1.1. Contact organisation
State Data Agency (Statistics Lithuania)
1.2. Contact organisation unit
Agricultural Statistics Division
1.3. Contact name
Confidential because of GDPR
1.4. Contact person function
Confidential because of GDPR
1.5. Contact mail address
29 Gedimino Ave.
LT-01500 Vilnius, Lithuania
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
9 July 2025
2.2. Metadata last posted
11 July 2025
2.3. Metadata last update
9 July 2025
3.1. Data description
The data describe the structure of agricultural holdings providing the general characteristics of farms and farmers and information on their land, livestock and labour force. They also describe production methods, rural development measures and agro-environmental aspects that look at the impact of agriculture on the environment.
The data are used by public, researchers, farmers and policy-makers to better understand the state of the farming sector and the impact of agriculture on the environment. The data follow up the changes in the agricultural sector and provide a basis for decision-making in the Common Agricultural Policy (CAP) and other European Union policies.
The statistical unit is the agricultural holding (farm). The aggregated results are disseminated through statistical tables. The data are presented at different geographical levels and over periods.
The data collections are organised in line with Regulation (EU) 2018/1091 and have a new structure, consisting of a core data set and several modules. The regulation covers the data collections in 2019/2020 (the agricultural census), 2023 and 2026. The data are as comparable and coherent as possible with the other European countries.
3.2. Classification system
Data are arranged in tables using many classifications. Please find below information on most classifications.
The classifications of variables are available in Annex III of Regulation (EU) 2018/1091 and in Commission Implementing Regulation (EU) 2021/2286.
The farm typology means a uniform classification of the holdings based on their type of farming and their economic size. Both are determined on the basis of the standard gross margin (SGM) (until 2007) or standard output (SO) (from 2010 onward) which is calculated for each crop and animal. The farm type is determined by the relative contribution of the different productions to the total standard gross margin or the standard output of the holding.
The territorial classification uses the NUTS classification to break down the regional data. The regional data is available at NUTS level 2.
3.3. Coverage - sector
The statistics cover agricultural holdings undertaking agricultural activities as listed in item 3.5 below and meeting the minimum coverage requirements (thresholds) as listed in item 3.6 below.
3.4. Statistical concepts and definitions
The list of core variables is set in Annex III of Regulation (EU) 2018/1091.
The descriptions of the core variables as well as the lists and descriptions of the variables for the modules collected in 2023 are set in Commission Implementing Regulation (EU) 2021/2286.
The following groups of variables are collected in 2023:
- for core: location of the holding, legal personality of the holding, manager, type of tenure of the utilised agricultural area, variables of land, organic farming, irrigation on cultivated outdoor area, variables of livestock, organic production methods applied to animal production;
- for the module "Labour force and other gainful activities": farm management, family labour force, non-family labour force, other gainful activities directly and not directly related to the agricultural holding;
- for the module "Rural development": support received by agricultural holdings through various rural development measures;
- for the module “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 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”
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 subset of agricultural holdings defined in item 3.6.2 with arable land or without arable land but with some elements of ecological focus areas or drainage.
3.6.6. Population covered by the data sent to Eurostat for the module “Orchard”
The subset of agricultural holdings defined in item 3.6.2, with any of the individual orchard variables that meet the threshold specified in Article 7(5) of Regulation (EU) 2018/1091.
3.6.7. Population covered by the data sent to Eurostat for the module “Vineyard”
Restricted from publication
3.7. Reference area
See sub-categories below.
3.7.1. Geographical area covered
The entire territory of the country.
3.7.2. Inclusion of special territories
Not applicable.
3.7.3. Criteria used to establish the geographical location of the holding
The main building for productionThe most important parcel by physical 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 in our country cover the period from 2003 onwards. Older time series are described in the previous quality reports (national methodological reports).
3.9. Base period
The 2023 data are processed (by Eurostat) with 2020 standard output coefficients (calculated as a 5-year average of the period 2018-2022). For more information, you can consult the definition of the standard output.
Two kinds of units are generally used:
- the units of measurement for the variables (area in hectares, livestock in (1000) heads or LSU (livestock units), labour force in persons or AWU (annual working units), standard output in Euro, places for animal housing etc.) and
- the number of agricultural holdings having these characteristics.
See sub-categories below.
5.1. Reference period for land variables
The use of land refers to the reference year 2023 or 12-month period ending on 1 June 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 1 June within the reference year 2023.
5.3. Reference day for variables on livestock and animal housing
The reference day is 1 June within the reference year 2023. This reference day applies for livestock variables, and 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 1 June 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 1 June 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
Law on Official Statistics
Official Statistics Programme 2023, Part I, which includes the statistical surveys conducted by Statistics Lithuania and other bodies managing official statistics.
6.1.3. Link to national legal acts and other agreements
Law on Official Statistics (in Lithuanian only)
Official Statistics Programme 2023 (in Lithuanian only)
6.1.4. Year of entry into force of national legal acts and other agreements
Law on Official Statistics: 12 October 1993, as last amended on 1 June 2024
Official Statistics Programme 2023: 2023
6.1.5. Legal obligations for respondents
Yes6.2. Institutional Mandate - data sharing
In Article 10 of the Law on Official Statistics, it is stated that institutions managing official statistics, while implementing the Official Statistics Programme, have the following rights: to receive, free of charge, the necessary statistical data from the sources of official statistics specified in Article 13 of this Law, including data that may allow for the direct or indirect identification of statistical observation units, as well as to combine such data with other statistical and/or state data.
