1.1. Contact organisation
Danmarks Statistik
1.2. Contact organisation unit
Division: Food industries / Section: Structure, Production and Organic production and trade
1.3. Contact name
Confidential because of GDPR
1.4. Contact person function
Confidential because of GDPR
1.5. Contact mail address
Sankt Kjelds Plads 13
2100 København Ø
Danmark
1.6. Contact email address
Confidential because of GDPR
1.7. Contact phone number
Confidential because of GDPR
1.8. Contact fax number
Confidential because of GDPR
2.1. Metadata last certified
2 March 2026
2.2. Metadata last posted
2 March 2026
2.3. Metadata last update
2 March 2026
3.1. Data description
The data describes the structure of agricultural holdings providing the general characteristics of farms and farmers and information on their land, livestock, labour force, irrigation and farm technology. It also describes rural development measures and agro-environmental aspects that look at the impact of agriculture on the environment.
The data is used by ministries, researchers, farmers, organisations, EU, and policy-makers to better understand the state of the farming sector and the impact of agriculture on the environment. The data follows up the changes in the agricultural sector and provides 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 is presented at different geographical levels and over periods.
The data collections are organised in line with Regulation (EU) 2018/1091 and have a new structure, consisting of a core data set and several modules. The regulation covers the data collections in 2019/2020 (the agricultural census), 2023 and 2026. The data is as comparable and coherent as possible with the other European countries.
3.2. Classification system
Data is arranged in tables using many classifications. Please find below information on most classifications.
The classifications of variables are available in Annex III of Regulation (EU) 2018/1091 and in Commission Implementing Regulation (EU) 2021/2286.
The farm typology means a uniform classification of the holdings based on their type of farming and their economic size. Both are determined on the basis of the standard gross margin (SGM) (until 2007) or standard output (SO) (from 2010 onward) which is calculated for each crop and animal. The farm type is determined by the relative contribution of the different productions to the total standard gross margin or the standard output of the holding.
The territorial classification uses the NUTS classification to break down the regional data. The regional data is available at NUTS level 2.
3.3. Coverage - sector
The statistics cover agricultural holdings undertaking agricultural activities as listed in item 3.5 below and meeting the minimum coverage requirements (thresholds) as listed in item 3.6 below.
3.4. Statistical concepts and definitions
The list of core variables is set in Annex III of Regulation (EU) 2018/1091.
The descriptions of the core variables as well as the lists and descriptions of the variables for the modules collected in 2023 are set in Commission Implementing Regulation (EU) 2021/2286.
The following groups of variables are collected in 2023:
- for core: location of the holding, legal personality of the holding, manager, type of tenure of the utilised agricultural area, variables of land, organic farming, irrigation on cultivated outdoor area, variables of livestock, organic production methods applied to animal production;
- for the module "Labour force and other gainful activities": farm management, family labour force, non-family labour force, other gainful activities directly and not directly related to the agricultural holding;
- for the module "Rural development": support received by agricultural holdings through various rural development measures;
- for the module “Irrigation”: availability of irrigation, irrigation methods, sources of irrigation water, technical parameters of the irrigation equipment, crops irrigated during a 12 months period;
- for the module “Soil management practices”: tillage methods, soil cover on arable land, crop rotation on arable land, ecological focus area;
- for the module “Machinery and equipment”: internet facilities, basic machinery, use of precision farming, machinery for livestock management, storage for agricultural products, equipment used for production of renewable energy on agricultural holdings;
- for the module “Orchards”: apples area, pears area, each one by age of plantation and density of trees.
3.5. Statistical unit
See sub-category below.
3.5.1. Definition of agricultural holding
The agricultural holding is a single unit, both technically and economically, that has a single management and that undertakes economic activities in agriculture in accordance with Regulation (EC) No 1893/2006 belonging to groups:
- A.01.1: Growing of non-perennial crops
- A.01.2: Growing of perennial crops
- A.01.3: Plant propagation
- A.01.4: Animal production
- A.01.5: Mixed farming or
- The “maintenance of agricultural land in good agricultural and environmental condition” of group A.01.6 within the economic territory of the Union, either as its primary or secondary activity.
Regarding activities of class A.01.49, only the activities “Raising and breeding of semi-domesticated or other live animals” (with the exception of raising of insects) and “Bee-keeping and production of honey and beeswax” are included.
3.6. Statistical population
See sub-categories below.
3.6.1. Population covered by the core data sent to Eurostat (main frame and if applicable frame extension)
The thresholds of agricultural holdings are available in the annex.
Annexes:
3.6.1. Thresholds of agricultural holdings
3.6.1.1. Raised thresholds compared to Regulation (EU) 2018/1091
No3.6.1.2. Lowered and/or additional thresholds compared to Regulation (EU) 2018/1091
No3.6.2. Population covered by the data sent to Eurostat for the modules “Labour force and other gainful activities”, “Rural development” and “Machinery and equipment”
The same population of agricultural holdings defined in item 3.6.1.
3.6.3. Population covered by the data sent to Eurostat for the module “Animal housing and manure management”
Restricted from publication
3.6.4. Population covered by the data sent to Eurostat for the module “Irrigation”
The subset of agricultural holdings defined in item 3.6.2 with irrigable area.
3.6.5. Population covered by the data sent to Eurostat for the module “Soil management practices”
The module “Soil management practices” consists of all farms in the sample, also farms with no arable land and even farms with no utilised agricultural area. Such farms could for instance have greenhouse area but no crops on free land.
3.6.6. Population covered by the data sent to Eurostat for the module “Orchard”
The subset of agricultural holdings defined in item 3.6.2, with any of the individual orchard variables that meet the threshold specified in Article 7(5) of Regulation (EU) 2018/1091. Collecting apples is mandatory in Denmark but we also collect pears even if the area is smaller than 1 000 ha.
3.6.7. Population covered by the data sent to Eurostat for the module “Vineyard”
Restricted from publication
3.7. Reference area
See sub-categories below.
3.7.1. Geographical area covered
The entire territory of the country.
3.7.2. Inclusion of special territories
The figures do not include Greenland and Faroe Islands.
3.7.3. Criteria used to establish the geographical location of the holding
The majority of the area of the holdingThe residence of the farmer (manager) not further than 5 km straight from the farm
3.7.4. Additional information reference area
The most detailed geographical location of the farm is the municipality. Normally, figures for municipalities are not published. Statistics Denmark publishes agricultural figures at the municipal level only during total agricultural census years. For other years, figures are published at the NUTS 3 level.
3.8. Coverage - Time
Farm structure statistics in Denmark cover the period from 1982 onwards with almost complete comparability. Older time series are described in the previous quality reports (national methodological reports).
