1.1. Contact organisation
Istat - Italian National Statistical Institute
1.2. Contact organisation unit
Division for agricultural statistics and surveys (ATC)
1.3. Contact name
Confidential because of GDPR
1.4. Contact person function
Confidential because of GDPR
1.5. Contact mail address
Piazza Guglielmo Marconi, 26 - 00144 Roma
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
12 November 2025
2.2. Metadata last posted
19 November 2025
2.3. Metadata last update
12 November 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;
- 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, nectarines area, apricots area, oranges area, small citrus fruit area, lemons area, olives area, grapes for table use area, each one by age of plantation and density of trees.
3.5. Statistical unit
See sub-category below.
3.5.1. Definition of agricultural holding
The agricultural holding is a single unit, both technically and economically, that has a single management and that undertakes economic activities in agriculture in accordance with Regulation (EC) No 1893/2006 belonging to groups:
- A.01.1: Growing of non-perennial crops
- A.01.2: Growing of perennial crops
- A.01.3: Plant propagation
- A.01.4: Animal production
- A.01.5: Mixed farming or
- The “maintenance of agricultural land in good agricultural and environmental condition” of group A.01.6 within the economic territory of the Union, either as its primary or secondary activity.
Regarding activities of class A.01.49, only the activities “Raising and breeding of semi-domesticated or other live animals” (with the exception of raising of insects) and “Bee-keeping and production of honey and beeswax” are included.
3.6. Statistical population
See sub-categories below.
3.6.1. Population covered by the core data sent to Eurostat (main frame and if applicable frame extension)
The thresholds of agricultural holdings are available in the annex.
Annexes:
3.6.1. Thresholds of agricultural holdings
3.6.1.1. Raised thresholds compared to Regulation (EU) 2018/1091
No3.6.1.2. Lowered and/or additional thresholds compared to Regulation (EU) 2018/1091
No3.6.2. Population covered by the data sent to Eurostat for the modules “Labour force and other gainful activities”, “Rural development” and “Machinery and equipment”
The same population of agricultural holdings defined in item 3.6.1.
3.6.3. Population covered by the data sent to Eurostat for the module “Animal housing and manure management”
Restricted from publication
3.6.4. Population covered by the data sent to Eurostat for the module “Irrigation”
The subset of agricultural holdings defined in item 3.6.2 with irrigable area.
3.6.5. Population covered by the data sent to Eurostat for the module “Soil management practices”
The subset of agricultural holdings defined in item 3.6.2 with arable land or with drainage.
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 following territory is covered by the data: Livigno
3.7.3. Criteria used to establish the geographical location of the holding
The main building for productionThe majority of the area of the holding
The 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 1961 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) 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 reference year 2022/2023; in particular the crop year was considered (1 November 2022 - 31 October 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
The reference period for variables on irrigation and soil management practices is the crop year (1 November 2022 - 31 October 2023).
5.3. Reference day for variables on livestock and animal housing
According to Regulation (EU) 2018/1091, the reference day for livestock variables is 1 December 2023, with the exception of poultry for which the average number in a 12-month period including 1 December 2023 was considered.
The 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
The 12-month period ending on 31 October 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 for all other variables is 31 October 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
- Lex n. 205 of 27 December 2017 (Art. 1, subsections 227-237)
- National Statistical Plan (PSN)
6.1.3. Link to national legal acts and other agreements
6.1.4. Year of entry into force of national legal acts and other agreements
- Lex n. 205 of 27 December 2017: 2018
- PSN: 2022, 2023, 2024
6.1.5. Legal obligations for respondents
Yes6.2. Institutional Mandate - data sharing
In Italy, the main stakeholders that produce administrative data or carry out surveys and analyses as regards agriculture, forestry and fishery are: Istat, the Ministry of Agriculture (MASAF), AGEA (the Italian IACS authority), ISMEA (research body managed by the MASAF), CREA (research body managed by MASAF that is responsible for FADN), the Italian Regions. These six bodies signed a Memorandum of Understanding on 12 December 2017, which has been renewed until 2031. All these bodies belong to the Italian National Statistical System. The Memorandum states that the administrative data owned by the authorities that signed it shall be available for statistical purposes without any additional financial cost for Istat. The most important administrative data managed on the basis of the Memorandum concern:
- The "Fascicoli aziendali" (Farm files), managed by AGEA in order to guarantee the EU financial support to farmers.