In Article 13, it is stated that the sources of official statistics are as follows:
- Statistical data provided by or collected from respondents;
- Administrative data;
- Data legally obtained by institutions managing official statistics from legal and/or natural persons, which are publicly accessible and/or collected and managed by legal persons, including data from electronic transactions or other records, mobile communication data, dynamic data, or other data collected and managed by private legal persons, including personal data, among them special categories of personal data;
- Statistical data legally obtained by institutions managing official statistics from international organisations;
- State data.
The exchange of statistical data required for the implementation of the Official Statistics Programme is also defined in Article 17 of the Law on Official Statistics.
7.1. Confidentiality - policy
In the process of statistical data collection, processing, analysis, and dissemination, Statistics Lithuania fully guarantees confidentiality of the data submitted by respondents (households, enterprises, institutions, organisations and other statistical units), as defined in the Confidentiality Policy Guidelines of the State Data Agency (Order No DĮ-111, 04 May 2023).
7.2. Confidentiality - data treatment
See sub-categories below.
7.2.1. Aggregated data
See sub-categories below.
7.2.1.1. Rules used to identify confidential cells
Threshold rule (The number of contributors is less than a pre-specified threshold)Dominance rule (The n largest contributions make up for more than k% of the cell total)
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
If there was any confidential information in aggregated data, the symbol "•" was inserted instead of the exact value, if:
- statistical information was prepared using data obtained from less than three respondents;
- statistical data from one respondent represents more than 70 per cent of the total volume of the statistical indicator;
- aggregated statistical data of two respondents represents more than 85 per cent of the volume of the whole statistical indicator.
7.2.2. Microdata
See sub-categories below.
7.2.2.1. Use of EU methodology for microdata dissemination
No7.2.2.2. Methods of perturbation
Recoding of variablesRemoval of variables
Reduction of information
Merging categories
Rounding
Other
7.2.2.3. Description of methodology
To obtain confidential statistical microdata for scientific purposes, an application must be submitted to Statistics Lithuania. The application is submitted via an electronic form available on the Official Statistics Portal. Once permission is granted, the microdata can only be used in a secure environment, in compliance with all confidentiality requirements.
The applied information reduction methods reduce the detail or distinctiveness of the data, thus reducing the risk of recognition without changing the values. These methods are:
- Global recoding: where values are combined into broader categories;
- Upper and lower coding: where extreme values are generalised;
- Rounding: where numbers are presented with less precision;
- Local suppression: where certain values are hidden.
8.1. Release calendar
Statistical information is published on the Official Statistics Portal according to the Official Statistics Calendar.
8.2. Release calendar access
8.3. Release policy - user access
Statistical information is prepared and disseminated under the principle of impartiality and objectivity, i.e. in a systematic, reliable and unbiased manner, following professional and ethical standards (the European Statistics Code of Practice), and the policies and practices followed are transparent to users and survey respondents.
All users have equal access to statistical information. All statistical information is published at the same time – at 9 a.m. on the day of publication of statistical information as indicated in the calendar on the Official Statistics Portal. Relevant statistical information is sent automatically to news subscribers.
The President and Prime Minister of the Republic of Lithuania, their advisers, the Ministers of Finance, Economy and Innovation, as well as Social Security and Labour or their authorised persons, as well as, in exceptional cases, external experts and researchers have the right to receive early statistical information. The specified persons are entitled to receive statistical reports on GDP, inflation, employment and unemployment and other particularly relevant statistical reports one day prior to the publication of this statistical information on the Official Statistics Portal. Before exercising the right of early receipt of statistical information, a person shall sign an undertaking not to disseminate the statistical information received before it has been officially published.
Statistical information is published following the Official Statistics Dissemination Policy Guidelines and Statistical Information Dissemination and Communication Rules of Statistics Lithuania (in Lithuanian) approved by Order No DĮ-101 of 20 April 2023 of the Director General of Statistics Lithuania.
8.3.1. Use of quality rating system
Yes, the EU quality rating system8.3.1.1. Description of the quality rating system
The methodology is described in the EU Handbook.
Data are disseminated at the national level every 3-4 years.
10.1. Dissemination format - News release
See sub-categories below.
10.1.1. Publication of news releases
No10.1.2. Link to news releases
Not applicable.
10.2. Dissemination format - Publications
See sub-categories below.
10.2.1. Production of paper publications
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
We do not monitor and record the number of consultations of data tables in the field of farm structure.
10.3.2. Accessibility of online database
Yes10.3.3. Link to online database
Online database (Agriculture, hunting, forestry and fishing → Agriculture → Farming structure and agricultural censuses).
The Indicators Database page allows for viewing and analysing statistical information. For detailed information, refer to the Database of Indicators User Guide.
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
Statistics Lithuania publishes metadata for Farming structure and agricultural census indicators on its website.
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 present quality report is a quality-related document.
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
Designated quality manager, quality unit and/or senior level committee
Compliance monitoring
Self-assessment
11.1.3. Description of the quality management system and procedures
Quality of statistical information and its production process is ensured by the provisions of the European Statistics Code of Practice and ESS Quality Assurance Framework.
In 2007, a quality management system, conforming to the requirements of the international quality management system standard ISO 9001, was introduced at Statistics Lithuania. The main trends in activity of Statistics Lithuania aimed at quality management and continuous development in the institution are established in the Quality Policy.
