1.1. Contact organisation
Central Statistical Bureau (CSB) of Latvia
1.2. Contact organisation unit
Agricultural and Environmental Statistics Department, Agricultural Statistics Section
1.3. Contact name
Restricted from publication1.4. Contact person function
Restricted from publication1.5. Contact mail address
Lāčplēša Street 1, Riga LV-1301, Latvia
1.6. Contact email address
Restricted from publication1.7. Contact phone number
Restricted from publication1.8. Contact fax number
Restricted from publication2.1. Metadata last certified
3 June 20222.2. Metadata last posted
3 June 20222.3. Metadata last update
3 June 20223.1. 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 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) 2018/1874.
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 2020 are set in Commission Implementing Regulation (EU) 2018/1874.
The following groups of variables are collected in 2020:
- 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 "Animal housing and rural development module": animal housing, nutrient use and manure on the farm, manure application techniques, facilities for manure.
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 modules ‘Labour force and other gainful activities’ and ‘Rural development’ covered the same population of agricultural holdings defined in item 3.6.1.
The module ‘Machinery and equipment’ is not collected in 2020.
3.6.3. Population covered by the data sent to Eurostat for the module “Animal housing and manure management”
The subset of the population of agricultural holdings defined in item 3.6.2 with at least one of the following: bovine animals, pigs, sheep, goats, poultry.
3.6.4. Population covered by the data sent to Eurostat for the module “Irrigation”
Restricted from publication3.6.5. Population covered by the data sent to Eurostat for the module “Soil management practices”
Restricted from publication3.6.6. Population covered by the data sent to Eurostat for the module “Orchard”
Restricted from publication3.6.7. Population covered by the data sent to Eurostat for the module “Vineyard”
Restricted from publication3.7. 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 2020 data are processed (by Eurostat) with 2017 standard output coefficients (calculated as a 5-year average of the period 2015-2019). 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 2020 or 12-month period ending on 1. July 2020. 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
A 12-month period for variables on irrigation ending on 1. July 2020. Variables on soil management practices are not part of the IFS 2020.
5.3. Reference day for variables on livestock and animal housing
The reference day is July 1 within the reference year 2020.
5.4. Reference period for variables on manure management
The 12-month period ends on 01.07.2020. This period includes the reference day used for livestock and animal housing.
5.5. Reference period for variables on labour force
The 12-month period ends on July 1 within the reference year 2020.
5.6. Reference period for variables on rural development measures
The three-year period ends on 31 December 2020.
5.7. Reference day for all other variables
The reference day is July 1 within the reference year 2020.
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 Rules for the approval of model official statistical forms and for the completion and submission of forms (Latvian only).
- Regulations of the Cabinet of Ministers No. 813 Regulations on the Official Statistics Program 2017-2019 (Latvian only).
- Regulations of the Cabinet of Ministers No. 763 Regulations on the Official Statistics Program 2019-2021 (Latvian only).
- Regulations of the Cabinet of Ministers No. 782 Regulations on the Official Statistics Program 2022-2024 (Latvian only).
6.1.3. Link to national legal acts and other agreements
- Statistics Law
- Cabinet Regulation No. 812 Rules for the approval of model official statistical forms and for the completion and submission of forms
- Regulations of the Cabinet of Ministers No. 813 Regulations on the Official Statistics Program 2017-2019
- Regulations of the Cabinet of Ministers No. 763 Regulations on the Official Statistics Program 2019-2021
- Regulations of the Cabinet of Ministers No. 782 Regulations on the Official Statistics Program 2022-2024
6.1.4. Year of entry into force of national legal acts and other agreements
- 01.01.2016.
- 01.01.2017.
- 01.01.2017.
- 01.01.2019.
- 01.01.2022.