- The microdata concerning rural development measures adopted by farmers, managed by AGEA.
- Regional farm registers including the active agricultural holdings at regional level.
- Organic farming data managed by MASAF.
Please find attached the text of the Memorandum (available only in Italian).
Moreover, a special agreement has been signed between Istat and the Ministry of Health. Under this agreement, Istat receives from the Ministry the yearly database of holdings that own livestock, for the main kinds of animals.
Annexes:
6.2. Institutional Mandate - data sharing - Memorandum of Understanding
7.1. Confidentiality - policy
Several national legal acts guarantee the confidentiality of data requested for statistical purposes. According to art. 9, paragraph 1 of the Legislative Decree n. 322 of 1989, personal data cannot be disseminated but in aggregated form, in order to make it impossible to make any reference to identifiable individuals. They can only be used for statistical purposes. Legislative Decree n. 322 of 1989, art. 6 bis and Legislative Decree n. 196 of 2003, Annex A3 (Code of conduct and professional practice applying to the processing of personal data for statistical and scientific research purposes within the framework of the national statistical system), art. 8, provide that the exchange of personal data within the National Statistical System (Sistan) is possible if it is necessary to fulfil requirements provided by the National Statistical Programme or to allow the pursuit of institutional purposes. The supply of the identification data of statistical units is allowed within the framework of entities included in the National Statistical System if the requesting party declares that no identical statistical result can be obtained otherwise. Regarding subjects who do not belong to Sistan, art. 7 of the Code of conduct (Legislative Decree n. 196 of 2003, Annex A3) states that it is possible to transmit individual data files without direct identifiers within the framework of specific laboratories set up by entities included in the National Statistical System, under certain conditions and only if that the data are protected by the application of different statistical methods that make it highly unlikely the identification of statistical units.
7.2. Confidentiality - data treatment
See sub-categories below.
7.2.1. Aggregated data
See sub-categories below.
7.2.1.1. Rules used to identify confidential cells
Threshold rule (The number of contributors is less than a pre-specified threshold)7.2.1.2. Methods to protect data in confidential cells
Cell suppression (Completely suppress the value of some cells)7.2.1.3. Description of rules and methods
The threshold rule indicated in item 7.2.1.1 defines confidential cells as those with 3 or fewer contributors.
In these cases, a specific notation (..) is used to indicate to users that the information is confidential.
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 methodology is described in the dedicated section of Eurostat's website.
Microdata can only be requested by users belonging to Sistan, using a specific form. The form must specify the purpose of the request and the variables of interest. The form is subject to approval by two Directorates (Directorate for Data collection and Directorate for Territory and Environment), which verify that the applicant's requirements are met, the purposes are legitimate and the principle of parsimony is respected, according to which no additional variables may be released other than those strictly necessary for the stated purposes. The confidentiality provisions applied are those laid down in Legislative Decree n. 322 of 1989 (on statistical confidentiality), as amended by Legislative Decree n. 281 of 1999 and Legislative Decree n. 196 of 2003.
8.1. Release calendar
There is no release calendar for Integrated Farm Statistics because Istat only publishes calendars for products not disseminated on the data warehouse, which is where the IFS tables are currently published.
8.2. Release calendar access
Not applicable.
8.3. Release policy - user access
Istat policy provides for free access to data by citizens. The main means of dissemination is the corporate website. Dissemination formats are designed for different audiences, so they range from infographics, more accessible to non-experts, to microdata for researchers.