Monitoring of the quality indicators of statistical processes and their results and self-evaluation of statistical survey managers is regularly carried out in order to identify areas which need improvement and to promptly eliminate shortcomings.
More information on assurance of quality of statistical information and its preparation is published in the Quality Management section on the Statistics Lithuania website.
11.1.4. Improvements in quality procedures
Validation rules will be improved by updating existing rules and adding new ones.
11.2. Quality management - assessment
The quality of the statistical results meets the requirements of accuracy, timeliness and punctuality, comparability and consistency.
In 2022, the review of the Farm structure survey 2023 statistical forms was carried out, recommendations received have been implemented through adjustments to the structure and content of the forms.
Quality of the obtained statistics is analysed during the evaluation of the indicators. Outstanding values of indicators are identified and analysed. In case of significant deviations, the data provider is contacted and the reasons for the deviation are clarified.
At the micro level, data are compared with the data of declarations of agricultural and other areas, the Farm Animal Register, Database of the State Social Insurance Fund Board, other agricultural statistical surveys (crop production, animal production, etc.). Differences, if significant, are identified and farms are contacted. Differences are mainly due to inconsistencies in definitions and methodological provisions.
Additional quality control of statistics is performed at the macro level. Results of the calculation are compared with the results of the previous Farm Structure Surveys, Agricultural Censuses and other agricultural statistical surveys (crop, animal, etc.). Results of the calculation are also compared with administrative data: the Register of Agricultural and Rural Business (Holdings' Register). If significant differences in indicators are identified, microdata are analysed in more detail.
12.1. Relevance - User Needs
The main users of statistical information are state and municipal institutions, international organisations, the media, representatives of business and science, and students, with all needs met while maintaining confidentiality.
12.1.1. Main groups of variables collected only for national purposes
Data for national needs were collected, including information on direct sales (via the question do direct sales to consumers account for more than 50% of total sales?).
12.1.2. Unmet user needs
There is no information about unmet user needs.
12.1.3. Plans for satisfying unmet user needs
Not applicable.
12.2. Relevance - User Satisfaction
Since 2005, user opinion surveys have been conducted on a regular basis. Official Statistics Portal traffic is monitored, website visitor opinion polls, general opinion poll on the products and services of Statistics Lithuania, target user group opinion polls and other surveys are conducted.
In 2007, the compilation of a user satisfaction index was launched. The said surveys are aimed at the assessment of the overall demand for and necessity of statistical information in general and specific statistical indicators in particular.
More information on user opinion surveys and results thereof are published in the User Surveys section on the Statistics Lithuania website.
12.2.1. User satisfaction survey
Yes12.2.2. Year of user satisfaction survey
2023
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
The precision requirements set in the Regulation (EU) 2018/1091 are all met.
However, there is one indicator whose RSE is high: F1110 (Apples), LT01, MORC, RSE = 3.31%.
There is a small amount of farms with apples in this NUTS region (LT01) of Lithuania. Areas of apples are relatively small as well. However, a sample design is one for all indicators of a certain module and a small amount of farmers (respondents) leads to higher RSEs.
13.2.3. Reference on method of estimation
See in annex.
Annexes:
13.2.3 Methodology used to calculate relative standard errors
13.2.4. Impact of sampling error on data quality
Low13.3. Non-sampling error
See sub-categories below.
13.3.1. Coverage error
See sub-categories below.
13.3.1.1. Over-coverage - rate
The over-coverage rate is available on Eurostat’s website, at the link: CircaBC.
The over-coverage rate is unweighted.
The over-coverage rate is calculated as the share of ineligible holdings to the holdings designated for the core data collection. The ineligible holdings include those holdings with unknown eligibility status that are not imputed nor re-weighted for (therefore considered ineligible).
The over-coverage rate is calculated over the holdings in the main frame and if applicable frame extension, for which core data are sent to Eurostat.
13.3.1.1.1. Types of holdings included in the frame but not belonging to the population of the core (main frame and if applicable frame extension)
Below thresholds during the reference periodCeased activities
13.3.1.1.2. Actions to minimize the over-coverage error
Removal of ineligible units from the records, leaving unchanged the weights for the other units13.3.1.1.3. Additional information over-coverage error
Not available.
13.3.1.2. Common units - proportion
Not requested.
13.3.1.3. Under-coverage error
See sub-categories below.
13.3.1.3.1. Under-coverage rate
Under-coverage rate was 3%.
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
The population was checked via various administrative data sources.
13.3.1.3.4. Additional information under-coverage error
We attempted to include all holdings belonging to the population. However, in some cases, new births and new units derived from splits may not have been included in the population. This could have happened if a new farm appeared during the reference period, but the population was created using data from administrative sources of previous years. Additionally, if a farm's size was below the thresholds in the previous year but above the thresholds during the reference period, it may not have been included.
13.3.1.4. Misclassification error
Yes13.3.1.4.1. Actions to minimise the misclassification error
Several misclassification errors occurred due to units changing municipalities between the sampling design and the reference period. Some of these changes were incorrect and therefore disregarded, leaving those units in their previous strata. However, other changes were correctly identified, and both the municipality and strata were updated. Misclassifications of unit size were not corrected.
Misclassification errors are estimated to be minimal.