6.1.5. Legal obligations for respondents
Yes6.2. Institutional Mandate - data sharing
The procedure for the use of administrative data sources for the provision and quality control of statistics data and the maintenance of static registers, is regulated by Article 15 of the Statistics Law and Article 6(1)(e) and Article 89(2) of Regulation (EU) No 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 Agricultural Census 2020. In accordance with the procedure specified in Article 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 and support payments on agricultural holdings. The agreement with the Agricultural Data Center provides data on the number of livestock, their husbandry methods and manure management, as well as 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 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 recognized as restricted access information, he or she shall be disciplinary or criminally liable.
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), to ensure data security and confidentiality, the CSB of Latvia takes administrative, technical, and organizational measures to protect the individual information of the respondents at its disposal:
- excludes unauthorized 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 rule and dominance rule (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
Other7.2.2.3. Description of methodology
Remote access to microdata without direct identifiers.
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
8.3. Release policy - user access
The results of the Agricultural Census (AC) 2020 are published in accordance with the CSB publication plan on the Official Statistics Portal. The data are published according to the data publication calendar at 1:00 PM. Data users are informed about the availability of statistical data through the data release calendar, as well as data users can subscribe to receive alerts by e-mail.
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
12.05.2021. The size of agricultural holdings, and the utilized agricultural land they manage, increased.
16.11.2021. Most Latvian agricultural holdings specialized in field cropping.
17.12.2021. Large agricultural holdings are farming 51 % of the agricultural area.
31.03.2022. Agricultural holdings managed 30 % of the territory of Latvia.
10.2. Dissemination format - Publications
See sub-categories below.
10.2.1. Production of paper publications
Yes, in English also10.2.2. Production of on-line publications
Yes, in English also10.2.3. Title, publisher, year and link
15.06.2022. Results of the Agricultural Census, 2020.
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
17.12.2021. Online data user seminar on provisional results of the Agricultural Census 2020.
31.03.2022. Online data user seminar on results of the Agricultural Census 2020.
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
Metadata on AC 2020, published in Official statistics portal.
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 2020 is organized in accordance with activities of the Total Quality Management System of CSB of Latvia: to identify statistical and organizational processes and develop their descriptions in compliance with the requirements of the quality management system. Components are fundamental processes such as project preparation, data collection, data processing, data analysis, data dissemination and support processes as metadata and documentation of processes. Quality Management System is maintained and updated electronically in the QPR (Quality. Process. Results) portal.
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 organizations. To implement Quality Policy the CSB has set up two management systems which are going to be integrated. The Quality Management System is certified according to the international standard ISO 9001:2015 “Quality Management Systems – Requirements” since 2018 and Information Security Management System is certified according to the standard ISO 27001:2013 “Information technology. Security techniques. Information security management systems” since 2017.
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 persons responsible 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. During the Covid-19 pandemic, other problems were identified during the data collection process, which was considered in subsequent surveys. Respondents should be more encouraged to fill in the questionnaires online, so we will continue to simplify the wording of the indicators and their explanations to promote the understanding of the indicator 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 in accordance with 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 harmonized 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 and rural development 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 AC 2020 are required by the Latvian CAP Strategic Plan for 2021–2027. Preparation and implementation of the programme, in particular the justification for direct payments, rural development measures and the development, modernization 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.
Agricultural census 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
AC 2020 questionnaire 8 variables which identify the respondents and are necessary for the maintenance of the information of the Statistical Farm Register; they also facilitate the use of administrative data in agricultural surveys. For the identification of respondents and update of the CSB Statistical Farm Register information included.
Additional information on the age structure of other regular labour force in agriculture (4 variables) is needed to provide Population Census data.
7 indicators included for the needs of government, and ministries are used to evaluate and ensure the state agricultural policy. The Ministry of Agriculture requests detailed information on the use of land on agricultural holdings, including a detailed breakdown by crop and pig, unutilised agricultural land, etc. AC 2020 data will be used in the regional planning of the Ministry of Environmental Protection and Regional Development and the municipality.
12.1.2. Unmet user needs
Main data users were involved in the organization of the AC 2020 and 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 AC 2020 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 users with statistical information with independent high-quality official statistics for decision-making, research, and discussions. In general, users have the possibility to express their opinion on data quality to e-mail.
