1.1. Contact organisation
Federal Statistical Office of the Federal Republic of Germany
1.2. Contact organisation unit
Division G1 – Agriculture and Forestry, Fisheries
1.3. Contact name
Confidential because of GDPR
1.4. Contact person function
Confidential because of GDPR
1.5. Contact mail address
Bonn Branch Office
Postfach 17 03 77
53029 Bonn
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
29 April 2025
2.2. Metadata last posted
14 May 2025
2.3. Metadata last update
29 April 2025
3.1. Data description
The data describe the structure of agricultural holdings providing the general characteristics of farms and farmers and information on their land, livestock and labour force. They also describe production methods, rural development measures and agro-environmental aspects that look at the impact of agriculture on the environment.
The data are used by public, researchers, farmers and policy-makers to better understand the state of the farming sector and the impact of agriculture on the environment. The data follow up the changes in the agricultural sector and provide a basis for decision-making in the Common Agricultural Policy (CAP) and other European Union policies.
The statistical unit is the agricultural holding (farm). The aggregated results are disseminated through statistical tables. The data are presented at different geographical levels and over periods.
The data collections are organised in line with Regulation (EU) 2018/1091 and have a new structure, consisting of a core data set and several modules. The regulation covers the data collections in 2020 (the agricultural census), 2023 and 2026. The data are as comparable and coherent as possible with the other European countries.
3.2. Classification system
Data are arranged in tables using many classifications. Please find below information on most classifications.
The classifications of variables are available in Annex III of Regulation (EU) 2018/1091 and in Commission Implementing Regulation (EU) 2021/2286.
The farm typology means a uniform classification of the holdings based on their type of farming and their economic size. Both are determined on the basis of the standard gross margin (SGM) (until 2007) or standard output (SO) (from 2010 onward) which is calculated for each crop and animal. The farm type is determined by the relative contribution of the different productions to the total standard gross margin or the standard output of the holding.
The territorial classification uses the NUTS classification to break down the regional data. The regional data is available at NUTS level 2.
3.3. Coverage - sector
The statistics cover agricultural holdings undertaking agricultural activities as listed in item 3.5 below and meeting the minimum coverage requirements (thresholds) as listed in item 3.6 below.
3.4. Statistical concepts and definitions
The list of core variables is set in Annex III of Regulation (EU) 2018/1091.
The descriptions of the core variables as well as the lists and descriptions of the variables for the modules collected in 2023 are set in Commission Implementing Regulation (EU) 2021/2286.
The following groups of variables are collected in 2023:
- for core: location of the holding, legal personality of the holding, manager, type of tenure of the utilised agricultural area, variables of land, organic farming, irrigation on cultivated outdoor area, variables of livestock, organic production methods applied to animal production;
- for the module "Labour force and other gainful activities": farm management, family labour force, non-family labour force, other gainful activities directly and not directly related to the agricultural holding;
- for the module "Rural development": support received by agricultural holdings through various rural development measures;
- for the module “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. Due to article 7 (8) of Regulations (EU) 2018/1091 peaches area, nectarines area, apricots area, oranges area, small citrus fruits area, lemons area, olives area, grapes for table use area, grapes for raisins area, each one by age of plantation and density of trees are not part of the German IFS 2023
* In line with the definition of Eurostat and the IFS 2023 handbook, a holding that owns, rents or uses a milking robot or an automatic feeding system is considered to use robotics. In context of IFS 2023 robotics refer to machines where increased levels of intelligence are added to the machines for its autonomous work that perform crop or livestock production tasks under human supervision, albeit without direct human intervention.
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.
Agricultural holdings that produce olive oil from olives for personal use are included in the scope of the regulation, but are not relevant for Germany in the context of the IFS 2023. The breeding and keeping of ostriches, emus and rabbits, as well as bee-keeping are not relevant for Germany, too.
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
Yes3.6.1.2. 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 as 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.
In Germany the irrigation module has been designed to capture only irrigation in the outdoor areas. There is no data available for the area under glass.
3.6.5. Population covered by the data sent to Eurostat for the module “Soil management practices”
The subset of agricultural holdings defined in item 3.6.2 with arable land.
3.6.6. Population covered by the data sent to Eurostat for the module “Orchard”
The subset of agricultural holdings defined in item 3.6.2, with any of the individual orchard variables that meet the threshold specified in Article 7(5) of Regulation (EU) 2018/1091.
3.6.7. Population covered by the data sent to Eurostat for the module “Vineyard”
Restricted from publication
3.7. Reference area
See sub-categories below.
3.7.1. Geographical area covered
The entire territory of the country.
3.7.2. Inclusion of special territories
The entire territory of the country.
3.7.3. Criteria used to establish the geographical location of the holding
Other3.7.4. Additional information reference area
The location of the holding was, in most cases, the parcel on which the farm building was located. If farm buildings were located on multiple parcels, the holding location was the parcel on which the most important farm buildings or buildings were located.
3.8. Coverage - Time
Farm structure statistics in Germany cover the period from the 1930s onwards. The results are comparable to a limited extent, as the survey characteristics and thresholds have changed over time. 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.
Three kinds of units are generally used:
- the units of measurement for the variables (area in hectares, livestock in heads, places or LSU (livestock units), labour force in persons or AWU (annual working units), standard output in Euro,
- storage and volume of water in cubic meters and
- the number of agricultural holdings having these characteristics.
See sub-categories below.
5.1. Reference period for land variables
The land variables refer to the reference year 2023, which begins on 1 January 2023 and ends on 31 December 2023. In the case of successive crops, the land variables refer to a crop that is harvested during the reference year, regardless of when the crop is sown.
5.2. Reference period for variables on irrigation and soil management practices
Irrigation:
- a 3-year period for average irrigated utilised agricultural outdoor area.
- a 12-month period for other variables on irrigation ending on the 31 December 2022.
Soil management practices:
- the variables of drainage and ecological focus areas refer the reference year 2023
- the 12-month period ending on the 28th February within the reference year is the reference period of tillage methods.
- the 5-month period ending on the 28th February within the reference year is the reference period of soil cover on agricultural land (from previous year to May in reference year of catch crops)
- the cultivation years 2022 and 2023 are the reference periods of crop rotation on arable land
5.3. Reference day for variables on livestock and animal housing
The reference day is the March 1st of the reference year 2023 for livestock variables. Animal housing variables are not applicable for 2023.
5.4. Reference period for variables on manure management
The manure management variables are not applicable for 2023.
5.5. Reference period for variables on labour force
A 12-month period ending on the 28th February within the reference year 2023. The calendar year 2022 is the reference period of the other gainful activities.
5.6. Reference period for variables on rural development measures
The three-year period ending on 31 December 2023.
5.7. Reference day for all other variables
The reference day first of March of the reference year 2023.
Reference period for variables not mentioned in 5.1. - 5.6.
- variables for the orchard module: calendar year 2022 as part of a preceding survey (survey of tree fruit growing)
- variables for machinery and equipment: 12-month period ending on 28 February within the reference year.
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
- Federal Statistics Law (Bundesstatistikgesetz – BStatG) of 20 October 2016 (Federal Law Gazette I p. 2394) as amended
- Law on Agricultural Statistics (Agrarstatistikgesetz – AgrStatG) of 17 December 2009 (Federal Law Gazette I p. 3886) as amended
- Act on Equal status for Set-Aside and Agricultural used Areas (Gesetz zur Gleichstellung stillgelegter und landwirtschaftlich genutzter Flächen) of 10 July 1995 (Federal Law Gazette I p. 910) as amended
6.1.3. Link to national legal acts and other agreements
National legal acts (mentioned in item 6.1.2) are part of the annex.
Annexes:
6.1.3. Federal Statistics Law (DE)
6.1.3. Law on Agricultural Statistics (DE)
6.1.3. Act on Equal status for Set-Aside and Agricultural used Areas (DE)
6.1.4. Year of entry into force of national legal acts and other agreements
- Bundesstatistikgesetz of 20 October 2016, amended on 14 June 2021
- Agrarstatistikgesetz of 17 December 2009, amended on 14 November 2022
- Gesetz zur Gleichstellung stillgelegter und landwirtschaftlich genutzter Flächen of 10 July 1995, amended on 14 June 2021
6.1.5. Legal obligations for respondents
Yes6.2. Institutional Mandate - data sharing
Due to the German federal system, the IFS 2023 was conducted as a decentralised statistic. The development of the survey instruments and the methodological preparation is mainly the task domain of the Federal Statistical Office, while the Statistical Offices of the Länder are responsible for the implementation, data collection, validation assuring quality and dissemination for the Bundesland and lower regional units. This distribution of responsibilities requires a close collaboration between the Statistical Offices. Basically each Bundesland is the owner of his data and the rules of data sharing are defined by law.