3.9. Base period
The 2023 data are processed (by Eurostat) with 2020 standard output coefficients (calculated as a 5-year average of the period 2018-2022). For more information, you can consult the definition of the standard output.
Two kinds of units are generally used:
- the units of measurement for the variables (area in hectares, livestock in (1000) heads or LSU (livestock units), labour force in persons or AWU (annual working units), standard output in Euro, places for animal housing etc.) and
- the number of agricultural holdings having these characteristics.
See sub-categories below.
5.1. Reference period for land variables
For farms applying for crops subsidies, which constitute the vast majority, crops on free land reflect what the farms have reported to the Ministry of Agriculture in April 2023.
The deadline for applying for crop subsidies in 2023 was 21 April. Thus, the 12-month period is 22 April 2022 - 21 April 2023.
For forestry and greenhouse areas, the reference day 14 June 2023 applies to all farms.
5.2. Reference period for variables on irrigation and soil management practices
For variables on irrigation and soil management practices, the 12-month period prior to the reference day 14 June 2023 applies.
5.3. Reference day for variables on livestock and animal housing
For cattle variables, Statistics Denmark receives the livestock register on 1 June 2023. For all other livestock variables, the reference day 14 June 2023 applies. Animal housing characteristics are not applicable for 2023.
5.4. Reference period for variables on manure management
The manure management variables are not applicable for 2023.
5.5. Reference period for variables on labour force
The 12-month period prior to the reference day 14 June 2023.
5.6. Reference period for variables on rural development measures
The years 2021, 2022 and 2023.
5.7. Reference day for all other variables
The reference day 14 June 2023.
6.1. Institutional Mandate - legal acts and other agreements
See sub-categories below.
6.1.1. National legal acts and other agreements
Legal act6.1.2. Name of national legal acts and other agreements
Lov om Danmarks Statistik (LBK nr 610 af 30/05/2018) - Act on Statistics Denmark (Act no. 610 of 30 May 2018).
6.1.3. Link to national legal acts and other agreements
Act on Statistics Denmark (in Danish)
Act on Statistics Denmark (in English - unofficial translation)
6.1.4. Year of entry into force of national legal acts and other agreements
2018
6.1.5. Legal obligations for respondents
Yes6.2. Institutional Mandate - data sharing
Statistics Denmark conducted all work for IFS 2023. The work was divided among the agricultural statistics division, a special data collection division and our methodological division.
- Agricultural statistics division: Designing of the questionnaire, data validation, telephone contact with the farmer, integrating the survey data with administrative sources, publishing of survey results, delivering of data and metadata to Eurostat.
- Data collection division: Sending of letters to farmers (including reminders), design of electronic version of the questionnaire.
- Methodological division: Selecting the sample, calculation of extrapolation factors.
There are no data exchange arrangements with other institutions.
7.1. Confidentiality - policy
Individual information from surveys is treated as strictly confidential. This applies to IFS data as well as data from all other surveys.
In practice, this means that only a limited number of colleagues at Statistics Denmark have access to the IFS survey registers.
Such a practice is in line with principle 5 of the European Statistics Code of Practice on statistical confidentiality.
See more at this website about our policy on confidentiality.
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)Other primary confidentiality rules
7.2.1.2. Methods to protect data in confidential cells
Cell suppression (Completely suppress the value of some cells)7.2.1.3. Description of rules and methods
All datasets regarding IFS are stored at a special library on Statistics Denmark’s computer network as SAS data sets going back to 1982. Only authorised colleagues can access the individual farm information. The surveys are delivered to the Danish National Archive, which keeps the information as strictly confidential for 80 years.
When designing statistical tables, the aim is to ensure that no table cells contain very few farms. There are no strict rules regarding "very few farms" but all cells with less than five farms are marked as missing.
7.2.2. Microdata
See sub-categories below.
7.2.2.1. Use of EU methodology for microdata dissemination
Yes7.2.2.2. Methods of perturbation
Recoding of variablesRemoval of variables
Reduction of information
Merging categories
Rounding
Micro-aggregation
7.2.2.3. Description of methodology
Researchers can obtain access to the surveys but only as anonymous information. If a researcher publishes statistical tables based on the individual information, it must take place in agreement with Statistics Denmark. It is not very common that researchers request microdata from IFS.
Read more at this website.
8.1. Release calendar
IFS data is normally published 9-12 months after the reference day. IFS 2023 data was published on 4 June 2024 with the reference day 14 June 2023. For all statistics, the release calendar is available at this website.
8.2. Release calendar access
For all tables published online, users can see when the next update is expected to take place, see for instance here: BDF11: Farms by region, unit, type of farms and area - table information.
Users also have access to a general release calendar.
An online table may or may not be accompanied by a newsletter.
8.3. Release policy - user access
There is no specific release policy in Statistics Denmark. Users can read about our publications at this website.
User can read about Statistics Denmark's dissemination policy at this website.
8.3.1. Use of quality rating system
No8.3.1.1. Description of the quality rating system
Not applicable.
The farm statistics are published yearly with statistical tables on number of farms by size and geography as well as statistical tables on crops and livestock.
Labour force is not included in the questionnaire every year. In recent years, Statistics Denmark has published agricultural labour force tables for the years 2013, 2016, 2018, 2020, and 2023.
10.1. Dissemination format - News release
See sub-categories below.
10.1.1. Publication of news releases
Yes10.1.2. Link to news releases
Conservation tillage is on the rise (available in Danish only)
This newsletter focuses on the development of conservation tillage for the period 2016-2023.
10.2. Dissemination format - Publications
See sub-categories below.
10.2.1. Production of paper publications
Yes, but not in English10.2.2. Production of on-line publications
Yes, but not in English10.2.3. Title, publisher, year and link
Statistical Ten-Year Review 2024
10.3. Dissemination format - online database
See sub-categories below.
10.3.1. Data tables - consultations
We do not monitor and record the number of consultations of data tables in the field of farm structure.
10.3.2. Accessibility of online database
Yes10.3.3. Link to online database
- Farms and agricultural and horticultural labour tables
- Find information about our online statistical tables at this website.
10.4. Dissemination format - microdata access
See sub-category below.
10.4.1. Accessibility of microdata
Yes10.5. Dissemination format - other
Not available.
10.5.1. Metadata - consultations
Not requested.
10.6. Documentation on methodology
See sub-categories below.
10.6.1. Metadata completeness - rate
Not requested.
10.6.2. Availability of national reference metadata
Yes10.6.3. Title, publisher, year and link to national reference metadata
Documentation of statistics: Farm Structure Survey (in Danish)
Documentation of statistics: Farm Structure Survey (in English)
This documentation on farm structure statistics was published by Statistics Denmark on 4 June 2024.
