1.1. Contact organisation
Hellenic Statistical Authority (ELSTAT)
1.2. Contact organisation unit
Agriculture, Livestock, Fishery and Environment Statistics Division / Farm Structure Statistics Section
1.3. Contact name
Confidential because of GDPR
1.4. Contact person function
Confidential because of GDPR
1.5. Contact mail address
Pireos 46 & Eponiton Str., 18510 – Piraeus, P.O.Box 80847
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
26 June 2025
2.2. Metadata last posted
22 July 2025
2.3. Metadata last update
26 June 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 modular 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 is 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, oranges area, small citrus fruit area, lemons area, olives 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 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 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
Mount Athos
3.7.3. Criteria used to establish the geographical location of the holding
The main building for productionThe location where all agricultural activities are situated
The most important parcel by economic size
The residence of the farmer (manager) not further than 5 km straight from the farm
3.7.4. Additional information reference area
Not available.
3.8. Coverage - Time
Farm structure statistics for Greece cover the period from 1983 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 and more specifically to the period 01 October 2022-30 September 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.
5.2. Reference period for variables on irrigation and soil management practices
The 12-month period ending on 30 September within the reference year 2023.
5.3. Reference day for variables on livestock and animal housing
The reference day for livestock variables is 01 November 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 30 September 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 30 September 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
Legal act6.1.2. Name of national legal acts and other agreements
- Greek Statistical Law No 3832/2010, as in force
- Regulation on the Operation and Administration of the Hellenic Statistical Authority (ELSTAT) (Government Gazette 2390/B/28.08.2012)
- Regulation on the Statistical Obligations of the agencies of the Hellenic Statistical System (ELSS) (Government Gazette 4083/Β/20.12.2016)
- Act no 5791/Β2-567/07.09.2023 (Government Gazette 5525/B/18.09.2023) on the “Approval, proclamation, assignment and distribution of costs for conducting the farm survey structure for the year 2023, as well as approval of using statistical representatives and determination of their fee, for the year 2023”
- 1st amendment: Act no 7974/Β2-758/18.12.2023 (Government Gazette 7652/B/31.12.2023) on the amendment of Act no 5791/Β2-567/07.09.2023.
- 2nd amendment: Act no 6692/Β2-700/04.11.2024 (Government Gazette 6145/B/06.11.2024) on the 2nd amendment of Act no 5791/Β2-567/07.09.2023.
6.1.3. Link to national legal acts and other agreements
- Greek Statistical Law No 3832/2010, as in force
- Regulation on the Operation and Administration of the Hellenic Statistical Authority (ELSTAT) (Government Gazette 2390/B/28.08.2012)
- Regulation on the Statistical Obligations of the agencies of the Hellenic Statistical System (ELSS) (Government Gazette 4083/Β/20.12.2016)
- Act no 5791/Β2-567/07.09.2023 (Government Gazette 5525/B/18.09.2023) on the “Approval, proclamation, assignment and distribution of costs for conducting the farm survey structure for the year 2023, as well as approval of using statistical representatives and determination of their fee, for the year 2023”
6.1.4. Year of entry into force of national legal acts and other agreements
- Greek Statistical Law No 3832/2010: 2010
- Regulation on the Operation and Administration of the Hellenic Statistical Authority (ELSTAT): 2012
- Regulation on the Statistical Obligations of the Agencies of the Hellenic Statistical System (ELSS): 2016
- Act on the “Approval, proclamation, assignment and distribution of costs for conducting the farm survey structure for the year 2023, as well as approval of using statistical representatives and determination of their fee, for the year 2023”: 2023
- 1st amendment: 2023
- 2nd amendment: 2024
6.1.5. Legal obligations for respondents
Yes6.2. Institutional Mandate - data sharing
For data producing agencies that are part of the Hellenic Statistical System (ELSS), issues pertaining to the development, production and dissemination of statistics, are arranged by ELSTAT and the agencies and laid down in form of memoranda of cooperation and written agreements between them (Regulation on the Statistical Obligations of the agencies of the Hellenic Statistical System (ELSS)).
7.1. Confidentiality - policy
The issues concerning the observance of statistical confidentiality by the Hellenic Statistical Authority (ELSTAT) are arranged by Articles 7, 8 and 9 of the Greek Statistical Law No 3832/2010 as in force, by Articles 8, 10 and 11(2) of the Regulation on the Statistical Obligations of the agencies of the Hellenic Statistical System (ELSS) and by Articles 10 and 15 of the Regulation on the Operation and Administration of the Hellenic Statistical Authority (ELSTAT).
Specifically, ELSTAT disseminates statistics in compliance with the statistical principles of the European Statistics Code of Practice and in particular with the principle of statistical confidentiality.
