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For any question on data and metadata, please contact: Eurostat user support |
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1.1. Contact organisation | Central Statistical Bureau (CSB) of Latvia |
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1.2. Contact organisation unit | Agricultural and Environmental Statistics Department, Agricultural Statistics Section |
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1.5. Contact mail address | Lāčplēša Street 1, Riga LV-1301, Latvia |
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2.1. Metadata last certified | 03/06/2022 | ||
2.2. Metadata last posted | 03/06/2022 | ||
2.3. Metadata last update | 03/06/2022 |
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3.1. Data description | |||
The data describe the structure of agricultural holdings, providing the general characteristics of farms and farmers and information on their land, livestock and labour force. They also describe production methods, rural development measures and agro-environmental aspects that look at the impact of agriculture on the environment. The data are used by public, researchers, farmers and policy-makers to better understand the state of the farming sector and the impact of agriculture on the environment. The data follow up the changes in the agricultural sector and provide a basis for decision-making in the Common Agricultural Policy (CAP) and other European Union policies. The statistical unit is the agricultural holding (farm). The aggregated results are disseminated through statistical tables. The data are presented at different geographical levels and over periods. |
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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. |
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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. |
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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:
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3.5. Statistical unit | |||
See sub-category below. |
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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. |
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3.6. Statistical population | |||
See sub-categories below. |
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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 |
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3.6.1.1. Raised thresholds compared to Regulation (EU) 2018/1091 | |||
No | |||
3.6.1.2. Lowered and/or additional thresholds compared to Regulation (EU) 2018/1091 | |||
Yes | |||
3.6.2. 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. |
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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. |
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3.7. Reference area | |||
See sub-categories below. |
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3.7.1. Geographical area covered | |||
The entire territory of the country. |
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3.7.2. Inclusion of special territories | |||
Not applicable. |
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3.7.3. Criteria used to establish the geographical location of the holding | |||
The main building for production The 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 |
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3.7.4. Additional information reference area | |||
Not available. |
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3.8. Coverage - Time | |||
Farm structure statistics in Latvia cover the period from 2001 onwards. |
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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. |
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Two kinds of units are generally used:
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See sub-categories below. |
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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. |
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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. |
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5.3. Reference day for variables on livestock and animal housing | |||
The reference day is July 1 within the reference year 2020. |
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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. |
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5.5. Reference period for variables on labour force | |||
The 12-month period ends on July 1 within the reference year 2020. |
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5.6. Reference period for variables on rural development measures | |||
The three-year period ends on 31 December 2020. |
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5.7. Reference day for all other variables | |||
The reference day is July 1 within the reference year 2020. |
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6.1. Institutional Mandate - legal acts and other agreements | |||
See sub-categories below. |
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6.1.1. National legal acts and other agreements | |||
Legal act | |||
6.1.2. Name of national legal acts and other agreements | |||
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6.1.3. Link to national legal acts and other agreements | |||
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6.1.4. Year of entry into force of national legal acts and other agreements | |||
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6.1.5. Legal obligations for respondents | |||
Yes | |||
6.2. 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 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. |
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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:
The Freedom of Information Law provides for the protection of restricted access Information (Section 16):
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:
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7.2. Confidentiality - data treatment | |||
See sub-categories below. |
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7.2.1. Aggregated data | |||
See sub-categories below. |
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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) |
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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. |
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7.2.2. Microdata | |||
See sub-categories below. |
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7.2.2.1. Use of EU methodology for microdata dissemination | |||
Yes | |||
7.2.2.2. Methods of perturbation | |||
Other | |||
7.2.2.3. Description of methodology | |||
Remote access to microdata without direct identifiers. |
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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). |
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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. |
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8.3.1. Use of quality rating system | |||
Yes, the EU quality rating system | |||
8.3.1.1. Description of the quality rating system | |||
The methodology is described in the EU handbook. |
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Every 3 – 4 years. |
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10.1. Dissemination format - News release | |||