10.6.4. Availability of national handbook on methodology
No10.6.5. Title, publisher, year and link to handbook
Not applicable.
10.6.6. Availability of national methodological papers
No10.6.7. Title, publisher, year and link to methodological papers
Not applicable.
10.7. Quality management - documentation
The quality of the IFS data is described in our national system of quality reports with text in both Danish and English:
- Documentation of statistics: Farm Structure Survey (in Danish)
- Documentation of statistics: Farm Structure Survey (in English)
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
Self-assessment
11.1.3. Description of the quality management system and procedures
The quality policy of Statistics Denmark can be seen at this website.
It is based on the 16 principles in the European statistics Code of Practice:
- Professional independence
1bis Coordination and cooperation - Mandate for data collection and access to data
- Adequacy of resources
- Commitment to quality
- Statistical confidentiality and data protection
- Impartiality and objectivity
- Sound Methodology
- Appropriate statistical procedures
- Non-excessive burden on respondents
- Cost-effectiveness
- Relevance
- Accuracy and reliability
- Timeliness and punctuality
- Coherence and comparability
- Accessibility and clarity
11.1.4. Improvements in quality procedures
The procedures of IFS 2020 and 2023 took place in the same frame with an online questionnaire and a validation system where the online answers were transferred. An improvement in 2023 compared to 2020 was a better validation system, meaning that data is now stored and validated in a more standardised manner, making it easier for IT staff to correct errors and implement improvements.
11.2. Quality management - assessment
The precision of the statistics varies for different items. The precision is thus highest for the total agricultural area and less precise for specific crops, especially crops grown by only few farmers. Likewise, precision is highest for livestock that many farmers have. This is in particular true for cattle. Similarly, the uncertainty is higher for small geographical units, such as Bornholm.
Overall accuracy
Coverage: The population includes all active farms in Denmark and is integrated in the Statistical Business Register (ESR), which is kept by Statistics Denmark. In order to ensure that the population is up to date Statistics Denmark regularly makes register merges with IACS and the Central Livestock Register (CHR). The assumption is that if a farm applies for crop subsidies or reports livestock to the livestock register it must be expected to be active in agriculture and should accordingly be marked as such in the register of Statistics Denmark. The sample is selected so that the lowest possible sample error is obtained with respect to agricultural area, pigs, cattle, fur animals and standard output. The farms are divided into groups - strata- by typology and size of standard output. The 2023 survey had 68 strata. Farms known to be specialised horticultural or poultry farms are selected exhaustively if possible. As a general rule the bigger a farm is the more likely it is to be selected.
Information on crops is selected from IACS kept by the Ministry of Agriculture. When a farmer applies for subsidies, he has to specify his crops carefully. IACS must therefore be assumed to be an extremely reliable source. Information on cattle is collected from the Central Livestock Register and fur animals are collected from the Association of Danish Fur animals farmers. For both these types of livestock the farmer answers yes/no, and for farmers having answered yes, the number of animals is taken from respectively the Central Livestock Register and the Association of Danish Fur animals farmers.
Control: Several computer validations and checks are made before publishing the results.
12.1. Relevance - User Needs
The farm statistics fulfil a general need for structural statistics on the Danish agriculture where the business is described by size, geography, type of farming and other aspects.
However, agricultural statistics are more than just business statistics. It is also environmental statistics, and the farm structure statistics provide also the users with number of animals and land use in agriculture.
The users are in particular the European Commission, the FAO, national ministries, and farmers' organisations, as well as students and the general public. The European Commission uses these statistics as a tool for planning the Common Agricultural Policy.
12.1.1. Main groups of variables collected only for national purposes
In IFS 2023 data collection, certain questions were asked that were not required by the Regulation (EU) 2018/1091:
- more detailed questions on precision technology than requested by the Regulation,
- areas and production of berries (to meet other EU demands),
- production of apples and pears (to meet other EU demands).
12.1.2. Unmet user needs
Many users are interested in figures by municipalities. This need, however, can only be met for years where Statistics Denmark has carried through total censuses. For sample surveys reliable figures by municipalities cannot be made. The most recent total censuses took place in 2010 and 2020.
12.1.3. Plans for satisfying unmet user needs
No such plans exist so far.
12.2. Relevance - User Satisfaction
The main impression is that most users are satisfied with the statistics but often they have wishes about more detailed regional figures with figures for municipalities and also more agro-environmental statistics.
There is no user board for agricultural statistics, nor has there ever been conducted any survey on user satisfaction.
12.2.1. User satisfaction survey
No12.2.2. Year of user satisfaction survey
Not applicable.
12.2.3. Satisfaction level
Not applicable12.3. Completeness
Information on not collected, not-significant and not-existent variables is available on Eurostat’s website, at the link: Additional data - Eurostat (europa.eu).
12.3.1. Data completeness - rate
Not applicable for Integrated Farm Statistics as the not collected variables, not-significant variables and not-existent variables are completed with 0.
13.1. Accuracy - overall
See categories below.
13.2. Sampling error
See sub-categories below.
13.2.1. Sampling error - indicators
Please find the relative standard errors on Eurostat’s website, at the link: CircaBC website.
13.2.2. Reasons for non-compliant precision requirements in relation to Regulation (EU) 2018/1091
There are no cases where estimated RSEs are above thresholds.
13.2.3. Reference on method of estimation
We used Eurostat's variance estimation method for the computation of the relative standard errors. The method is based on the ultimate cluster approximation. It accounts for the sampling design and for the presence of unequal weights within strata, however it does not account for the effect of calibration residuals on the estimated variance. For the description of the method, see the IFS 2023 Handbook, chapter “4.Data processing”, sub-chapter “4.6. Calculation of weights, variance estimation and quality rating system”, section "TOTALS OF CONTINUOUS VARIABLES", sub-section "Variance estimation in IFS".
13.2.4. Impact of sampling error on data quality
Moderate13.3. Non-sampling error
See sub-categories below.
13.3.1. Coverage error
See sub-categories below.
13.3.1.1. Over-coverage - rate
The over-coverage rate is available on Eurostat’s website, at the link: CircaBC.
The over-coverage rate is unweighted.
The over-coverage rate is calculated as the share of ineligible holdings to the holdings designated for the core data collection. The ineligible holdings include those holdings with unknown eligibility status that are not imputed nor re-weighted for (therefore considered ineligible).
The over-coverage rate is calculated over the holdings in the main frame and if applicable frame extension, for which core data are sent to Eurostat.