13.3.1.5. Contact error
Yes13.3.1.5.1. Actions to minimise the contact error
Interviewers were unable to survey certain farmers and family farms due to inaccurate addresses or because they were only present seasonally or temporarily. Contact errors represented 1.4%.
If the farmer was not at their registered address, interviewers used an available address from the IACS Crop Declaration Database and contacted the farmer there.
Incorrect phone numbers were corrected using information from the IACS, telecommunication companies and other statistical surveys. Additionally, Statistics Lithuania obtained email addresses from the IACS and other government institutions, enabling interviewers to contact farmers by email.
13.3.1.6. Impact of coverage error on data quality
Low13.3.2. Measurement error
See sub-categories below.
13.3.2.1. List of variables mostly affected by measurement errors
Most questionnaire questions were either clear to the farmers or clarified by the interviewers. If significant discrepancies were identified when cross-checking the data with administrative sources and data from previous surveys, the holdings were contacted again and asked to clarify the data.
The measurement errors were most commonly found in the following variables:
- UAAS – utilised agricultural area - under glass or high accessible cover;
- U1000 – cultivated mushrooms;
- UAAT_IB – UAA - outdoor - irrigable;
- BLD_ST_SED – storage of seeds (cereals, oilseeds, and pulses); and
- BLD_ST_RF – refrigerated storage.
13.3.2.2. Causes of measurement errors
Complexity of variablesRespondents’ inability to provide accurate answers
13.3.2.3. Actions to minimise the measurement error
Pre-testing questionnaireExplanatory notes or handbooks for enumerators or respondents
On-line FAQ or Hot-line support for enumerators or respondents
Training of enumerators
13.3.2.4. Impact of measurement error on data quality
Low13.3.2.5. Additional information measurement error
We attempted to correct these errors during data collection. Both interviewers and farmers were consulted by phone.
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
13.3.3.1.2. Actions to minimise or address unit non-response
RemindersImputation
Weighting
Other
13.3.3.1.3. Unit non-response analysis
The unit non-response rate was very low, and no special analysis was made.
13.3.3.2. Item non-response - rate
Item non-response rate was not calculated.
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
Imputation13.3.3.3. Impact of non-response error on data quality
Low13.3.3.4. Additional information non-response error
In order to minimise non-response, all respondents received an information letter detailing the survey, including when, how, and where they could provide their data.
Only electronic questionnaires were used, with tailored navigation, and all relevant questions were mandatory.
We imputed data from various sources into the IFS 2023 database (ORACLE software) for holdings not found or refusing to answer. Data imputations for non-response units were performed using the IACS Crop Declaration Database, the Animal Register and the State Social Insurance Fund Board Register. Also, the AC 2020 and FSS 2016 data were used for imputation. Data were prepared for imputation using SAS software and Excel tables.
13.3.4. Processing error
See sub-categories below.
13.3.4.1. Sources of processing errors
Imputation methods13.3.4.2. Imputation methods
Previous data for the same unit13.3.4.3. Actions to correct or minimise processing errors
Data were imported from the electronic questionnaire to the database using a special computer program. Logical and arithmetic controls were implemented. Data were compared with data from other statistical data sources, including previous crop surveys, etc. Thus, the probability of processing errors was minimised as much as possible. Statistics Lithuania can assess that most processing errors were discovered, thoroughly reviewed, and corrected.
13.3.4.4. Tools and staff authorised to make corrections
The following computer programs were used to process and analyse the data received:
- The electronic statistical submission and collection system e. Statistics was used to fill in the electronic questionnaire for agricultural companies and enterprises;
- The electronic statistical submission and collection system e. Statistics for the Population was used to fill in the electronic questionnaire for farmers' and family farms;
- A special program created using ORACLE software was used for statistical data processing at Statistics Lithuania;
- Statistical programs SAS and R were used for linking statistical data of several sources according to the selected criterion and for the calculation of derived statistical indicators;
- The results received were transferred into MS Office Excel worksheet tables. Excel was also used for the comparison of statistical IFS 2023 data with statistical data of the previous year and the results of the AC 2020.
Corrections and imputations were made by employees of Agricultural Statistics Division of Statistics Lithuania, who were responsible for the IFS 2023.
13.3.4.5. Impact of processing error on data quality
Low13.3.4.6. Additional information processing error
All available statistical data were combined into a single data file, which was checked and then automatically exported to the survey database (ORACLE software). After that, logical and arithmetic controls were performed on the entire IFS 2023 database.
13.3.5. Model assumption error
Not applicable.
14.1. Timeliness
See sub-categories below.
14.1.1. Time lag - first result
The first provisional results of the IFS 2023 were published on 24 October 2024 in the Database of Indicators of Statistics Lithuania, i.e., 9 months and 24 days after the reference date of 31 December 2023.
14.1.2. Time lag - final result
It is planned that the final IFS 2023 results will be published 16 months after the reference date of 31 December 2023, i.e. in April 2025.
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
First results were published according to the approved release calendar. Also, we do not expect delays in dissemination of final results.
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
The definition of agricultural holdings is in accordance with 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
We included all holdings meeting at least one of the physical thresholds listed in Annex II of Regulation (EU) 2018/1091.
15.1.3.2. Differences between the national thresholds and the thresholds used for the data sent to Eurostat
There are no differences between the national thresholds and the thresholds of agricultural holdings used for the data sent to Eurostat.
