1.1. Contact organisation
Central Statistical Bureau (CSB) of Latvia
1.2. Contact organisation unit
Environment Statistics Department, Environment Statistics Methodology Section
1.3. Contact name
Confidential because of GDPR
1.4. Contact person function
Confidential because of GDPR
1.5. Contact mail address
Lāčplēša Street 1, Riga LV-1301, Latvia
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
12 October 2025
2.2. Metadata last posted
24 October 2025
2.3. Metadata last update
12 October 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 agri-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
Yes3.6.2. Population covered by the data sent to Eurostat for the modules “Labour force and other gainful activities”, “Rural development” and “Machinery and equipment”
The same population of agricultural holdings defined in item 3.6.1.
3.6.3. Population covered by the data sent to Eurostat for the module “Animal housing and manure management”
Restricted from publication
3.6.4. Population covered by the data sent to Eurostat for the module “Irrigation”
Not applicable for our country, according to Article 7(7) of Regulation (EU) 2018/1091.
3.6.5. Population covered by the data sent to Eurostat for the module “Soil management practices”
The same population of agricultural holdings defined in item 3.6.1.
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 majority of the area of the holding
The most important parcel by physical size
The most important parcel by economic size
The residence of the farmer (manager) not further than 5 km straight from the farm
3.7.4. Additional information reference area
Not available.
3.8. Coverage - Time
Farm structure statistics in Latvia cover the period from 2001 onwards.
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, including irrigable area, refers to the reference year 2023 or 12-month period ending on 1 July 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 July 2023 for variables on soil management practices. Variables on irrigation are not applicable for our country, according to Article 7(7) of Regulation (EU) 2018/1091.
5.3. Reference day for variables on livestock and animal housing
The reference day is 1 July within the reference year 2023 for livestock variables. The animal housing variables are not applicable for 2023.
5.4. Reference period for variables on manure management
The manure management variables are not applicable for 2023.
5.5. Reference period for variables on labour force
The 12-month period ends on 1 July within the reference year 2023.
5.6. Reference period for variables on rural development measures
The three-year period ends on 31 December 2023.
5.7. Reference day for all other variables
The reference day is 1 July 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
- Statistics Law
- Cabinet Regulation No. 812 - Regulations Regarding Approval of Questionnaire Forms for Official Statistics and Completion and Submission of Questionnaires
- Cabinet Regulation No. 782 - Regulations on the Official Statistics Program 2022-2024 (Latvian only)
- Cabinet Regulation No. 741 - Regulations Regarding the Official Statistics Programme for 2023-2025
- Cabinet Regulation No. 645 - Regulations on the Official Statistics Program 2024-2026 (Latvian only)
6.1.3. Link to national legal acts and other agreements
- Statistics Law
- Cabinet Regulation No. 812 - Regulations Regarding Approval of Questionnaire Forms for Official Statistics and Completion and Submission of Questionnaires
- Cabinet Regulation No. 782 - Regulations on the Official Statistics Program 2022-2024
- Cabinet Regulation No. 741 - Regulations Regarding the Official Statistics Programme for 2023-2025
- Cabinet Regulation No. 645 - Regulations on the Official Statistics Program 2024-2026
6.1.4. Year of entry into force of national legal acts and other agreements
- 2016
- 2017
- 2022
- 2023
- 2024
6.1.5. Legal obligations for respondents
Yes6.2. Institutional Mandate - data sharing
Reducing the administrative burden on respondents in the production of statistics is important both for the creation of new administrative registers, databases, or information systems that meet the needs of statistics and for the adequacy of existing administrative data sources for statistical purposes. In accordance with the strategic directions of the European Statistical System (ESS) and the tendencies of obtaining statistical data, one of the priorities of the CSB in providing statistics is to routinely expand the use of administrative data sources and information presented by the CSB regular surveys. In co-operation with holders of administrative data, the CSB, in accordance with the competence specified in the Statistics Law, regularly solves problems related to the use of administrative data in order to provide the most complete and high-quality information from administrative data sources, thus reducing the administrative burden on both businesses and households.
The procedure for the use of administrative data sources for the provision and quality control of statistics data and the maintenance of statistical registers, is regulated by Section 15 of the Statistics Law and Article 6(1)(e) and Article 89(2) of Regulation (EU) 2016/679, ensuring that the authorities transmit the information in their competence to the CSB free of charge.
Information from administrative data sources as defined in Article 2 of Regulation (EU) 2018/1091 was used to provide the data for the Integrated Farm Statistics 2023. In accordance with the procedure specified in Section 15 of the Statistics Law, two inter-ministerial agreements were concluded with state institutions in charge of administrative data sources. The agreement with the Rural Support Service provides for giving the information on sown areas, support payments on agricultural holdings and ecological focus areas. The agreement with the Agricultural Data Center provides data on the number of livestock and organic farming.
7.1. Confidentiality - policy
The CSB ensures the confidentiality and protection of the information provided by the respondents, as well as the information received from other sources, in accordance with the requirements of the applicable legal acts.