13.3.1.1.1. Types of holdings included in the frame but not belonging to the population of the core (main frame and if applicable frame extension)
Below thresholds during the reference periodCeased activities
13.3.1.1.2. Actions to minimize the over-coverage error
Maintain of ineligible units in the records, recalculating weights of all units by considering the corrected population13.3.1.1.3. Additional information over-coverage error
Statistics Denmark currently works to keep its farm register up to date. This is done, for example, by comparing the current IACS edition with the previous years. Farms that applied for crop subsidies in year N-1 but not in year N (reference year of the data collection) are typically removed from the farm register, unless they are also found in the livestock register.
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
The degree of under-coverage is not known. We have never carried out any analysis of under-coverage nor has it ever been requested by users.
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)
None13.3.1.3.3. Actions to minimise the under-coverage error
None
13.3.1.3.4. Additional information under-coverage error
Not available.
13.3.1.4. Misclassification error
No13.3.1.4.1. Actions to minimise the misclassification error
Not applicable.
13.3.1.5. Contact error
No13.3.1.5.1. Actions to minimise the contact error
Not applicable.
13.3.1.6. Impact of coverage error on data quality
None13.3.2. Measurement error
See sub-categories below.
13.3.2.1. List of variables mostly affected by measurement errors
There is no information on measurement errors.
13.3.2.2. Causes of measurement errors
Not applicable13.3.2.3. Actions to minimise the measurement error
None13.3.2.4. Impact of measurement error on data quality
Unknown13.3.2.5. Additional information measurement error
Statistics Denmark has no knowledge of the size of measurement errors in IFS 2023, nor do we know whether certain items are more subject to measurement errors than others.
13.3.3. Non response error
See sub-categories below.
13.3.3.1. Unit non-response - rate
See item 13.3.1.1.
The unit non-response rate is unweighted.
The unit non-response rate is calculated as the share of eligible non-respondent holdings to the eligible holdings. The eligible holdings include those holdings with unknown eligibility status which are imputed or re-weighted for (therefore considered eligible).
The unit non-response rate is calculated over the holdings in the main frame and if applicable frame extension, for which core data are sent to Eurostat.
13.3.3.1.1. Reasons for unit non-response
Failure to make contact with the unitRefusal to participate
13.3.3.1.2. Actions to minimise or address unit non-response
RemindersWeighting
13.3.3.1.3. Unit non-response analysis
Unlike IFS 2020, the IFS 2023 was a sample survey, which simplified the process significantly. In a total census like IFS 2020, non-responding units need to be imputed so that we end up with a register of all farms in the population with complete answers for all characteristics.
In a sample survey, it is different, since we can treat non-response units as if they had never been selected by adjusting extrapolation factors in the relevant strata. In the Danish IFS 2023, there were 504 farms out of 14 000 which failed to complete the questionnaire.
13.3.3.2. Item non-response - rate
Farmers typically complete all sections of the questionnaire. Online, the system prevents farmers from proceeding to the next section without answering all questions. During telephone interviews, interviewers ask all questions. The only exception is the few cases – 217 in 2023 – where farmers receive a paper questionnaire by post. In such cases incomplete parts of the questionnaire might lead to telephone contact with the farmer or 'qualified guesses'.
It should be noted that paper questionnaires sent by post are not part of the standard solution. They are reserved for farmers, typically elderly individuals, who have been exempt from digital requirements. Whether incomplete answers should lead to telephone contact depends on the situation, as no strict rules exist.
13.3.3.2.1. Variables with the highest item non-response rate
Item non-response rate could affect all parts of the questionnaire. However, in the Danish IFS 2023, there was only one instance of item non-response: about 15% (unweighted) of the farms with irrigation did not answer the question on water volume for irrigation. For these farms, the item was imputed.
13.3.3.2.2. Reasons for item non-response
Farmers do not know the answer13.3.3.2.3. Actions to minimise or address item non-response
Imputation13.3.3.3. Impact of non-response error on data quality
Moderate13.3.3.4. Additional information non-response error
Not available.
13.3.4. Processing error
See sub-categories below.
13.3.4.1. Sources of processing errors
Data processing13.3.4.2. Imputation methods
None13.3.4.3. Actions to correct or minimise processing errors
We emphasise carefulness and encourage colleagues to do the same. At Statistics Denmark, we acknowledge that mistakes happen. When they do, we prioritise correction over placing blame.
13.3.4.4. Tools and staff authorised to make corrections
All staff at Statistics Denmark are authorised to correct mistakes within their areas of responsibility, using standard tools like Excel and SAS, when necessary.
13.3.4.5. Impact of processing error on data quality
Unknown13.3.4.6. Additional information processing error
Not available.
13.3.5. Model assumption error
Not applicable, no model assumptions have been used.
14.1. Timeliness
See sub-categories below.
14.1.1. Time lag - first result
There is no provisional publication.
14.1.2. Time lag - final result
Data was published on 4 June 2024, i.e. 5 months after the end of the reference year.
14.2. Punctuality
See sub-categories below.
14.2.1. Punctuality - delivery and publication
See sub-categories below.
14.2.1.1. Punctuality - delivery
Not requested.
14.2.1.2. Punctuality - publication
In time.
15.1. Comparability - geographical
See sub-categories below.
15.1.1. Asymmetry for mirror flow statistics - coefficient
Not applicable, because there are no mirror flows in Integrated Farm Statistics.
15.1.2. Definition of agricultural holding
See sub-categories below.
15.1.2.1. Deviations from Regulation (EU) 2018/1091
No such deviations exist. The definitions are in line with Regulation (EU) 2018/1091.
15.1.2.2. Reasons for deviations
Not applicable.
15.1.3. Thresholds of agricultural holdings
See sub-categories below.
15.1.3.1. Proofs that the EU coverage requirements are met
The population contains all farms fulfilling at least one of the physical thresholds listed in Annex II of Regulation (EU) 2018/1091. See more information in annex.
Annexes:
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 farms meeting at least one of thresholds 1-18 presented in the annex of the item 3.6.1 are submitted to Eurostat. These farms were covered by census in 2020 and by sample in 2023. However, only farms meeting at least one of thresholds 1-12 (presented in the annex of the item 3.6.1) are included in the national statistical release.
15.1.3.3. Reasons for differences
The introduction of new FSS thresholds in 2010 meant a slight break in comparability with respect to the 1982-2009 figures, also because we - for national reasons - included farms with mink for the first time. A total of 1 162 farms were included in 2010 which would not have been included with thresholds applied till 2009 (a utilised agricultural area of at least 5.0 ha or a standard gross margin of at least 5 000 euros). But only 431 farms were due to the new EU thresholds, the remaining 731 farms were due to the inclusion of farms with mink as the only farm activity. Since 2010, we have maintained the same threshold in our national publication despite the introduction of new thresholds for IFS 2020, 2023 and 2026. In this way, we have kept comparability.
