1.1. Contact organisation
Statistics Austria
1.2. Contact organisation unit
Directorate Spatial Statistics
Agriculture and Forestry, VIS
1.3. Contact name
Confidential because of GDPR
1.4. Contact person function
Confidential because of GDPR
1.5. Contact mail address
Statistics Austria
Directorate Spatial Statistics
Guglgasse 13
1110 Vienna
Austria
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
24 April 2025
2.2. Metadata last posted
5 May 2025
2.3. Metadata last update
24 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 2019/2020 (the agricultural census), 2023 and 2026. The data are as comparable and coherent as possible with the other European countries.
3.2. Classification system
Data are arranged in tables using many classifications. Please find below information on most classifications.
The classifications of variables are available in Annex III of Regulation (EU) 2018/1091 and in Commission Implementing Regulation (EU) 2021/2286.
The farm typology means a uniform classification of the holdings based on their type of farming and their economic size. Both are determined on the basis of the standard gross margin (SGM) (until 2007) or standard output (SO) (from 2010 onward) which is calculated for each crop and animal. The farm type is determined by the relative contribution of the different productions to the total standard gross margin or the standard output of the holding.
The territorial classification uses the NUTS classification to break down the regional data. The regional data is available at NUTS level 2.
3.3. Coverage - sector
The statistics cover agricultural holdings undertaking agricultural activities as listed in item 3.5 below and meeting the minimum coverage requirements (thresholds) as listed in item 3.6 below.
3.4. Statistical concepts and definitions
The list of core variables is set in Annex III of Regulation (EU) 2018/1091.
The descriptions of the core variables as well as the lists and descriptions of the variables for the modules collected in 2023 are set in Commission Implementing Regulation (EU) 2021/2286.
The following groups of variables are collected in 2023:
- for core: location of the holding, legal personality of the holding, manager, type of tenure of the utilised agricultural area, variables of land, organic farming, irrigation on cultivated outdoor area, variables of livestock, organic production methods applied to animal production;
- for the module "Labour force and other gainful activities": farm management, family labour force, non-family labour force, other gainful activities directly and not directly related to the agricultural holding;
- for the module "Rural development": support received by agricultural holdings through various rural development measures;
- for the module “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, peaches area, apricots area, each one by age of plantation and density of trees. In Austria, there is only an obligation to collect the relevant data on apple orchards due to the underlying requirements.
3.5. Statistical unit
See sub-category below.
3.5.1. Definition of agricultural holding
The agricultural holding is a single unit, both technically and economically, that has a single management and that undertakes economic activities in agriculture in accordance with Regulation (EC) No 1893/2006 belonging to groups:
- A.01.1: Growing of non-perennial crops;
- A.01.2: Growing of perennial crops;
- A.01.3: Plant propagation;
- A.01.4: Animal production;
- A.01.5: Mixed farming or;
- The “maintenance of agricultural land in good agricultural and environmental condition” of group A.01.6 within the economic territory of the Union, either as its primary or secondary activity.
Regarding activities of class A.01.49, only the activities “Raising and breeding of semi-domesticated or other live animals” (with the exception of raising of insects) and “Bee-keeping and production of honey and beeswax” are included.
3.6. Statistical population
See sub-categories below.
3.6.1. Population covered by the core data sent to Eurostat (main frame and if applicable frame extension)
The thresholds of agricultural holdings are available in the annex.
Annexes:
3.6.1 Thresholds of agricultural holdings
3.6.1.1. Raised thresholds compared to Regulation (EU) 2018/1091
No3.6.1.2. Lowered and/or additional thresholds compared to Regulation (EU) 2018/1091
Yes3.6.2. Population covered by the data sent to Eurostat for the modules “Labour force and other gainful activities”, “Rural development” and “Machinery and equipment”
The same population of agricultural holdings defined in item 3.6.1
3.6.3. Population covered by the data sent to Eurostat for the module “Animal housing and manure management”
Restricted from publication
3.6.4. Population covered by the data sent to Eurostat for the module “Irrigation”
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 subset of agricultural holdings defined in item 3.6.2 with arable land and farms with drainage area.
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.
According to these thresholds, Austria would only have to transmit the data for apple orchards. However, Austria also provides other types of fruits that are considered relevant nationally: pears, peaches and apricots (exception: nectarines are included with peaches).
3.6.7. Population covered by the data sent to Eurostat for the module “Vineyard”
Restricted from publication
3.7. Reference area
See sub-categories below.
3.7.1. Geographical area covered
The entire territory of the country.
3.7.2. Inclusion of special territories
Not applicable.
3.7.3. Criteria used to establish the geographical location of the holding
The main building for productionThe location where all agricultural activities are situated
The residence of the farmer (manager) not further than 5 km straight from the farm
3.7.4. Additional information reference area
Not available.
3.8. Coverage - Time
Farm structure statistics in our country cover the period from 1970 onwards. 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 etc.) and
- the number of agricultural holdings having these characteristics.
See sub-categories below.
5.1. Reference period for land variables
The use of land refers to the 12-month period ending on 31 December within the reference year 2023. In the case of successive crops from the same piece of land, the land use refers to a crop that is harvested during the reference year, regardless of when the crop in question is sown.
5.2. Reference period for variables on irrigation and soil management practices
Variables on irrigation: The 12-month period ending on 31 March 2023.
Variables on soil management practices: The 12-month period ending on 31 December within the reference year 2023.
5.3. Reference day for variables on livestock and animal housing
Variables on livestock: The reference day 1 April within the reference year 2023. For animals with short production cycles, the average herd was used if no animal was present on the farm on the reference date.
Variables on animal housing 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 ending on 31 December within the reference year 2023.
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 1 April within the reference year 2023.
6.1. Institutional Mandate - legal acts and other agreements
See sub-categories below.