We do not offer pre-release access to external users. Exceptions are made for colleagues in other areas of Istat (e.g. national accounts) who need aggregate variables for their work.
An ebook on Istat's dissemination policies was recently published. It can be found on Istat's website.
8.3.1. Use of quality rating system
No8.3.1.1. Description of the quality rating system
Not applicable.
At national level, IFS data are disseminated every 3 years.
10.1. Dissemination format - News release
See sub-categories below.
10.1.1. Publication of news releases
No10.1.2. Link to news releases
Not applicable.
10.2. Dissemination format - Publications
See sub-categories below.
10.2.1. Production of paper publications
No10.2.2. Production of on-line publications
No10.2.3. Title, publisher, year and link
Not applicable.
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
10.4. Dissemination format - microdata access
See sub-category below.
10.4.1. Accessibility of microdata
Yes10.5. Dissemination format - other
Not available.
10.5.1. Metadata - consultations
Not requested.
10.6. Documentation on methodology
See sub-categories below.
10.6.1. Metadata completeness - rate
Not requested.
10.6.2. Availability of national reference metadata
Yes10.6.3. Title, publisher, year and link to national reference metadata
Metadata are available online on Istat's website.
10.6.4. Availability of national handbook on methodology
Yes10.6.5. Title, publisher, year and link to handbook
Survey on agricultural holding structure and output
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
Metadata on IFS are stored in SIQual, which is an information system for documenting the process and quality of all surveys carried out by Istat in a standard manner.
11.1. Quality assurance
See sub-categories below.
11.1.1. Quality management system
Yes11.1.2. Quality assurance and assessment procedures
Training coursesQuality guidelines
Designated quality manager, quality unit and/or senior level committee
Compliance monitoring
Other
11.1.3. Description of the quality management system and procedures
Istat is highly committed to quality and its quality management system is consistent with the principles of the ES Code of Practice and the European Statistical System Common Quality Framework.
Quality management has a long tradition at Istat. It dates back to the ‘90s, when Istat adopted a systematic approach in order to ensure quality in statistical products, processes and services offered to the community. The Istat quality management system is based on several tools like information systems for the documentation of the statistical process and products and their quality, quality guidelines, quality reporting, training courses on quality, and procedures for the quality assessment of statistical processes and products.
SIDI-SIQual is the official information system for documenting quality and reference metadata of the Istat statistical production processes. It is aimed at documenting and supporting quality monitoring and assessment. The system describes the production process and its features: information content; survey phases and operations; activities to prevent, monitor and evaluate sampling and non-sampling errors. The system has two versions: one for external users (SIQual), and one for internal users. The internal version of the system includes standard quality indicators that are both process- and product- oriented.
Quality Report Card for Administrative data (QRCA) is a documentation system managed by the Directorate for Data Collection, aimed at evaluating the input quality for the statistical production processes based on administrative data. It is also aimed at informing internal users about the usability and quality of administrative data acquired. It contains information about the administrative sources and their relevance for Istat, the dataset's compliance with respect to data requested, its timeliness, integrability, stability, and metadata description.
Istat Quality guidelines for statistical processes contain the principles for planning, executing and assessing statistical processes and the description of the methods to ensure the compliance with the principles. The quality guidelines are considered as the reference standard for the assessment of both process and product quality.
As is well known, statistical processes that produce European statistics are required to accompany their results with metadata information, which is organised in standard quality reports. Activities related to quality reporting are carried out by the production sector; however, the Istat quality team coordinates and offers support for quality reports compilation and transmission. Internal training courses for quality reporting are also organised. In addition, in many cases, it is possible to produce quality reports by retrieving the quality information already provided in SIDI-SIQual. At the national level, the so-called "Schede standard di qualità" represents the Istat standard quality reports for Istat’s users and the Italian public. They offer similar information to that included in the standard quality reports but, unlike them, they are published only in Italian.