Small farms which are omitted from our national publication but delivered to Eurostat amount to 3 863 in 2020 and 1 853 in 2023 (extrapolated).
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
No such deviations exist. The definitions are in line with the EU handbook, Regulation (EU) 2018/1091 and Commission Implementing Regulation (EU) 2021/2286.
15.1.4.1.1. The number of working hours and days in a year corresponding to a full-time job
The information is available on Eurostat’s website, at the link: CircaBC.
The number of working hours and days in a year for a full-time job correspond to one annual working unit (AWU) in the country. One annual work unit corresponds to the work performed by one person who is occupied on an agricultural holding on a full-time basis. Annual working units are used to calculate the farm work on the agricultural holdings.
15.1.4.1.2. Point chosen in the Annual work unit (AWU) percentage band to calculate the AWU of holders, managers, family and non-family regular workers
See item 15.1.4.1.1.
15.1.4.1.3. AWU for workers of certain age groups
See item 15.1.4.1.1.
15.1.4.1.4. Livestock coefficients
The livestock unit coefficients are the same as laid down in the 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 Denmark includes under the heading “Other livestock n.e.c.” and the types of livestock that should be included according to the EU handbook.
15.1.4.2. Reasons for deviations
Not applicable.
15.1.5. Reference periods/days
See sub-categories below.
15.1.5.1. Deviations from Regulation (EU) 2018/1091
The reference periods/days are in line with Regulation (EU) 2018/1091.
15.1.5.2. Reasons for deviations
Not applicable.
15.1.6. Common land
The concept of common land does not exist15.1.6.1. Collection of common land data
Not applicable15.1.6.2. Reasons if common land exists and data are not collected
Not applicable.
15.1.6.3. Methods to record data on common land
Not applicable15.1.6.4. Source of collected data on common land
Not applicable15.1.6.5. Description of methods to record data on common land
Not applicable.
15.1.6.6. Possible problems in relation to the collection of data on common land and proposals for future data collections
Not applicable.
15.1.7. National standards and rules for certification of organic products
See sub-categories below.
15.1.7.1. Deviations from Council Regulation (EC) No 834/2007
The organic farm certification is administrated by the Ministry of Agriculture. It is in line with 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
Data for 2023 was collected uniformly across the country.
15.2. Comparability - over time
See sub-categories below.
15.2.1. Length of comparable time series
2 years.
15.2.2. Definition of agricultural holding
See sub-categories below.
15.2.2.1. Changes since the last data transmission to Eurostat
There have been no changes15.2.2.2. Description of changes
There are no changes as both 2020 and 2023 are data collection years covered by the same Regulation (EU) 2018/1091.
15.2.3. Thresholds of agricultural holdings
See sub-categories below.
15.2.3.1. Changes in the thresholds of holdings for which core data are sent to Eurostat since the last data transmission
There have been no changes15.2.3.2. Description of changes
Not applicable.
15.2.4. Geographical coverage
See sub-categories below.
15.2.4.1. Change in the geographical coverage since the last data transmission to Eurostat
There have been no changes15.2.4.2. Description of changes
Not applicable.
15.2.5. Definitions and classifications of variables
See sub-categories below.
15.2.5.1. Changes since the last data transmission to Eurostat
There have been no changes15.2.5.2. Description of changes
There are no changes as both 2020 and 2023 are data collection years covered by the same Regulation (EU) 2018/1091.
15.2.6. Reference periods/days
See sub-categories below.
15.2.6.1. Changes since the last data transmission to Eurostat
There have been some changes but not enough to warrant the designation of a break in series15.2.6.2. Description of changes
There are slight modifications in the reference periods/dates for land variables, irrigation, livestock variables other than cattle, labour force and “all other variables” between 2020 and 2023.
15.2.7. Common land
See sub-categories below.
15.2.7.1. Changes in the methods to record common land since the last data transmission to Eurostat
There have been no changes15.2.7.2. Description of changes
Not applicable.
15.2.8. Explanations for major trends of main variables compared to the last data transmission to Eurostat
The total number of farms in Denmark decreased from 2020 to 2023 but this trend was not uniform if observed by the legal form breakdown of the holdings. In fact, FARM_SPOU (manager is spouse of holder) holdings and HLD_GRP (holding group) are sharply increasing, on the contrary, the FARM_HLD (holding is a single manager) and FARM_NFAM (manager is not a member of holder’s family) are remarkably decreasing.
The impact of the reduction of farms over time also shows that there is an overall tendency in reduction of smaller farms and consequent increase of share of larger farms in terms of UAA size and standard output.
By observing the evolution over time of holdings by farm type, FT15 (specialist cereals, oilseeds and protein crops) has increased its share in 2023 compared to 2020.
The LEG_FORM (legal personality of the holding) is not directly a part of the data collection but is derived from information from the business register and from labour force section of the questionnaire.
The evolution of holdings by age manager shows a robust increase of the manager age class less than 25 years old. The age is collected from registers except in the cases where the holder is not the manager, and it could also be influenced by sample error as the non-response rate differs from different age groups.
The evolution over time of IFS variables shows that:
- F3000T (Berries (excluding strawberries) - outdoor): Berries are in general in decline in Denmark.
- F4000T (Nuts - outdoor): Nuts have become more popular in the recent years, that is why their area increased remarkably but it is still a small crop. It is collected from IACS.
- A2300G (Non dairy cows): There are fewer suckler cows in Denmark primarily due to pressure on land and high fodder prices. Additionally, improvements in productivity of dairy cows have also contributed to a decrease in the number of cows needed to produce the same amount of milk.
- A3130 (Other pigs): Due to lower prices and higher fodder prices, the pigs have declined in the recent years in Denmark.
- A5110O (Laying hens): Lower egg prices may have contributed to the decline in the number of laying hens. It should be noted that poultry can fluctuate rather much and also are subject to a sample error higher than for other animals.
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
See in annex.
Annexes:
15.3.3.2. Results of analysis at micro level
15.3.4. Coherence at macro level with data collections in other domains in agriculture
See sub-categories below.
15.3.4.1. Analysis of coherence at macro level
Yes15.3.4.2. Results of analysis at macro level
Coherence cross-domain: IFS vs CROP PRODUCTION (main area in 1 000 ha) in relative terms
Fallow land is collected from IACS so the results for the region are good enough. IFS results are subject to sample errors and thereby cannot be expected to be identical to IACS results.
Coherence cross-domain: IFS vs ANIMAL PRODUCTION (1 000 heads) in relative terms
IFS results on pigs are based on information from sample survey 2023 from questions on the questionnaire and are subject to sample errors.