6.1.1. National legal acts and other agreements
Legal act6.1.2. Name of national legal acts and other agreements
National IFS-Regulation 2023: Verordnung des Bundesministers für Land- und Forstwirtschaft, Regionen und Wasserwirtschaft betreffend die Statistik über die Struktur der landwirtschaftlichen Betriebe im Jahr 2023 (Agrarstrukturstatistik-Verordnung 2023)
Federal Statistics Act 2000: Bundesgesetz über die Bundesstatistik (Bundesstatistikgesetz 2000)
6.1.3. Link to national legal acts and other agreements
National IFS-Regulation 2023: BGBl. II Nr. 69/2023
Federal Statistics Act 2000: BGBl. I Nr. 163/1999, as last amended by BGBl. I Nr. 136/2001, BGBl. I Nr. 71/2003, BGBl. I Nr. 92/2007, BGBl. I Nr. 125/2009, BGBl. I Nr. 111/2010, BGBl. I Nr. 40/2014, BGBl. I Nr. 30/2018, BGBl. I Nr. 32/2018, BGBl. I Nr. 205/2021 and BGBl. I Nr. 185/2022.
6.1.4. Year of entry into force of national legal acts and other agreements
National IFS-Regulation 2023: 2023
Federal Statistics Act 2000: 2000
6.1.5. Legal obligations for respondents
Yes6.2. Institutional Mandate - data sharing
Under the Federal Statistics Act 2000, Statistics Austria is required to use the available administrative data instead of information obtained using its own questionnaires, so as to minimise the respondents' workload.
On the other hand, there is an obligation on the holders of administrative data to cooperate. According to the national IFS-Regulation 2023 (BGBl. II Nr. 69/2023) § 10,
(1) Area characteristics pursuant to § 4 para. 2 no. 3, which are recorded within the scope of the multiple application area, shall be transferred directly to the electronic questionnaire.
(2) Pursuant to § 4 para. 2 no. 2 to 8, the holders of administrative data shall, at the request of the Federal Agency, transmit the data to the Federal Agency electronically free of charge within four weeks.
7.1. Confidentiality - policy
National level: The legal base for statistical confidentiality is:
- The general obligation to publish statistics and the strict fulfilment of statistical confidentiality is regulated by the Federal Statistics Act 2000 ("Bundesstatistikgesetz 2000", in the current version). It contains provisions governing data protection. The surveyed data are subject of confidentiality (§ 17 Federal Statistics Act – Statistical Confidentiality) and will be treated in the strictest confidence; a forwarding of data to third parties is permitted only under the legal bases mentioned conditions (e.g. the transmission of data to the LFBIS: see above - administrative and financial provisions).
- The protection of personal data is covered by the Data Protection Act 2000 ("Datenschutzgesetz 2000", in the current version).
EU level: Regulation (EC) No 223/2009 on European statistics.
In the national dissemination of the IFS 2023 data, the deepest regional breakdown is the NUTS 2 - level.
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
No rules applied7.2.1.2. Methods to protect data in confidential cells
Other7.2.1.3. Description of rules and methods
In the dissemination of the IFS 2023 data, the deepest regional breakdown is the NUTS 2 - level. In the database, there is a restriction of the combination of variables in more detailed spatial levels.
7.2.2. Microdata
See sub-categories below.
7.2.2.1. Use of EU methodology for microdata dissemination
Not applicable7.2.2.2. Methods of perturbation
None7.2.2.3. Description of methodology
Not applicable.
8.1. Release calendar
There is a publication calendar for all statistical areas; an individual publication calendar for the Integrated Farm Statistics does not exist.
8.2. Release calendar access
A release calendar as well as a press calendar are on the homepage of Statistics Austria at Statistik Release calendar and Statistik Press releases.
8.3. Release policy - user access
No prior access before official data release.
The Federal Statistics Act 2000 ("Bundesstatistikgesetz 2000", in the current version) explicitly states in §30 (3) the obligation of Statistics Austria to inform without delay the Federal Ministry responsible for the subject matter concerned about the results of statistical surveys and to publish them simultaneously.
8.3.1. Use of quality rating system
No8.3.1.1. Description of the quality rating system
Not applicable.
Every 3-4 years.
10.1. Dissemination format - News release
See sub-categories below.
10.1.1. Publication of news releases
Yes10.1.2. Link to news releases
Press release preliminary information
There was no press release containing preliminary national information for the IFS 2023.
Press release final data
Final data were available in the form of a press release containing final national information in April 2025. The press release was made available on the Internet at Statistik Press releases.
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 report concerning the IFS 2023 is not yet available and will be provided under: Statistik publikationen.
Statistical Yearbook of Austria: Various Statistics Austria publications contain contributions setting out the results of the IFS/FSS - Statistik publikationen (select product type "Statistical Yearbook Austria")
10.3. Dissemination format - online database
See sub-categories below.