Protection of personal data
ELSTAT abides by the commitments and obligations arising from the applicable EU and national legislation on the protection of the individual from the processing of personal data and the relevant decisions, guidelines and regulatory acts of the Hellenic Data Protection Authority.
Pursuant to the Regulation on the protection of natural persons with regard to the processing of personal data [Regulation (EU) 2016/679 of the European Parliament and of the Council of 27 April 2016 (General Data Protection Regulation - GDPR)], ELSTAT implements the appropriate technical and organisational measures for ensuring adequate level of security against risks for the personal data it collects and has access to, in the context of carrying out its tasks, in order to meet the requirements of this Regulation and to protect these personal data from any unauthorised access or illegal processing.
The personal data collected by ELSTAT are used exclusively for purposes related to the conduct of surveys and the production of relevant statistics. Only ELSTAT has access to the data. The controller is the person appointed by law pursuant to the relevant provisions concerning the Legal Entities of Public Law and the Independent Authorities. The data are stored in the databases of ELSTAT for as long as required by the relevant legislation.
Legal basis of the processing: Article 6, paragraphs 1(c) and 1(d) of the General Data Protection Regulation (GDPR)
General information on the processing of your data by ELSTAT
7.2. Confidentiality - data treatment
See sub-categories below.
7.2.1. Aggregated data
See sub-categories below.
7.2.1.1. Rules used to identify confidential cells
Threshold rule (The number of contributors is less than a pre-specified threshold)Secondary confidentiality rules
7.2.1.2. Methods to protect data in confidential cells
Cell suppression (Completely suppress the value of some cells)7.2.1.3. Description of rules and methods
The above procedures are implemented using the free version of τ-argus software v4.1.0 distributed by Statistics Netherlands, therefore the identification of confidential cells and their suppression (primary and secondary) is automated.
To ensure adherence to the confidentiality provisions set out in section 7.1 Confidentiality – policy, prior to their publication ΙFS data are subject to the following procedures:
- Micro-aggregation at NUTS 3 level,
- Primary cell suppression on the aggregated data, using a minimum frequency/threshold less than three (3), according to the recommendations of the Statistical Confidentiality Committee (SCC) of ELSS, and
- Secondary cell suppression with full singleton handling, using Optimal Salazar solution.
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
ELSTAT may grant researchers conducting statistical analyses for scientific purposes access to data that enable the indirect identification of the statistical units concerned, upon completion of an application procedure and a favourable recommendation from the Statistical Confidentiality Committee (SCC) within the ELSS.
8.1. Release calendar
Yes
8.2. Release calendar access
There is a press release calendar, planned during the previous calendar year, that also concerns data releases.
Changes that may occur, regarding either delays or ad-hoc press releases are communicated on the ELSTAT's webpage.
8.3. Release policy - user access
ELSTAT publishes sets of tables containing aggregated data, usually at the NUTS 3 level, on its official webpage, along with the respective metadata. Both data and metadata are published according to the release calendar and are accessible by anyone and free of charge. The dissemination time is 12 p.m. (noon). There is no pre-release access for users.
For IFS data, the variables included represent the main crop/livestock/labour force categories and classifications.
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 Integrated Farm Statistics Manual, 2023 edition.
Every three years for sample survey data and every 10 years for census survey data.
10.1. Dissemination format - News release
See sub-categories below.
10.1.1. Publication of news releases
Yes10.1.2. Link to news releases
Not yet available for IFS 2023.
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
Not yet available for IFS 2023.
10.3. Dissemination format - online database
See sub-categories below.
10.3.1. Data tables - consultations
IFS tables: 21 912 consultations in 2022, 21 807 consultations in 2023 and 18 930 consultations in 2024, including consultations of metadata.
10.3.2. Accessibility of online database
Yes10.3.3. Link to online database
Online database (see 'Census of Agricultural and Livestock Holdings' and 'Farm Structure Survey (FSS)')
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
Not yet available for IFS 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
The quality report is not made directly available to the public through the ELSTAT's webpage. The SIMS and Euro-SDMX reports are provided instead.
However, users interested in the validated quality report can submit a request on the following page, under the section 'European Statistics', and the quality report will be emailed to them.
11.1. Quality assurance
See sub-categories below.
11.1.1. Quality management system
Yes11.1.2. Quality assurance and assessment procedures
Training coursesQuality guidelines
Self-assessment
Peer review
11.1.3. Description of the quality management system and procedures
ELSTAT aims at ensuring and continuously improving the quality of the produced statistics and maintaining users’ confidence in these statistics. These goals are achieved, as described in the ELSTAT Quality Policy and the ELSTAT Quality Guidelines, through the following principles:
- Safeguard and substantiate the operational independence of ELSTAT;
- Produce timely and relevant statistics using scientifically sound methods;
- Establish and maintain users’ confidence in the reliability of the statistics;
- Safeguard the confidence of the statistical units who provide their confidential information for the production of the statistics.