The Regulation (EC) No 223/2009 establishes a legal framework for the development, production, and dissemination of European statistics.
The confidentiality of information provided by respondents is protected by the Statistics Law:
- Section 7, which imposes an obligation on the statistical authority to ensure statistical confidentiality,
- Section 17, which sets out the procedures for the processing of data and the requirements for their protection,
- Section 19, paragraph 1, which stipulates that the statistical authority shall disseminate official statistics in a manner which does not allow either directly or indirectly identify a private individual or a State institution,
- Section 19, paragraph 2, stipulates that a statistical institution shall publish the official statistics which have been produced within the framework of the Official Statistics Programme in a publicly available form and by a predetermined deadline on the Official Statistics Portal. Until the moment of publication of official statistics, this statistic shall not be published.
The Freedom of Information Law provides for the protection of restricted access Information (Section 16):
- An institution shall ensure that the duty to protect restricted access information is known by all persons to whom this duty applies, if it is not otherwise laid down in law. A written confirmation shall be required from the persons who process restricted access information that they know the regulations and undertake to observe them.
- If, due to illegal disclosure of restricted access information, harm has been caused to its owner or another person, or his or her legal interests have been significantly infringed, these persons have the right to bring an action for damages for the harm done, or for restoration of the rights infringed.
- If a person has unlawfully disclosed information, which has been recognised as restricted access information, he or she shall be disciplinary or criminally liable.
The State Administration Structure Law defines the principles of State Administration (Section 10). State administration in its activities shall observe the principles of good administration. Such principles shall include openness with respect to private individuals and the public, the protection of data, the fair implementation of procedures within a reasonable time and other regulations, the aim of which is to ensure that the State administration observes the rights and lawful interests of private individuals.
The CSB of Latvia has implemented internal information security management and the CSB of Latvia Information Security Policy has been adopted within it. In accordance with item 5 of Quality Guidelines of CSB (in Latvian only, and not publicly available), to ensure data security and confidentiality, the CSB of Latvia takes administrative, technical, and organisational measures to protect the individual information of the respondents at its disposal:
- excludes unauthorised access to respondents held by the CSB individual data,
- in the process of disseminating information, prevents respondents from being identified by the individual information they provide, while providing the best possible analysis of results for scientific and research purposes.
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
Rounding: controlled, deterministic or random (Round each cell value to a pre-specified rounding base)7.2.1.3. Description of rules and methods
In agricultural statistics, cells are defined as confidential according to the threshold and dominance rules (n, k). Cells are safe to be published if contributed by at least 5 respondents (n=4) as well as a share of a single contributor is less than 80% (1,80) or two contributors' share is less than 90% (2,90).
Rounding was applied to the number of farms, especially when published at the municipal level. If the number of holdings was from 0 to 4 in a cell, then the number of holdings was randomly rounded to 0 or 5 considering totals.
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
The methodology is described in the dedicated section of Eurostat's website.
The Central Statistical Bureau of Latvia offers the opportunity to use IFS microdata for research purposes by entering into an appropriate agreement with researchers. The methodology, data protection and confidentiality conditions, as well as contract forms, are available on the Official Statistics Portal.
8.1. Release calendar
The Integrated Farm Statistics (IFS) release calendar is part of the release calendar of the CSB of Latvia.
The calendar includes all planned dates and types of publication of IFS data (press releases, publications, and data tables).
8.2. Release calendar access
Data release calendar (on the Official Statistics Portal)
8.3. Release policy - user access
Data are published following the data release calendar, at 1:00 PM.
Data users are informed of statistical data availability via the data release calendar and can subscribe to email alerts.
Pre-release access to some key indicators, including IFS data, is reserved for policymakers in the agricultural and environmental sectors for the purpose of assessing trends (but not for publication) no earlier than one week before the publication of the preliminary data on the Official Statistics Portal.
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.
Every 3 – 4 years.
10.1. Dissemination format - News release
See sub-categories below.
10.1.1. Publication of news releases
Yes10.1.2. Link to news releases
15 May 2024. Average size of an agricultural holding is growing
17 October 2024. Share of large holdings is growing
20 December 2024. Utilised agricultural area per one farm keeps growing
10.2. Dissemination format - Publications
See sub-categories below.
10.2.1. Production of paper publications
No10.2.2. Production of on-line publications
Yes, in English also10.2.3. Title, publisher, year and link
07 April 2025. Integrated Farm Statistics 2023
10.3. Dissemination format - online database
See sub-categories below.
10.3.1. Data tables - consultations
Information is not available. A number of consultations is not logged.
10.3.2. Accessibility of online database
Yes10.3.3. Link to online database
10.4. Dissemination format - microdata access
See sub-category below.
10.4.1. Accessibility of microdata
Yes10.5. Dissemination format - other
09 January 2025. Online data user seminar on results of the Integrated Farm Statistics 2023
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
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
IFS 2023 is organised in accordance with the requirements of the Latvian Central Statistical Office Quality Management System: to identify statistical and organisational processes and develop their descriptions. The basic processes of practical project implementation, such as project preparation, data collection, data processing, data analysis, data distribution, and support processes such as process metadata and documentation, are described in the internal Quality Management System (QMS). See description in item 11.1.3.