10.3.1. Data tables - consultations
| DATA ID | Description in English | 2022 | 2023 | 2024 |
|---|---|---|---|---|
| Agrarstrukturerhebung 2010 - Bodennutzung [as1001] | FSS 2010 areas | 554 | 288 | 205 |
| Agrarstrukturerhebung 2010 - Landw. Nebentätigkeiten [as1004] | FSS 2010 other gainful activities | 179 | 85 | 26 |
| Agrarstrukturerhebung 2010 - Personen und Arbeitskräfte [as1003] | FSS 2010 labour | 183 | 104 | 41 |
| Agrarstrukturerhebung 2010 - Viehbestand [as1002] | FSS 2010 livestock | 161 | 132 | 126 |
| Agrarstrukturerhebung 2010 - Überblick [as1005] | FSS 2010 overview | 703 | 248 | 138 |
| Agrarstrukturerhebung 2013 - Bodennutzung [as1301] | FSS 2013 areas | 79 | 40 | 47 |
| Agrarstrukturerhebung 2013 - Landw. Nebentätigkeiten [as1304] | FSS 2013 other gainful activities | 18 | 29 | 6 |
| Agrarstrukturerhebung 2013 - Maschinen und Geräte [as1306] | FSS 2013 machinery | 31 | 43 | 31 |
| Agrarstrukturerhebung 2013 - Personen und Arbeitskräfte [as1303] | FSS 2013 labour | 39 | 28 | 2 |
| Agrarstrukturerhebung 2013 - Viehbestand [as1302] | FSS 2013 livestock | 33 | 21 | 35 |
| Agrarstrukturerhebung 2013 - Überblick [as1305] | FSS 2013 overview | 72 | 28 | 15 |
| Agrarstrukturerhebung 2016 - Bodennutzung [as1601] | FSS 2016 areas | 1 049 | 80 | 93 |
| Agrarstrukturerhebung 2016 - Landw. Nebentätigkeiten [as1604] | FSS 2016 other gainful activities | 175 | 6 | 18 |
| Agrarstrukturerhebung 2016 - Personen und Arbeitskräfte [as1603] | FSS 2016 labour | 229 | 49 | 14 |
| Agrarstrukturerhebung 2016 - Viehbestand [as1602] | FSS 2016 livestock | 516 | 100 | 61 |
| Agrarstrukturerhebung 2016 - Überblick [as1605] | FSS 2016 overview | 609 | 108 | 86 |
| Agrarstrukturerhebung 2020 - Bio [as2007] | IFS 2020 organic | 312 | 349 | 361 |
| Agrarstrukturerhebung 2020 - Bodennutzung [as2001] | IFS 2020 areas | 1 110 | 1 583 | 1 074 |
| Agrarstrukturerhebung 2020 - Landw. Nebentätigkeiten [as2004] | IFS 2020 other gainful activities | 238 | 142 | 123 |
| Agrarstrukturerhebung 2020 - Personen und Arbeitskräfte [as2003] | IFS 2020 labour | 593 | 350 | 285 |
| Agrarstrukturerhebung 2020 - Viehbestand [as2002] | IFS 2020 livestock | 2 323 | 1 212 | 830 |
| Agrarstrukturerhebung 2020 - Überblick [as2005] | IFS 2020 overview | 1 665 | 1 346 | 900 |
| Q | STATcube - Statistical database of STATISTICS AUSTRIA | |||
| Number of tabulations EXTERNAL Status: 10 December 2024, 03:00:28 | This database allows the tabulations of the STATcube server specified in the title to be evaluated. A tabulation is counted when the “Retrieve data” button has been pressed or a download has taken place. |
|||
| Database title or ID | The German database title is used as the text. For better comparability. | |||
10.3.2. Accessibility of online database
Yes10.3.3. Link to online database
STATcube is available online at www.statcube.com.
A subscription provides access to more detailed data and additional features not available with the free guest access. This Guest access can be used to assess whether the purchase of a subscription is worthwhile. The database includes features which can only be fully accessed by subscribers (labelled with the tag [partly ABO]) and other features only accessible to subscribers (labelled with the tag [ABO]). Please note: the abbreviation "ABO" stands for the German translation of the term "subscription".
10.4. Dissemination format - microdata access
See sub-category below.
10.4.1. Accessibility of microdata
No10.5. 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
Standard documentation; meta-information
(Definitions, explanatory notes, methods, quality)
Concepts, definitions and explanations relating to the information on the IFS 2023, plus notes on the methods used and on quality, will be available end of 2025 free of charge, in a standardised form, at Statistik farms (see Documentation).
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
See 10.6 Documentation on methodology.
11.1. Quality assurance
See sub-categories below.
11.1.1. Quality management system
Yes11.1.2. Quality assurance and assessment procedures
Training coursesUse of best practices
Quality guidelines
Designated quality manager, quality unit and/or senior level committee
Peer review
11.1.3. Description of the quality management system and procedures
Statistics Austria has its own internal quality management department.
In the run-up to the field phase, there are separate training sessions for enumerators and hotline staff.
Reports are not only based on best practices, but also on the results of regular (quality) meetings amongst decision-making bodies and users.
A commitment to quality of Statistics Austria is available under Statistik - Responsibilities and principles - Standards.
A peer review report can be found under Statistik - ESS-Peer-Review-Report-2022_Austria.
11.1.4. Improvements in quality procedures
Not available.
11.2. Quality management - assessment
Not available.
12.1. Relevance - User Needs
National institutions:
- Federal Chancellery (BKA)
- Federal Ministry of Agriculture, Forestry, Environment and Water
- Political Institutions (National Assembly, Federal Assembly, etc.)
- Austrian Chamber of Agriculture (LKÖ), Chambers of Agriculture on regional level
- Regional authorities (Provincial Government, municipalities)
- Statistics Austria (Output statistics and subsequently supply balance sheets; Economic Accounts for Agriculture (EAA) and subsequently National Accounts; Environment and energy statistics)
- Austrian Institute of Economic Research (WIFO)
- Federal Institute of Agricultural Economics, Rural and Mountain Research (BAB)
- Agency for Health and Food Safety (AGES)
- Environment Agency Austria (UBA)
International institutions:
- European Commission (EUROSTAT, DG AGRI, DG CLIMA, JRC)
- Farm Accountancy Data Network (FADN)
- Food and Agriculture Organization of the United Nations (FAO)
Non-institutional users:
- Media
- Educational institutions (Agricultural colleges, universities)
- Research institutions (universities)
- Enterprises
- Non-Profit-Organisations
- General public
There is no monitoring of the requested topics (main groups of variables) according to the different data users and purposes.
12.1.1. Main groups of variables collected only for national purposes
In order to satisfy both national needs and various directives, the survey of the individual variables was more detailed for certain groups. Those variables were aggregated prior to transmission to Eurostat in line with the rules for the provision of IFS data (see annex).
Annexes:
12.1.1 Main groups of variables collected only for national purposes - national variables
12.1.2. Unmet user needs
So far, all known and reasonable user needs are met. User needs can already be brought forward in the course of the respective preparation of the legal basis. The most important data-users are heard in this process. It has to be stated that this question is also a matter of cost/benefit consideration and ultimately a question of commissioning and funding.
12.1.3. Plans for satisfying unmet user needs
Not applicable.
12.2. Relevance - User Satisfaction
There are no dedicated procedures to measure user satisfaction in IFS.
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
Restricted from publication
13.2.2. Reasons for non-compliant precision requirements in relation to Regulation (EU) 2018/1091
The sample size was designed to ensure sufficient accuracy, considering the rules of Annex V of Regulation (EU) 2018/1091. Calculations have shown, a posteriori, that the required mandatory specifications regarding simple relative standard errors are fulfilled with this sample size, with the exception of the variable A5000X5120_5130_LSU (Live poultry excluding cocks and chicks of chicken). This is probably due to concentration tendencies in this sector and a higher variability than expected. In future, this sector will be given greater consideration in the sample planning.