In order to reduce the burden, other national authorities provide data such as bovine variables of the Herkunfts- und Informationssystem für Tiere (HIT, Bovine Register), land variables of the Integriertes Verwaltungs- und Kontrollsystem (InVeKoS, Integrated Administration and Control System) or data of the European Agricultural Fund for Rural Development (ELER) or InVeKoS.
7.1. Confidentiality - policy
The individual data collected are generally kept confidential in compliance with Article 16 of the Federal Statistics Law. Only in exceptional cases explicitly regulated by law individual data can be transferred (e.g. the provision of anonymised individual data to the research data centres of the Federal Statistical Office and the Statistical Offices of the Länder, for universities and other independent, academic institutions), without citing names or addresses (factually anonymised individual data).
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
p% rule (A contributor is able to derive an estimate of some other contributor within p% of its true value)Secondary confidentiality rules
7.2.1.2. Methods to protect data in confidential cells
Cell suppression (Completely suppress the value of some 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
A number of steps must be done to ensure compliance with this legal requirement. To prevent individual information about holdings from being disclosed in the nationally published tables, the results were subject to harmonised, nationwide confidentiality. During the generation of the published tables, primary confidentiality was carried out automatically based on the p-percent rule.
IFS 2023 was carried out as a sample survey and disseminated results were rounded by the nearest hundred (variables), the nearest ten (agricultural holdings) or the nearest one (ratio values, standard output coefficients). The coarsening effect of rounding was taken into account by means of a relevant adjustment of the p-percent rule.
To prevent the disclosure of the primarily suppressed table elements by forming sums or differences in the tables, so-called secondary suppressions were carried out (secondary confidentiality) in addition to the primary suppressions. The secondary confidentiality was carried out manually by the Federal Statistical Office and the Statistical Offices of the Länder. The suppressed table elements based on primary or secondary confidentiality are marked with a dot in the publication tables.
Tabulation and confidentiality were realised by using the AGRATAB Management Tool (AMT) and Geheimhaltungsmanagementtool (GHMAN). Both are Java applications developed especially for this purpose. While AMT supports the entire process of tabulation including primary confidentiality, GHMAN enables manual entry of secondary confidentiality in the table cells.
7.2.2. Microdata
See sub-categories below.
7.2.2.1. Use of EU methodology for microdata dissemination
Yes7.2.2.2. Methods of perturbation
Recoding of variablesRemoval of variables
Reduction of information
Merging categories
Rounding
Micro-aggregation
7.2.2.3. Description of methodology
The individual data collected are generally kept confidential in compliance with Article 16 of the Federal Statistics Law. Only in exceptional cases explicitly regulated by law can individual data be transmitted. The names and addresses of the respondents are never passed on to third parties.
The transmission of information collected to the responsible supreme federal or Länder authorities in the form of tables with statistical results is allowed according to Article 98 (1) of the Law on Agricultural Statistics in conjunction with Article 16 (4) of the Federal Statistics Law. This applies even if individual table cells only identify a single case. Under Article 16 (6) of the Federal Statistics Law, it is possible to provide individual data (microdata) to universities or other institutions tasked with independent scientific research for scientific projects if individual data can only be assigned to the respondents or parties concerned with a disproportionately large investment of time, cost and labour. Research institutions may, in addition, apply to the research data centres of the Federal Statistical Office and the Statistical Offices of the Länder where they have various ways of obtaining access to data, such as the on-site use (via safe centres or remote access) or off-site use (scientific use files, public use files).
The research data centres are obliged to ensure statistical confidentiality when offering access to microdata for scientific use. They may not release results that allow conclusions on individual cases.
For this reason, the research data centres check every result that is produced within an on-site use of microdata for statistical confidentiality. This happens according to set rules. If results allow conclusions on individual cases, these results will to be blocked. (You can find more information of the regulations on the analysis of microdata).
8.1. Release calendar
First preliminary results of the IFS 2023 were published in a press release in January 2024. The results of the IFS 2023 were gradually published between March and May 2024. IFS 2023 was not part of the annual release calendar. Press releases on selected topics of the IFS 2023 results were announced in the weekly preview.
(You can find more information of the current preview (weekly and annual release calendar).
8.2. Release calendar access
Not applicable for the result of the IFS 2023.
8.3. Release policy - user access
In Germany there is an extensive set of tables, which are created due to the process described above. The whole content of these tables is available for everyone: politicians, public authorities, the business community and all citizens. The results are published online, which is a credo for the publication of information by the Federal Statistical System in Germany. You can retrieve data and publications by theme in an interactive form or read the daily press releases on the website Destatis. In addition to this, the results on a lower regional level are published on the websites of the Statistical Offices of the Bundesländer.
In GENESIS-Online you should find all the data of the Federal Statistical Office updated daily in two languages. The result tables are available in Excel, HTML and CSV format.
8.3.1. Use of quality rating system
Yes, another quality rating system8.3.1.1. Description of the quality rating system
The quality rating system of representative results is based on the relative standard errors. Results that have a standard errors of ± 15 percent and more were replaced in the national publication tables by the character “/” because the estimation error is too high and the numerical value thus is not reliable enough. Information on all values is available on request.
The frequency of the IFS is every three to four years.
10.1. Dissemination format - News release
See sub-categories below.
10.1.1. Publication of news releases
Yes10.1.2. Link to news releases
- Around 7,800 fewer farms since 2020 (DE)
- Agricultural structure survey 2023: Number of livestock farms fell by 4% between 2020 and 2023 (DE)
- Number of organic farms in agriculture increased by 10% between 2020 and 2023 (DE)
- Rental prices for agricultural land increased by 9% between 2020 and 2023 (DE)
- Number of labour force decreased by 7% between 2020 and 2023 (DE)
- In 2022, there were 554,000 hectares of irrigated agricultural outdoor area (DE)
- In 2023, there were 26% of agricultural holdings with systems for generating renewable energy (DE)
- Plowing in agriculture became less important (DE)
- 4,800 agricultural farms were part of an enterprise group in 2022 (DE)
- 28% of labour force in agricultural farms were seasonal workers in 2023 (DE)
10.2. Dissemination format - Publications
See sub-categories below.
10.2.1. Production of paper publications
No10.2.2. Production of on-line publications
Yes, but not in English10.2.3. Title, publisher, year and link
The Federal Statistical Office published between March and May 2024 the following statistical reports as Excel files:
- Statistical report - agricultural holdings - land use
- Statistical report - agricultural holdings - livestock farming
- Statistical report - agricultural holdings - organic farming
- Statistical report - agricultural holdings - ownership and tenancy
- Statistical report - agricultural holdings - legal personality and socio-economic holding types
- Statistical report - agricultural holdings - labour force and vocational training of the holder
- Statistical report - agricultural holdings - other gainful activities
- Statistical report - agricultural holdings - business focuses and standard output
- Statistical report - agricultural holdings - irrigation
- Statistical report - agricultural holdings - viticulture
- Statistical report - agricultural holdings - machinery and storage
- Statistical report - agricultural holdings - systems for generating renewable energy
- Statistical report - agricultural holdings - promotion programmes
- Statistical report - agricultural holdings - soil management
10.3. Dissemination format - online database
See sub-categories below.
10.3.1. Data tables - consultations
Not available.
10.3.2. Accessibility of online database
Yes10.3.3. Link to online database
In GENESIS-Online, you find selected results of the IFS 2023. The German or English language can be selected at the top right of the website.
Navigating to
> Statistics
>> 41 Agriculture, forestry, fisheries
>>> 41141 Census of agricultural holdings: main survey
10.4. Dissemination format - microdata access
See sub-category below.