Coherence cross-domain: IFS vs ORGANIC ANIMAL PRODUCTION (heads) in relative terms
The IFS 2023 results are subject to high sample errors for sheep and goats but what might be more important is the fact that animal production figures come from the livestock register.
It is uncertain whether the register figures really reflect number of animals on one specific day but rather is a mix of production, expected numbers, out of date numbers, or even animal housing capacity.
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
Farmers who participated in the pig survey in April are not required to report pig data again for the IFS about two months later. However, they must complete all other sections of the IFS questionnaire.
16.2. Efficiency gains since the last data transmission to Eurostat
None16.2.1. Additional information efficiency gains
IFS 2023 used pretty much the same tools as IFS 2020. However, a more modern validation tool called DAF was introduced in 2021. It is a more standardised system, making it easier for IT staff to correct errors and implement new requirements.
16.3. Average duration of farm interview (in minutes)
See sub-categories below.
16.3.1. Core
As the questionnaire is normally completed online, we have no knowledge of how long it takes. In cases where the questionnaire is completed via telephone interviews the experience is that it most often can be completed in less than ten minutes. How the time is distributed by the different parts of the questionnaire is it not possible to say anything certain about.
It should be noted that telephone interviews are not used as a systematic tool; rather, they are reserved for cases where a farmer calls to report trouble with the online questionnaire. In such instances, we may complete the survey by phone as an additional service.
16.3.2. Module ‘Labour force and other gainful activities‘
There is no reliable information available.
16.3.3. Module ‘Rural development’
Not relevant since all items of this module are collected from administrative registers.
16.3.4. Module ‘Animal housing and manure management’
Restricted from publication
16.3.5. Module ‘Irrigation’
About three-quarters of Danish farms do not practice irrigation. They simply need to cross off “no irrigation”. For farms that do practice irrigation, we have no certain knowledge of how burdensome this part of the questionnaire is.
16.3.6. Module ‘Soil management practices’
We have no certain knowledge of how burdensome this part of the questionnaire is.
16.3.7. Module ‘Machinery and equipment’
We have no certain knowledge of how burdensome this part of the questionnaire is. However, most of the machinery questions are check-box, which should be relatively easy to answer.
16.3.8. Module ‘Orchard’
Only a few hundred farms have apple and pear trees. We have limited knowledge regarding the burdensomeness of this part of the questionnaire. However, our experience suggests that farms with small areas but several sorts of apples could encounter challenges completing the questionnaire.
16.3.9. Module ‘Vineyard’
Restricted from publication
17.1. Data revision - policy
Not relevant for IFS 2023 since there is no provisional version of the survey. If major mistakes are found after first publication, they will, of course, be corrected, and the correction will be announced on our homepage. No such thing happened to IFS 2023.
Statistics Denmark revises published figures in accordance with the Revision Policy for Statistics Denmark. The common procedures and principles of the Revision Policy are for some statistics supplemented by a specific revision practice.
Read more at this website.
17.2. Data revision - practice
There was only one publication of IFS 2023 (final results publication) and thereby no revisions.
17.2.1. Data revision - average size
Not requested.
18.1. Source data
See sub-categories below.
18.1.1. Sampling design & Procedure frame
See sub-categories below.
18.1.1.1. Type of frame
List frame18.1.1.2. Name of frame
IFS farmers population
18.1.1.3. Update frequency
Continuous18.1.2. Core data collection on the main frame
See sub-categories below.
18.1.2.1. Coverage of agricultural holdings
Sample18.1.2.2. Sampling design
The 14 000 farm-Danish sample was used for core and all modules. For instance, all farms were asked about irrigation, and even though about three-quarters crossed off 'no irrigation’, all farms are part of the irrigation module.
The sample is divided into 68 strata, categorised by farm typology and size. These strata are distributed proportionately among the five Danish NUTS 2 regions.
Annexes:
18.1.2.2. Sampling design
18.1.2.2.1. Name of sampling design
Stratified one-stage random sampling18.1.2.2.2. Stratification criteria
Unit sizeUnit specialization
18.1.2.2.3. Use of systematic sampling
No18.1.2.2.4. Full coverage strata
All horticultural and poultry farms are intended to receive questionnaires. However, due to technical limitations, such as invalid business numbers or farms in bankruptcy, questionnaires can only be sent to as many as are feasible.
18.1.2.2.5. Method of determination of the overall sample size
A sample size of 14 000 farms was determined. This sample size was not derived from an exact calculation, but rather from an expert estimation of the necessary size to ensure adequate accuracy, including for the five Danish NUTS 2 regions.
18.1.2.2.6. Method of allocation of the overall sample size
Optimal allocation considering costs18.1.3. Core data collection on the frame extension
See sub-categories below.
18.1.3.1. Coverage of agricultural holdings
Not applicable18.1.3.2. Sampling design
Not applicable.
18.1.3.2.1. Name of sampling design
Not applicable18.1.3.2.2. Stratification criteria
Not applicable18.1.3.2.3. Use of systematic sampling
Not applicable18.1.3.2.4. Full coverage strata
Not applicable.
18.1.3.2.5. Method of determination of the overall sample size
Not applicable.
18.1.3.2.6. Method of allocation of the overall sample size
Not applicable18.1.4. Module “Labour force and other gainful activities”
See sub-categories below.
18.1.4.1. Coverage of agricultural holdings
Sample18.1.4.2. Sampling design
The 14 000 farm-Danish sample was used for core and all modules. For instance, all farms were asked about irrigation, and even though about three-quarters crossed off 'no irrigation’, all farms are part of the irrigation module.
The sample is divided into 68 strata, categorised by farm typology and size. These strata are distributed proportionately among the five Danish NUTS 2 regions.
18.1.4.2.1. Name of sampling design
Stratified one-stage random sampling18.1.4.2.2. Stratification criteria
Unit sizeUnit specialization
18.1.4.2.3. Use of systematic sampling
No18.1.4.2.4. Full coverage strata
All horticultural and poultry farms are intended to receive questionnaires. However, due to technical limitations, such as invalid business numbers or farms in bankruptcy, questionnaires can only be sent to as many as are feasible.
18.1.4.2.5. Method of determination of the overall sample size
A sample size of 14 000 farms was determined. This sample size was not derived from an exact calculation, but rather from an expert estimation of the necessary size to ensure adequate accuracy, including for the five Danish NUTS 2 regions.
18.1.4.2.6. Method of allocation of the overall sample size
Optimal allocation considering costs18.1.4.2.7. If sampled from the core sample, the sampling and calibration strategy
Not applicable18.1.5. Module “Rural development”
See sub-categories below.
18.1.5.1. Coverage of agricultural holdings
Sample18.1.5.2. Sampling design
The 14 000 farm-Danish sample was used for core and all modules. For instance, all farms were asked about irrigation, and even though about three-quarters crossed off 'no irrigation’, all farms are part of the irrigation module.