13.2.3. Reference on method of estimation
See annex.
Annexes:
13.2.3. Methodology used to calculate relative standard errors
13.2.4. Impact of sampling error on data quality
None13.3. 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 europa.
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
Not available.
13.3.1.2. Common units - proportion
Not requested.
13.3.1.3. Under-coverage error
See sub-categories below.
13.3.1.3.1. Under-coverage rate
There is virtually no under-coverage of agricultural holdings, because newly created holdings usually submit subsidy applications and their administrative data are used for inclusion in the Farm Register (or Agricultural and Forestry Register, AFR).
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
Units with outdated information in the frame (variables below thresholds in the frame but above thresholds in the reference period)
13.3.1.3.3. Actions to minimise the under-coverage error
Under-coverage is minimised via routine maintenance of the AFR with checks against various types of administrative data (applications for subsidies, social insurance information, etc.).
New births, new units derived from split or units with outdated information and which became relevant were added to the frame during the survey period.
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
Yes13.3.1.5.1. Actions to minimise the contact error
Contact error is minimised via routine maintenance of the AFR with checks against various types of administrative data (applications for subsidies, social insurance information, etc.).
In the case of undeliverable and returned survey documents, the correct addresses were researched and the documents were resent.
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 were no variables with high measurement errors.
Occasionally respondents transferred area data, which was not prefilled by administrative sources from their land register without considering diverging units of area.
Examples:
- Other farmland (FA_OTH)
- Unutilised agricultural area (NUAA)
- Wooded areas (WA)
- Short rotation coppice areas (SRCAA)
- Other areas on the farms (FA9).
There were cases with wrong units regarding the volumes of storage capacities (cubic meters versus litres).
13.3.2.2. Causes of measurement errors
Respondents’ inability to provide accurate answers13.3.2.3. 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
Other
13.3.2.4. Impact of measurement error on data quality
None13.3.2.5. Additional information measurement error
Measurement errors are minimised by offering appropriate information in the questionnaire to use the correct units of measurement. Errors that nevertheless occurred were discovered in the course of plausibility checks (e.g. checking the data against previous surveys or against calculated bandwidths). Impact of measurement errors on data quality can be assumed to be extremely low.
13.3.3. Non response error
See sub-categories below.
13.3.3.1. Unit non-response - rate
See item 13.3.1.1.
The unit non-response rate is unweighted.
The unit non-response rate is calculated as the share of eligible non-respondent holdings to the eligible holdings. The eligible holdings include those holdings with unknown eligibility status which are imputed or re-weighted for (therefore considered eligible).
The unit non-response rate is calculated over the holdings in the main frame and if applicable frame extension, for which core data are sent to Eurostat.
13.3.3.1.1. Reasons for unit non-response
Failure to make contact with the unitRefusal to participate
Inability to participate (e.g. illness, absence)
13.3.3.1.2. Actions to minimise or address unit non-response
Follow-up interviewsReminders
Legal actions
Imputation
Weighting
13.3.3.1.3. Unit non-response analysis
In the end (after urgency and administrative penalty proceedings), 385 units remained with their refusal to provide information. Their data could be imputed by using administrative or other data-sources (Internet, etc.). As this number is very low and fairly equally distributed, no further analysis was made.
13.3.3.2. Item non-response - rate
Item non-response rates were not calculated. Item non-response can virtually be excluded for the main variables, if not for most variables due to the measures mentioned above (electronic questionnaire with plausibility checks). Occasional imputations by enumerators cannot be quantified. In the hindsight it is difficult to quantify item non-response with regard to the costs and response-burden.
13.3.3.2.1. Variables with the highest item non-response rate
In general it is obvious that the risk of non-response tends to be higher in the context of variables,
- which might be considered to be more confidential by the respondents or
- which can obviously and hardly be cross-checked.
Examples for variables with potential item non-response:
- Other farmland (FA_OTH)
- Unutilised agricultural area (NUAA)
- Wooded areas (WA)
- Other areas on the farms (FA9)
- Other gainful activities related to provision of health, social or educational services (OGA_HSES)
- Other gainful activities related to handicraft (OGA_HC)
- Other gainful activities related to processing of farm products (OGA_FPRDPRC)
- Other gainful activities related to production of renewable energy (OGA_NRGRPRD)
- Other gainful activities related to wood processing (OGA_WPRC)
- Other gainful activities related to agricultural contractual work (using production means of the holding) (OGA_ACW)
- Other gainful activities related to non-agricultural contractual work (using production means of the holding) (OGA_NACW)
- Other gainful activities related to forestry (OGA_FOR)
- Other gainful activities directly related to the agricultural holding n.e.c. (OGA_AGRHLD)
- Family members of holder-manager of the sole holder holding, who are working on the agricultural holding and have other gainful activities (not related to the agricultural holding) as their main activity (MOGA_FAM_NRH)
- Family members of holder-manager of the sole holder holding, who are working on the agricultural holding and have other gainful activities (not related to the agricultural holding) as their secondary activity (SOGA_FAM_NRH)
- Variables from the machines module that cannot be cross-checked with other hard facts or logical necessities (precision farming, automated systems for livestock management).
13.3.3.2.2. Reasons for item non-response
RefusalSkip of due question
13.3.3.2.3. Actions to minimise or address item non-response
Follow-up interviewsImputation
13.3.3.3. Impact of non-response error on data quality
Low13.3.3.4. Additional information non-response error
Item non-response is minimised by the electronic questionnaire, which was designed in such a way that it could only be sent once the compulsory fields had been correctly completed. To prevent individual pages of the questionnaire being inadvertently missed out, a marker was placed on each page of the internet questionnaire which had to be set to signify that the page had been completed. There were also cross-checks between dependent items in the questionnaire (e.g. livestock - manure). This range of well-targeted measures made it possible to reduce the item non-response rate. In the paper-based questionnaires in previous surveys, variables frequently came back uncompleted.
In addition, various tests and monitoring measures were carried out during the plausibility checks. Obviously missing data could be imputed after contacting the respondents again or by using other data sources (internet, etc.).
13.3.4. Processing error
See sub-categories below.