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.
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 for the main variables in the annex.
Annexes:
13.2.1 Relative Standard errors
13.2.2. Reasons for non-compliant precision requirements in relation to Regulation (EU) 2018/1091
The estimated relative standard errors (RSEs) for frame extension holdings are below the thresholds stipulated in Annex V of the Regulation (EU) 2018/1091.
13.2.3. Methodology used to calculate relative standard errors
Variance estimation is done by the ultimate cluster method (Hansen, Hurwitz and Madow, 1953). Software R package vardpoor is used for variance estimation.
Annexes:
13.2.3 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 in the annex. 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.
Annexes:
13.3.1.1 Over-coverage rate and Unit non-response rate
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
Calculated under-coverage rate is 2.75 %. The main reason for non-coverage is the creation of new farms. An annual analysis of changes in the number of agricultural holdings in the Statistical Farm register and Statistical Business register showed that every year about 1.5-3% of newly created agricultural holdings were not included in the surveys in a given year.
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 births13.3.1.3.3. Actions to minimise the under-coverage error
Under-coverage has no significant impact on AC 2020 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 minimized through the detection of outliers.
13.3.1.5. Contact error
Yes13.3.1.5.1. Actions to minimise the contact error
Due to the Covid-19 pandemic, the chosen data collection methods were changed. Scheduled face-to-face interviews were replaced by telephone interviews and online questionnaires.
When carrying out telephone interviews, agricultural holdings with incorrect phone numbers (number changed or disabled) were found. Not all farms included in AC 2020 had a known email address, or it was incorrect or inactive.
At the start of the pandemic data collection, CSB updated its extraordinary contact information from administrative data sources and mobile operators. Whenever it was possible, the interviewers looked for new phone numbers in public catalogues. In cases where new telephone numbers or e-mail addresses were not found, holdings were considered to be not responding.
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
Although IFS is a direct continuation of the previous FSS, which has been carried out in Latvia since 2001 and the survey materials, as well as the quality of training, are regularly improved, measurement errors have not been completely avoided. Due to Covid-19, IFS 2020 was rescheduled, especially the data collection methods, as face-to-face interviews were not allowed during the pandemic. The main sources of error in IFS 2020 have been respondents and interviewers. Face-to-face interviews were replaced by telephone or online interviews. Interviewers from the CSB Interview Organization who had not previously participated in the FSS and required in-depth training and explanatory work were interviewed.
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.
Main characteristics that caused most measurement errors:
- the labour force section seemed to be too complicated for respondents, as well as 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 the animal housing and manure treatment,
- manure, exported from the holding and manure imported to the holding.
The survey information on animal housing and manure treatment was compared to the data of Animal housing facilities infrastructure and manure storage register. In cases when several animal holders had to be merged in one statistical holding, corrections were made without contacting the holding. The information that was not available in administrative data registers 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 organized.
13.3.2.2. Causes of measurement errors
Complexity of variablesSensitivity of variables
Unclear questions
Insufficient preparation of interviewers
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
Not available.
13.3.3. Non response error
See sub-categories below.
13.3.3.1. Unit non-response - rate
The unit non-response rate is in the annex of 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
Follow-up interviewsReminders
Legal actions
Imputation
13.3.3.1.3. Unit non-response analysis
The CATI and CAWI methods were mainly used to obtain the AC 2020 data, as face-to-face interviews on the agricultural holding were almost impossible under Covid-19 conditions. Therefore, it was important to perform a non-responsive analysis already in the data collection process. That allowed timely implementation of non-response measures.
Unit non–response had two reasons:
- respondent was not met;
- respondent refused to answer.
During the survey, the reasons for non-response were analyzed, 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, 3573 questionnaires or 5.8% out of the total number were identified, which were 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,
- spouse of the holder was not indicated,
- permanent and temporary employees,
- items on manure storage facilities and capacity,
- items on the use and import/export of manure were completed only partially.