10.4.1. Accessibility of microdata
Yes10.5. Dissemination format - other
In the following overview tables information on specific topics of the IFS 2023 were presented:
- Agricultural holdings: selected features in a time comparison
- Agricultural holdings and utilised agricultural area by size of the utilised agricultural area
- Agricultural holdings by their legal form
- Representative agricultural holdings and holdings with organic farming 2023
- Agricultural holdings by general type of farming
- Persons employed in the agricultural holdings
- Soil tillage methods on arable land in the open air 2022/2023
- Farms with possibility of irrigation on open spaces - without frost protection - and irrigated area 2022
- Livestock of holdings with conventional and organic farming
- Holdings with poultry and other livestock
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
Explanations of the metadata are part of the national quality report and the production methods.
- National quality report (DE): Statistisches Bundesamt (Destatis), 2024: Agrarstrukturerhebung 2023, Qualitätsbericht
- Production methods (DE, available on request): Statistisches Bundesamt (Destatis), 2024: Methodische Grundlagen der Agrarstrukturerhebung 2023
10.6.4. Availability of national handbook on methodology
Yes10.6.5. Title, publisher, year and link to handbook
The national handbook is part of the production methods (available on request):
Statistisches Bundesamt (Destatis), 2024: Methodische Grundlagen der Agrarstrukturerhebung 2023
10.6.6. Availability of national methodological papers
Yes10.6.7. Title, publisher, year and link to methodological papers
Explanations of the content, the methodology and procedures of the IFS 2023 are part of the production methods (available on request):
Statistisches Bundesamt (Destatis), 2024: Methodische Grundlagen der Agrarstrukturerhebung 2023
10.7. Quality management - documentation
Explanations of the quality management are part of the national quality report (DE): Statistisches Bundesamt (Destatis), 2024: Agrarstrukturerhebung 2023, Qualitätsbericht
11.1. Quality assurance
See sub-categories below.
11.1.1. Quality management system
Yes11.1.2. Quality assurance and assessment procedures
Training coursesUse of best practices
Quality guidelines
Peer review
Other
11.1.3. Description of the quality management system and procedures
Due to the increasing quantity of data requirements - which are growing over the time - it is sometimes difficult to collect high-quality data. The willingness of respondents decreases due to fatigue, annoyance or lack of understanding and therefore the quality suffers. Despite all digital innovations and quality management, this must be observed.
Having said this, German statistics are naturally embedded in the European statistical system. This quality policy is based on four levels of quality assurance, namely principles, indicators, methods, tools, sector-specific quality assurance and established by Regulation (EC) No 223/2009. It aims to ensure and continuously improve the quality of European statistics whilst verifying that they meet user needs.
The guidelines focusing on quality:
- Code of Practice (CoP)
The European Statistics Code of Practice (CoP) is the cornerstone of the quality framework and sets the standards for developing, producing and disseminating European statistics.
- Quality Assurance Framework (QAF)
The QAF serves as a guidance and complement on how to implement the CoP. Like the CoP, it applies to statistical authorities of the ESS, which comprises the European Union Statistical Authority (Eurostat), the National Statistical Institutes (NSIs) and Other National Authorities (ONAs) responsible for the development, production and dissemination of European Statistics.
The QAF represents a collection of methods, tools and good practices that are suggested for use and/or are already in use in (some of) the statistical authorities of the ESS, where they have proved to be useful.
The aim of the QAF is to accompany the CoP by providing guidance and examples in the form of more detailed methods and tools as well as good practices for the high-level principles and indicators outlined in the CoP.
The Federal Statistical Office applies a variety of systematic quality assurance measures. Those measures ensure that we produce high-quality statistical information on the basis of methodologically sound and efficient production processes. With its Statistical Quality Offensive, the Federal Statistical Office has introduced a comprehensive quality management system based on Total Quality Management (TQM). The conceptual framework is the EFQM (European Foundation for Quality Management) Excellence Model.
The EFQM Model for Excellence provides us with the basic structure for this assessment and review of the capability and service delivery and fulfilment (performance) of the overall organisation.
Below is some information on the chosen quality assurance and assessment procedures which are carried out:
1. Peer Reviews
The purpose of the ESS peer review is to monitor the compliance with the ES CoP of all partners of the ESS and to identify forward-looking recommendations for improvement. The peer reviews will therefore cover Eurostat, the NSIs and other national authorities (ONAs) responsible for the development, production and dissemination of European Statistics. The peer review will focus on all areas of the ES CoP and cover a carefully selected number of ONAs from each country. The product level will not be monitored.
2. Quality guidelines (Quality Reports)
Since 2005, the Federal Statistical Office has offered Quality Reports on all federal statistics. Through that medium, users are provided with standard-format information on the methods and definitions applied and on the quality of statistical results. Also, the quality reports contain information on other publications and contacts. Such additional information may be used by users to properly interpret the data and to better assess the information value of the data obtained.
3. Other (Quality manual)
The quality of statistical data has always been of great importance in official statistics. The Statistical Offices are recognised as the leading providers of high-quality statistical information about Germany. The aim is to continue to guarantee and expand the quality level achieved. For this reason, the Statistical Offices of the Federation and the Länder have developed and approved a quality manual.
The Quality manual describes the framework for ensuring data quality in official German statistics. On the one hand, it is intended to inform users of statistical data on the quality assurance of statistical results and, on the other hand, to serve as a guideline for employees of the Statistical Offices of the Federation and the Länder and other offices in Germany that produce official statistics.
In addition, workshops and regular meetings are held for professional exchange between the Statistical Offices of the Federation and the Länder in order to minimise processing errors.
11.1.4. Improvements in quality procedures
Developing quality indicators or experimental statistics, e.g. geospatial aspects in statistics.
11.2. Quality management - assessment
Not available
12.1. Relevance - User Needs
Main users:
- Organisation for Economic Co-operation and Development (OECD)
- Food and Agriculture Organization of the United Nations (FAO)
- European Commission:
- Eurostat (ESTAT)
- Directorate-General for Agricultural and Rural Development (DG AGRI)
- Directorate-General for Environment (DG ENV)
- Directorate-General for Climate Action (DG CLIMA)
- Directorate-General for Health and Food Safety (DG SANTE)
- Joint Research Centre (JRC)
- European Environment Agency (EEA)
- Federal Ministry of Food and Agriculture of Germany (BMEL), Ministries of agriculture of the Länder
- Subordinate authorities or institutions like
- Federal Office of Agriculture and Food (BLE)
- Johann Heinrich von Thünen Institut, Federal Research Institute for Rural Areas, Forestry and Fisheries
- Universities, higher education institutions
- Associations like
- German Farmers’ Association (DBV)
- German Horticultural Association (ZVG)
- Companies with downstream and upstream agricultural sectors like
- Food industry
- Agricultural technology
Furthermore, communities, Chambers and offices of Agriculture, agricultural holdings, media and press representatives, political parties and interested private individuals are also the users of these statistics.
12.1.1. Main groups of variables collected only for national purposes
The national legislators supplement the programme of variables for the IFS 2023 by the Agricultural Statistics Law by:
- Land use
Some areas have a detailed subdivision such as permanent crops under glass or high accessible cover and were broken down in orchards for pome fruits, orchards for stone fruits, orchards for berry and nurseries.
- Catch crops
Cultivation of summer catch crop in 2022 and plowing of winter catch crops from winter 2022 until May 2023.
- Rented areas and rents
The leased UAA and the relevant annual rents were broken down by the types of use arable land, permanent grassland and other UAA. In addition, first leases in the last two years were listed as a separate item on the questionnaire. The rented UAA within an entire rented farm was identified separately. Furthermore, the annual lease payments were requested.
- Irrigation
Detailed subdivision of other arable land crops irrigated outdoor.
- Livestock
All livestock variables including equidae were also collected for animals in organic husbandry.
Furthermore, the number of places on the farm used in the last 12 months according to the respective types of poultry was surveyed.
- Labour force
The gender expression ‘diverse’ was asked but not published.
- Annual net income in sole holder holdings
Comparison of the holdings net income with the external net income of the holder/spouse.
Other gainful activities divided into the own agricultural holding and a legally outsourced business.
- Machinery and equipment
Detailed subdivision of number of tractors > 100 kW owned by the holding and refrigerated storage (into animal or planted-based products).
Equipment used for production of renewable energy on agricultural holdings, divided into the own agricultural holding and into a legally outsourced business.