The sample is divided into 68 strata, categorised by farm typology and size. These strata are distributed proportionately among the five Danish NUTS 2 regions.
18.1.5.2.1. Name of sampling design
Stratified one-stage random sampling18.1.5.2.2. Stratification criteria
Unit sizeUnit specialization
18.1.5.2.3. Use of systematic sampling
No18.1.5.2.4. Full coverage strata
All horticultural and poultry farms are intended to receive questionnaires. However, due to technical limitations, such as invalid business numbers or farms in bankruptcy, questionnaires can only be sent to as many as are feasible.
18.1.5.2.5. Method of determination of the overall sample size
A sample size of 14 000 farms was determined. This sample size was not derived from an exact calculation, but rather from an expert estimation of the necessary size to ensure adequate accuracy, including for the five Danish NUTS 2 regions.
18.1.5.2.6. Method of allocation of the overall sample size
Optimal allocation considering costs18.1.5.2.7. If sampled from the core sample, the sampling strategy and calibration strategy
Not applicable18.1.6. Module “Animal housing and manure management module”
Restricted from publication
18.1.6.1. Coverage of agricultural holdings
Restricted from publication
18.1.6.2. Sampling design
Restricted from publication
18.1.6.2.1. Name of sampling design
Restricted from publication
18.1.6.2.2. Stratification criteria
Restricted from publication
18.1.6.2.3. Use of systematic sampling
Restricted from publication
18.1.6.2.4. Full coverage strata
Restricted from publication
18.1.6.2.5. Method of determination of the overall sample size
Restricted from publication
18.1.6.2.6. Method of allocation of the overall sample size
Restricted from publication
18.1.6.2.7. If sampled from the core sample, the sampling strategy and calibration strategy
Restricted from publication
18.1.7. Module ‘Irrigation’
See sub-categories below.
18.1.7.1. Coverage of agricultural holdings
Sample18.1.7.2. Sampling design
The 14 000 farm-Danish sample was used for core and all modules. For instance, all farms were asked about irrigation, and even though about three-quarters crossed off 'no irrigation’, all farms are part of the irrigation module.
The sample is divided into 68 strata, categorised by farm typology and size.
18.1.7.2.1. Name of sampling design
Stratified one-stage random sampling18.1.7.2.2. Stratification criteria
Unit sizeUnit specialization
18.1.7.2.3. Use of systematic sampling
No18.1.7.2.4. Full coverage strata
All horticultural and poultry farms are intended to receive questionnaires. However, due to technical limitations, such as invalid business numbers or farms in bankruptcy, questionnaires can only be sent to as many as are feasible.
18.1.7.2.5. Method of determination of the overall sample size
A sample size of 14 000 farms was determined for core and all modules. This sample size was not derived from an exact calculation, but rather from an expert estimation of the necessary size to ensure adequate accuracy, including for the five Danish NUTS 2 regions. Only a part of this sample size consists of farms with irrigable area.
18.1.7.2.6. Method of allocation of the overall sample size
Optimal allocation considering costs18.1.7.2.7. If sampled from the core sample, the sampling strategy and calibration strategy
Not applicable18.1.8. Module ‘Soil management practices’
See sub-categories below.
18.1.8.1. Coverage of agricultural holdings
Sample18.1.8.2. Sampling design
The 14 000 farm-Danish sample was used for core and all modules. For instance, all farms were asked about irrigation, and even though about three-quarters crossed off 'no irrigation’, all farms are part of the irrigation module.
The sample is divided into 68 strata, categorised by farm typology and size.
18.1.8.2.1. Name of sampling design
Stratified one-stage random sampling18.1.8.2.2. Stratification criteria
Unit sizeUnit specialization
18.1.8.2.3. Use of systematic sampling
No18.1.8.2.4. Full coverage strata
All horticultural and poultry farms are intended to receive questionnaires. However, due to technical limitations, such as invalid business numbers or farms in bankruptcy, questionnaires can only be sent to as many as are feasible.
18.1.8.2.5. Method of determination of the overall sample size
A sample size of 14 000 farms was determined for core and all modules. This sample size was not derived from an exact calculation, but rather from an expert estimation of the necessary size to ensure adequate accuracy, including for the five Danish NUTS 2 regions. Only a part of this sample size consists of farms with relevant for the module.
18.1.8.2.6. Method of allocation of the overall sample size
Optimal allocation considering costs18.1.8.2.7. If sampled from the core sample, the sampling strategy and calibration strategy
Not applicable18.1.9. Module ‘Machinery and equipment’
See sub-categories below.
18.1.9.1. Coverage of agricultural holdings
Sample18.1.9.2. Sampling design
The 14 000 farm-Danish sample was used for core and all modules. For instance, all farms were asked about irrigation, and even though about three-quarters crossed off 'no irrigation’, all farms are part of the irrigation module.
The sample is divided into 68 strata, categorised by farm typology and size.
18.1.9.2.1. Name of sampling design
Stratified one-stage random sampling18.1.9.2.2. Stratification criteria
Unit sizeUnit specialization
18.1.9.2.3. Use of systematic sampling
No18.1.9.2.4. Full coverage strata
All horticultural and poultry farms are intended to receive questionnaires. However, due to technical limitations, such as invalid business numbers or farms in bankruptcy, questionnaires can only be sent to as many as are feasible.
18.1.9.2.5. Method of determination of the overall sample size
A sample size of 14 000 farms was determined. This sample size was not derived from an exact calculation, but rather from an expert estimation of the necessary size to ensure adequate accuracy, including for the five Danish NUTS 2 regions.
18.1.9.2.6. Method of allocation of the overall sample size
Optimal allocation considering costs18.1.9.2.7. If sampled from the core sample, the sampling strategy and calibration strategy
Not applicable18.1.10. Module ‘Orchard’
See sub-categories below.
18.1.10.1. Coverage of agricultural holdings
Sample18.1.10.2. Sampling design
The 14 000 farm-Danish sample was used for core and all modules. For instance, all farms were asked about irrigation, and even though about three-quarters crossed off 'no irrigation’, all farms are part of the irrigation module.
The sample is divided into 68 strata, categorised by farm typology and size.
18.1.10.2.1. Name of sampling design
Stratified one-stage random sampling18.1.10.2.2. Stratification criteria
Unit sizeUnit specialization
18.1.10.2.3. Use of systematic sampling
No18.1.10.2.4. Full coverage strata
All horticultural and poultry farms are intended to receive questionnaires. However, due to technical limitations, such as invalid business numbers or farms in bankruptcy, questionnaires can only be sent to as many as are feasible.