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
There are no deviations from Regulation (EU) 2018/1091, Commission Implementing Regulation (EU) 2021/2286 and EU handbook.
15.1.4.1.1. The number of working hours and days in a year corresponding to a full-time job
The information is available on Eurostat’s website, at the link: CircaBC.
The number of working hours and days in a year for a full-time job correspond to one annual working unit (AWU) in the country. One annual work unit corresponds to the work performed by one person who is occupied on an agricultural holding on a full-time basis. Annual working units are used to calculate the farm work on the agricultural holdings.
15.1.4.1.2. Point chosen in the Annual work unit (AWU) percentage band to calculate the AWU of holders, managers, family and non-family regular workers
See item 15.1.4.1.1.
15.1.4.1.3. AWU for workers of certain age groups
See item 15.1.4.1.1.
15.1.4.1.4. Livestock coefficients
The same LSU coefficients as the ones set in Regulation (EU) 2018/1091 are used.
15.1.4.1.5. Livestock included in “Other livestock n.e.c.”
There are no differences between the types of livestock that are included under the heading “Other livestock n.e.c.” and the types of livestock that should be included according to the EU handbook.
15.1.4.2. Reasons for deviations
Not applicable.
15.1.5. Reference periods/days
See sub-categories below.
15.1.5.1. Deviations from Regulation (EU) 2018/1091
Data are collected, sent to Eurostat and published in compliance with the reference periods/days set in Regulation (EU) 2018/1091.
15.1.5.2. Reasons for deviations
Not applicable.
15.1.6. Common land
The concept of common land does not exist15.1.6.1. Collection of common land data
Not applicable15.1.6.2. Reasons if common land exists and data are not collected
Not applicable.
15.1.6.3. Methods to record data on common land
Not applicable15.1.6.4. Source of collected data on common land
Not applicable15.1.6.5. Description of methods to record data on common land
Not applicable.
15.1.6.6. Possible problems in relation to the collection of data on common land and proposals for future data collections
Not applicable.
15.1.7. National standards and rules for certification of organic products
See sub-categories below.
15.1.7.1. Deviations from Council Regulation (EC) No 834/2007
There are no deviations in the national standards and rules for certification of organic products from Council Regulation (EC) No 834/2007.
15.1.7.2. Reasons for deviations
Not applicable.
15.1.8. Differences in methods across regions within the country
There are no differences in the methods used across regions within the country.
15.2. Comparability - over time
See sub-categories below.
15.2.1. Length of comparable time series
1
15.2.2. Definition of agricultural holding
See sub-categories below.
15.2.2.1. Changes since the last data transmission to Eurostat
There have been no changes15.2.2.2. Description of changes
There are no changes as both 2020 and 2023 are data collection years covered by the same Regulation (EU) 2018/1091.
15.2.3. Thresholds of agricultural holdings
See sub-categories below.
15.2.3.1. Changes in the thresholds of holdings for which core data are sent to Eurostat since the last data transmission
There have been sufficient changes to warrant the designation of a break in series15.2.3.2. Description of changes
From 2023, the physical threshold values specified in Annex II of Regulation (EU) 2018/1091 were applied. Until then, the thresholds were defined in the previous metadata. Due to changes in thresholds, the smallest holdings were excluded, affecting all holding counts. While the impact on the total utilised agricultural area and number of livestock units is minor, it is significant for certain crops and livestock species.
15.2.4. Geographical coverage
See sub-categories below.
15.2.4.1. Change in the geographical coverage since the last data transmission to Eurostat
There have been no changes15.2.4.2. Description of changes
Not applicable.
15.2.5. Definitions and classifications of variables
See sub-categories below.
15.2.5.1. Changes since the last data transmission to Eurostat
There have been no changes15.2.5.2. Description of changes
There are no changes as both 2020 and 2023 are data collection years covered by the same Regulation (EU) 2018/1091.
15.2.6. Reference periods/days
See sub-categories below.
15.2.6.1. Changes since the last data transmission to Eurostat
There have been no changes15.2.6.2. Description of changes
Not applicable.
15.2.7. Common land
See sub-categories below.
15.2.7.1. Changes in the methods to record common land since the last data transmission to Eurostat
There have been no changes15.2.7.2. Description of changes
Not applicable.
15.2.8. Explanations for major trends of main variables compared to the last data transmission to Eurostat
The time series analysis between 2020 and 2023 should be performed considering that the population size in 2020 included both the main frame and the frame extension. However, in 2023, the population size includes only the main frame.
In 2020, the main frame consisted of 100 216 holdings, while the frame extension included 31 860 holdings. If compared the number of holdings in the main frame in 2020 with 2023, the decrease in the number of holdings will be relatively small – 11.8%. Additionally, the number of holdings in Lithuania has been decreasing every year, while the average size of holdings has been increasing. This trend has been observed throughout the entire period from 2003 to 2020. For example, in 2020, compared to 2016, the number of holdings decreased by 12.1%.
The consequences of the reduction of absolute number of holdings are reflected in the evolution of the share of holdings breakdown by SO EURO and UAA. The striking increase of average UAA and SO_EURO is the result of concentration of holdings (due to their sharp reduction, rather than a real increase of area and standard outputs.
The number of holdings has decreased significantly between 2020 and 2023, leading to an increase in the average values. Additionally, this trend may also be partly explained by a methodological change: in 2023, the population does not include small holdings below the thresholds, whereas such holdings were included in the 2020 population. As a result, the overall averages in 2023 reflect a population composed of relatively larger and more productive holdings.