13.3.4.1. Sources of processing errors
Imputation methods13.3.4.2. Imputation methods
Deductive imputationPrevious data for the same unit
Other
13.3.4.3. Actions to correct or minimise processing errors
Checks for missing, incorrect or implausible information were carried out in the validation tool. The occurrence of processing errors was practically prevented by a large number of numerical-logical checks and the permanent application of plausibility logic. Some checks were run immediately after the entry in the cell, others were run each time the save button was pressed.
13.3.4.4. Tools and staff authorised to make corrections
The data sets were checked for missing, incorrect or implausible information, using an extensive plausibility application. The corrections were made by the members of Statistics Austria's project team, who were specially trained and authorised to process and rectify the data sets.
13.3.4.5. Impact of processing error on data quality
Low13.3.4.6. Additional information processing error
Missing or incorrect entries were completed from other data sources wherever available (e.g. administrative data from IACS or ÖPUL, information on areas or "safety-plan-measures“ from the social insurance institution for farmers) to avoid burdening the respondents. The forestry yearbook, containing the areas of Austria’s largest forestry holdings, was another means of checking data. If these sources were not exhaustive, individual items from the 2020, 2016 or 2013 FSS were used, wherever possible, to check and/or supplement the data. Where this did not provide clarity, individual holdings had to be contacted by telephone.
Moreover, the nil returns were examined. If, for example, administrative information on the holding was available, the nil return was rejected and the holding was surveyed again. This was done in close collaboration with staff dealing with the Farm Register (or Agricultural and Forestry Register, AFR), as the information from the nil returns (business closure, leasing, etc.) was used for updating the registers.
13.3.5. Model assumption error
Not applicable.
14.1. Timeliness
See sub-categories below.
14.1.1. Time lag - first result
Not applicable.
14.1.2. Time lag - final result
16 months; last day of the reference period: 31 December 2023; day of publication of final results: April 2025.
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 planned national publication is scheduled for April 2025.
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
Concerning the data-subset relevant for Eurostat, the definition of the holding is consistent with the definition fixed in Regulation (EU) 2018/1091.
According to the national regulation, statistical units include forestry holdings with at least two hectares of wooded area. These holdings, which only are relevant due to their wooded areas, are not included in the dataset delivered to Eurostat.
15.1.2.2. Reasons for deviations
Holdings that are only relevant nationally due to their forest areas and do not reach the thresholds fixed in Regulation (EU) 2018/1091 are nevertheless recorded and published in the Austrian results as additional information on forestry holdings. The reason for this is that forest areas are important in Austria and should be quantified.
15.1.3. Thresholds of agricultural holdings
See sub-categories below.
15.1.3.1. Proofs that the EU coverage requirements are met
As can be seen in the annex 3.6.1 Thresholds of agricultural holdings, the thresholds of agricultural holdings applied are lower than those specified in Regulation (EU) 2018/1091.
UAA surveyed covers 98% of the total UAA (excluding kitchen gardens) and the livestock surveyed covers 99% of the livestock units in Austria:
| Total | Covered by the thresholds (main frame plus frame extension) | Attained coverage | Minimum requested coverage | |
|---|---|---|---|---|
| 1 | 2 | 3=2*100/1 | 4 | |
| UAA excluding kitchen gardens | 2 613 994 | 2 570 285 | 98.3% | 98% |
| LSU | 2 245 312 | 2 216 404 | 98.7% | 98% |
15.1.3.2. Differences between the national thresholds and the thresholds used for the data sent to Eurostat
According to the national regulation, statistical units include forestry holdings with at least two hectares of wooded area.
15.1.3.3. Reasons for differences
National importance of wooded area.
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
Definitions used in IFS 2023 are mainly based on the Commission Implementing Regulation (EU) 2021/2286. Furthermore, the guidelines according to the "Integrated farm statistics manual” are implemented as far as possible. There are following differences:
- % band Annual work unit (AWU): The number of hours for a "full-time employee" was set 2000 hours per year (250 working days of eight hours), whereas the Regulation cited above provides for only 1800 hours per year (225 working days of eight hours).
- F3000T Berries (excluding strawberries) - outdoor: Starting with the FSS 2016 elderberry (Sambucus nigra L.) was recorded under PECR9_H9000T Other permanent crops including other permanent crops for human consumption – outdoor.
- F1210 Peaches and F1220 Nectarines are recorded together under the variable F1210 Peaches.
- Livestock n.e.c.: For details, see sub-categories below.
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 europa.
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 coefficients are consistent with those fixed in Regulation (EU) 2018/1091.
15.1.4.1.5. Livestock included in “Other livestock n.e.c.”
“Other livestock n.e.c.” includes rabbits for breeding.
15.1.4.2. Reasons for deviations
% band Annual work unit (AWU): The number of hours for a "full-time employee" was set 2000 hours per year (250 working days of eight hours). As the European requirements (Commission Implementing Regulation (EU) 2021/2286) are, according to experts, too low for Austria, the national Working Party of the Agricultural Statistics Advisory Committee decided to increase the number of hours as from the 1995 Farm Structure Survey. This deviation has not changed in time series and can mostly be attributed to specific national circumstances. The worktime percentage bands for the regularly employed labour force are calculated by using the working hours reported by the respondents. Apparent excessive amounts regarding the labour input (e.g. 100% in case of retired people) were reduced considering interacting factors.
F3000T Berries (excluding strawberries) - outdoor: Starting with the FSS 2016 elderberry (Sambucus nigra L.) was recorded under PECR9_H9000T Other permanent crops including other permanent crops for human consumption – outdoor to ensure a better coherence with crop statistics. Elderberries are used predominantly for industrial purposes.
F1210 Peaches and F1220 Nectarines are recorded together under the variable F1210 Peaches, because nectarines are not significant in Austria.
Livestock n.e.c. includes rabbits for breeding, because rabbits for breeding are not significant in Austria.
15.1.5. Reference periods/days
See sub-categories below.
15.1.5.1. Deviations from Regulation (EU) 2018/1091
The national reference period for the irrigation module was a 12-month period ending on 31 March 2023.
15.1.5.2. Reasons for deviations
Since the survey phase started in April 2023 and therefore farmers had the option of completing the survey at that time – prior to the start of the cropping season, the only possible amount of irrigation and irrigated area, which can be reported at that time, is that of the previous cropping period (2022). The farmers cannot say beforehand, how much they will irrigate the coming season.