12.1.2. Unmet user needs
The international and national data requirements are defined by the amendment of the national Law on Agricultural Statistics. The mission is to provide objective and independent statistical information of high quality. However, the burden on survey should be within a tolerable range. Therefore, the integration of new variables is a balance act. If we would follow every single data need, the IFS-system would collapse. In a nutshell, all main data needs were met.
12.1.3. Plans for satisfying unmet user needs
Not applicable
12.2. Relevance - User Satisfaction
The international variables for the IFS 2023 were carried out by Eurostat in coordination with the national statistical offices. At the national level, variables are implemented in cooperation with the Federal Ministry of Food and Agriculture of Germany, which involves the ministries of the Länder via the Statistical Committee. In addition, the federal ministries, the Statistical Offices of the Länder, the central associations and representatives from economy and science are represented in the Statistical Advisory Committee, which shall advise the Federal Office on statistical matters and represent the interests of the users of federal statistics. The detailed discussion of individual statistics and of special methodological and technical questions take place in the technical committee Agricultural Statistics of the Statistical Advisory Committee.
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 website: 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
Restricted from publication
13.2.2. Reasons for non-compliant precision requirements in relation to Regulation (EU) 2018/1091
Not applicable. In the IFS, there are no cases where the estimated RSEs are above the determined thresholds.
13.2.3. Reference on method of estimation
See in annex.
Annexes:
13.2.3. Methodology used to calculate relative standard errors
13.2.4. Impact of sampling error on data quality
Low13.3. Non-sampling error
See sub-categories below.
13.3.1. Coverage error
See sub-categories below.
13.3.1.1. Over-coverage - rate
The over-coverage rate is available on Eurostat’s website, at the Circabc website.
The over-coverage rate is unweighted.
The over-coverage rate is calculated as the share of ineligible holdings to the holdings designated for the core data collection. The ineligible holdings include those holdings with unknown eligibility status that are not imputed nor re-weighted for (therefore considered ineligible).
The over-coverage rate is calculated over the holdings in the main frame and if applicable frame extension, for which core data are sent to Eurostat.
13.3.1.1.1. Types of holdings included in the frame but not belonging to the population of the core (main frame and if applicable frame extension)
Below thresholds during the reference periodTemporarily out of production during the reference period
Ceased activities
Merged to another unit
13.3.1.1.2. Actions to minimize the over-coverage error
Removal of ineligible units from the records, leaving unchanged the weights for the other units13.3.1.1.3. Additional information over-coverage error
Over-coverage occurs when holdings do not or no longer belong to the survey population and are therefore not (or no longer) obligated to respond in the survey. In order to prevent this, holdings that are identified as below the threshold or that have abandoned agricultural production are labelled accordingly in the Farm Register and no longer considered when drawing the sampling frame. The Farm Register is regularly updated by the Statistical Offices of the Länder.
Moreover, the questionnaire contains a question whether the holding reaches the coverage thresholds. Holdings which do not fulfil the thresholds are marked during data processing and excluded from further data processing.
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
Errors can basically occur during the determination of the survey population regardless of the method. Under-coverage can occur when holdings that are agricultural holdings in the legal sense are not identified as such and are therefore not surveyed.
The Farm register, which serves as the basis for determining the population, is regularly updated by the statistical offices of the Länder. Primarily various administrative sources as well as information from past surveys are used to update the register.
In order to prevent multiple listings (particularly when adding new respondents), a duplicate search is conducted in the Farm Register. The integrated duplicate search (carried out using the names and locations of the holdings) and constant comparisons with various administrative sources practically exclude multiple listings of the same unit. If a number of holdings are listed under one address – not necessarily an error – this situation is checked immediately (e.g. by telephone). In case of doubt they were surveyed as new respondents.
Accordingly, we consider the degree of under-coverage being very low.
13.3.1.3.2. Types of holdings belonging to the population of the core but not included in the frame (main frame and if applicable frame extension)
New birthsNew units derived from split
13.3.1.3.3. Actions to minimise the under-coverage error
Cf. item 13.3.1.3.1. Under-coverage rate
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
The variables used for the classification are surveyed and checked in the IFS 2023. So misclassification errors cannot occur in this survey.
13.3.1.5. Contact error
No13.3.1.5.1. Actions to minimise the contact error
The respondents can enter changes of address or correct errors in the address in the questionnaire. The address changes provided are checked for postal correctness, then transferred to the Farm Register and promptly displayed during the processing procedure.
Contact data are not always changed entirely in the questionnaire. Obvious incomplete or erroneous information (e.g. post code) or survey documents that cannot be delivered by post are corrected using public registries (telephone books, Internet), in part also using administrative sources, through enquiries with the register of residents, municipalities, trade or regulatory agencies as well as through queries among respondents.
13.3.1.6. Impact of coverage error on data quality
Low13.3.2. Measurement error
See sub-categories below.
13.3.2.1. List of variables mostly affected by measurement errors
The main reasons for missing or erroneous information in the IFS 2023 are the size of the questionnaire and the complexity of the survey. The same problem will occur in the IFS 2026. Furthermore, in the IFS 2023 some variables are considered sensitive by respondents (e.g. ownership or the breakdown of the number of employees and working hours), which lessens response willingness. In addition, and in despite of the great care that was taken in preparing the questionnaire, comprehension difficulties frequently occurred in the questionnaire sections rented areas and rents, areas under high accessible protective covers including greenhouses, irrigation, machinery and storage, as identified by the large number of follow-up enquiries.
All measurement errors were corrected – if recognised as such, for example through distinct deviations from previous year or experienced values – during data editing. Moreover, a small pre-test was conducted in three federal states with voluntary farmers to improve the questionnaire.
13.3.2.2. Causes of measurement errors
Complexity of variablesSensitivity of variables
Unclear questions
Respondents’ inability to provide accurate answers
13.3.2.3. Actions to minimise the measurement error
Pre-testing questionnairePre-filled questions
Explanatory notes or handbooks for enumerators or respondents
On-line FAQ or Hot-line support for enumerators or respondents
Training of enumerators
13.3.2.4. Impact of measurement error on data quality
Moderate13.3.2.5. Additional information measurement error
Not available
13.3.3. Non response error
See sub-categories below.
13.3.3.1. Unit non-response - rate
See item 13.3.1.1.
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
Refusal to participateInability to participate (e.g. illness, absence)
13.3.3.1.2. Actions to minimise or address unit non-response
Follow-up interviewsReminders
Legal actions
Imputation
Weighting
13.3.3.1.3. Unit non-response analysis
A non-response analysis was not conducted.
13.3.3.2. Item non-response - rate
Item non-responses were primarily solved/corrected by means of telephone follow-ups with the farmers. There is no summary of the results on non-responses items.
13.3.3.2.1. Variables with the highest item non-response rate
There were problems with response willingness mainly with variables considered as sensitive such as ownership, the breakdown of the number of workers and work hours and irrigation which required a comparatively large amount of follow-ups with the respondents or machinery and equipment due to difficulties of understanding.
13.3.3.2.2. Reasons for item non-response
RefusalSkip of due question
Farmers do not know the answer
13.3.3.2.3. Actions to minimise or address item non-response
Follow-up interviewsReminders
Legal actions
Imputation
Weighting
13.3.3.3. Impact of non-response error on data quality
Low13.3.3.4. Additional information non-response error
Although answering the survey was obligatory, nevertheless it happened that a survey respondent refused to answer. To increase the willingness to provide information several reminders were sent by the Statistical Offices of the Länder, before – in the last instance – fines were issued.
Some missing values were supplemented by using an imputation method.
13.3.4. Processing error
See sub-categories below.
13.3.4.1. Sources of processing errors
Internet problems affecting filled-in web questionnairesImputation methods
Data processing
13.3.4.2. Imputation methods
Cold-deck imputationPrevious data for the same unit
13.3.4.3. Actions to correct or minimise processing errors
The plausibility checks minimised possible processing errors.
Most missing or erroneous information should be identified by the extensive data editing program. Furthermore, plausibility checks are part of the online questionnaire. Where implausible or missing information occurred in the data material, they were completed or corrected by means of telephone follow-ups with the farmers, comparison with individual data of other holdings, comparison with previous surveys or comparison with administrative sources.