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 of 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, IACS, Housing facilities infrastructure and manure storage register database were used, repeatedly contacting respondents to specify the 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
Previous data for the same unitOther
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,
- information about the owner’s spouse,
- forests and another land,
- permanent and temporary employees,
- other gainful activities,
- items on manure storage facilities and capacity,
- items on the use and import/export of manure.
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, the Housing facilities infrastructure and manure storage register, as well as FSS 2016 data and other agricultural survey information was used.
13.3.4.4. Tools and staff authorised to make corrections
Data were imputed by CSB staff involved in the IFS 2020 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 11 months.
Time lag – 3rd provisional results after 12 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 15.06.2022 a publication of the AC 2020 results was planned.
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 the 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, % | |
|---|---|---|---|---|
| UAA excluding kitchen gardens, ha | 1968298 | 1966905 | 99,9 | 98% |
| LSU | 470911 | 474447 | 100,7 | 98% |
15.1.3.2. Differences between the national thresholds and the thresholds used for the data sent to Eurostat
All data (core and modules) were obtained by counting and frame extensions, and the applicable thresholds comply with the Regulation. For data published at the national level, the results will be broken down according to the thresholds set out in the Regulation and the frame extension.
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 the Regulation (EU) 2018/1091 and the EU handbook for IFS 2020.
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 in the annex.
The number of working hours and days in a year for a full-time job corresponds 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.
Annexes:
15.1.4.1.1. AWU
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
The information is available in the annex of item 15.1.4.1.1.
15.1.4.1.3. AWU for workers of certain age groups
The information is available in the annex of item 15.1.4.1.1.
15.1.4.1.4. Livestock coefficients
The LSU coefficients used to publish the data shall be those set out 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 2020.
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.
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 the 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
8 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 some changes but not enough to warrant the designation of a break in series15.2.2.2. Description of changes
Regulation (EU) 2018/1091 newly considers agricultural holdings with only fur animals. However, even if Latvia raises fur animals, holdings with only fur animals are not included in our data collection because they do not meet the thresholds. The thresholds for animals are expressed in livestock units (LSU) and fur animals are not associated with LSU coefficients. We did not add thresholds related to fur animals; there is no reason for it (fur animals do not contribute towards 98% of the total LSU).
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 some changes but not enough to warrant the designation of a break in series15.2.5.2. Description of changes
Legal personality of the agricultural holding
In IFS, there is a new class (“shared ownership”) for the legal personality of the holding compared to FSS 2016, which trigger fluctuations of holdings in the classes of sole holder holdings and group holdings.
Other livestock n.e.c.
In FSS 2016, deer were included in this class, but in IFS they are classified separately.
Also in FSS 2016, there was a class for the collection of Equidae. That has been dropped and Equidae are included in IFS in "other livestock n.e.c."
Livestock units
In FSS 2016, turkeys, ducks, geese, ostriches and other poultry were considered each one in a separate class with a coefficient of 0.03 for all the classes except for ostriches (coefficient 0.035). In IFS 2020, the coefficients were adjusted accordingly, with turkeys remaining at 0.03, ostriches remaining at 0.35, ducks adjusted to 0.01, geese adjusted to 0.02 and other poultry fowls n.e.c. adjusted to 0.001.
Organic animals
While in FSS only fully compliant (certified converted) animals were included, in IFS both animals under conversion and fully converted are included.
15.2.6. Reference periods/days
See sub-categories below.
15.2.6.1. Changes since the last data transmission to Eurostat
There have been some changes but not enough to warrant the designation of a break in series15.2.6.2. Description of changes
There have been very slight changes in the reference period for land variables, irrigation, and the labour force from the 12-month period ending on 30 June to the 12-month period ending on 1 July 2020.
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
Since 2016, the number of agricultural holdings in Latvia has decreased and the structure of agricultural holdings has also changed, and the type of farming has changed significantly.