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 agricultural holdings renting or being allotted the land based on written or oral agreements.15.1.6.4. Source of collected data on common land
SurveysAdministrative sources
15.1.6.5. Description of methods to record data on common land
Common land has been included in the FSS as special agricultural holdings (AGRARIAN COMMUNITIES) since 1993. The UAA of the common land is predominantly made up of grassland (99.9996%).
This approach (separate record) met the practice of the subsidy system where, common land units themselves, can apply for subsidies, are therefore part of administrative data and are treated like other farms.
There were no particular questions and no separate questionnaire for common land units. The administrative data were provided within the questionnaire after transmission from IACS via web service interface.
Since 2020, in contrast to the surveys before, the recorded common land units were not kept in the data set as separate individual holdings. Their UAA (non-material shares) was then assigned to the individual farms of the members of the agrarian community. In terms of tenure classification, these allocated areas are recorded as 'common land'.
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
There are no deviations.
15.1.7.2. Reasons for deviations
Not applicable.
15.1.8. Differences in methods across regions within the country
There are no differences in the methods used across regions within the country.
15.2. Comparability - over time
See sub-categories below.
15.2.1. Length of comparable time series
2
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 some changes but not enough to warrant the designation of a break in series15.2.3.2. Description of changes
Due to the module for orchards with the IFS 2023, an extended threshold for commercial apple orchards or commercial apricot orchards was introduced with the IFS 2023, which applies in addition to the existing threshold for orchards.
(0.15 ha Commercial apple orchards or commercial apricot orchards (sum (F1110, F1230) OR 0.30 ha Fruits, berries and nuts (excluding citrus fruits, grapes and strawberries) - outdoor and other permanent crops including other permanent crops for human consumption - outdoor" sum (F0000T, PECR9_H9000T)).
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
The reference date for “all other variables” referred to in item 5.7 changed from 1 March (2020) to 1 April (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
Austrian data for IFS 2023 were fully comparable with 2020 data because the country provided survey data for holdings from both main frame and frame extension.
- Overall, the number of holdings decreased, this was particularly evident for holding group farms, on the contrary, non-family farms increased (although the latter have very small absolute number).
- There was a general trend of increase of share of holdings falling into bigger UAA size and SO_EURO classes.
- The decline of share of holdings having other gainful activities (OGA) from 2020 to 2023 could be partially explained by the sampling, as the difference to the 2016 values is less pronounced.
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
No15.3.3.2. Results of analysis at micro level
Since the same data sources are in use, a deeper analysis does not make much sense. On the other hand, it is evident, that discrepancies can be caused by different reference days (livestock).
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 ANIMAL PRODUCTION
The differences identified were mostly due to:
- different methodology (IFS = sample, animal production for cattle = census)
- different reference date (December for IFS, April for animal production). This was particularly true for lambs whose stock is generally higher in April compared to December
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
Double burden of very few farms occurred in the context of IFS and animal surveys. In the trade-off between the use of administrative data with a given reference date (1 April 2023) for all farms in the IFS and a double burden of some farms also chosen for the animal surveys, the project team decided to run two separated surveys with an optimised use of administrative data and precisely tailored questionnaires. Another reason not to join the two surveys on the reference date of the animal survey in June or December is that time is rather short to survey all farms of the IFS and process the livestock data for the animal survey in time.
16.2. Efficiency gains since the last data transmission to Eurostat
Further automationIncreased use of administrative data
16.2.1. Additional information efficiency gains
Since the IFS 2020, a web service interface transfers IACS data directly into the questionnaire. This data transfer was possible as soon as the IACS data was available and was triggered the moment the IFS electronic questionnaire was opened.
Since the IFS 2023, plausibility checks have been carried out using a new application that is part of the so-called Digital Agriculture (DLW).
The DLW is a standardised database for the compilation of agricultural statistics.
The main objectives of the “DLW” are as follows:
- Increased efficiency through centralised data maintenance
- Creation of new reporting channels for data
- Ensuring uniform and consistent data
- Centralised data preparation for the individual statistics
16.3. Average duration of farm interview (in minutes)
See sub-categories below.
16.3.1. Core
Respondents could voluntarily indicate in the questionnaire how much time it took them to complete it. However, no distinction was made between the individual parts (core/modules) of the questionnaire. The evaluation of this information yields a median of 60 minutes (first quartile=30 minutes; third quartile=120 minutes).
16.3.2. Module ‘Labour force and other gainful activities‘
Not available.
16.3.3. Module ‘Rural development’
Not relevant, use of administrative data.
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
General principles relating to the data revision policy can be found at Statistik austrias revisions policy.
17.2. Data revision - practice
No data revision is planned so far.
17.2.1. Data revision - average size
Not requested.
18.1. Source data
See sub-categories below.
18.1.1. Sampling design & Procedure frame
See sub-categories below.
18.1.1.1. Type of frame
List frame18.1.1.2. Name of frame
Statistical Farm Register
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 sample was designed as a one-stage stratified random sample of holdings and therefore is a probability design.
Concerning the unit location, the sample was stratified by NUTS 2 (federal state/province/"Bundesland").
Besides the criteria mentioned in item 18.1.2.2.2, the presence of non-family workers was another stratification criterion.
There is no sub-sampling for modules or some of the variables.
Annexes:
18.1.2.2 Sampling design - stratification conditions
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
Other
18.1.2.2.3. Use of systematic sampling
No18.1.2.2.4. Full coverage strata
Full coverage strata can be found highlighted in grey in the annex (NUTS 2 (federal state/province/"Bundesland")).
Annexes:
18.1.2.2.4 Full coverage strata - stratification delimitations
18.1.2.2.5. Method of determination of the overall sample size
The sample survey covered 31 081 holdings. Farms with orchards were surveyed as a full coverage stratum. The sample size was designed to ensure sufficient accuracy, taking into account the rules of Annex V of Regulation (EU) 2018/1091. Calculations have shown, a posteriori, that the required mandatory specifications regarding simple relative standard errors are fulfilled with this sample size, with the exception of the variable A5000X5120_5130_LSU (Live poultry excluding cocks and chicks of chicken), which is presumably due to concentration trends in this sector and a higher variability than expected.