The AGRA2010 processing program was the main instrument for completeness and plausibility checks. This program stores more than 600 obligatory and facultative error tests and many automated corrections. In the following, we explain the differentiation of these error messages.
- Obligatory errors must be adjusted in all cases (e.g. missing age for an individual) and are obvious, unacceptable erroneous information or inconsistencies in correlations of data.
- Facultative errors occur when information or correlations of information are possible, but either are improbable or rare, taking into consideration the operating and economic circumstances in agriculture, or originate from chronologically different individual surveys and therefore need not necessarily match (e.g. maximum controls). In such cases, we checked whether and, if so, in what way correction of the relevant information is necessary through individual and targeted follow-ups with the holding or, from case to case, drawing on other information.
Automatically adjusted errors are errors that can be corrected without a doubt and unequivocally based on the available information without follow-up interviews or data matching (e.g. by inserting missing total values).
13.3.4.4. Tools and staff authorised to make corrections
All subsequent work on the data (follow-ups, corrections, input of data from administrative sources, etc.) was done by the staff members of the Statistical Offices of the Länder using the AGRA2010 processing and data editing program for agricultural statistics.
13.3.4.5. Impact of processing error on data quality
Low13.3.4.6. Additional information processing error
Processing errors occur during processing of statistics, for example during signing, data capture or corrections made during data editing. To prevent processing errors, the programs used were tested extensively. To prevent signature errors or data capture errors, corresponding signature and value range checks were recorded in the data editing program.
13.3.5. Model assumption error
No model assumptions were applied.
14.1. Timeliness
See sub-categories below.
14.1.1. Time lag - first result
In Germany, the Main Survey of Land Use was part of the IFS 2023. Preliminary national results referring to agricultural land use were published approximately 5 months after the survey's reference day (1 March 2023), i.e. in August 2023.
14.1.2. Time lag - final result
The publication of the final results (statistical reports) of the IFS 2023 was divided up into 14 parts (series) (cf. Section 10.2.3 Title, publisher, year and link) and carried out in a number of steps. The first set of statistical reports was published 3 months (March 2024) and the last 5 months (May 2024) 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
The results of the IFS 2023 were delivered to Eurostat on time on 11 November 2024. The national publication of the national results was also on time with the first press release on 16 January 2024 and the last, final results were published on 29 May 2024.
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 deviations
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
Before data collection, Germany requested approval from Eurostat for using higher thresholds than those foreseen by Regulation (EU) 2018/1091 on integrated farm statistics. According to article 3 of said regulation, Member States can ask permission to raise thresholds fixed in Annex II of the regulation in case they can prove that the required coverage is met. Germany submitted the request in May 2022. The provided calculations showed that:
- The holdings above at least one of the IFS threshold would cover more that 98% of the total standard output in Germany
- The holdings above at least one of the raised thresholds would cover at least 98% of the UAA and at least 98% of the LSUs in Germany
Eurostat accepted the suggested thresholds in June 2022.
15.1.3.2. Differences between the national thresholds and the thresholds used for the data sent to Eurostat
No differences.
15.1.3.3. Reasons for differences
Not applicable.
15.1.4. Definitions and classifications of variables
See sub-categories below.
15.1.4.1. Deviations from Regulation (EU) 2018/1091 and EU handbook
In Germany the irrigation module has been designed to capture only irrigation in the outdoor areas. There is no data available for the area under glass.
The nationally published data differ from the data published by Eurostat, concerning the variables UAAT_IT (Total irrigated utilised agricultural area - outdoor), SPLR (Sprinkler irrigation) and DRIP (Drip irrigation). This is following data adjustments requested by Eurostat to ensure that Total irrigable utilised agricultural area - outdoor (UAAT_IB) is less than or equal to Total utilised agricultural area - outdoor (UAAT), and that Total irrigable utilised agricultural area - outdoor (UAAT_IB) is larger than or equal to Total irrigated utilised agricultural area - outdoor (UAAT_IT).
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 Circabc website.
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
LSU coefficients used in the data published at national level:
| Variable | National Livestock Unit |
|---|---|
| Bovine animals, less than 1 year old | 0.300 |
| Bovine animals, 1 to less than 2 years old | 0.700 |
| Bovine animals, 2 years old and over including cows | 1.000 |
| Piglets | 0.020 |
| Breeding sows | 0.300 |
| Other pigs | 0.120 |
| Sheep less than 1 year old excluding mated young ewes | 0.050 |
| Ewes including milk ewes | 0.100 |
| Breeding rams and other sheep | 0.100 |
| Goats | 0.080 |
| Poultry | 0.004 |
15.1.4.1.5. Livestock included in “Other livestock n.e.c.”
There are no differences for "Other livestock n.e.c." according to the EU handbook.
15.1.4.2. Reasons for deviations
Due to a more detailed subdivision of area variables the national classification of the general type of farming* differs slightly from the IFS.
* Commission delegated regulation (EU) No 1198/2014.
The national livestock units are harmonised with the other national agricultural statistics. Furthermore, these national livestock units have not been adjusted. Therefore, a time comparison is possible.
In Germany, areas collected in the irrigation module only include outdoor areas. In line with that, all other irrigation variables refer to outdoor areas as well. Because of that, variables of the irrigation module are comparable with former data collections.
15.1.5. Reference periods/days
See sub-categories below.
15.1.5.1. Deviations from Regulation (EU) 2018/1091
Deviations from Regulation (EU) 2018/1091 are
- Irrigation: Calendar year 2022
- Other gainful activities: Calendar year 2022
- Orchard module: Calendar year 2022
15.1.5.2. Reasons for deviations
- In order to ensure comparability over time of the variables of other gainful activities and irrigated area, a different reference period was selected.
- The survey of tree fruit growing 2022 served as a preceding survey for variables of the orchard module.
15.1.6. Common land
The concept of common land exists15.1.6.1. Collection of common land data
Yes15.1.6.2. Reasons if common land exists and data are not collected
Not applicable
15.1.6.3. Methods to record data on common land
Common land is included in the land of entities meeting the definition of agricultural holdings, having own managers.15.1.6.4. Source of collected data on common land
Surveys15.1.6.5. Description of methods to record data on common land
The Commission Regulation (EU) 2015/1391 defines common land as a virtual unit created for the purposes of data collection and compilation, which includes the agricultural land used by agricultural holdings but not directly owned by them, i.e. land for which common rights exist (commons).
It does not include common land that is leased or given for cultivation free of charge. Each common land unit has a holder and a manager.
Common land only exists in Bavaria.
15.1.6.6. Possible problems in relation to the collection of data on common land and proposals for future data collections
We do not experience problems to collect data on common land.
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
According to the Commission Implementing Regulation (EU) 2021/2286 variables for organic farming were collected according to Regulation (EU) 2018/848 of 30 May 2018 on organic production and labelling of organic products that repeals 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
No differences across regions within the country.
15.2. Comparability - over time
See sub-categories below.
15.2.1. Length of comparable time series
Since the last adjustment of the survey design and coverage limits for the FSS 2010, the data collected can be considered comparable. The last change was in the threshold category poultry, where the number of poultry were replaced by the holding places for poultry.
For the IFS surveys prior to that, the possibilities for comparison exist, but some aspects have to be considered.
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 some changes but not enough to warrant the designation of a break in series15.2.5.2. Description of changes
Separated recording of "Processing of agricultural products, excluding production of wine (e.g. meat processing, cheese production)" and "Direct marketing of agricultural products".
Taking into account the IFS 2023 handbook, it is sufficient for OGA_FPRDPRC (Other gainful activities related to processing of farm products) if a holding processes agricultural products - direct marketing of these products is not necessarily required. According to the IFS 2023 handbook "Shops where own products are sold" belongs to OGA_AGRHLD (Other gainful activities directly related to the agricultural holding n.e.c.).
This differentiated filling of OGA_FPRDPRC and OGA_AGRHLD is now possible by recording the two separate variables. As a result, there is an increase in the number of farms in OGA_AGRHLD and a decrease in OGA_FPRDPRC for IFS 2023 compared to IFS 2020.
15.2.6. Reference periods/days
See sub-categories below.
15.2.6.1. Changes since the last data transmission to Eurostat
There have been no changes15.2.6.2. Description of changes
Not applicable
15.2.7. Common land
See sub-categories below.
