3.3 Questionnaire - family farm - in Slovenian
18.3.3 Questionnaire - legal unit - in Slovenian
18.4. Data validation
See sub-categories below.
18.4.1. Type of validation checks
Data format checksCompleteness checks
Range checks
Relational checks
Data flagging
Comparisons with previous rounds of the data collection
Comparisons with other domains in agricultural statistics
18.4.2. Staff involved in data validation
Staff from central department18.4.3. Tools used for data validation
Data validation is carried out using a combination of automated and manual procedures. Automated validation checks are integrated into the data processing system and include consistency checks, range checks, and logical controls between variables.
Validation is performed using SAS software as well as the internal application SOP (Statistical Data Editing Program), developed at SURS. These tools enable systematic data editing, detailed analysis, and cross-checks with previous data and administrative sources. Suspicious or inconsistent records are further reviewed and corrected where necessary.
18.5. Data compilation
Data compilation includes data cleaning, editing, imputation (where necessary), weighting and aggregation in accordance with established statistical procedures. Data from different sources are integrated and processed to ensure consistency and completeness. Derived variables are calculated based on predefined rules and classifications.
Design weights were calculated as the inverse of the inclusion probabilities, reflecting the sampling design. These weights were subsequently adjusted to account for unit non-response.
All steps of data compilation were implemented using standardised procedures, ensuring coherence and comparability of the final results.
18.5.1. Imputation - rate
A significant proportion of the data was obtained from administrative sources, which reduces the need for imputation. Imputation was applied only in limited cases where data were missing or inconsistent. Because administrative sources were used for replacing values, it is difficult to calculate the actual imputation rate.
18.5.2. Methods used to derive the extrapolation factor
Design weightNon-response adjustment
Trimming of extreme weights
18.6. 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
CATI – Computer Assisted Telephone Interview
CORE – General, crops and livestock variables of Annex III of Regulation (EU) 2018/1091
CV – Coefficient of Variation
ESS – European Statistical System
EU – European Union
IFS – Integrated Farm Statistics
LSU – Livestock unit
NUTS – Nomenclature of territorial units for statistics
OJ RS – Official Gazette of the Republic of Slovenia
SGM – Standard gross margin
SO – Standard output
SOP – Statistical Data Editing Program
SURS – Statistical Office of the Republic of Slovenia
UN – United Nations
ZDSta – National Statistics Act (Zakon o državni statistiki)
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.
27 February 2026
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 “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, olives 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.
Data compilation includes data cleaning, editing, imputation (where necessary), weighting and aggregation in accordance with established statistical procedures. Data from different sources are integrated and processed to ensure consistency and completeness. Derived variables are calculated based on predefined rules and classifications.
Design weights were calculated as the inverse of the inclusion probabilities, reflecting the sampling design. These weights were subsequently adjusted to account for unit non-response.
All steps of data compilation were implemented using standardised procedures, ensuring coherence and comparability of the final results.
See sub-categories below.
Data on Integrated Farm Statistics (IFS) are disseminated at the national level following each IFS reference year, which occurs every 3-4 years.
See sub-categories below.
See sub-categories below.
See sub-categories below.