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
Peer review
External review or audit
Certification
11.1.3. Description of the quality management system and procedures
The CSB Quality Policy is designed and implemented in accordance with the CSB mid-term strategy. The Quality Policy covers vision, mission, core values and strategic priorities as well as commitment to comply with the requirements of binding laws and regulations and to follow recommendations of international statistical organisations.
To implement its Quality Policy, the CSB has established two management systems that will be integrated. The Quality Management System has been certified according to the international standard ISO 9001:2015 “Quality Management Systems – Requirements” and the Information Security Management System is certified according to the standard ISO/IEC 27001:2022 “Information technology. Security techniques. Information security management systems”.
CSB has introduced internal Quality Management System (QMS). The system is directed towards providing high user satisfaction and ensuring compliance with regulatory enactments. Based on the structure of the Generic Statistical Business Process Model (GSBPM), QMS defines and at the level of procedures describes processes of statistical production as well as sets the responsible persons for the monitoring of processes at all stages of the statistical production considering the EU and international quality standards. QMS defines the sequence of how processes are implemented (i.e., activities to be performed (incl. verification of processes and statistics, sequence and implementation requirements thereof, as well as persons responsible for the implementation)), procedures used in the evaluation of processes and statistics, as well as any improvements needed. QMS is regularly audited by internal and external parties. External audits of subject areas are performed by the Statistical Office of the European Union (Eurostat) and other institutions.
Also, the CSB has been a subject to regular European Statistical System's peer review (a form of assessment of the National Statistical Offices and the National Statistical Systems of the European Union) since 2007, proving its compliance with the requirements of the European Statistics Code of Practice.
11.1.4. Improvements in quality procedures
Quality requirements for the CSB and its structural units, for the processes, products and services are an integral part of the CSB management system. Different types of documentation support planning at different stages and levels (the CSB mid-term strategy, annual Action plan, Official Statistics Programme, Work Plan, QMS Processes’ portal, quality criteria for processes and products, etc.). Fulfilment of the quality requirements are monitored in the framework of the management system.
To ensure high-quality statistical data, ways to improve the statistical production process are sought regularly. In the survey planning process, the methodology, data acquisition types and data sources are evaluated in detail. In the data entry programme, we are improving validations to obtain more accurate microdata.
Respondents are increasingly completing questionnaires online, so we will continue to simplify the wording of the indicators and their explanations to promote the understanding of the indicators and provide a correct answer.
Reducing the respondent burden is of great importance in obtaining agricultural statistics, therefore opportunities to use new sources of administrative data in providing statistical information are sought regularly. Coverage and quality of administrative data sources are assessed according to the descriptions developed by QMS, analysing administrative data in comparison with survey information and other data sources.
11.2. Quality management - assessment
Quality of statistics is assessed in accordance with the existing requirements of external and internal regulatory enactments and the established quality criteria.
Regulation (EC) No 223/2009 of the European Parliament and of the Council on European statistics states that European Statistics shall be developed, produced and disseminated on the basis of uniform standards and of harmonised methods. In this respect, the following quality criteria shall apply: relevance, accuracy, timeliness, punctuality, accessibility, clarity, comparability and coherence.
12.1. Relevance - User Needs
Public administration, government, and ministries, in particular the Ministry of Agriculture, the Ministry of Environmental Protection and Regional Development and the Ministry of Economics
The results will provide information on agricultural indicators for sectoral analysis and decision-making in the field of agricultural policy, rural development and environment in Latvia, the European Union, as well as for the planning and implementation of the CAP. The information obtained will be the basis for a reasoned assessment of the impact of agriculture on the environment and climate change, the quality and safety of agricultural products, and will provide comparable statistics on agricultural activity in the EU Member States.
The data obtained in the IFS 2023 are required by the Latvian CAP Strategic Plan for 2021–2027 and further. Implementation and assessment of the programme, in particular the justification for direct payments, rural development measures and the development, modernisation and reduction of administrative burdens of sectoral strategies, justification, and implementation plan for the management and coordination systems for beneficiaries and direct payments to beneficiaries.
Integrated farm statistics data will be used for the National Energy and Climate Plan 2021–2030 to assess the implementation of measures to reduce greenhouse gas emissions and increase CO2 sequestration and increase the share of renewable energy in agriculture.
Agricultural sciences and education institutions with access to data tables and specially designed microdata for research purposes.
12.1.1. Main groups of variables collected only for national purposes
To enable the linkage of survey information with administrative data sources and to maintain the Statistical Farm Register, 8 variables were included in the “Respondents” section of the IFS 2023 questionnaire. These variables are:
- name of holding,
- address of agricultural holding,
- website address,
- mailing address,
- telephone number and e-mail address,
- VAT payer’s registration number,
- customer number in the Rural Support Service,
- name, surname, telephone number and e-mail address of the person filling in the IFS 2023 form.