These quality objectives are achieved by incorporating the guidelines listed above in all the stages of collection, production and dissemination of the statistics.
The IFS data collection was done by electronic self-enumeration of the owner or manager of the agricultural holding, or by personal interview and registration of the data by the Enumerator, when the self-enumeration was not possible, by taking all the necessary public health protection measures.
The Enumerators before undertaking the collection of the IFS data attended a relevant seminar by the IFS Supervisors for the correct completion of the questionnaire. A Work Team, consisting of competent employees of ELSTAT and the Heads of its two General Directorates, coordinated the work of organising, conducting, processing data, exporting and disseminating the results of the IFS, providing relevant instructions and guidelines on various issues concerning the procedures and the output, including on quality issues.
11.1.4. Improvements in quality procedures
Evaluation and possible improvement measures will take place after the final validation of the data.
11.2. Quality management - assessment
The quality assessment procedures include:
- Evaluation of the statistical procedures and output of every statistical survey/work, on the basis of CoP principles and best practices in the European Statistical System;
- Participation in the Peer Reviews, which are periodically conducted throughout the European Statistical System;
- Evaluation of the implementation of CoP principles 1 - 6 in the ELSS by the Good Practice Advisory Committee, which is an independent advisory committee, whose members are selected from among experts with exceptional skills and national and/or international experience in matters relating to the CoP.
12.1. Relevance - User Needs
The main users of IFS data are:
- Private consulting companies that require the full array of IFS variables to identify sectors and regions eligible for funding under the various development initiatives;
- The Ministry of Rural Development and Food and the Ministry of Environment and Climate Change that require various IFS data for policy planning and assessment reasons.
12.1.1. Main groups of variables collected only for national purposes
There are no characteristics that are surveyed only for national purposes.
12.1.2. Unmet user needs
All user needs are met.
12.1.3. Plans for satisfying unmet user needs
Not applicable.
12.2. Relevance - User Satisfaction
An annual user satisfaction survey has been conducted since 2011/2012, using an online questionnaire. The survey is general and not specific to IFS.
12.2.1. User satisfaction survey
Yes12.2.2. Year of user satisfaction survey
2023
12.2.3. Satisfaction level
Highly 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
Over-coverage, misclassification and non-response increased the variances and the standard errors of the survey estimates.
For the 2023 IFS survey, among 28 precision variables calculated for each of the 13 regions (NUTS 2) of the country, the precision requirements of Regulation (EU) 2018/1091 were not met for the variable "Live poultry excluding cocks and chicks of chicken (LSU)" for the region of Eastern Macedonia and Thrace (EL51) and the variable "Other bovine animals (bovine animals less than 1 year, bovine animals 1 to less than 2 years, male bovine animals 2 years old and over, heifers 2 years old and over) (LSU)" for the region of Western Greece (EL63). This is due to misclassification errors, which are random errors but have increased the variance of the estimates of these variables and consequently the sampling error of their estimates.
Measures will therefore be taken to ensure that ELSTAT's Farm Register will be updated as close as possible to the reference period of the survey, as agricultural and livestock holdings may generally exhibit dynamic variability due to changes in their technical and economic orientation in terms of the dominant crop or type of livestock, the size of the holding, the location of the dominant type of agricultural crop or breeding livestock, etc.
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
High13.3. Non-sampling error
See sub-categories below.
13.3.1. Coverage error
See sub-categories below.
13.3.1.1. Over-coverage - rate
The unweighted over-coverage rate is 10.7%, while the corresponding weighted over-coverage rate is 14.2%.
The unweighted over-coverage rate is also published on Eurostat’s website, at the link: CircaBC.
The weighted over-coverage rate is greater than the corresponding unweighted rate, because the holdings that were found to be out of scope through the survey were mainly small-scale holdings belonging to sampling strata with design weights greater than 1.
The over-coverage rate is the proportion of units accessible via the frame that do not belong to the target population (are out-of-scope). Over-coverage rate was computed as the proportion of units from the sample which do not belong to the target population to the overall sample size.
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
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
The over-coverage units were detected during the data collection, and they were removed. Τhis resulted in an increase of the sampling errors, but no significant biases.
Since the allocation of sample units in the strata is not proportional but it is based on the optimal allocation, the sampling fractions of small holdings are lower than the corresponding fractions of medium and large holdings. As a result, small ineligible holdings are more difficult to detect than medium and large ineligible holdings.