See sub-categories below. |
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10.1.1. Publication of news releases | |||
Yes | |||
10.1.2. 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. |
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10.2. Dissemination format - Publications | |||
See sub-categories below. |
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10.2.1. Production of paper publications | |||
Yes, in English also | |||
10.2.2. Production of on-line publications | |||
Yes, in English also | |||
10.2.3. Title, publisher, year and link | |||
15.06.2022. Results of the Agricultural Census, 2020. |
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10.3. Dissemination format - online database | |||
See sub-categories below. |
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10.3.1. Data tables - consultations | |||
Information is not available. A number of consultations is not logged. |
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10.3.2. Accessibility of online database | |||
Yes | |||
10.3.3. Link to online database | |||
10.4. Dissemination format - microdata access | |||
See sub-category below. |
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10.4.1. Accessibility of microdata | |||
Yes | |||
10.5. 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. |
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10.5.1. Metadata - consultations | |||
Not requested. |
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10.6. Documentation on methodology | |||
See sub-categories below. |
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10.6.1. Metadata completeness - rate | |||
Not requested. |
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10.6.2. Availability of national reference metadata | |||
Yes | |||
10.6.3. Title, publisher, year and link to national reference metadata | |||
Metadata on AC 2020, published in Official statistics portal. |
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10.6.4. Availability of national handbook on methodology | |||
No | |||
10.6.5. Title, publisher, year and link to handbook | |||
Not applicable. |
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10.6.6. Availability of national methodological papers | |||
No | |||
10.6.7. Title, publisher, year and link to methodological papers | |||
Not applicable. |
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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. |
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11.1. Quality assurance | |||
See sub-categories below. |
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11.1.1. Quality management system | |||
Yes | |||
11.1.2. Quality assurance and assessment procedures | |||
Training courses Use of best practices Quality guidelines Designated quality manager, quality unit and/or senior level committee Compliance monitoring Peer review External review or audit Certification |
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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. |
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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. |
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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. |
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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. |
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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. |
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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. |
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12.1.3. Plans for satisfying unmet user needs | |||
Not applicable. |
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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. |
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12.2.1. User satisfaction survey | |||
No | |||
12.2.2. Year of user satisfaction survey | |||
Not applicable. |
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12.2.3. Satisfaction level | |||
Not applicable | |||
12.3. Completeness | |||
Information on not collected, not-significant and not-existent variables is available on Eurostat's website. |
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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. |
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13.1. Accuracy - overall | |||
See categories below. |
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13.2. Sampling error | |||
See sub-categories below. |
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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 |
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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. |
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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 |
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13.2.4. Impact of sampling error on data quality | |||
Low | |||
13.3. Non-sampling error | |||
See sub-categories below. |
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13.3.1. Coverage error | |||
See sub-categories below. |
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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 |
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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 period Temporarily out of production during the reference period Ceased activities Merged to another unit |
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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 units Maintain of ineligible units in the records, recalculating weights of all units by considering the corrected population |
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13.3.1.1.3. Additional information over-coverage error | |||
Not available. |
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13.3.1.2. Common units - proportion | |||
Not requested. |
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13.3.1.3. Under-coverage error | |||
See sub-categories below. |
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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. |
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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 births | |||
13.3.1.3.3. 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. |
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13.3.1.3.4. Additional information under-coverage error | |||
Not available. |
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13.3.1.4. Misclassification error | |||
Yes | |||
13.3.1.4.1. Actions to minimise the misclassification error | |||
The misclassification error was minimized through the detection of outliers. |
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13.3.1.5. Contact error | |||
Yes | |||
13.3.1.5.1. 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. |
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13.3.1.6. Impact of coverage error on data quality | |||
Low | |||
13.3.2. Measurement error | |||
See sub-categories below. |
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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 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. |
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13.3.2.2. Causes of measurement errors | |||
Complexity of variables Sensitivity of variables Unclear questions Insufficient preparation of interviewers |
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13.3.2.3. Actions to minimise the measurement error | |||