Additionally, 4 indicators are included to support the needs of the government and ministries in evaluating and implementing national agricultural policy. The Ministry of Agriculture requires detailed information on:
- the use of land on agricultural holdings,
- detailed breakdown by crop,
- destination of the holding’s production,
- total utilised agricultural area irrigated at least once during the last 12 months, methods and equipment used.
IFS 2023 data will be used for regional planning purposes by the Ministry of Environmental Protection and Regional Development, as well as by the municipality.
12.1.2. Unmet user needs
Main data users were involved in the organisation of the IFS 2023. An internal working group and an inter-institutional working group of the CSB were established. The needs of main data users were discussed in working groups and included in the list of IFS 2023 variables. The variables are given in item 12.1.1.
12.1.3. Plans for satisfying unmet user needs
Not applicable.
12.2. Relevance - User Satisfaction
The mission of the CSB is to provide independent, high-quality official statistics that support decision-making, research, and public debate. However, if users encounter issues or have suggestions for improvement, they are encouraged to share their feedback by email.
Communication with data users is carried out by the Communication and Services Governance Department. So far, we have only received requests for additional data, no questions or objections regarding the quality of the data have been received.
12.2.1. User satisfaction survey
No12.2.2. Year of user satisfaction survey
Not applicable.
12.2.3. Satisfaction level
Not applicable12.3. Completeness
Information on not collected, not-significant and not-existent variables is available on Eurostat’s website, at the link: Additional data - Eurostat (europa.eu).
12.3.1. Data completeness - rate
Not applicable for Integrated Farm Statistics as the not collected variables, not-significant variables and not-existent variables are completed with 0.
13.1. Accuracy - overall
See categories below.
13.2. Sampling error
See sub-categories below.
13.2.1. Sampling error - indicators
Please find the relative standard errors on Eurostat’s website, at the link: CircaBC website.
13.2.2. Reasons for non-compliant precision requirements in relation to Regulation (EU) 2018/1091
The estimated relative standard errors (RSEs) for the holdings in the main frame are below the thresholds stipulated in Annex V of the Regulation (EU) 2018/1091.
13.2.3. Reference on method of estimation
Variance estimation is done by the ultimate cluster method (Hansen, M. H., Hurwitz, W. N., & Madow, W. G. (1953)). Software R, package vardpoor, is used for variance estimation.
13.2.4. Impact of sampling error on data quality
Low13.3. Non-sampling error
See sub-categories below.
13.3.1. Coverage error
See sub-categories below.
13.3.1.1. Over-coverage - rate
The over-coverage rate is available on Eurostat’s website, at the link: CircaBC.
The over-coverage rate is unweighted.
The over-coverage rate is calculated as the share of ineligible holdings to the holdings designated for the core data collection. The ineligible holdings include those holdings with unknown eligibility status that are not imputed nor re-weighted for (therefore considered ineligible).
The over-coverage rate is calculated over the holdings in the main frame and if applicable frame extension, for which core data are sent to Eurostat.
13.3.1.1.1. Types of holdings included in the frame but not belonging to the population of the core (main frame and if applicable frame extension)
Below thresholds during the reference periodTemporarily out of production during the reference period
Ceased activities
Merged to another unit
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 unitsMaintain of ineligible units in the records, recalculating weights of all units by considering the corrected population
13.3.1.1.3. 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 – 1.2%.
Under-coverage error value is irrelevant and does not significantly affect the quality of calculations.
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
13.3.1.3.3. Actions to minimise the under-coverage error
Under-coverage has no significant impact on IFS 2023 results. To include as many new agricultural holdings as possible in future surveys of the agricultural sector, it is necessary to update the list of agricultural holdings shortly before sampling.
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
The misclassification error was minimised through the detection of outliers.
13.3.1.5. Contact error
Yes13.3.1.5.1. Actions to minimise the contact error
At the start of the data collection, CSB updated holdings' contact information from administrative data sources.
In cases where phone numbers or email addresses were not found, paper letters were sent to farm owners.
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
Main characteristics that caused most measurement errors:
- the labour force section seemed too complicated for respondents and interviewers. Respondents do not want to reveal information on employees, their working time, and other income-generating activities, as they believe that the respective information is sensitive and confidential,
- questions related to storage for agricultural products. Not always, the farmer could separate storage of roots, tubers and bulbs from storage of vegetables and fruits, especially for farms that do not specialise in growing specific crops.
13.3.2.2. Causes of measurement errors
Complexity of variablesSensitivity of variables
Unclear questions
Respondents’ inability to provide accurate answers
13.3.2.3. Actions to minimise the measurement error
Pre-filled questionsExplanatory 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
Although IFS is a direct continuation of the previous FSS, which has been carried out in Latvia since 2001 and the survey materials and the quality of trainings are regularly improved, measurement errors have not been completely avoided.
The questionnaires were filled in incorrectly due to several reasons, such as problems with the Internet connection and the speed thereof, the survey was comprehensive and very detailed information was asked, thus farmers considered this information confidential, and there was also a need for additional explanations for the indicators in the questionnaire.