15.2.7.1. Changes in the methods to record common land since the last data transmission to Eurostat
There have been some changes but not enough to warrant the designation of a break in series15.2.7.2. Description of changes
- In 2020, holdings have been questioned if they are a common land unit.
- In 2023, the information of common land unit has been delivered via the register of holdings.
15.2.8. Explanations for major trends of main variables compared to the last data transmission to Eurostat
Evolution of main aggregates for quantitative variables
- PECRS: relevant increase compared to 2020, which could be possible. but also maybe due to some farmers that wrongly include areas covered by hail protection.
- J3000TE: There is a visible increase between the survey of land use 2022 and IFS 2023 mainly caused by DE1 and DEE. The survey of land use 2024 shows comparable values for these regions.
- K0000T is part of the NSNE variables for IFS 2023 but was collected for IFS 2020.
- Land use: the comparison with the survey of land use 2021 and 2022 and IFS 2020 and 2016 did not point out errors, the difference in years may be due to, for example, comparable variations between survey years, a continuous increase/decrease over time or confirmed values by the Federal States.
- There is a decrease in the number of female family workers (12%) that can affect the AWU of this employee group.
- Pigs: The comparison with the annual livestock surveys confirms the development.
- A5000X5100: Development mainly caused by DE9, but methodological reasons cannot be ruled out. A further reason might be the avian influenza that also could be responsible for lower values in DE9.
Evolution of holdings breakdown by legal personality
In the light of very stable trends between 2020 and 2023 under the point of view of farm composition by UAA LSU size and SO class, it appears interesting to see the increase of share of FARM_HLD at the expenses of FARM_HLD_SPOUFAM.
For IFS 2020 cold deck imputation was used to fill in the variable expression “FARM_HLD_SPOUFAM” for some farms that only were part of CORE but not of the LAFO module. If holdings were part of LAFO module a hot deck imputation was possible.
It seems that the use of different imputation methods in 2020 might have affected the results. If you only compare the results of holdings that are part of the LAFO module (2020 and 2023) then there are less differences visible.
Evolution of holdings breakdown by working time of managers
It appears that from 2020 there was a partial shift of working time from PC100 to PC75T99, as well as the relative increase of share of lower working time classes in 2023.
Farms that only were part of CORE received a slightly different questionnaire than holdings that were part of the LAFO module in IFS 2020.
In LAFO farms had to provide the average number of hours worked per week for the total holding and the average number of hours worked per week in other gainful activities in the agricultural holding. With these two variables we computed the average number of hours of farm work per week for the agricultural holding. Holdings that answered only CORE directly had to provide the average number of hours of farm work per week for the agricultural holding.
For IFS 2023 all holdings received the same questionnaire as described for the IFS 2020 LAFO module. The stated differences might result in a different kind of response behaviour and the visible differences in the diagram.
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
The holding-ID is unique and therefore, in principle, the micro data can only be blended nationally with other agricultural statistics (e.g. livestock and area statistics).
Comparisons with other data sources (previous land use, livestock surveys, FSS or administrative sources) were done for specific variables for data editing purposes. Striking deviations were then clarified by means of follow-ups with the respondents.
15.3.4. Coherence at macro level with data collections in other domains in agriculture
See sub-categories below.
15.3.4.1. Analysis of coherence at macro level
Yes15.3.4.2. Results of analysis at macro level
Coherence cross-domain: IFS vs ORGANIC CROP PRODUCTION
There are slight differences in the ARAT_ORG and UAAXK0000_ORG between the 2 data sets, the differences result from qualitative limitations of the estimation methods used for the reference year 2022, which resulted in a significant underestimation for sheep stocks (A4100) and industrial crops (I0000). In the case of the area data, this underreporting is also reflected in the aggregated results (ARA; UAAXK0000). Based on the new primary survey in 2023, high-quality results are now once again available, which, however, lead to a jump in the time series.
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 lower the burden of the respondents and the Statistical Offices of the Länder and lessen costs, in IFS years the Survey of Land Use is conducted as an integrated element of the IFS. Additionally, the online reporting is mandatory. This obligation also lowered the survey costs.
Reporting burden for the holdings was also reduced by using as many administrative data sources as possible. For example, data from the Integrated Administration and Control System, Bovine Register and European Agricultural Fund for Rural Development. Furthermore, the part of enterprise groups was derived from information available in statistical database. However, this procedure not always reduced the cost of data handling in the Statistical Offices, too.
16.2. Efficiency gains since the last data transmission to Eurostat
None16.2.1. Additional information efficiency gains
Not available
16.3. Average duration of farm interview (in minutes)
See sub-categories below.
16.3.1. Core
Not available.
16.3.2. Module ‘Labour force and other gainful activities‘
Not available.
16.3.3. Module ‘Rural development’
Not available.
16.3.4. Module ‘Animal housing and manure management’
Restricted from publication
16.3.5. Module ‘Irrigation’
Not available.
16.3.6. Module ‘Soil management practices’
Not available.
16.3.7. Module ‘Machinery and equipment’
Not available.
16.3.8. Module ‘Orchard’
Not available.
16.3.9. Module ‘Vineyard’
Restricted from publication
17.1. Data revision - policy
We define a data revision as all subsequent modifications to data that have already been released to the public. This is the case when at first preliminary statistical results are published and final results at a later time. On principle, preliminary data are always identified as such in all publications.
Errors in publications can also be the reason for data revisions. The treatment of errors in publications is prescribed at the Federal Statistical Office in a special guideline (Richtlinie zum Umgang mit Veröffentlichungsfehlern). Should publication errors occur they are allocated to error categories – depending on the severity of the error – and treated depending on the error category. Corrected data are then identified in the national publications by a special signature. The Statistical Offices of the Länder have comparable guidelines for handling publication errors or use a comparable procedure for revisions.
17.2. Data revision - practice
In Germany, the Main Survey of Land Use was part of the IFS 2023. Preliminary national results referring to agricultural land use were published approximately 5 months after the survey's reference day (1 March 2023), i.e. in August 2023. The publication of the first IFS 2023 final results was approximately 3 months after the last day of the reference period in March 2024. So, there was one planned data revision for the IFS 2023.
Up to now, no publication errors emerged.
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
The population for the IFS 2023 was established on the basis of the Farm Register which is regularly updated by results from various agricultural statistical surveys and by information from administrative sources such as the Herkunfts- und Informationssystem für Tiere (HIT, Bovine Register). The adds and outs (e.g. newly established or abandoned holdings) ascertained in intermediate years in the Farm Register, which result from the regular updates of survey units using administrative sources, were taken into consideration.
With a population of 254,000 holdings, the sampling fraction is approx. 0.3 (n/N)*.
The information from the Farm Register is not subject to any fixed updating schedules, the contents of the Farm Register are constantly (but at least once a year) updated by the statistical offices of the Länder.
* Ratio of the size of a sample (n) to the population size (N)
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
For IFS 2023, a single-stage stratified random sample of holdings is used. No sub-samples are drawn, neither for individual survey variables.
The following stratification variables are used for the stratification procedure: NUTS 2 regions, the size classes of the utilised agricultural area, the relevant crop and livestock variables (e.g. cereals for the production of grain, bovine animals, the farming methods of the holdings (organic/conventional) and the field of specialisation of holdings at NUTS 2 level. The latter encompass holdings that stand out from the farm population through fields of specialisation (e.g. large amounts of livestock, special crops, horticulture) or through the special importance of this production. There is also an additional stratum for the new holdings.
18.1.2.2.1. Name of sampling design
Stratified one-stage random sampling18.1.2.2.2. Stratification criteria
Unit sizeUnit location
Unit specialization
18.1.2.2.3. Use of systematic sampling
No18.1.2.2.4. Full coverage strata
The strata plan for the IFS 2023 included various full coverage strata, e.g. holdings with large numbers of livestock and organic farming. This report does not contain a list of the full coverage strata for the NUTS 2 regions since these are classified nationally as confidential.
18.1.2.2.5. Method of determination of the overall sample size
Since a sample of maximum 80,000 holdings is permitted by national law for the IFS 2023, only approximately 79,771 holdings were selected during the sampling procedure for the IFS in order to take all new holdings into consideration. After excluding holdings that were found below thresholds or did not provide their data, the sample size for the IFS 2023 was 74,782 holdings, according to the sampling fraction.