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
There is no information available to assess the under-coverage rate.
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 birthsNew units derived from split
Units 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
Updating the Farm Register of ELSTAT by administrative sources and agricultural and livestock surveys minimises the under-coverage error.
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 lead to inaccurate holding stratification, reducing the benefits of sample unit stratification. Because misclassification is considered a random error, the sampling error increases.
Updating the Farm Register of ELSTAT by administrative sources and agricultural and livestock surveys minimises the misclassification error.
13.3.1.5. Contact error
Yes13.3.1.5.1. Actions to minimise the contact error
There were cases where contact with the holdings was not possible due to inaccurate address information in the Farm Register.
In these cases, the initial sample was reduced, and the eligibility status of the holdings remained unknown.
During data processing, the eligibility of these "unknown" units was estimated using information from the remaining units in the same stratum.
These contact errors increase sampling errors and may introduce bias because it is not possible to survey these units.
Updating the Farm Register of ELSTAT by administrative sources and agricultural and livestock surveys minimises the contact error.
13.3.1.6. Impact of coverage error on data quality
Unknown13.3.2. Measurement error
See sub-categories below.
13.3.2.1. List of variables mostly affected by measurement errors
The interview was conducted with the owner or the manager of the holding. However, if the owner or the manager was temporarily absent, the required information could be retrieved by interviewing another member of the holder’s family or an employee with knowledge (e.g. foreman) of the holding.
The most common problematic questions/characteristics identified during the quality control of the data were the following:
- Location of the holding,
- Kitchen gardens vs. outdoor fresh vegetables,
- Permanent grassland vs. common land, in some cases difficult to discern,
- Categories by tree density,
- Categories by plantation age,
- Water volume used for irrigation.
13.3.2.2. Causes of measurement errors
Complexity of variablesRespondents’ inability to provide accurate answers
Insufficient preparation of interviewers
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
Unknown13.3.2.5. Additional information measurement error
Measurement error variability can be assessed by comparing survey microdata with corresponding microdata from other sources for the same reference period.
13.3.3. Non response error
See sub-categories below.
13.3.3.1. Unit non-response - rate
The unit unweighted non-response rate of eligible units is 7.1%, while the corresponding weighted non-response rate is 8.7%.
The unit unweighted non-response rate is also published on Eurostat’s website, at the link: CircaBC.
The weighted non-response rate is greater than the corresponding unweighted rate, because the non-respondent holdings are mainly small-scale holdings that belong to sampling strata with design weights greater than 1.
The unit response rate (Rr) is calculated as the share of eligible respondent holdings to the eligible holdings. The unit non-response rate is: NRr = 1-Rr.
The eligibility status of the unknown holdings was determined in ultimate strata by taking into account the relationship of the eligibility and non-eligibility status of the remaining units.
13.3.3.1.1. Reasons for unit non-response
Failure to identify the unitFailure to make contact with the unit
Refusal 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
Using data from the Farm Register of ELSTAT in the sample of eligible holdings, it was observed that:
- The utilised agricultural area for non-respondent holdings represents 3.1% of the corresponding area of the overall eligible sample of holdings.
- The number of livestock units for non-respondent holdings represents 2.2% of the corresponding livestock units of the overall eligible sample of holdings.
13.3.3.2. Item non-response - rate
Item non-response does not exist because item non-response controls were incorporated in the web questionnaire to ensure complete sets of answers.
13.3.3.2.1. Variables with the highest item non-response rate
Not applicable.
13.3.3.2.2. Reasons for item non-response
Not applicable13.3.3.2.3. Actions to minimise or address item non-response
None13.3.3.3. Impact of non-response error on data quality
Low13.3.3.4. Additional information non-response error
Weight adjustment was used to compensate for differential non-response rates in design strata and domains (NUTS 2 regions). The weight adjustment increases the weights of specified respondents so that they represent the non-respondents. The non-response adjustment has been conducted in homogeneous strata where the means of the variable values of respondents and non-respondents are approximately equal as it occurs in expectation when the non-respondents are missing at random within strata. In this case, the weight adjustment eliminates the bias due to non-response.
The aim of the applied weight adjustment was to reduce the bias that non-response causes in survey estimates. However, the effect of weight adjustment was to increase the sampling errors of the survey estimates.
13.3.4. Processing error
See sub-categories below.
13.3.4.1. Sources of processing errors
Data entry13.3.4.2. Imputation methods
Ratio imputation13.3.4.3. Actions to correct or minimise processing errors
To minimise processing errors, data collection and processing were automated where feasible. Specifically, a Computer Assisted Interview (CAI) approach was adopted, using a web questionnaire that incorporated as many logical and quality controls as possible. Remaining processing errors were corrected through imputation.