Surveys aimed at assessing users’ satisfaction for products and services available on Istat website are regularly carried out by the Directorate for Communication. The surveys are carried out through a web questionnaire on a voluntary basis and results are published on the website.
Training on quality has a long tradition at Istat. Since 2000, Istat has provided different types of training courses: general and introductory courses aim at involving all staff on quality and creating a quality culture, advanced courses describing non-sampling errors affecting accuracy and methods to assess and deal with them in statistical processes, specific courses on quality reporting or on audit procedures. In the period 2010-2016, a first cycle of audits and self-assessments on 80 Istat statistical processes was also carried out. Assessment was carried out against the quality guidelines.
In 2016, Istat started a modernisation process that brought about significant changes in the statistical production. After the modernisation, a renovation of quality policy was deemed necessary to adapt the quality methods and tools to the new production environment. In this new environment, in 2020, the Quality Committee was re-constituted (a first Quality Committee was in charge from 2010 to 2016) and a Quality Manager was appointed. A new quality policy was then developed and approved by Istat top management in October 2021. It included a set of activities to be implemented in the next 5 years. The implementation is currently being finalised and a new quality policy is being developed.
The quality policy focuses on the quality assessment of statistical processes, with different methods and tools according to the type of statistical processes:
- For traditional processes, e.g. surveys, the compliance with sound methodologies and practices was verified through a checklist, and the compliant processes obtained an internal “quality” label. Other processes are implementing improvement actions to reach compliance. The checklist, applied to all statistical processes, is complemented by an audit procedure applied to a limited number of processes (3 per year). Indeed, 3 audits were carried out in 2024 and 3 audits are ongoing in 2025.
- For complex multisource statistical processes that led to the creation of statistical registers, an ad hoc quality framework has been defined and is currently being implemented. The framework is based on a set of quality indicators useful to monitor and evaluate the quality of the different phases of the processes. Such quality indicators, together with metadata useful for their interpretation, will be stored in the new metadata system METAstat.
- For innovative processes, e.g. Trusted Smart Statistics, work is in progress to define a specific quality framework.
For further information, see the Istat’s website.
11.1.4. Improvements in quality procedures
As mentioned in item 11.1.3, the new Istat quality policy is currently being implemented. It was designed as a five-year program that should be completed.
Renovations or improvements of the existing quality tools are also ongoing, e.g. a new system for managing metadata and quality information, named METAstat, is being developed to substitute SIDI-SIQual, while the development of quality guidelines for statistical registers is planned.
Meanwhile, assessment procedures are applied to statistical processes; they identify improvement actions whose implementation is monitored by the quality team. Similarly, the quality framework for statistical registers will be implemented on more and more registers, and the quality framework for Trusted Smart Statistics will be developed.
11.2. Quality management - assessment
Not available.
12.1. Relevance - User Needs
The main groups of users and the groups of variables they need are the following:
- National Account Service of Istat (all the variables on labour force)
- Environment Service of Istat (all the variables on irrigation)
- ISPRA (variables connected with environmental impact of agriculture)
- Regions (variables on land use and livestock)
- Ministry of Agriculture (variables on land use)
- Universities and other research institutions (all variables for research, studies and analyses on the agricultural sector)
12.1.1. Main groups of variables collected only for national purposes
The main groups of variables collected for national purposes concern:
- Use of fertilisers: use and class of quantity of fertilisers, soil conditioners, correctives. This information was collected to enable Istat to gain a better understanding of the topic;
- Other gainful activities: additional information such as for how long the farm has been carrying out the OGAs and whether the OGAs were also initiated thanks to public support sources. This information was collected to enable researchers in the agricultural field to analyse the effect of public subsidies on the OGAs;
- Quality products: whether the farm participates in food quality labels for farm products: organic, PDO/PGI, other. This information was collected to enable researchers in the agricultural field to make analyses on the characteristics of farms adopting quality labels.
12.1.2. Unmet user needs
User needs that are not met are those relating to very detailed information, e.g. on municipal territorial domains, or concerning very specific and rare phenomena not covered by the Regulation (EU) 2018/1091.