In terms of holdings breakdown by legal personality, in 2023 there was an increase of number of FARM_HLD holdings (Sole holders who are the single manager).
When it comes to the holdings breakdown by farm typology, there has been a significant increase in the share of FT15 and decline in the shares of FT45 and FT84. During last few years such a tendency is observed in Lithuania. More and more farms grow only crops (mainly cereals). Farms with dairy cows, sell their cows due to low purchase prices of milk. Moreover, dairy farms become crop farms. Data on crops were taken from IACS (questionnaires were prefilled), data on farm animals were taken from Animal register (number dairy cows was taken directly, without questioning).
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 micro-level data were compared with the data from declarations in agricultural and other areas, the Farm Animal Register, the Database of the State Social Insurance Fund Board, and the Annual Crop Statistics 2023 data collection. No significant differences were found. The comparison with animal production statistics was not conducted due to differences in reference dates.
15.3.4. Coherence at macro level with data collections in other domains in agriculture
See sub-categories below.
15.3.4.1. Analysis of coherence at macro level
Yes15.3.4.2. Results of analysis at macro level
Coherence cross-domain: IFS vs CROP PRODUCTION (main area in 1000 ha) in relative terms
The differences arise due to the different coverage of the surveys. Crop statistics also include farms with less than one hectare of agricultural land, as well as gardening communities.
Coherence cross-domain: IFS vs ORGANIC CROP PRODUCTION (hectares) in relative terms
The discrepancies arise due to differences in crop classification. Therefore, LT categorise the areas according to the data provided in the survey. Also, they receive administrative data for the last day of 2023, but by this date, some farms decide to withdraw fields or livestock from organic certification, resulting in the loss of some organic areas and livestock that are included in the organic animal production statistics.
Coherence cross-domain: IFS vs ANIMAL PRODUCTION (1000 heads) in relative terms
Differences due to different reporting periods and scopes.
Coherence cross-domain: IFS vs ORGANIC ANIMAL PRODUCTION (heads) in relative terms
LT receives administrative data for the last day of 2023, but by this date, some farms decide to withdraw fields or livestock from organic certification, resulting in the loss of some organic areas and livestock that are included in the organic animal production statistics.
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
In order to reduce costs, all holdings had the possibility to provide their data electronically. In order to reduce the burden, administrative data were used as much as possible.
In case of all agricultural data collections, data from administrative data sources are used directly or prefilled into questionnaires. In such a way the duplication of asking the same questions is avoided, because farmers have only look at prefilled numbers and approve their correctness.
Statistics Lithuania pays a lot of attention to reducing the statistical reporting burden on respondents. Its obligations to implement the Law on Reducing the Administrative Burden of the Republic of Lithuania and to reduce the statistical reporting burden on the respondents are defined in the Respondents’ statistical reporting burden reduction policy.
More information can be found here.
16.2. Efficiency gains since the last data transmission to Eurostat
Further automationIncreased use of administrative data
16.2.1. Additional information efficiency gains
Statistics Lithuania makes efforts to improve the IFS 2023 efficiency. Firstly, agricultural companies and enterprises as well as farmers' and family farms had the possibility to fill in the electronic questionnaire by themselves and submit it to the Statistics Lithuania. Secondly, the market research company collected data using portable computers and the statistical data were provided through the electronic statistical submission and collection system, e. Statistics for the Population. Variables on land areas and farm animals (pigs, poultry, rabbits, beehives) were prefilled into the IFS 2023 questionnaires. Moreover, a lot of variables, such as farm animals (cattle, sheep, goats) as well as all characteristics about support for rural development and organic farming were taken directly from administrative data sources without questioning the farmers.
Additionally, we automated routine data checks by integrating logical and arithmetic controls into the data entry programs.
16.3. Average duration of farm interview (in minutes)
See sub-categories below.
16.3.1. Core
The average duration of farm interview was 16 minutes for farmers’ and family farms and 39 minutes for agricultural companies and enterprises. There is no information about the separate duration for core and modules.
16.3.2. Module ‘Labour force and other gainful activities‘
The average duration of farm interview was 16 minutes for farmers’ and family farms and 39 minutes for agricultural companies and enterprises. There is no information about the separate duration for core and modules.
16.3.3. Module ‘Rural development’
Not relevant (data were collected from the administrative data source).
16.3.4. Module ‘Animal housing and manure management’
Restricted from publication
16.3.5. Module ‘Irrigation’
Not applicable (exemption from data collection).
16.3.6. Module ‘Soil management practices’
The average duration of farm interview was 16 minutes for farmers’ and family farms and 39 minutes for agricultural companies and enterprises. There is no information about the separate duration for core and modules.
16.3.7. Module ‘Machinery and equipment’
The average duration of farm interview was 16 minutes for farmers’ and family farms and 39 minutes for agricultural companies and enterprises. There is no information about the separate duration for core and modules.
16.3.8. Module ‘Orchard’
The average duration of farm interview was 16 minutes for farmers’ and family farms and 39 minutes for agricultural companies and enterprises. There is no information about the separate duration for core and modules.
16.3.9. Module ‘Vineyard’
Restricted from publication
17.1. Data revision - policy
The revision policy applied by Statistics Lithuania is described in the Description of Procedure for Performance, Analysis and Publication of Revisions of Statistical Information.