13.3.4.4. Tools and staff authorised to make corrections
Corrections were made through the web questionnaire application, developed in Oracle APEX. Excel was also used in order to detect errors in the dataset.
Only authorised staff of ELSTAT was allowed to make changes in the data. Authorisation was provided to staff from the Agriculture, Livestock, Fishery and Environment Statistics Division, the IT Division and the Regional Statistical Offices, that participated in the survey.
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
Not applicable, because first results were not published.
14.1.2. Time lag - final result
The final results will be published in the second half of 2025. The time lag will be around 20 months compared to the end of the reference year.
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
Not yet available for IFS 2023.
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
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
The data collection covered all holdings meeting at least one of the physical thresholds listed in Annex II of Regulation (EU) 2018/1091.
During the design period of the survey, it was calculated and verified that the coverage of 98% UAA excluding kitchen gardens and 98% LSU (livestock units) was ensured. See the table below (figures are prior to the survey):
| Total | Covered by the thresholds | Attained coverage | Minimum requested coverage | |
|---|---|---|---|---|
| 1 | 2 | 3=2*100/1 | 4 | |
| UAA excluding kitchen gardens | 4 303 496.0 | 4 261 935.7 | 99.0% | 98% |
| LSU | 2 090 417.9 | 2 087 738.3 | 99.9% | 98% |
15.1.3.2. Differences between the national thresholds and the thresholds used for the data sent to Eurostat
Data sent to Eurostat are collected using the thresholds set by Regulation (EU) 2018/1091. For national needs, the survey was also carried out on farms whose threshold was lower, than that set by the Regulation, based on the data from the Farm Register. The survey used stratified random sampling, with the holding as the surveyed unit. The sample size focused solely on national needs consisted of 13 897 holdings, representing a sampling fraction of 21.5% within the population of holdings pertinent solely to national needs. The stratification criteria were: geographical location (NUTS 3), holding type and holding size. The estimates of the survey variables show that the number of farms surveyed for national needs represent 7.8% of all holdings and their UAA represent 1.8% of the total one.
The Horvitz–Thompson (H-T) estimator was applied, and the design weights were adjusted in order to reduce the bias caused by non-response units. The H-T estimator estimates the population total by its weighted sample total, which equals the sum of all the observed values multiplied by the corresponding sample weights. Sample weights are used to correct the estimates for unequal selection probabilities due to stratified sampling and to adjust for nonresponse in surveys. The sample weight of each respondent unit is equal to the product of the inverse of its probability of selection multiplied by inverse of the response rate in each adjustment class determined by variables of interest (e.g. geography, size of holding, etc.).
15.1.3.3. Reasons for differences
The extension of the survey to small-scale holdings ensures data coherence with corresponding statistics from other agricultural and livestock surveys in Greece, and enhances data comparability over time.
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
There is a difference in the way equidae are handled. Whereas in Regulation (EU) 2018/1091 equidae are under "Other livestock n.e.c.", in Greece we retained the representation used until 2020, with two additional separate categories for Horses (including mules) and Donkeys. Nevertheless, equidae are reported to Eurostat under "Other livestock n.e.c.".
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, over an entire year, that is 8 working hours per day for 275 workdays.
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 LSU coefficients used, are those set in Regulation (EU) 2018/1091.
15.1.4.1.5. Livestock included in “Other livestock n.e.c.”
This category includes hares, wild boars, equidae and other. Whereas in Regulation (EU) 2018/1091 equidae are under "Other animals n.e.c.", in the Greek questionnaire there are two additional separate categories for Horses (including mules) and Donkeys. Nevertheless, equidae are reported to Eurostat, according to the Regulation (EU) 2018/1091, under "Other animals n.e.c.".
15.1.4.2. Reasons for deviations
Greece deviates from Regulation (EU) 2018/1091, as far as the collection and publication of data on equidae are concerned, for reasons of comparability with previous surveys and because horses, mules and donkeys are considered traditional animals for Greek agriculture.
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 separate records representing virtual entities without managers.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
Common lands were recorded as common land units, meaning virtual entities, one for each NUTS 3 region, created for the purposes of data collection and recording, consisting of the utilised agricultural area used by agricultural holdings of that region, but not belonging directly to them.
The common land data was obtained from the Payment and Control Agency for Guidance and Guarantee Community Aid (OPEKEPE) which, in turn, has collected the data from the applicants for the Community Aid (farm holders), under its competence as the Integrated Administration and Control System (IACS) operator.