12.1.3. Plans for satisfying unmet user needs
No plans for satisfying unmet user needs.
12.2. Relevance - User Satisfaction
Not applicable because there are no specific user satisfaction surveys for IFS.
Istat conducts user satisfaction surveys but the main focus of the surveys is in measuring overall satisfaction with the products consulted on the institutional website. They are related to ease of finding products, adequacy of content, accuracy and reliability of data, timeliness of updates, clarity of presentation and comparability with other data.
12.2.1. User satisfaction survey
No12.2.2. Year of user satisfaction survey
Not applicable.
12.2.3. Satisfaction level
Not applicable12.3. Completeness
Information on not collected, not-significant and not-existent variables is available on Eurostat’s website, at the link: Additional data - Eurostat (europa.eu).
12.3.1. Data completeness - rate
Not applicable for Integrated Farm Statistics as the not collected variables, not-significant variables and not-existent variables are completed with 0.
13.1. Accuracy - overall
See categories below.
13.2. Sampling error
See sub-categories below.
13.2.1. Sampling error - indicators
Please find the relative standard errors on Eurostat’s website, at the link: CircaBC website.
13.2.2. Reasons for non-compliant precision requirements in relation to Regulation (EU) 2018/1091
The cases in which the estimated RSEs are above the thresholds laid down in the Regulation (EU) 2018/1091 relate mainly to certain variables in the MORC module, such as, for example, T1000 (Oranges), T2000 (Small citrus fruits) and T3000 (Lemons) at country level.
This is probably due to two main causes:
- in order to limit the sample size, it was not possible to include constraints on these variables in the sample definition. We will take this into account in the future, as far as possible.
- some orchards (for example citrus) are quite rare in some regions.
In addition to the variables of the MORC module above, RSEs are high for the variables: A5000X5120_5130_LSU (Live poultry excluding cocks and chicks of chicken) in the ITH5 region and A3120_LSU (Breeding sows, live weight 50 kg or over) in the ITC4 region. For both variables, this is due to the strong heterogeneity of values among the surveyed units. The bulk of poultry and pig production appears to be concentrated in very few units in the ITH5 region and ITC4 region respectively. In the future, in order to overcome this problem, one may consider including these variables in the sample definition.
Other cases with estimated RSEs above the thresholds concern: A2300F_LSU (Dairy cows) in ITF3 region, A2300G_LSU (Non-dairy cows) in ITF6 region, A3110_3130_LSU (Piglets live weight of under 20 kg and other pigs) in ITH3 region, J1000_3000TE (Permanent grassland excluding rough grazing) in ITH2, ITH4 and ITF3 regions and UAA_IB (Total irrigable area) in ITH1 region. In these cases, the non-compliance with precision requirements is due to the strong heterogeneity of values among the surveyed units. To avoid these results in future, we will try to take them into account during the sample design phase.
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 link: CircaBC.
The over-coverage rate is unweighted.
The over-coverage rate is calculated as the share of ineligible holdings to the holdings designated for the core data collection. The ineligible holdings include those holdings with unknown eligibility status that are not imputed nor re-weighted for (therefore considered ineligible).
The over-coverage rate is calculated over the holdings in the main frame and if applicable frame extension, for which core data are sent to Eurostat.
13.3.1.1.1. Types of holdings included in the frame but not belonging to the population of the core (main frame and if applicable frame extension)
Temporarily out of production during the reference periodCeased activities
Merged to another unit
Duplicate units
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
We consider the degree of under-coverage to be 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 births13.3.1.3.3. Actions to minimise the under-coverage error
Consider the most up-to-date Farm Register.
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
The most up-to-date contact information possible was used to build the list used for the sample selection. The contact information of this list is updated through the official database of the Italian Chambers of Commerce (TELEMACO), managed by InfoCamere, which allows online access to the Business Register. Through the Telemaco platform, it is possible to update master data (company names, physical addresses, PEC addresses, etc.) on companies registered in Italy.