The main reasons for the changes could be related to changes in the purchase price policy of agricultural products, global economic and political trends, and the unbalanced economic situation in the country. 2016-2020 purchase prices of milk decreased during the period, thus making dairy farming economically unprofitable and reducing the number of dairy cows. According to Animal statistics, exports of live piglets (up to 30 kg) abroad have increased during this period.
In the period 2016 – 2020, there is a trend that the number of livestock in Latvia is decreasing, especially in holdings that raised livestock only for their own needs. There could be several reasons for this, such as the specialization of farms or a change in farm type. The number of holdings specialized in field cropping has increased significantly and holdings have become larger.
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 IFS 2020 and the annual crop and livestock surveys takes place simultaneously, the data are asked to the respondent once, and the data obtained are used to provide annual 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
- with regards to animal statistics: The number of sheep in Latvia is highly seasonal, and differences in animal statistics and IFS are related to the reference date. The number of animals in IFS is fixed at 1.07.2020, but in Animal Statistics – 31.12.2020. Very similar situation is the one of goats and differences in animal statistics and IFS are related to the reference date.
- with regards to crops statistics: ACS data shows harvested areas, IFS 2020 data – sown areas. The areas of other oilseeds not mentioned elsewhere are small and not economically significant for Latvia, so they are listed under Other industrial crops not mentioned elsewhere since 2020.
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 2020 was conducted at the same time as the Crop Survey 2020 and the Animal Survey 2020. The IFS 2020 and Crop Survey 2020 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
15 minutes
16.3.2. Module ‘Labour force and other gainful activities‘
20 minutes
16.3.3. Module ‘Rural development’
Not relevant.
16.3.4. Module ‘Animal housing and manure management’
20 minutes.
16.3.5. Module ‘Irrigation’
Restricted from publication16.3.6. Module ‘Soil management practices’
Restricted from publication16.3.7. Module ‘Machinery and equipment’
Restricted from publication16.3.8. Module ‘Orchard’
Restricted from publication16.3.9. Module ‘Vineyard’
Restricted from publication17.1. Data revision - policy
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.
Routine data revisions include revision of the provisional data published. The first data published 11 months after the end of the reference year and the second data published 12 months after the end of the reference year are provisional. The data are considered final a year after the validation in Eurostat thereof.
Unplanned revision of the IFS 2020 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.
17.2. Data revision - practice
There has been no need to perform data revision.
17.2.1. Data revision - average size
Not requested.
Annexes:
18. Timetable of statistical process
18.1. Source data
See sub-categories below.
18.1.1. Population 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
Census18.1.2.2. Sampling design
Not applicable for 2019/2020.
18.1.2.2.1. Name of sampling design
Not applicable18.1.2.2.2. Stratification criteria
Not applicable18.1.2.2.3. Use of systematic sampling
Not applicable18.1.2.2.4. Full coverage strata
Not applicable for 2019/2020.
18.1.2.2.5. Method of determination of the overall sample size
Not applicable for 2019/2020.
18.1.2.2.6. Method of allocation of the overall sample size
Not applicable18.1.3. 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
- total SO is not less than 70 EUR
Sample stratification was performed:
- by location of the holding - 6 regions (NUTS 3 level): 1 - Riga, 2 - Pieriga, 3 - Vidzeme, 4 - Kurzeme, 5 - Zemgale, 6 - Latgale;
- by UAA groups - 4 groups (1 – 0 ha <= UAA < 2 ha; 2 – 2 ha <= UAA < 3 ha; 3 – 3 ha <= UAA < 4 ha; 4 – 4 ha <= UAA)
- by LSU groups - 2 groups (1 – LSU2020 <= 0.5; 2 – LSU 2020 > 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
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 size of the sample was decided in accordance with the precision requirements provided in the Regulation (EU) 2018/1091 and financial and organizational possibilities.
Sampling size of FEF – 1149 eligible holdings, which covers a population of 14023 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
Census18.1.4.2. Sampling design
Not applicable.