Common land is reported as assigned to 52 special/virtual 'common land agricultural holdings' which represent the 52 NUTS 3 regions of the country. These special units were added in the dataset and considered as agricultural holdings.
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
No
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
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
Not applicable.
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 last dataset transmitted to Eurostat (from IFS 2020) included data from both the main and extended frames, whereas the dataset of 2023 only includes data from the main frame. The thresholds applicable to the main frame of both the IFS 2020 and IFS 2023, are those of Annex II of Regulation (EU) 2018/1091, whereas the thresholds used for the extended frame of IFS 2020 were the following:
| LABEL | VARIABLE_CODE_2020 | VALUE | UNIT |
|---|---|---|---|
| Utilised agricultural area | UAA | 0.1 | ha |
| Greenhouses, regardless of the production type, ownership, or the location of the holding | sum(V0000_S0000S, N0000S, PECRS, ARA09S, UAA09S) | 0.01 | ha |
| Cows | sum(A2300F, A2300G) | 1 | heads |
| Other "large animals" of any type and age (oxen, horses, donkeys, mules) | sum(A2010, A2120, A2220, A2130, A2230, A1000) | 2 | heads |
| Small animals (sheep, goats, pigs of any age and type) | sum(A4100, A4200, A3100) | 5 | heads |
| Poultry birds | A5000X5120_5130 | 50 | heads |
| Hives of “domestic” or “European” bees | A6710R | 20 | hives |
| Mushrooms | U1000 | 0 | ha |
| Ostriches | A5410 | 5 | heads |
| Female rabbits | A6111 | 50 | heads |
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
Variations over time on the basis of the legal form of the holdings
The evolution between 2020 and 2023 of the number of holdings by legal personality shows a sharp reduction for FARM_SPOU and a remarkable increase for the combination PER_LEG_EG + PER_LEG_NEG (although the absolute values are smaller).
These trends are impacted by the fact that the data cover main frame and frame extension in 2020 and only main frame in 2023.
For the combination PER_LEG_EG + PER_LEG_NEG, in particular, prior to the IFS, Greece updated the Farm Register from administrative sources, mainly IACS/OPEKEPE, and this resulted in adding in the register a lot of new holdings that were not previously included. Τhe percentage of legal personalities among these holdings was higher, resulting in the increase of the combination PER_LEG_EG + PER_LEG_NEG.
Variations over time of the products
C2000T: Differences are largely due to the decrease in water availability resulting from reduced rainfall and prolonged periods of drought. Taking into consideration that rice cultivation requires large quantities of water, the decrease in available water negatively affects the cultivated areas, while it also generally follows the trend of decreasing arable land in Greece.
In general, differences in variables of arable land are most likely due to the rotation of land with other arable crops. Especially for C1600T + C1700T + C1900T the decrease is due, to some extent, to the increase in durum wheat cultivation (C1120T) resulting from higher prices and related subsidies in the final price, due to the war in Ukraine.
I3000T: This decrease is likely due to the low final price of the product, which has been declining in recent years. As this is a contract farming crop for our country, any fluctuations in the signing of contracts correspondingly affect its cultivation.
I1110T: The decrease is due to the difficulty in promoting the product in the market, resulting from a reduction in contracts as well as a decrease in the number of factories that process the final product.
Ι1120Τ: As a contract farming crop, a reduction in contracts and a decrease in the number of collection and processing factories for the agricultural product results in the recorded decrease.
I1190T: The reduction is due to, as already mentioned:
- the increase in cereal cultivation due to higher prices and related subsidies caused by the war in Ukraine,
- the reduction of areas cultivated through contract farming,
- the decrease in available water resources for irrigation and the prolonged drought.
I2100T: The small number of land areas and farms in absolute numbers means that any change causes large percentage increases or decreases. Essentially, this concerns a small number of farms in just two regional units.
UAAT_IB: The decrease follows, to some extent, the observed trend of decreasing arable land in Greece, but further decrease is also attributed to the reduction in water availability due to prolonged drought and longer periods of dryness. Consequently, areas that were irrigated in the past may no longer be feasible to irrigate again.
Livestock: The average livestock per holding has increased in 2023 compared to 2020 but the median is still 0 meaning that the majority of Greek farms have no livestock at all.
15.2.9. Maintain of statistical identifiers over time
Yes15.3. 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
IFS 2023 microdata were compared to relevant agricultural surveys, as well as to corresponding IACS data whenever a full match could be secured between an IFS and an IACS holding, on the basis of the holder’s personal data. The results indicated differences in several cases, triggering corrective actions.