The information was clarified via phone with an interviewer or by directly calling the holding.
To carry out data validation, additional logical controls of source data and summary data were organised.
13.3.3. Non response error
See sub-categories below.
13.3.3.1. Unit non-response - rate
See item 13.3.1.1.
The unit non-response rate is unweighted.
The unit non-response rate is calculated as the share of eligible non-respondent holdings to the eligible holdings. The eligible holdings include those holdings with unknown eligibility status which are imputed or re-weighted for (therefore considered eligible).
The unit non-response rate is calculated over the holdings in the main frame and if applicable frame extension, for which core data are sent to Eurostat.
13.3.3.1.1. Reasons for unit non-response
Failure to make contact with the unitRefusal to participate
Inability to participate (e.g. illness, absence)
13.3.3.1.2. Actions to minimise or address unit non-response
Follow-up interviewsReminders
Legal actions
Weighting
13.3.3.1.3. Unit non-response analysis
The CATI and CAWI methods were mainly used to obtain the IFS 2023 data. Therefore, performing a non-responsive analysis already in the data collection process was important. That allowed the timely implementation of non-response measures.
There were two reasons for the unit non-response:
- it was not possible to contact the unit - the respondent did not answer the calls and was not available at the farm (Failure to make contact with the unit),
- the respondent did not want to participate (Refusal to participate) or was unable to participate (Inability to participate, e.g. due to illness, absence).
During the survey, the reasons for non-response were analysed, additional sources of information were sought, such as various sources of administrative data, databases of mobile phone operators, in order to provide useful contact information for communicating with respondents.
13.3.3.2. Item non-response - rate
During the survey and data processing, 1 400 questionnaires (6.4% of the total) were identified as partially completed.
13.3.3.2.1. Variables with the highest item non-response rate
Most common missing items:
- the year when classified as manager of agricultural holdings,
- spouse of the holder was not indicated, and
- permanent and temporary employees.
13.3.3.2.2. Reasons for item non-response
Interview interruptionRefusal
Skip of due question
Other
13.3.3.2.3. Actions to minimise or address item non-response
Follow-up interviewsReminders
Imputation
13.3.3.3. Impact of non-response error on data quality
Low13.3.3.4. Additional information non-response error
Partially completed questionnaires were received mainly from web respondents.
The problem was also caused by the difference in the definitions of the respondent units in IFS and administrative sources; for example, answers were not provided for the whole area available to the holding, but only for the part, for which support payments were received, or for the part belonging to the agricultural holding registered in the Business Register.
To obtain the missing information, data from the Statistical Farm Register, Population Register and IACS database were used. Respondents were contacted repeatedly to clarify information when necessary.
13.3.4. Processing error
See sub-categories below.
13.3.4.1. Sources of processing errors
Imputation methodsData processing
13.3.4.2. Imputation methods
Mean imputationPrevious data for the same unit
13.3.4.3. Actions to correct or minimise processing errors
Data imputation was performed for partially completed questionnaires. The key imputed indicators include:
- the year when classified as manager of agricultural holdings,
- information about the owner’s spouse,
- forests and another land,
- permanent and temporary employees, and
- other gainful activities.
In order to improve the quality of the data and fill in the missing information, we contacted the respondents repeatedly or made data imputation from the Statistical Farm Register, Population Register, IACS data, as well as IFS 2020 data and other agricultural survey information was reused.
13.3.4.4. Tools and staff authorised to make corrections
Data were imputed by CSB staff involved in the IFS 2023 organisation and execution – Agriculture statistics experts and IT specialists. Access and SQL tools were used for data correction.
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
Time lag — 1st provisional results: after 5 months.
Time lag — 2nd provisional results: after 10 months.
14.1.2. Time lag - final result
Time lag — final results: after 15 months.
14.2. Punctuality
See sub-categories below.
14.2.1. Punctuality - delivery and publication
See sub-categories below.
14.2.1.1. Punctuality - delivery
Not requested.
14.2.1.2. Punctuality - publication
On time.
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 an agricultural holding is in line 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
| Total | Covered by the thresholds | Attained coverage | Minimum requested coverage | |
|---|---|---|---|---|
| 1 | 2 | 3=2*100/1 | 4 | |
| UAA excluding kitchen gardens | 1 970 700 | 1 967 908 | 99.86% | 98% |
| LSU | 444 500 | 443 699 | 99.82% | 98% |
The data collection covers all holdings meeting at least one of the 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
No differences.
15.1.3.3. Reasons for differences
Not applicable.
15.1.4. Definitions and classifications of variables
See sub-categories below.
15.1.4.1. Deviations from Regulation (EU) 2018/1091 and EU handbook
There are no deviations from Regulation (EU) 2018/1091, Commission Implementing Regulation (EU) 2021/2286 and the EU handbook for IFS 2023.
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 LSU coefficients are the ones set in Regulation (EU) 2018/1091.
15.1.4.1.5. Livestock included in “Other livestock n.e.c.”
There are no differences between the types of livestock that are included under the heading “Other livestock n.e.c.” and the types of livestock that should be included according to the EU handbook for IFS 2023.