Pre-filled questions Explanatory notes or handbooks for enumerators or respondents On-line FAQ or Hot-line support for enumerators or respondents Training of enumerators |
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13.3.2.4. Impact of measurement error on data quality | |||
Unknown | |||
13.3.2.5. Additional information measurement error | |||
Not available. |
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13.3.3. Non response error | |||
See sub-categories below. |
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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. |
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13.3.3.1.1. Reasons for unit non-response | |||
Failure to make contact with the unit Refusal to participate |
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13.3.3.1.2. Actions to minimise or address unit non-response | |||
Follow-up interviews Reminders Legal actions Imputation |
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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:
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. |
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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. |
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13.3.3.2.1. Variables with the highest item non-response rate | |||
Most common missing items:
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13.3.3.2.2. Reasons for item non-response | |||
Interview interruption Refusal Skip of due question Other |
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13.3.3.2.3. Actions to minimise or address item non-response | |||
Follow-up interviews Reminders Imputation |
|||
13.3.3.3. Impact of non-response error on data quality | |||
Low | |||
13.3.3.4. 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 methods Data processing |
|||
13.3.4.2. Imputation methods | |||
Previous data for the same unit Other |
|||
13.3.4.3. Actions to correct or minimise processing errors | |||
Data imputation was performed for partially completed questionnaires. The key imputed indicators include:
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 | |||
Low | |||
13.3.4.6. 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 | |||||||||||||||
|
|||||||||||||||
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. 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 exist | |||||||||||||||
15.1.6.1. Collection of common land data | |||||||||||||||
Not applicable | |||||||||||||||
15.1.6.2. Reasons if common land exists and data are not collected | |||||||||||||||
Not applicable. |
|||||||||||||||
15.1.6.3. Methods to record data on common land | |||||||||||||||
Not applicable | |||||||||||||||
15.1.6.4. Source of collected data on common land | |||||||||||||||
Not applicable | |||||||||||||||
15.1.6.5. 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 series | |||||||||||||||
15.2.2.2. 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 changes | |||||||||||||||
15.2.3.2. 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 changes | |||||||||||||||
15.2.4.2. 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 series | |||||||||||||||
15.2.5.2. 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 series | |||||||||||||||
15.2.6.2. 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 changes | |||||||||||||||
15.2.7.2. 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 | |||||||||||||||
No | |||||||||||||||
15.3. 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 | |||||||||||||||
Yes | |||||||||||||||
15.3.3.2. 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 | |||||||||||||||
Yes | |||||||||||||||
15.3.4.2. 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 surveys Further 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. |
|
|||
17.1. 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 frame | |||
18.1.1.2. Name of frame | |||
Statistical farm register (SFR) |
|||
18.1.1.3. Update frequency | |||
Continuous | |||
18.1.2. Core data collection on the main frame | |||
See sub-categories below. |
|||
18.1.2.1. Coverage of agricultural holdings | |||
Census | |||
18.1.2.2. Sampling design | |||
Not applicable for 2019/2020. |
|||
18.1.2.2.1. Name of sampling design | |||
Not applicable | |||
18.1.2.2.2. Stratification criteria | |||
Not applicable | |||
18.1.2.2.3. Use of systematic sampling | |||
Not applicable | |||
18.1.2.2.4. 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 applicable | |||
18.1.3. Core data collection on the frame extension | |||
See sub-categories below. |
|||
18.1.3.1. Coverage of agricultural holdings | |||
Sample | |||
18.1.3.2. Sampling design | |||
The sample includes economically active holdings with the following characteristics:
Sample stratification was performed:
|
|||
18.1.3.2.1. Name of sampling design | |||
Stratified one-stage random sampling | |||
18.1.3.2.2. Stratification criteria | |||
Unit size Unit location |
|||
18.1.3.2.3. Use of systematic sampling | |||
No | |||
18.1.3.2.4. 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. |
|||
18.1.3.2.6. Method of allocation of the overall sample size | |||
Neymann allocation | |||
18.1.4. Module “Labour force and other gainful activities” | |||
See sub-categories below. |
|||
18.1.4.1. Coverage of agricultural holdings | |||
Census | |||
18.1.4.2. Sampling design | |||
Not applicable. |
|||
18.1.4.2.1. Name of sampling design | |||
Not applicable | |||
18.1.4.2.2. Stratification criteria | |||
Not applicable | |||
18.1.4.2.3. Use of systematic sampling | |||
Not applicable | |||
18.1.4.2.4. 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 applicable | |||
18.1.4.2.7. If sampled from the core sample, the sampling and calibration strategy | |||
Not applicable | |||
18.1.5. Module “Rural development” | |||
See sub-categories below. |
|||
18.1.5.1. Coverage of agricultural holdings | |||
Census | |||
18.1.5.2. Sampling design | |||
Not applicable. |
|||
18.1.5.2.1. Name of sampling design | |||
Not applicable | |||
18.1.5.2.2. Stratification criteria | |||
Not applicable | |||
18.1.5.2.3. Use of systematic sampling | |||
Not applicable | |||
18.1.5.2.4. 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 applicable | |||
18.1.5.2.7. If sampled from the core sample, the sampling strategy and calibration strategy | |||
Not applicable | |||
18.1.6. Module “Animal housing and manure management module” | |||
See sub-categories below. |
|||
18.1.6.1. Coverage of agricultural holdings | |||
Census | |||
18.1.6.2. Sampling design | |||
Not applicable. |
|||
18.1.6.2.1. Name of sampling design | |||
Not applicable | |||
18.1.6.2.2. Stratification criteria | |||
Not applicable | |||
18.1.6.2.3. Use of systematic sampling | |||
Not applicable | |||
18.1.6.2.4. 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 applicable | |||
18.1.6.2.7. If sampled from the core sample, the sampling strategy and calibration strategy | |||
Not applicable | |||
18.1.12. 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 source The 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 version Postal, electronic version (email) Face-to-face, electronic version Telephone, electronic version Use of Internet |
|||
18.3.2. Data entry method, if paper questionnaires | |||
Not applicable | |||
18.3.3. 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 checks Completeness 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 | |||
Interviewers Supervisors 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 weight Non-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. |
|
|||
|
|||