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
In order to understand the reasons behind the differences between IFS and the Annual Crop Survey (ACS), the following general remarks need to be considered:
ACS is conducted by the Ministry of Rural Development and Food (MRDF) according to Regulation (EC) No 543/2009. The methodology described in the Regulation differs from that of IFS. Examples of methodological differences include:
- ACS data are not collected through a survey, but are obtained primarily from the regional Directorates of Agricultural Economy and Veterinary, of the country and are based on experts’ opinions. This raises the issue of accuracy/subjectivity of the estimations and according to the relevant metadata, there has not been a peer-review carried out for ACS.
- In ACS, UAA is counted more than once in the case of successive crops, leading to higher values of the related variables being reported by the ACS compared to the IFS.
- There are differences between the definitions for some of the examined variables, according to the EC regulations relevant for the ACS and the IFS.
- Data validation and cross-checking with external sources, namely ELSTAT-FSS/IFS and Payment and Control Agency for Guidance and Guarantee Community Aid (OPEKEPE)-IACS, is reported by the MRDF however the relevant procedures are not documented, and the results are not provided in the metadata.
Coherence cross-domain: IFS vs ORGANIC CROP PRODUCTION (hectares) in relative terms
Despite our efforts to collaborate with the Ministry of Rural Development and Food in order to eliminate the discrepancies, the situation remains, especially for ARA_ORG, PECRT_ORG and UAAXK0000_ORG.
Coherence cross-domain: IFS vs ANIMAL PRODUCTION (1000 heads) in relative terms
EL30 and EL43 for variable A3100 (pigs). One of the biggest pig farms in Greece has its headquarters in EL30 (Attica-where Athens is) but the location of the livestock facilities is in EL43 (Island of Crete). So, for IFS the holding is attributed to EL43, whereas for the animal production survey to EL30, causing the differences in the two regions.
Coherence cross-domain: IFS vs ORGANIC ANIMAL PRODUCTION (heads) in relative terms
Despite our efforts to collaborate with the Ministry of Rural Development and Food in order to eliminate the discrepancies, the situation remains, especially for A2000_ORG, A2300F_ORG, A4100_ORG and A4200_ORG.
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
There was coordination with the 2023 Livestock Surveys.
16.2. Efficiency gains since the last data transmission to Eurostat
On-line surveysFurther automation
16.2.1. Additional information efficiency gains
Adopting the CAI methodology resulted in a reduced burden for both respondents and ELSTAT. Significant efficiency gains in data verification and validation were achieved, which had direct and positive consequences on data quality.
16.3. Average duration of farm interview (in minutes)
See sub-categories below.
16.3.1. Core
9.6 minutes
16.3.2. Module ‘Labour force and other gainful activities‘
1.6 minutes
16.3.3. Module ‘Rural development’
0.8 minutes (even holdings that had no answer in the module are included in the calculations, so the duration appears very small)
16.3.4. Module ‘Animal housing and manure management’
Restricted from publication
16.3.5. Module ‘Irrigation’
1.8 minutes (even holdings that had no answer in the module are included in the calculations, so the duration appears very small)
16.3.6. Module ‘Soil management practices’
0.7 minutes (even holdings that had no answer in the module are included in the calculations, so the duration appears very small)
16.3.7. Module ‘Machinery and equipment’
2.0 minutes (even holdings that had no answer in the module are included in the calculations, so the duration appears very small)
16.3.8. Module ‘Orchard’
7.7 minutes (even holdings that had no answer in the module are included in the calculations, so the duration appears small)
16.3.9. Module ‘Vineyard’
Restricted from publication
17.1. Data revision - policy
The revision policy of the Hellenic Statistical Authority (ELSTAT) defines standard rules and principles for data revisions, in accordance with the European Statistics Code of Practice and the principles for a common revision policy for European Statistics contained in the Annex of the European Statistical System (ESS) guidelines on revision policy. For more details: ELSTAT Revision Policy.
17.2. Data revision - practice
No data revisions.
17.2.1. Data revision - average size
Not requested.
18.1. Source data
See sub-categories below.
18.1.1. Sampling design & Procedure frame
See sub-categories below.
18.1.1.1. Type of frame
List frame18.1.1.2. Name of frame
Farm Register of ELSTAT
18.1.1.3. Update frequency
Annual18.1.2. 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
The sampling method used was one-stage stratified sampling, with the agricultural and livestock holdings from the Farm Register of ELSTAT as the survey units. Stratification criteria included farm size, location (NUTS 3 level), and typology, as defined by Commission Regulation (EC) No 1242/2008.
Before selecting the sample, the holdings in each stratum were sorted according to the codes of the municipalities to which they belong. After this sorting, a systematic sampling was carried out in each stratum.