18.1.10.2.5. Method of determination of the overall sample size
A sample size of 14 000 farms was determined for core and all modules. This sample size was not derived from an exact calculation, but rather from an expert estimation of the necessary size to ensure adequate accuracy, including for the five Danish NUTS 2 regions. Only a part of this sample size consists of farms with relevant for the module.
18.1.10.2.6. Method of allocation of the overall sample size
Optimal allocation considering costs18.1.10.2.7. If sampled from the core sample, the sampling strategy and calibration strategy
Not applicable18.1.11. Module ‘Vineyard’
Restricted from publication
18.1.11.1. Coverage of agricultural holdings
Restricted from publication
18.1.11.2. Sampling design
Restricted from publication
18.1.11.2.1. Name of sampling design
Restricted from publication
18.1.11.2.2. Stratification criteria
Restricted from publication
18.1.11.2.3. Use of systematic sampling
Restricted from publication
18.1.11.2.4. Full coverage strata
Restricted from publication
18.1.11.2.5. Method of determination of the overall sample size
Restricted from publication
18.1.11.2.6. Method of allocation of the overall sample size
Restricted from publication
18.1.11.2.7. If sampled from the core sample, the sampling strategy and calibration strategy
Restricted from publication
18.1.12. Software tool used for sample selection
SAS and Oracle are used as tools when selecting the sample.
18.1.13. Administrative sources
See sub-categories below.
18.1.13.1. Administrative sources used and the purposes of using them
The information is available on Eurostat’s website, at the link: Additional data - Eurostat (europa.eu).
18.1.13.2. Description and quality of the administrative sources
See the Excel file in the annex.
Annexes:
18.1.13.2. Description and quality of administrative sources
18.1.13.3. Difficulties using additional administrative sources not currently used
Other18.1.14. Innovative approaches
The information on the innovative approaches and the quality methods applied is available on Eurostat’s website, at the link: Additional data - Eurostat (europa.eu).
18.2. Frequency of data collection
The agricultural census is conducted every 10 years. The decennial agricultural census is complemented by sample or census-based data collections organised every 3-4 years in-between.
18.3. Data collection
See sub-categories below.
18.3.1. Methods of data collection
Postal, electronic version (email)18.3.2. Data entry method, if paper questionnaires
Not applicable18.3.3. Questionnaire
Please find the questionnaire in annex.
Annexes:
18.3.3. Questionnaire in Danish
18.3.3. Questionnaire in English
18.4. Data validation
See sub-categories below.
18.4.1. Type of validation checks
Completeness checksComparisons with previous rounds of the data collection
Comparisons with other domains in agricultural statistics
Annexes:
18.4.1. Type of validation checks
18.4.2. Staff involved in data validation
Staff from central department18.4.3. Tools used for data validation
Oracle and SAS
18.5. Data compilation
See item 18.5.2.
18.5.1. Imputation - rate
Imputation has been used to a modest degree, only for water consumption for irrigation, and it applies to about 15% (unweighted) of all farms with irrigation.
18.5.2. Methods used to derive the extrapolation factor
CalibrationAnnexes:
18.5.2. Methods used to derive the extrapolation factor
18.6. Adjustment
Covered under Data compilation.
18.6.1. Seasonal adjustment
Not applicable to Integrated Farm Statistics, because it collects structural data on agriculture.
See sub-categories below.
19.1. List of abbreviations
AWU – Annual Working Unit
CAP – Common Agricultural Policy
CHR – Central Livestock Register
ESR – Statistical Business Register
EU – European Union
FAO – Food and Agriculture Organization of the United Nations
FSS – Farm Structure Survey
IACS – Integrated Administration and Control System
IFS – Integrated Farm Statistics
LSU – Livestock unit
NUTS – Nomenclature of territorial units for statistics
RSE – Relative standard error
SGM – Standard Gross Margin
SO – Standard output
UAA – Utilised agricultural area
19.2. Additional comments
No additional comments.
Annexes:
19.2. Additional comments
The data describes the structure of agricultural holdings providing the general characteristics of farms and farmers and information on their land, livestock, labour force, irrigation and farm technology. It also describes rural development measures and agro-environmental aspects that look at the impact of agriculture on the environment.
The data is used by ministries, researchers, farmers, organisations, EU, and policy-makers to better understand the state of the farming sector and the impact of agriculture on the environment. The data follows up the changes in the agricultural sector and provides 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 is presented at different geographical levels and over periods.
The data collections are organised in line with Regulation (EU) 2018/1091 and have a new structure, consisting of a core data set and several modules. The regulation covers the data collections in 2019/2020 (the agricultural census), 2023 and 2026. The data is as comparable and coherent as possible with the other European countries.
2 March 2026
The list of core variables is set in Annex III of Regulation (EU) 2018/1091.
The descriptions of the core variables as well as the lists and descriptions of the variables for the modules collected in 2023 are set in Commission Implementing Regulation (EU) 2021/2286.
The following groups of variables are collected in 2023:
- for core: location of the holding, legal personality of the holding, manager, type of tenure of the utilised agricultural area, variables of land, organic farming, irrigation on cultivated outdoor area, variables of livestock, organic production methods applied to animal production;
- for the module "Labour force and other gainful activities": farm management, family labour force, non-family labour force, other gainful activities directly and not directly related to the agricultural holding;
- for the module "Rural development": support received by agricultural holdings through various rural development measures;
- for the module “Irrigation”: availability of irrigation, irrigation methods, sources of irrigation water, technical parameters of the irrigation equipment, crops irrigated during a 12 months period;
- for the module “Soil management practices”: tillage methods, soil cover on arable land, crop rotation on arable land, ecological focus area;
- for the module “Machinery and equipment”: internet facilities, basic machinery, use of precision farming, machinery for livestock management, storage for agricultural products, equipment used for production of renewable energy on agricultural holdings;
- for the module “Orchards”: apples area, pears area, each one by age of plantation and density of trees.
See sub-category below.
See sub-categories below.
See sub-categories below.
See sub-categories below.
See categories below.
Two kinds of units are generally used:
- the units of measurement for the variables (area in hectares, livestock in (1000) heads or LSU (livestock units), labour force in persons or AWU (annual working units), standard output in Euro, places for animal housing etc.) and
- the number of agricultural holdings having these characteristics.
See item 18.5.2.
See sub-categories below.
The farm statistics are published yearly with statistical tables on number of farms by size and geography as well as statistical tables on crops and livestock.
Labour force is not included in the questionnaire every year. In recent years, Statistics Denmark has published agricultural labour force tables for the years 2013, 2016, 2018, 2020, and 2023.
See sub-categories below.
See sub-categories below.
See sub-categories below.