18.1.4.2.1. Name of sampling design
Not applicable18.1.4.2.2. Stratification criteria
Not applicable18.1.4.2.3. Use of systematic sampling
Not applicable18.1.4.2.4. Full coverage strata
Not applicable.
18.1.4.2.5. Method of determination of the overall sample size
Not applicable.
18.1.4.2.6. Method of allocation of the overall sample size
Not applicable18.1.4.2.7. 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
Census18.1.5.2. Sampling design
Not applicable.
18.1.5.2.1. Name of sampling design
Not applicable18.1.5.2.2. Stratification criteria
Not applicable18.1.5.2.3. Use of systematic sampling
Not applicable18.1.5.2.4. Full coverage strata
Not applicable.
18.1.5.2.5. Method of determination of the overall sample size
Not applicable.
18.1.5.2.6. Method of allocation of the overall sample size
Not applicable18.1.5.2.7. If sampled from the core sample, the sampling strategy and calibration strategy
Not applicable18.1.6. Module “Animal housing and manure management module”
See sub-categories below.
18.1.6.1. Coverage of agricultural holdings
Census18.1.6.2. Sampling design
Not applicable.
18.1.6.2.1. Name of sampling design
Not applicable18.1.6.2.2. Stratification criteria
Not applicable18.1.6.2.3. Use of systematic sampling
Not applicable18.1.6.2.4. Full coverage strata
Not applicable.
18.1.6.2.5. Method of determination of the overall sample size
Not applicable.
18.1.6.2.6. Method of allocation of the overall sample size
Not applicable18.1.6.2.7. If sampled from the core sample, the sampling strategy and calibration strategy
Not applicable18.1.7. Module ‘Irrigation’
Restricted from publication18.1.7.1. Coverage of agricultural holdings
Restricted from publication18.1.7.2. Sampling design
Restricted from publication18.1.7.2.1. Name of sampling design
Restricted from publication18.1.7.2.2. Stratification criteria
Restricted from publication18.1.7.2.3. Use of systematic sampling
Restricted from publication18.1.7.2.4. Full coverage strata
Restricted from publication18.1.7.2.5. Method of determination of the overall sample size
Restricted from publication18.1.7.2.6. Method of allocation of the overall sample size
Restricted from publication18.1.7.2.7. If sampled from the core sample, the sampling strategy and calibration strategy
Restricted from publication18.1.8. Module ‘Soil management practices’
Restricted from publication18.1.8.1. Coverage of agricultural holdings
Restricted from publication18.1.8.2. Sampling design
Restricted from publication18.1.8.2.1. Name of sampling design
Restricted from publication18.1.8.2.2. Stratification criteria
Restricted from publication18.1.8.2.3. Use of systematic sampling
Restricted from publication18.1.8.2.4. Full coverage strata
Restricted from publication18.1.8.2.5. Method of determination of the overall sample size
Restricted from publication18.1.8.2.6. Method of allocation of the overall sample size
Restricted from publication18.1.8.2.7. If sampled from the core sample, the sampling strategy and calibration strategy
Restricted from publication18.1.9. Module ‘Machinery and equipment’
Restricted from publication18.1.9.1. Coverage of agricultural holdings
Restricted from publication18.1.9.2. Sampling design
Restricted from publication18.1.9.2.1. Name of sampling design
Restricted from publication18.1.9.2.2. Stratification criteria
Restricted from publication18.1.9.2.3. Use of systematic sampling
Restricted from publication18.1.9.2.4. Full coverage strata
Restricted from publication18.1.9.2.5. Method of determination of the overall sample size
Restricted from publication18.1.9.2.6. Method of allocation of the overall sample size
Restricted from publication18.1.9.2.7. If sampled from the core sample, the sampling strategy and calibration strategy
Restricted from publication18.1.10. Module ‘Orchard’
Restricted from publication18.1.10.1. Coverage of agricultural holdings
Restricted from publication18.1.10.2. Sampling design
Restricted from publication18.1.10.2.1. Name of sampling design
Restricted from publication18.1.10.2.2. Stratification criteria
Restricted from publication18.1.10.2.3. Use of systematic sampling
Restricted from publication18.1.10.2.4. Full coverage strata
Restricted from publication18.1.10.2.5. Method of determination of the overall sample size
Restricted from publication18.1.10.2.6. Method of allocation of the overall sample size
Restricted from publication18.1.10.2.7. If sampled from the core sample, the sampling strategy and calibration strategy