There are no planned revisions of published IFS 2023 data.
17.2. Data revision - practice
Final results are published and are not subject to subsequent revision. The exception is only in case of significant errors, changes in classifications, methodologies, new statistical data sources, etc. If significant changes or revisions occur, the revised data will be published with an informative note.
Individual depersonalised data are validated by Statistics Lithuania and Eurostat using strict rules; later, aggregated data are checked again. IFS 2023 data revision was not planned because data were carefully checked against administrative data sources and are consistent with validation rules.
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
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
Agricultural companies and farmers are assigned to different strata. Agricultural companies are assigned strata based on their location (municipality), while farmers are assigned strata based on their standard output and municipality. Therefore, the stratification variables were legal status, standard output and Local Administrative Units (municipalities at the NUTS 5 level).
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
Unit legal status
18.1.2.2.3. Use of systematic sampling
No18.1.2.2.4. Full coverage strata
Within the full coverage strata were holdings that met the following criteria:
- holdings with a standard output of EUR 20 000 or more;
- certified organic farms;
- holdings belonging to specific groups:
- walnut growing;
- nursery;
- perennial plants for twining and weaving;
- flax;
- oilseeds;
- tobacco;
- hops;
- aromatic, medicinal and culinary plants;
- seed and seedling plants;
- other energy and industrial plants;
- fibre plants;
- farms where ostriches are kept.
18.1.2.2.5. Method of determination of the overall sample size
A relevant analysis of the IFS 2023 data was performed to determine the sample size.
The sample size was determined based on previous surveys, where a similar size ensured satisfactory estimate quality. Considering the ongoing decline in population, the sample was slightly reduced to optimise costs without compromising data reliability.
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
Agricultural companies and farmers are assigned to different strata. Agricultural companies are assigned strata based on their location (municipality), while farmers are assigned strata based on their standard output and municipality. Therefore, the stratification variables were legal status, standard output and Local Administrative Units (municipalities at the NUTS 5 level).
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
Unit legal status
18.1.4.2.3. Use of systematic sampling
No18.1.4.2.4. Full coverage strata
Within the full coverage strata were holdings that met the following criteria:
- holdings with a standard output of EUR 20 000 or more;
- certified organic farms;
- holdings belonging to specific groups:
- walnut growing;
- nursery;
- perennial plants for twining and weaving;
- flax;
- oilseeds;
- tobacco;
- hops;
- aromatic, medicinal and culinary plants;
- seed and seedling plants;
- other energy and industrial plants;
- fibre plants;
- farms where ostriches are kept.
18.1.4.2.5. Method of determination of the overall sample size
A relevant analysis of the IFS 2023 data was performed to determine the sample size.
The sample size was determined based on previous surveys, where a similar size ensured satisfactory estimate quality. Considering the ongoing decline in population, the sample was slightly reduced to optimise costs without compromising data reliability.
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
Agricultural companies and farmers are assigned to different strata. Agricultural companies are assigned strata based on their location (municipality), while farmers are assigned strata based on their standard output and municipality. Therefore, the stratification variables were legal status, standard output and Local Administrative Units (municipalities at the NUTS 5 level).
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
Unit legal status
18.1.5.2.3. Use of systematic sampling
No18.1.5.2.4. Full coverage strata
Within the full coverage strata were holdings that met the following criteria:
- holdings with a standard output of EUR 20 000 or more;
- certified organic farms;
- holdings belonging to specific groups:
- walnut growing;
- nursery;
- perennial plants for twining and weaving;
- flax;
- oilseeds;
- tobacco;
- hops;
- aromatic, medicinal and culinary plants;
- seed and seedling plants;
- other energy and industrial plants;
- fibre plants;
- farms where ostriches are kept.
18.1.5.2.5. Method of determination of the overall sample size
A relevant analysis of the IFS 2023 data was performed to determine the sample size.
The sample size was determined based on previous surveys, where a similar size ensured satisfactory estimate quality. Considering the ongoing decline in population, the sample was slightly reduced to optimise costs without compromising data reliability.
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
Agricultural companies and farmers are assigned to different strata. Agricultural companies are assigned strata based on their location (municipality), while farmers are assigned strata based on their standard output and municipality. Therefore, the stratification variables were legal status, standard output and Local Administrative Units (municipalities at the NUTS 5 level).
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
Unit legal status
18.1.8.2.3. Use of systematic sampling
No18.1.8.2.4. Full coverage strata
Within the full coverage strata were holdings that met the following criteria:
- holdings with a standard output of EUR 20 000 or more;
- certified organic farms;
- holdings belonging to specific groups:
- walnut growing;
- nursery;
- perennial plants for twining and weaving;
- flax;
- oilseeds;
- tobacco;
- hops;
- aromatic, medicinal and culinary plants;
- seed and seedling plants;
- other energy and industrial plants;
- fibre plants;
- farms where ostriches are kept.
18.1.8.2.5. Method of determination of the overall sample size
A relevant analysis of the IFS 2023 data was performed to determine the sample size.
The sample size was determined based on previous surveys, where a similar size ensured satisfactory estimate quality. Considering the ongoing decline in population, the sample was slightly reduced to optimise costs without compromising data reliability.
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
Agricultural companies and farmers are assigned to different strata. Agricultural companies are assigned strata based on their location (municipality), while farmers are assigned strata based on their standard output and municipality. Therefore, the stratification variables were legal status, standard output and Local Administrative Units (municipalities at the NUTS 5 level).