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
18.1.2.2.3. Use of systematic sampling
Yes18.1.2.2.4. Full coverage strata
Take-all strata are the strata with large scale holdings that present high population variance for the main variables. The boundaries of the size classes were determined by applying the Rule of Cumulative Root.
18.1.2.2.5. Method of determination of the overall sample size
The sample size was determined to ensure that the relative standard errors of the estimates for the main, non-rare variables at the NUTS 2 regional level would not exceed 5%. This determination was made in accordance with the precision requirements of Regulation (EU) 2018/1091.
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
Same as 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 specialization
18.1.4.2.3. Use of systematic sampling
Yes18.1.4.2.4. Full coverage strata
Same as 18.1.2.2.4.
18.1.4.2.5. Method of determination of the overall sample size
Same as 18.1.2.2.5.
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
Same as 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 specialization
18.1.5.2.3. Use of systematic sampling
Yes18.1.5.2.4. Full coverage strata
Same as 18.1.2.2.4.
18.1.5.2.5. Method of determination of the overall sample size
Same as 18.1.2.2.5.
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
Same as 18.1.2.2.
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
18.1.7.2.3. Use of systematic sampling
Yes18.1.7.2.4. Full coverage strata
Same as 18.1.2.2.4.
18.1.7.2.5. Method of determination of the overall sample size
Same as 18.1.2.2.5.
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
Same as 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 specialization
18.1.8.2.3. Use of systematic sampling
Yes18.1.8.2.4. Full coverage strata
Same as 18.1.2.2.4.
18.1.8.2.5. Method of determination of the overall sample size
Same as 18.1.2.2.5.
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
Same as 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 specialization
18.1.9.2.3. Use of systematic sampling
Yes18.1.9.2.4. Full coverage strata
Same as 18.1.2.2.4.
18.1.9.2.5. Method of determination of the overall sample size
Same as 18.1.2.2.5.
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
Same as 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 specialization
18.1.10.2.3. Use of systematic sampling
Yes18.1.10.2.4. Full coverage strata
Same as 18.1.2.2.4.
18.1.10.2.5. Method of determination of the overall sample size
Same as 18.1.2.2.5.
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
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
None18.1.14. 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
Sample-based data collection is organised every 3 years, in-between and complementing the decennial agricultural census.
18.3. Data collection
See sub-categories below.
18.3.1. Methods of data collection
Face-to-face, electronic versionTelephone, electronic version
Use of Internet
18.3.2. Data entry method, if paper questionnaires
Not applicable18.3.3. Questionnaire
For transcribed versions of the web questionnaire in Greek and English, see the pdf files in the annexes.
Annexes:
18.3.3 Questionnaire in Greek
18.3.3 Questionnaire 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 other domains in agricultural statistics
18.4.2. Staff involved in data validation
Staff from local departmentsStaff from central department
18.4.3. Tools used for data validation
Custom Oracle SQL based applications, developed in-house, and Microsoft Excel.
18.5. Data compilation
See details on the methods used to derive the extrapolation factors (18.5.2) in the annex.
Annexes:
18.5 Data compilation
18.5.1. Imputation - rate
For a total number of 30 633 holdings, irrigation water volume was imputed by Eurostat using modelling.
18.5.2. Methods used to derive the extrapolation factor
Design weightNon-response adjustment
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
ACS – Annual Crop Survey
AWU – Annual Working Unit
CAI – Computer Assisted Interview
CAP – Common Agricultural Policy
CAPI – Computer Assisted Personal Interview
CATI – Computer Assisted Telephone Interview
CAWI – Computer Assisted Web Interview
CoP – Code of Practice
EC – European Community
ELSS – Hellenic Statistical System
ELSTAT – Hellenic Statistical Authority
ESS – European Statistical System
EU – European Union
FSS – Farm Structure Survey
GDPR – General Data Protection Regulation
IACS – Integrated Administration and Control System
IFS – Integrated Farm Statistics
LSU – Livestock unit
MRDF – Ministry of Rural Development and Food
NUTS – Nomenclature of territorial units for statistics
OPEKEPE – Payment and Control Agency for Guidance and Guarantee Community Aid
SCC – Statistical Confidentiality Committee
SDMX – Statistical Data and Metadata eXchange
SGM – Standard Gross Margin
SIMS – Single Integrated Metadata Structure
SO – Standard output
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 modular 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 is as comparable and coherent as possible with the other European countries.
26 June 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, oranges area, small citrus fruit area, lemons area, olives 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.
See details on the methods used to derive the extrapolation factors (18.5.2) in the annex.
Annexes:
18.5 Data compilation
See sub-categories below.
Every three years for sample survey data and every 10 years for census survey data.
See sub-categories below.
See sub-categories below.
See sub-categories below.