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
There are no deviations in reference periods/days.
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
9 years
15.2.2. Definition of agricultural holding
See sub-categories below.
15.2.2.1. Changes since the last data transmission to Eurostat
There have been no changes15.2.2.2. Description of changes
There are no changes as both 2020 and 2023 are data collection years covered by the same Regulation (EU) 2018/1091.
15.2.3. Thresholds of agricultural holdings
See sub-categories below.
15.2.3.1. Changes in the thresholds of holdings for which core data are sent to Eurostat since the last data transmission
There have been no changes15.2.3.2. Description of changes
Not applicable.
15.2.4. Geographical coverage
See sub-categories below.
15.2.4.1. Change in the geographical coverage since the last data transmission to Eurostat
There have been no changes15.2.4.2. Description of changes
Not applicable.
15.2.5. Definitions and classifications of variables
See sub-categories below.
15.2.5.1. Changes since the last data transmission to Eurostat
There have been no changes15.2.5.2. Description of changes
There are no changes as both 2020 and 2023 are data collection years covered by the same Regulation (EU) 2018/1091.
15.2.6. Reference periods/days
See sub-categories below.
15.2.6.1. Changes since the last data transmission to Eurostat
There have been 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
One out of five holdings in Latvia ceased its activities from 2020 to 2023, which caused major shifts in the structure of agricultural crops and livestock herds on farms. These changes were influenced by both socio-economic aspects and climate, especially for winter crops. As the number of livestock changes, the area of fodder crops on arable land also decreases, except for grassland areas.
With regards to the other gainful activities of the holding, there was a significant increase of those related to the holding and a decrease of those non-related to the holding.
Compared to 2020 there was a relevant increase in holdings without livestock in 2023.
Since 2010, there has been a trend that agricultural holdings in Latvia are gradually becoming economically larger for various reasons - both because holdings with lower outputs are decreasing, and because production is concentrated in larger farms, and also because SO is increasing.
The change of farm typology breakdown may be attributed to economic factors and the opportunity to provide the farm with various sources of income, as well as to the likelihood that farms are managed by multiple generations with differing visions for future development.
The farms that benefitted from rural development measures remarkably decreased from being more than seven out of ten in 2020, to three out of ten in 2023.
The shrink in total number of farms had as impact the increase of median SO_EURO and, at a more limited scale, of the UAA, in 2023 compared to 2020. This process reflects a trend of consolidation of agricultural holdings.
15.2.9. Maintain of statistical identifiers over time
No15.3. Coherence - cross domain
See sub-categories below.
15.3.1. Coherence - sub annual and annual statistics
Not applicable to Integrated Farm Statistics, because there are no sub annual data collections in agriculture.
15.3.2. Coherence - National Accounts
Not applicable, because Integrated Farm Statistics have no relevance for national accounts.
15.3.3. Coherence at micro level with data collections in other domains in agriculture
See sub-categories below.
15.3.3.1. Analysis of coherence at micro level
Yes15.3.3.2. Results of analysis at micro level
Data collection for the IFS 2023 and the annual crop survey 2023 took place simultaneously. The data were requested from the respondent once, and the collected data were used to provide agricultural statistics in each survey in accordance with the requirements of the regulations.
15.3.4. Coherence at macro level with data collections in other domains in agriculture
See sub-categories below.
15.3.4.1. Analysis of coherence at macro level
Yes15.3.4.2. Results of analysis at macro level
Coherence cross-domain: IFS vs ORGANIC CROP PRODUCTION
The statistics cover agricultural holdings (including organic holdings), carrying out agricultural activities in accordance with Regulation (EC) No 1893/2006 and in accordance with the thresholds set by Latvia: the area used for agriculture is at least 1 ha, and SO = 70 euros or more.
Agricultural holdings that do not meet these conditions were not surveyed in IFS 2023; therefore, differences in areas with organic statistics are possible.
Coherence cross-domain: IFS vs ANIMAL PRODUCTION
The IFS 2023 reference date for livestock is 01 July 2023, while the reference period for animal production statistics is 31 December 2023. The number of livestock in Latvia varies seasonally - there are more animals, especially grazing animals, during the summer months than in winter. This entails the existence of fluctuations in animal numbers.
Coherence cross-domain: IFS vs ORGANIC ANIMAL PRODUCTION
Same comments as per animal production cross-domain checks.
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
To reduce the burden on respondents and to avoid duplication of questions in statistical surveys, IFS 2023 was conducted at the same time as the Crop Survey 2023. The IFS 2023 and Crop Survey 2023 questionnaires were designed in such a way that they would not require the defendant to provide the same information several times.
16.2. Efficiency gains since the last data transmission to Eurostat
On-line surveysFurther automation
Increased use of administrative data
Further training
16.2.1. Additional information efficiency gains
Not available.
16.3. Average duration of farm interview (in minutes)
See sub-categories below.
16.3.1. Core
10 minutes
16.3.2. Module ‘Labour force and other gainful activities‘
15 minutes
16.3.3. Module ‘Rural development’
Not relevant. Rural development module is collected via administrative sources.