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
Unit legal status
18.1.9.2.3. Use of systematic sampling
No18.1.9.2.4. Full coverage strata
Within the full coverage strata were holdings that met the following criteria:
- holdings with a standard output of EUR 20 000 or more;
- certified organic farms;
- holdings belonging to specific groups:
- walnut growing;
- nursery;
- perennial plants for twining and weaving;
- flax;
- oilseeds;
- tobacco;
- hops;
- aromatic, medicinal and culinary plants;
- seed and seedling plants;
- other energy and industrial plants;
- fibre plants;
- farms where ostriches are kept.
18.1.9.2.5. Method of determination of the overall sample size
A relevant analysis of the IFS 2023 data was performed to determine the sample size.
The sample size was determined based on previous surveys, where a similar size ensured satisfactory estimate quality. Considering the ongoing decline in population, the sample was slightly reduced to optimise costs without compromising data reliability.
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
Agricultural companies and farmers are assigned to different strata. Agricultural companies are assigned strata based on their location (municipality), while farmers are assigned strata based on their standard output and municipality. Therefore, the stratification variables were legal status, standard output and Local Administrative Units (municipalities at the NUTS 5 level).
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
Unit legal status
18.1.10.2.3. Use of systematic sampling
No18.1.10.2.4. Full coverage strata
Within the full coverage strata were holdings that met the following criteria:
- holdings with a standard output of EUR 20 000 or more;
- certified organic farms;
- holdings belonging to specific groups:
- walnut growing;
- nursery;
- perennial plants for twining and weaving;
- flax;
- oilseeds;
- tobacco;
- hops;
- aromatic, medicinal and culinary plants;
- seed and seedling plants;
- other energy and industrial plants;
- fibre plants;
- farms where ostriches are kept.
18.1.10.2.5. Method of determination of the overall sample size
A relevant analysis of the IFS 2023 data was performed to determine the sample size.
The sample size was determined based on previous surveys, where a similar size ensured satisfactory estimate quality. Considering the ongoing decline in population, the sample was slightly reduced to optimise costs without compromising data reliability.
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
The software tool used for sample selection was 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 attached 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
The agricultural census is conducted every 10 years. The decennial agricultural census is complemented by sample or census-based data collections organised every 3-4 years in-between.
18.3. Data collection
See sub-categories below.
18.3.1. Methods of data collection
Face-to-face, electronic versionTelephone, electronic version
Use of Internet
18.3.2. Data entry method, if paper questionnaires
Not applicable18.3.3. Questionnaire
Please find the questionnaire in annex.
Annexes:
18.3.3. Questionnaire for farmers' and family farms in English
18.3.3. Questionnaire for agricultural companies and enterprises in English
18.3.3. Questionnaire for farmers' and family farms in Lithuanian
18.3.3. Questionnaire for agricultural companies and enterprises in Lithuanian
18.4. Data validation
See sub-categories below.
18.4.1. Type of validation checks
Data format checksCompleteness checks
Range checks
Relational checks
Comparisons with previous rounds of the data collection
Comparisons with other domains in agricultural statistics
18.4.2. Staff involved in data validation
InterviewersSupervisors
Staff from central department
18.4.3. Tools used for data validation
For IFS 2023, only electronic questionnaires with integrated validation rules were used. Moreover, additional validation rules were prepared for data processing software ORACLE. Also, some mistakes or inconsistencies were found during IFS 2023 data comparison at macro level.
18.5. Data compilation
The weights of holdings in the modules' sample were adjusted for non-response. Over–coverage holdings were not taken into account.
18.5.1. Imputation - rate
The unweighted unit imputation rate was 9% (3 860 holdings were imputed). Administrative data from the current year were used for inputting information on land, animals, and the regular labour force of the holdings. Item imputation rate was not calculated.
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
AC – Agricultural Census
AWU – Annual Working Unit
CAP – Common Agricultural Policy
ESS – European Statistical System
EU – European Union
EUR – Euro
Eurostat – Statistical office of the European Union
FSS – Farm Structure Survey
GDP – Gross domestic product
IACS – Integrated Administration and Control System
IFS – Integrated Farm Statistics
ISO – International Organization for Standardization
LSU – Livestock unit
MORC – Module 'Orchards'
NUTS – Nomenclature of territorial units for statistics
RSE – Relative standard error
SECR – State Enterprise Centre of Registers
SGM – Standard Gross Margin
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 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.
9 July 2025
The list of core variables is set in Annex III of Regulation (EU) 2018/1091.
The descriptions of the core variables as well as the lists and descriptions of the variables for the modules collected in 2023 are set in Commission Implementing Regulation (EU) 2021/2286.
The following groups of variables are collected in 2023:
- for core: location of the holding, legal personality of the holding, manager, type of tenure of the utilised agricultural area, variables of land, organic farming, irrigation on cultivated outdoor area, variables of livestock, organic production methods applied to animal production;
- for the module "Labour force and other gainful activities": farm management, family labour force, non-family labour force, other gainful activities directly and not directly related to the agricultural holding;
- for the module "Rural development": support received by agricultural holdings through various rural development measures;
- for the module “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 by age of plantation and density of trees.
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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.
The weights of holdings in the modules' sample were adjusted for non-response. Over–coverage holdings were not taken into account.
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Data are disseminated at the national level every 3-4 years.
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