18.1.2.2.6. Method of allocation of the overall sample size
Neymann allocation18.1.3. Core data collection on the frame extension
See sub-categories below.
18.1.3.1. Coverage of agricultural holdings
Sample18.1.3.2. Sampling design
The sample was designed as a one-stage stratified random sample of holdings and therefore is a probability design.
Concerning the unit location, the sample was stratified by NUTS 2 (federal state/province/"Bundesland").
18.1.3.2.1. Name of sampling design
Stratified one-stage random sampling18.1.3.2.2. Stratification criteria
Unit sizeUnit location
Unit specialization
18.1.3.2.3. Use of systematic sampling
No18.1.3.2.4. Full coverage strata
No full coverage strata.
18.1.3.2.5. Method of determination of the overall sample size
The sample size was designed to ensure sufficient accuracy, considering the rules of Annex V of Regulation (EU) 2018/1091. For this reason, the thresholds were lowered (see item 3.6.1).
18.1.3.2.6. Method of allocation of the overall sample size
Neymann allocation18.1.4. Module “Labour force and other gainful activities”
See sub-categories below.
18.1.4.1. Coverage of agricultural holdings
Sample18.1.4.2. Sampling design
See 18.1.2.2.
18.1.4.2.1. Name of sampling design
Stratified one-stage random sampling18.1.4.2.2. Stratification criteria
Unit sizeUnit location
Unit specialization
Other
18.1.4.2.3. Use of systematic sampling
No18.1.4.2.4. Full coverage strata
See 18.1.2.2.4.
18.1.4.2.5. Method of determination of the overall sample size
See 18.1.2.2.5.
18.1.4.2.6. Method of allocation of the overall sample size
Neymann allocation18.1.4.2.7. If sampled from the core sample, the sampling and calibration strategy
Not applicable18.1.5. Module “Rural development”
See sub-categories below.
18.1.5.1. Coverage of agricultural holdings
Sample18.1.5.2. Sampling design
See 18.1.2.2.
18.1.5.2.1. Name of sampling design
Stratified one-stage random sampling18.1.5.2.2. Stratification criteria
Unit sizeUnit location
Unit specialization
Other
18.1.5.2.3. Use of systematic sampling
No18.1.5.2.4. Full coverage strata
See 18.1.2.2.4.
18.1.5.2.5. Method of determination of the overall sample size
See 18.1.2.2.5.
18.1.5.2.6. Method of allocation of the overall sample size
Neymann allocation18.1.5.2.7. If sampled from the core sample, the sampling strategy and calibration strategy
Not applicable18.1.6. Module “Animal housing and manure management module”
Restricted from publication
18.1.6.1. Coverage of agricultural holdings
Restricted from publication
18.1.6.2. Sampling design
Restricted from publication
18.1.6.2.1. Name of sampling design
Restricted from publication
18.1.6.2.2. Stratification criteria
Restricted from publication
18.1.6.2.3. Use of systematic sampling
Restricted from publication
18.1.6.2.4. Full coverage strata
Restricted from publication
18.1.6.2.5. Method of determination of the overall sample size
Restricted from publication
18.1.6.2.6. Method of allocation of the overall sample size
Restricted from publication
18.1.6.2.7. If sampled from the core sample, the sampling strategy and calibration strategy
Restricted from publication
18.1.7. Module ‘Irrigation’
See sub-categories below.
18.1.7.1. Coverage of agricultural holdings
Sample18.1.7.2. Sampling design
See 18.1.2.2.
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
Other
18.1.7.2.3. Use of systematic sampling
No18.1.7.2.4. Full coverage strata
See 18.1.2.2.4.
18.1.7.2.5. Method of determination of the overall sample size
See 18.1.2.2.5.
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
See 18.1.2.2.
18.1.8.2.1. Name of sampling design
Stratified one-stage random sampling18.1.8.2.2. Stratification criteria
Unit sizeUnit location
Unit specialization
Other
18.1.8.2.3. Use of systematic sampling
No18.1.8.2.4. Full coverage strata
See 18.1.2.2.4.
18.1.8.2.5. Method of determination of the overall sample size
See 18.1.2.2.5.
18.1.8.2.6. Method of allocation of the overall sample size
Neymann allocation18.1.8.2.7. If sampled from the core sample, the sampling strategy and calibration strategy
Not applicable18.1.9. Module ‘Machinery and equipment’
See sub-categories below.
18.1.9.1. Coverage of agricultural holdings
Sample18.1.9.2. Sampling design
See 18.1.2.2.
18.1.9.2.1. Name of sampling design
Stratified one-stage random sampling18.1.9.2.2. Stratification criteria
Unit sizeUnit location
Unit specialization
Other
18.1.9.2.3. Use of systematic sampling
No18.1.9.2.4. Full coverage strata
See 18.1.2.2.4.
18.1.9.2.5. Method of determination of the overall sample size
See 18.1.2.2.5.
18.1.9.2.6. Method of allocation of the overall sample size
Neymann allocation18.1.9.2.7. If sampled from the core sample, the sampling strategy and calibration strategy
Not applicable18.1.10. Module ‘Orchard’
See sub-categories below.
18.1.10.1. Coverage of agricultural holdings
Census18.1.10.2. Sampling design
Not applicable.
18.1.10.2.1. Name of sampling design
Not applicable18.1.10.2.2. Stratification criteria
Not applicable18.1.10.2.3. Use of systematic sampling
Not applicable18.1.10.2.4. Full coverage strata
Not applicable.
18.1.10.2.5. Method of determination of the overall sample size
Not applicable.
18.1.10.2.6. Method of allocation of the overall sample size
Not applicable18.1.10.2.7. 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
R-Package SamplingStrata
18.1.13. Administrative sources
See sub-categories below.
18.1.13.1. Administrative sources used and the purposes of using them
The information is available on Eurostat’s website, at the link: Additional data - Eurostat (europa.eu).
18.1.13.2. Description and quality of the administrative sources
See the Excel file in the annex.
Annexes:
18.1.13.2 Description and quality of administrative sources
18.1.13.3. Difficulties using additional administrative sources not currently used
Problems related to data quality of the sourceOther
18.1.14. Innovative approaches
The information on the innovative approaches and the quality methods applied is available on Eurostat’s website, at the link: Additional data - Eurostat (europa.eu).