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 variables mostly affected by measurement errors are: UAA (utilised agricultural area), WA (wooded areas), and NUAA (unutilised agricultural area).
13.3.2.2. Causes of measurement errors
Respondents’ inability to provide accurate answers13.3.2.3. Actions to minimise the measurement error
Explanatory notes or handbooks for enumerators or respondentsOn-line FAQ or Hot-line support for enumerators or respondents
Training of enumerators
13.3.2.4. Impact of measurement error on data quality
High13.3.2.5. 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 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 identify the unitFailure to make contact with the unit
Refusal 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
Imputation
Weighting
13.3.3.1.3. Unit non-response analysis
Not carried out.
13.3.3.2. Item non-response - rate
The electronic questionnaire did not allow skipping the most important questions. So that only few variables were affected by non-response. For these variables imputation has been performed.
The minimum item non-response rate (unweighted) is: 0.00371
The maximum item non-response rate (unweighted) is: 2.668
13.3.3.2.1. Variables with the highest item non-response rate
A6710R (Bees (hives))
13.3.3.2.2. Reasons for item non-response
Skip of due questionFarmers do not know the answer
13.3.3.2.3. Actions to minimise or address item non-response
Imputation13.3.3.3. Impact of non-response error on data quality
Low13.3.3.4. Additional information non-response error
Not available.
13.3.4. Processing error
See sub-categories below.
13.3.4.1. Sources of processing errors
Internet problems affecting filled-in web questionnairesData processing
13.3.4.2. Imputation methods
Deductive imputationMean imputation
Random hot deck imputation
Sequential hot deck imputation
Nearest neighbour imputation
13.3.4.3. Actions to correct or minimise processing errors
Intervention on electronic questionnaire: introduction of tooltips to explain the content of the questions or some definitions.
13.3.4.4. Tools and staff authorised to make corrections
The following software tools were used:
- R package SeleMix was used to detect outlier and influential errors for the main quantitative variables;
- Banff is a SAS module used for editing and imputation of quantitative variables which violated linear constraints;
- R packages such as validate, validatetools, errorlocate and VIM were mainly used for editing and imputation of qualitative variables.
Staff profiles: researchers and technical assistants of the Directorate for Methodology and Statistical Process Design.
13.3.4.5. Impact of processing error on data quality
Low13.3.4.6. Additional information processing error
Not available.
13.3.5. Model assumption error
Not applicable.
14.1. Timeliness
See sub-categories below.
14.1.1. Time lag - first result
Not applicable, because first results were not published.
14.1.2. Time lag - final result
24 months.
14.2. Punctuality
See sub-categories below.
14.2.1. Punctuality - delivery and publication
See sub-categories below.
14.2.1.1. Punctuality - delivery
Not requested.
14.2.1.2. Punctuality - publication
The punctuality cannot be assessed as there is no release calendar.
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 from Regulation (EU) 2018/1091.
15.1.2.2. Reasons for deviations
Not applicable.
15.1.3. Thresholds of agricultural holdings
See sub-categories below.
15.1.3.1. Proofs that the EU coverage requirements are met
We selected the initial sample in order to represent all active agricultural holdings which are above at least one of the thresholds set in Annex II of Regulation (EU) 2018/1091, without any exclusion.
15.1.3.2. Differences between the national thresholds and the thresholds used for the data sent to Eurostat
The thresholds applied for the national data collection are the same as those applied for the data sent to Eurostat.
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
We collect, publish and send to Eurostat data with the same definitions and classification of variables compared to Regulation (EU) 2018/1091, Commission Implementing Regulation (EU) 2021/2286 and EU handbook, with the following exceptions:
- Other gainful activities (OGA) do not include existing hunting or fishing activities, since they are assumed to be very rare activities on Italian farms.