18.1.2.2.6. Method of allocation of the overall sample size
Neymann allocation18.1.3. Core data collection on the frame extension
See sub-categories below.
18.1.3.1. Coverage of agricultural holdings
Not applicable18.1.3.2. Sampling design
Not applicable
18.1.3.2.1. Name of sampling design
Not applicable18.1.3.2.2. Stratification criteria
Not applicable18.1.3.2.3. Use of systematic sampling
Not applicable18.1.3.2.4. Full coverage strata
Not applicable
18.1.3.2.5. Method of determination of the overall sample size
Not applicable
18.1.3.2.6. Method of allocation of the overall sample size
Not applicable18.1.4. Module “Labour force and other gainful activities”
See sub-categories below.
18.1.4.1. Coverage of agricultural holdings
Sample18.1.4.2. Sampling design
For IFS 2023 modules, a single-stage stratified random sample of holdings is used. No sub-samples are drawn, neither for individual survey variables.
The following stratification variables are used for the stratification procedure: NUTS 2 regions, the size classes of the utilised agricultural area, the relevant crop and livestock variables (e.g. cereals for the production of grain, bovine animals, the farming methods of the holdings (organic/conventional) and the field of specialisation of holdings at NUTS 2 level. The latter encompass holdings that stand out from the farm population through fields of specialisation (e.g. large amounts of livestock, special crops, horticulture) or through the special importance of this production. There is also an additional stratum for the new holdings.
18.1.4.2.1. Name of sampling design
Stratified one-stage random sampling18.1.4.2.2. Stratification criteria
Unit sizeUnit location
Unit specialization
18.1.4.2.3. Use of systematic sampling
No18.1.4.2.4. Full coverage strata
The strata plan for the IFS 2023 included various full coverage strata, e.g. holdings with large numbers of livestock and organic farming. This report does not contain a list of the full coverage strata for the NUTS 2 regions since these are classified nationally as confidential.
18.1.4.2.5. Method of determination of the overall sample size
Since a sample of maximum 80,000 holdings is permitted by national law for the IFS 2023, only approximately 79,771 holdings were selected during the sampling procedure for the IFS in order to take all new holdings into consideration. After excluding holdings that were found below thresholds or did not provide their data, the sample size for the IFS 2023 was 74,782 holdings, according to the sampling fraction.
18.1.4.2.6. Method of allocation of the overall sample size
Neymann allocation18.1.4.2.7. If sampled from the core sample, the sampling and calibration strategy
Not applicable18.1.5. Module “Rural development”
See sub-categories below.
18.1.5.1. Coverage of agricultural holdings
Sample18.1.5.2. Sampling design
Cf. item 18.1.4.2 Sampling design
18.1.5.2.1. Name of sampling design
Stratified one-stage random sampling18.1.5.2.2. Stratification criteria
Unit sizeUnit location
Unit specialization
18.1.5.2.3. Use of systematic sampling
No18.1.5.2.4. Full coverage strata
Cf. item 18.1.4.2.4 Full coverage strata.
18.1.5.2.5. Method of determination of the overall sample size
Cf. item 18.1.4.2.5 Method of determination of the overall sample size.
18.1.5.2.6. Method of allocation of the overall sample size
Neymann allocation18.1.5.2.7. If sampled from the core sample, the sampling strategy and calibration strategy
Not applicable18.1.6. Module “Animal housing and manure management module”
Restricted from publication
18.1.6.1. Coverage of agricultural holdings
Restricted from publication
18.1.6.2. Sampling design
Restricted from publication
18.1.6.2.1. Name of sampling design
Restricted from publication
18.1.6.2.2. Stratification criteria
Restricted from publication
18.1.6.2.3. Use of systematic sampling
Restricted from publication
18.1.6.2.4. Full coverage strata
Restricted from publication
18.1.6.2.5. Method of determination of the overall sample size
Restricted from publication
18.1.6.2.6. Method of allocation of the overall sample size
Restricted from publication
18.1.6.2.7. If sampled from the core sample, the sampling strategy and calibration strategy
Restricted from publication
18.1.7. Module ‘Irrigation’
See sub-categories below.
18.1.7.1. Coverage of agricultural holdings
Sample18.1.7.2. Sampling design
Cf. item 18.1.4.2 Sampling design
18.1.7.2.1. Name of sampling design
Stratified one-stage random sampling18.1.7.2.2. Stratification criteria
Unit sizeUnit location
Unit specialization
18.1.7.2.3. Use of systematic sampling
No18.1.7.2.4. Full coverage strata
Cf. item 18.1.4.2.4 Full coverage strata.
18.1.7.2.5. Method of determination of the overall sample size
Cf. item 18.1.4.2.5 Method of determination of the overall sample size.
18.1.7.2.6. Method of allocation of the overall sample size
Neymann allocation18.1.7.2.7. 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
Cf. item 18.1.4.2 Sampling design
18.1.8.2.1. Name of sampling design
Stratified one-stage random sampling18.1.8.2.2. Stratification criteria
Unit sizeUnit location
Unit specialization
18.1.8.2.3. Use of systematic sampling
No18.1.8.2.4. Full coverage strata
Cf. item 18.1.4.2.4 Full coverage strata.
18.1.8.2.5. Method of determination of the overall sample size
Cf. item 18.1.4.2.5 Method of determination of the overall sample size.
18.1.8.2.6. Method of allocation of the overall sample size
Neymann allocation18.1.8.2.7. If sampled from the core sample, the sampling strategy and calibration strategy
Not applicable18.1.9. Module ‘Machinery and equipment’
See sub-categories below.
18.1.9.1. Coverage of agricultural holdings
Sample18.1.9.2. Sampling design
Cf. item 18.1.4.2 Sampling design
18.1.9.2.1. Name of sampling design
Stratified one-stage random sampling18.1.9.2.2. Stratification criteria
Unit sizeUnit location
Unit specialization
18.1.9.2.3. Use of systematic sampling
No18.1.9.2.4. Full coverage strata
Cf. item 18.1.4.2.4 Full coverage strata.
18.1.9.2.5. Method of determination of the overall sample size
Cf. item 18.1.4.2.5 Method of determination of the overall sample size.
18.1.9.2.6. Method of allocation of the overall sample size
Neymann allocation18.1.9.2.7. If sampled from the core sample, the sampling strategy and calibration strategy
Not applicable18.1.10. Module ‘Orchard’
See sub-categories below.
18.1.10.1. Coverage of agricultural holdings
Sample18.1.10.2. Sampling design
Cf. item 18.1.4.2 Sampling design
18.1.10.2.1. Name of sampling design
Stratified one-stage random sampling18.1.10.2.2. Stratification criteria
Unit sizeUnit location
Unit specialization
18.1.10.2.3. Use of systematic sampling
No18.1.10.2.4. Full coverage strata
Cf. item 18.1.4.2.4 Full coverage strata.
18.1.10.2.5. Method of determination of the overall sample size
Cf. item 18.1.4.2.5 Method of determination of the overall sample size.
18.1.10.2.6. Method of allocation of the overall sample size
Neymann allocation18.1.10.2.7. If sampled from the core sample, the sampling strategy and calibration strategy
Not applicable18.1.11. Module ‘Vineyard’
Restricted from publication
18.1.11.1. Coverage of agricultural holdings
Restricted from publication
18.1.11.2. Sampling design
Restricted from publication
18.1.11.2.1. Name of sampling design
Restricted from publication
18.1.11.2.2. Stratification criteria
Restricted from publication
18.1.11.2.3. Use of systematic sampling
Restricted from publication
18.1.11.2.4. Full coverage strata
Restricted from publication
18.1.11.2.5. Method of determination of the overall sample size
Restricted from publication
18.1.11.2.6. Method of allocation of the overall sample size
Restricted from publication
18.1.11.2.7. If sampled from the core sample, the sampling strategy and calibration strategy
Restricted from publication
18.1.12. Software tool used for sample selection
The preliminary assessments for the sample design were conducted using the statistical software SAS. The method of “controlled sampling” was used by the Statistical Offices of the Länder for the random selection of the sample holdings. Using the national STIA sampling program it was possible to draw any number of independent samples for this. For each of these samples an extrapolation of the stratification variables was carried out. The extrapolated results were then compared with the corresponding totals of the sampling frame and the sample with the least deviations compared with the corresponding total values of the control variables was chosen.