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’
10 minutes
16.3.7. Module ‘Machinery and equipment’
5-7 minutes
16.3.8. Module ‘Orchard’
10 minutes
16.3.9. Module ‘Vineyard’
Restricted from publication
17.1. Data revision - policy
CSB of Latvia has developed Revision Policy Guidelines, which are available in item 18 of the national reference metadata. Revision policy is an important component of good governance practice, addressed more and more often in the international statistical society. The objective of the Revision policy is to lay down the order of review or revision of the prepared and published data.
The planned revision is carried out after the data has been approved by Eurostat and published in the Eurostat database.
Unplanned revision of the IFS 2023 may be carried out. It may be necessary to carry out the unplanned revision if a mistake in data sources or calculations is found, or due to unexpected changes in the methodology or data sources. To date, there has been no need to perform unplanned IFS data revision. If, however, errors are discovered in the published data, they would be corrected in the Official Statistics Portal databases or PDF publications, and the last information update date would be added.
17.2. Data revision - practice
There has been no need to perform data revision.
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 (SFR)
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
The main frame includes the economically active agricultural holdings with UAA greater than or equal to 5 hectares and with a total SO greater than or equal to 70 EUR.
Of the holdings in the main frame, the sample includes those holdings with a total SO less than 100 000 EUR or with UAA less than 30 ha. See the full coverage strata in item 18.1.2.2.4.
Sample stratification was performed by location of the holding - 6 regions (NUTS 3 level): LV003 (Kurzeme), LV005 (Latgale), LV006 (Riga), LV007 (Pieriga), LV008 (Vidzeme) and LV009 (Zemgale).
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
No18.1.2.2.4. Full coverage strata
The full coverage strata included all economically active holdings from the main frame with the following characteristics:
- total SO in a holding is greater than or equal to 100 000 EUR, and
- the area of UAA in a holding is greater than or equal to 30 ha.
Sample stratification was performed by location of the holding - 6 regions (NUTS 3 level): LV003 (Kurzeme), LV005 (Latgale), LV006 (Riga), LV007 (Pieriga), LV008 (Vidzeme) and LV009 (Zemgale).
18.1.2.2.5. Method of determination of the overall sample size
The sample size was decided in accordance with the precision requirements provided in Regulation (EU) 2018/1091, as well as financial and organisational possibilities.
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
Sample18.1.3.2. Sampling design
The sample includes economically active holdings with the following characteristics:
- the area of UAA in a holding is less than 5 ha, and
- total SO is greater than or equal to 70 EUR.
Sample stratification was performed:
- by location of the holding - 6 regions (NUTS 3 level)
- LV003 (Kurzeme)
- LV005 (Latgale)
- LV006 (Riga)
- LV007 (Pieriga)
- LV008 (Vidzeme)
- LV009 (Zemgale)
- by UAA groups - 4 groups
- 0 ha <= UAA < 2 ha
- 2 ha <= UAA < 3 ha
- 3 ha <= UAA < 4 ha
- 4 ha <= UAA
- by LSU groups - 2 groups
- LSU <= 0.5
- LSU > 0.5
18.1.3.2.1. Name of sampling design
Stratified one-stage random sampling18.1.3.2.2. Stratification criteria
Unit sizeUnit location
Unit specialization
18.1.3.2.3. Use of systematic sampling
No18.1.3.2.4. Full coverage strata
The full coverage strata were not used for frame extension.
18.1.3.2.5. Method of determination of the overall sample size
The sample size was decided in accordance with the precision requirements provided in Regulation (EU) 2018/1091, as well as financial and organisational possibilities.
Sampling size of FEF (frame extension farms) – 1 149 eligible holdings, which covered a population of 14 023 holdings.
18.1.3.2.6. Method of allocation of the overall sample size
Neymann allocation18.1.4. 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
Module data were collected for all farms in the overall survey sample, no separate sample was prepared for the module. See sample description in item 18.1.2.2.
18.1.4.2.1. Name of sampling design
Stratified one-stage random sampling18.1.4.2.2. Stratification criteria
Unit sizeUnit location
Unit specialization
18.1.4.2.3. Use of systematic sampling
No18.1.4.2.4. Full coverage strata
See description in item 18.1.2.2.4.
18.1.4.2.5. Method of determination of the overall sample size
See description in item 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
Module data were collected for all farms in the overall survey sample, no separate sample was prepared for the module. See sample description in item 18.1.2.2.
18.1.5.2.1. Name of sampling design
Stratified one-stage random sampling18.1.5.2.2. Stratification criteria
Unit sizeUnit location
Unit specialization
18.1.5.2.3. Use of systematic sampling
No18.1.5.2.4. Full coverage strata
See description in item 18.1.2.2.4.
18.1.5.2.5. Method of determination of the overall sample size
See description in item 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
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
Module data were collected for all farms in the overall survey sample, no separate sample was prepared for the module. See sample description in item 18.1.2.2.