Restricted from publication18.1.11. Module ‘Vineyard’
Restricted from publication18.1.11.1. Coverage of agricultural holdings
Restricted from publication18.1.11.2. Sampling design
Restricted from publication18.1.11.2.1. Name of sampling design
Restricted from publication18.1.11.2.2. Stratification criteria
Restricted from publication18.1.11.2.3. Use of systematic sampling
Restricted from publication18.1.11.2.4. Full coverage strata
Restricted from publication18.1.11.2.5. Method of determination of the overall sample size
Restricted from publication18.1.11.2.6. Method of allocation of the overall sample size
Restricted from publication18.1.11.2.7. If sampled from the core sample, the sampling strategy and calibration strategy
Restricted from publication18.1.12. Software tool used for sample selection
Not applicable.
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 here on Eurostat's website.
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_quality_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 innovative approaches and the quality methods applied is available on Eurostat's website.
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 organized every 3-4 years in-between.
18.3. Data collection
See sub-categories below.
18.3.1. Methods of data collection
Postal, non-electronic versionPostal, electronic version (email)
Face-to-face, electronic version
Telephone, 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 ENG
18.3.3. Questionnaire LV
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 “Integrated farm statistics manual — 2020 edition”. 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).
167 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 – Agricultural Data Centre Housing facilities infrastructure and manure storage register on housing facilities and manure management and SFR. The primary source used to specify the information was the respondent – CSB employees called the respondents and asked them to give the precise incorrect or missing 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, 3573 questionnaires or 5.8% out of the total number were identified, which were partially completed and for which the imputation from administrative sources and other agricultural surveys were 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
AC – Agricultural Census
AWU – annual working units
CAP – Common Agricultural Policy
CAPI – Computer Assisted Personal Interview
CATI – Computer Assisted Telephone Interview
CAWI – Computer Assisted Web Interview
CSB - Central Statistical Bureau
EC – European Commission
ESS – European Statistical System
EU – European Union
FSS – Farm Structure Survey
IACS – Integrated Administration and Control System
IFS – Integrated Farm Statistics
ISDAVS CASIS - Integrated statistical data processing and management system, Computer Assisted Statistical Information System
LSU – Livestock units
NACE – Nomenclature of Economic Activities
NUTS – Nomenclature of territorial units for statistics
SFR – Statistical farm register
SGM – standard gross margin
SO – Standard output
UAA – Utilized 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 2020 (the agricultural census), 2023 and 2026. The data are as comparable and coherent as possible with the other European countries.
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 2020 are set in Commission Implementing Regulation (EU) 2018/1874.
The following groups of variables are collected in 2020:
- 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 "Animal housing and rural development module": animal housing, nutrient use and manure on the farm, manure application techniques, facilities for manure.
See sub-category below.
See sub-categories below.
See sub-categories below.
See sub-categories below.
See categories below.
Two kinds of units are generally used:
- the units of measurement for the variables (area in hectares, livestock in (1000) heads or LSU (livestock units), labour force in persons or AWU (annual working units), standard output in Euro, places for animal housing etc.) and
- the number of agricultural holdings having these characteristics.
During the survey and data processing, 3573 questionnaires or 5.8% out of the total number were identified, which were partially completed and for which the imputation from administrative sources and other agricultural surveys were made.
See sub-categories below.
Every 3 – 4 years.
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See sub-categories below.
See sub-categories below.