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 website: 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
None18.1.14. Innovative approaches
The information on the innovative approaches and the quality methods applied is available on Eurostat’s website, at the website: Additional data - Eurostat (europa.eu).
18.2. Frequency of data collection
The IFS is conducted every 3 to 4 years.
18.3. Data collection
See sub-categories below.
18.3.1. Methods of data collection
Paper auto-questionnaireTelephone, non-electronic version
Use of Internet
18.3.2. Data entry method, if paper questionnaires
Manual18.3.3. Questionnaire
Please find the questionnaire in annex.
Annexes:
18.3.3. Questionnaire in English
18.3.3. Questionnaire in German
18.4. Data validation
See sub-categories below.
18.4.1. Type of validation checks
Data format checksCompleteness checks
Routing 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
Staff from local departments18.4.3. Tools used for data validation
The national AGRA2010 processing program was the main instrument for completeness and plausibility checks. This program stores more than 600 obligatory and facultative error tests and many automated corrections.
18.5. Data compilation
Since IFS 2023 is a random sample, the data compiled must be extrapolated. The results of the sample were extrapolated using the Horvitz–Thompson estimator. The weight is the inverse value of the sampling fraction, i.e. per stratum N/n whereby N = stratum size and n = sample size per stratum. The smaller the sample size in each stratum, the greater the extrapolation factor. Holdings from a full coverage stratum, e.g. new holdings or holdings with large amounts of livestock and organic farming are given the weight 1.
The extrapolation factor for sample holdings is adjusted for “true” non-responses. For this, a correction factor was included in the extrapolation method in the sample survey. Under the assumption that the “true” non-responses possess the same structure as the units that responded, the mathematical adjustment was made so that only the observed values of the effective sample size were used to identify the extrapolation factor, i.e. nstrata minus the number of “true” non-responses within strata.
18.5.1. Imputation - rate
Not available. Some imputations for different missing values were used, but there is no imputation rate.
18.5.2. Methods used to derive the extrapolation factor
Design weightNon-response adjustment
18.6. Adjustment
Covered under Data compilation.
18.6.1. Seasonal adjustment
Not applicable to Integrated Farm Statistics, because it collects structural data on agriculture.
See sub-categories below.
19.1. List of abbreviations
AGRA2010 – Processing program for completeness and plausibility checks
AgrStatG – Agrarstatistikgesetz (Law on Agricultural Statistics)
AMT – AGRATAB Management Tool
AWU – Annual working unit
BLE – Federal Office of Agriculture and Food
BMEL – Federal Ministry of Food and Agriculture of Germany
BStatG – Bundesstatistikgesetz (Federal Statistics Law)
CAP – Common Agricultural Policy
CoP – Code of Practice
CORE – General, crops and livestock variables of Annex III of Regulation (EU) 2018/1091
CSV – Comma-separated values
DBV – German Farmers’ Association
DG AGRI – Directorate-General for Agricultural and Rural Development
DG CLIMA – Directorate-General for Climate Action
DG ENV – Directorate-General for Environment
DG SANTE – Directorate-General for Health and Food Safety
EEA – European Environment Agency
EFQM – European Foundation for Quality Management
ELER – European Agricultural Fund for Rural Development
ES CoP – European Statistics Code of Practice
ESS – European Statistical System
ESTAT – Eurostat
FAO – Food and Agriculture Organization of the United Nations
FAQ – Frequently Asked Questions
FSS – Farm Structure Survey
GHMAN – Geheimhaltungsmanagementtool
HIT – Herkunfts- und Informationssystem für Tiere (Bovine Register)
HTML – Hypertext Markup Language
IACS – Integrated Administration and Control System
IFS – Integrated Farm Statistics
InVeKoS – Integriertes Verwaltungs- und Kontrollsystem (IACS)
JRC – Joint Research Centre
LAFO – Labour force and other gainful activities
LSU – Livestock unit
NSI – National Statistical Institute
NSNE – Non-significant and non-existent
NUTS – Nomenclature of Territorial Units for Statistics
OECD – Organisation for Economic Co-operation and Development
ONAs – Other National Authorities
QAF – Quality Assurance Framework
RSE – Relative standard error
SGM – Standard gross margin
SO – Standard output
STIA – Sampling program
TQM – Total Quality Management
UAA – Utilised agricultural area
ZVG – German Horticultural Association
19.2. Additional comments
When a variable has a low or zero prevalence in a Member State, that variable may be excluded from the data collection subject to providing information duly justifying the exclusion to the Commission (Eurostat) in the calendar year preceding the reference year (Regulation (EU) 2018/1091 Article 7 Nr. 9).
In Germany, IFS 2023 was carried out as sample survey that is designed for NUTS 2 regions. Therefore, information on non-significant and non-existent variables (NSNE) below the NUTS 2 regions is not reliable.
The data describe the structure of agricultural holdings providing the general characteristics of farms and farmers and information on their land, livestock and labour force. They also describe production methods, rural development measures and agro-environmental aspects that look at the impact of agriculture on the environment.
The data are used by public, researchers, farmers and policy-makers to better understand the state of the farming sector and the impact of agriculture on the environment. The data follow up the changes in the agricultural sector and provide a basis for decision-making in the Common Agricultural Policy (CAP) and other European Union policies.
The statistical unit is the agricultural holding (farm). The aggregated results are disseminated through statistical tables. The data are presented at different geographical levels and over periods.
The data collections are organised in line with Regulation (EU) 2018/1091 and have a new structure, consisting of a core data set and several modules. The regulation covers the data collections in 2020 (the agricultural census), 2023 and 2026. The data are as comparable and coherent as possible with the other European countries.
29 April 2025
The list of core variables is set in Annex III of Regulation (EU) 2018/1091.
The descriptions of the core variables as well as the lists and descriptions of the variables for the modules collected in 2023 are set in Commission Implementing Regulation (EU) 2021/2286.
The following groups of variables are collected in 2023:
- for core: location of the holding, legal personality of the holding, manager, type of tenure of the utilised agricultural area, variables of land, organic farming, irrigation on cultivated outdoor area, variables of livestock, organic production methods applied to animal production;
- for the module "Labour force and other gainful activities": farm management, family labour force, non-family labour force, other gainful activities directly and not directly related to the agricultural holding;
- for the module "Rural development": support received by agricultural holdings through various rural development measures;
- for the module “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. Due to article 7 (8) of Regulations (EU) 2018/1091 peaches area, nectarines area, apricots area, oranges area, small citrus fruits area, lemons area, olives area, grapes for table use area, grapes for raisins area, each one by age of plantation and density of trees are not part of the German IFS 2023
* In line with the definition of Eurostat and the IFS 2023 handbook, a holding that owns, rents or uses a milking robot or an automatic feeding system is considered to use robotics. In context of IFS 2023 robotics refer to machines where increased levels of intelligence are added to the machines for its autonomous work that perform crop or livestock production tasks under human supervision, albeit without direct human intervention.
See sub-category below.
See sub-categories below.
See sub-categories below.
See sub-categories below.
See categories below.
Three kinds of units are generally used:
- the units of measurement for the variables (area in hectares, livestock in heads, places or LSU (livestock units), labour force in persons or AWU (annual working units), standard output in Euro,
- storage and volume of water in cubic meters and
- the number of agricultural holdings having these characteristics.
Since IFS 2023 is a random sample, the data compiled must be extrapolated. The results of the sample were extrapolated using the Horvitz–Thompson estimator. The weight is the inverse value of the sampling fraction, i.e. per stratum N/n whereby N = stratum size and n = sample size per stratum. The smaller the sample size in each stratum, the greater the extrapolation factor. Holdings from a full coverage stratum, e.g. new holdings or holdings with large amounts of livestock and organic farming are given the weight 1.
The extrapolation factor for sample holdings is adjusted for “true” non-responses. For this, a correction factor was included in the extrapolation method in the sample survey. Under the assumption that the “true” non-responses possess the same structure as the units that responded, the mathematical adjustment was made so that only the observed values of the effective sample size were used to identify the extrapolation factor, i.e. nstrata minus the number of “true” non-responses within strata.
See sub-categories below.
The frequency of the IFS is every three to four years.
See sub-categories below.
See sub-categories below.
See sub-categories below.