18.1.8.2.1. Name of sampling design
Stratified one-stage random sampling18.1.8.2.2. Stratification criteria
Unit sizeUnit location
Unit specialization
18.1.8.2.3. Use of systematic sampling
No18.1.8.2.4. Full coverage strata
See description in item 18.1.2.2.4.
18.1.8.2.5. Method of determination of the overall sample size
See description in item 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
Module data were collected for all farms in the overall survey sample, no separate sample was prepared for the module. See sample description in item 18.1.2.2.
18.1.9.2.1. Name of sampling design
Stratified one-stage random sampling18.1.9.2.2. Stratification criteria
Unit sizeUnit location
Unit specialization
18.1.9.2.3. Use of systematic sampling
No18.1.9.2.4. Full coverage strata
See description in item 18.1.2.2.4.
18.1.9.2.5. Method of determination of the overall sample size
See description in item 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
Module data were collected for all farms in the overall survey sample, no separate sample was prepared for the module. See sample description in item 18.1.2.2.
18.1.10.2.1. Name of sampling design
Stratified one-stage random sampling18.1.10.2.2. Stratification criteria
Unit sizeUnit location
Unit specialization
18.1.10.2.3. Use of systematic sampling
No18.1.10.2.4. Full coverage strata
See description in item 18.1.2.2.4.
18.1.10.2.5. Method of determination of the overall sample size
See description in item 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
Software R
18.1.13. Administrative sources
See sub-categories below.
18.1.13.1. Administrative sources used and the purposes of using them
The information is available on Eurostat’s website, at the link: Additional data - Eurostat (europa.eu).
18.1.13.2. Description and quality of the administrative sources
See the Excel file in the annex.
Annexes:
18.1.13.2 Description and quality of administrative sources
18.1.13.3. Difficulties using additional administrative sources not currently used
Problems related to data quality of the sourceThe final validated data in the source would not be in time to meet statistical deadlines or would relate to a period which does not coincide with the reference period
18.1.14. Innovative approaches
The information on the innovative approaches and the quality methods applied is available on Eurostat’s website, at the link: Additional data - Eurostat (europa.eu).
18.2. Frequency of data collection
The agricultural census is conducted every 10 years. The decennial agricultural census is complemented by sample or census-based data collections organised every 3-4 years in-between.
18.3. Data collection
See sub-categories below.
18.3.1. Methods of data collection
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 questionnaires in annex.
Annexes:
18.3.3 Questionnaire in English
18.3.3 Questionnaire in Latvian
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
Data control was made in all data collection and data processing levels.
Mathematical and logical controls were developed in compliance with the requirements of the EU handbook. In order to obtain more precise information and facilitate further data processing, they were supplemented with other necessary controls. With respect to the validations failing during the data input process, an error notification appeared that indicated the place of the error and the correct value (if possible).
172 controls were incorporated in the data input application ISDAVS CASIS (Integrated statistical data processing and management system, Computer Assisted Statistical Information System). This did not only ensure mathematical and logical control but also technically correct data input.
When data were sent to the CSB server, the personnel engaged carried out deeper mathematical and logical controls at the level of holdings. When necessary, the information was revised by contacting the interviewer or holder/manager of agricultural holding.
Data comparison was based on the administrative data sources – State Technical Control Agency of Latvia and SFR. The primary source used to specify the information was the respondent – CSB employees called the respondents and asked them to clarify survey information.
For data processing and validation purposes, SQL and access for individual data were used. For data set validation purposes, a standalone validation tool, developed by Eurostat, was used.
18.5. Data compilation
During the survey and data processing, 1 400 questionnaires (6.4% of the total) were identified as partially completed and for which the imputation from administrative sources and other agricultural surveys was made.
18.5.1. Imputation - rate
Not available.
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
AWU – Annual Working Unit
CAP – Common Agricultural Policy
CATI – Computer Assisted Telephone Interview
CAWI – Computer Assisted Web Interview
CO2 – Carbon dioxide
CSB – Central Statistical Bureau
ESS – European Statistical System
EU – European Union
EUR – Euro
Eurostat – Statistical Office of the European Union
FEF – Frame extension farms
FSS – Farm Structure Survey
GSBPM – Generic Statistical Business Process Model
IACS – Integrated Administration and Control System
IEC – International Electrotechnical Commission
IFS – Integrated Farm Statistics
ISDAVS CASIS – Integrated statistical data processing and management system, Computer Assisted Statistical Information System
ISO – International Organization for Standardization
LSU – Livestock unit
NUTS – Nomenclature of territorial units for statistics
QMS – Quality Management System
RSE – Relative standard error
SFR – Statistical Farm Register
SGM – Standard gross margin
SO – Standard output
UAA – Utilised agricultural area
VAT – Value-added tax
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 agri-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.
12 October 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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See sub-categories below.
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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.
During the survey and data processing, 1 400 questionnaires (6.4% of the total) were identified as partially completed and for which the imputation from administrative sources and other agricultural surveys was made.
See sub-categories below.
Every 3 – 4 years.
See sub-categories below.
See sub-categories below.
See sub-categories below.