18.2. Frequency of data collection
The agricultural census is conducted every 10 years. The decennial agricultural census is complemented by sample or census-based data collections organised every 3-4 years in-between.
18.3. Data collection
See sub-categories below.
18.3.1. Methods of data collection
Face-to-face, electronic versionTelephone, electronic version
Use of Internet
18.3.2. Data entry method, if paper questionnaires
Not applicable18.3.3. Questionnaire
Please find the questionnaire in annex.
The Farm Structure Survey was held solely using an electronic questionnaire (eQuest). The farmers were able to submit their return either directly at their computer (direct respondents, CAWI, 73%), at their competent chamber of agriculture (CAPI, 20%) or during a personal interview by telephone with staff of Statistics Austria (CATI, 7%) using the same electronic questionnaire.
A dedicated free hotline was set up by Statistics Austria to answer any questions that arose during the survey phase. In addition, queries could be sent by e-mail to Agrarstrukturerhebung@statistik.gv.at.
Annexes:
18.3.3 Questionnaire in German
18.3.3 Questionnaire in English
18.4. Data validation
See sub-categories below.
18.4.1. Type of validation checks
Data format checksCompleteness checks
Range checks
Relational checks
Data flagging
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 central department18.4.3. Tools used for data validation
The data sets were checked for missing, incorrect or implausible information, using an extensive plausibility application. The program was newly developed as an integral part of the Digital Agriculture (DLW), a standardised database for the compilation of agricultural statistics. Care was taken to ensure that missing, incorrect and implausible entries were detected by the program and either highlighted or immediately corrected. The functionality of the plausibility program was first checked using fictitious holdings. The correction applications contained a number of deliberate errors in order to check whether the program would recognise and report them.
For the IFS 2023, about 96% of the questionnaires needed further checking due to information errors or "real" errors. This share was again higher than in the previous survey due to new variables. For each holding, all errors and information errors were listed and categorised. The errors detected (incorrect entries, missing or implausible data) had to be investigated and rectified by the processing team. Errors were eliminated and plausibility checks carried out repeatedly directly via the application. The staff themselves could correct logical obvious errors. Missing or incorrect entries were completed from other data sources wherever available (e.g. administrative data from IACS or ÖPUL, VIS, “total area information” from the social insurance institution for farmers) to avoid burdening the respondents. The forestry yearbook, containing the areas of Austria’s largest forestry holdings, was another means of checking data. If these sources were not exhaustive, individual items from the 2020, 2016 or 2013 Farm Structure Survey were used, wherever possible, to supplement and/or check the data. Where this did not provide clarity, individual holdings had to be contacted by telephone.
18.5. Data compilation
The IFS 2023 was conducted as a sample survey. Data collected and administrative data were merged via the unique farm number/identifier.
18.5.1. Imputation - rate
The imputation-rate could be kept very low by the measures mentioned in 13.3.3.1.2. Actions to minimise or address unit non-response and 13.3.3.2.3. Actions to minimise or address item non-response. The imputation-rate cannot be quantified for single variables.
18.5.2. Methods used to derive the extrapolation factor
Design weightNon-response adjustment
Calibration
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
AFR – Agricultural Farm Register
AGES – Agency for Health and Food Safety
AMA – Agrarmarkt Austria
AWU – Annual Work Unit
BAB – Federal Institute of Agricultural Economics, Rural and Mountain Research
BGBl – Bundesgesetzblatt - Federal Law Gazette
BKA – Federal Chancellery
CAP – Common Agricultural Policy
CAPI – Computer Assisted Personal Interview
CATI – Computer Assisted Telephone Interview
CAWI – Computer Assisted Web Interview
DG AGRI – Directorate-General for Agriculture and Rural Development
DG CLIMA – Directorate-General for Climate Action
DLW – Digital Agriculture, Statistics Austria's standardised database for the compilation of agricultural statistics
EAA – Economic Accounts for Agriculture
EU – European Union
EUROSTAT – European Statistical Office
FADN – Farm Accountancy Data Network
FAO – Food and Agriculture Organization of the United Nations
FSS – Farm Structure Survey
IACS – Integrated Administration and Control System
IFS – Integrated Farm Statistics
JRC – Joint Research Centre
LFBIS – National identification system of agricultural and forestry holdings
LKÖ – Austrian Chamber of Agriculture
LSU – Livestock unit
NUTS – Nomenclature of territorial units for statistics
OGA – Other gainful activities
ÖPUL – Austrian agro-environmental programme
RGBl – Reichsgesetzblatt - Imperial Law Gazette
SGM – Standard gross margin
SO – Standard output
UAA – Utilised agricultural area
UBA – Environment Agency Austria
VIS – Veterinary Information System
WIFO – Austrian Institute of Economic Research
19.2. Additional comments
No additional comments.
The data describe the structure of agricultural holdings providing the general characteristics of farms and farmers and information on their land, livestock and labour force. They also describe production methods, rural development measures and agro-environmental aspects that look at the impact of agriculture on the environment.
The data are used by public, researchers, farmers and policy-makers to better understand the state of the farming sector and the impact of agriculture on the environment. The data follow up the changes in the agricultural sector and provide a basis for decision-making in the Common Agricultural Policy (CAP) and other European Union policies.
The statistical unit is the agricultural holding (farm). The aggregated results are disseminated through statistical tables. The data are presented at different geographical levels and over periods.
The data collections are organised in line with Regulation (EU) 2018/1091 and have a new structure, consisting of a core data set and several modules.
The regulation covers the data collections in 2019/2020 (the agricultural census), 2023 and 2026. The data are as comparable and coherent as possible with the other European countries.
24 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, peaches area, apricots area, each one by age of plantation and density of trees. In Austria, there is only an obligation to collect the relevant data on apple orchards due to the underlying requirements.
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 etc.) and
- the number of agricultural holdings having these characteristics.
The IFS 2023 was conducted as a sample survey. Data collected and administrative data were merged via the unique farm number/identifier.
See sub-categories below.
Every 3-4 years.
See sub-categories below.
See sub-categories below.
See sub-categories below.