- The definition of the categories for agricultural training of the manager is not exactly the same as that from the EU legislation and handbook (our used definition also considers the level of education), in order to keep the comparison with previous farm statistics data.
- "Other livestock n.e.c." does not include equidae. However, in the data sent to Eurostat, "Other livestock n.e.c." includes equidae.
In the national dissemination, some variables (i.e. livestock) have a different format than in the data disseminated by Eurostat.
15.1.4.1.1. The number of working hours and days in a year corresponding to a full-time job
The information is available on Eurostat’s website, at the link: CircaBC.
The number of working hours and days in a year for a full-time job correspond to one annual working unit (AWU) in the country. One annual work unit corresponds to the work performed by one person who is occupied on an agricultural holding on a full-time basis. Annual working units are used to calculate the farm work on the agricultural holdings.
15.1.4.1.2. Point chosen in the Annual work unit (AWU) percentage band to calculate the AWU of holders, managers, family and non-family regular workers
See item 15.1.4.1.1.
15.1.4.1.3. AWU for workers of certain age groups
See item 15.1.4.1.1.
15.1.4.1.4. Livestock coefficients
Italy uses the same LSU coefficients as the ones set in Regulation (EU) 2018/1091 in order to transmit data to Eurostat.
However, data on livestock at national level are published in number of heads (not in LSU).
15.1.4.1.5. Livestock included in “Other livestock n.e.c.”
Italy does not include equidae in "Other livestock n.e.c.". However, in the data sent to Eurostat, "Other livestock n.e.c." includes equidae.
15.1.4.2. Reasons for deviations
Other gainful activities (OGA) do not include existing hunting or fishing activities, since they are assumed to be very rare activities on Italian farms.
The definition of the categories for agricultural training of the manager is not exactly the same as that from the EU legislation and handbook (our used definition also considers the level of education), in order to keep the comparison with previous farm statistics data.
15.1.5. Reference periods/days
See sub-categories below.
15.1.5.1. Deviations from Regulation (EU) 2018/1091
Data collected, sent to Eurostat and published are in compliance with the reference periods/days set in Regulation (EU) 2018/1091.
15.1.5.2. Reasons for deviations
Not applicable.
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 separate records representing virtual entities without managers.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
Due to the substantial stability of common land areas and survey difficulties, the microdata figures for IFS 2023 were kept the same as those from 2020.
The virtual common land units have been created at municipality level, on the basis of the information collected during 2020 census by:
- in 14 regions (NUTS 2): a direct survey, on optional basis, managed by the regions,
- in 7 regions (NUTS 2): administrative sources (in regions that did not participate in the survey).
15.1.6.6. Possible problems in relation to the collection of data on common land and proposals for future data collections
‘Common land agricultural units’ means an entity of land on which common rights apply. They are normally under the responsibility of a public authority (state, parish, etc.) over which another person is entitled to exercise rights of common, and these rights are generally exercisable in common with others.
The main problem in collecting data on common land in Italy concerns the lack of information from public authorities on this type of land. In many cases (regions, municipalities, etc.), there is no digitised management of information, making it very hard to discriminate between land on which common rights apply and land allotted to individual farms.
15.1.7. National standards and rules for certification of organic products
See sub-categories below.
15.1.7.1. Deviations from Council Regulation (EC) No 834/2007
There are no deviations in the national standards and rules for certification of organic products from Council Regulation (EC) No 834/2007.
15.1.7.2. Reasons for deviations
Not applicable.
15.1.8. Differences in methods across regions within the country
There are no differences in the methods used across regions within the country.
15.2. Comparability - over time
See sub-categories below.
15.2.1. Length of comparable time series
1 year
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 sufficient changes to warrant the designation of a break in series15.2.3.2. Description of changes
In IFS 2023, the thresholds provided for in the Regulation (EU) 2018/1091 were applied (in IFS 2020, some thresholds have been lowered). This had a negligible effect on UAA and LSU, as only very small units were excluded, but had a significant effect on number of holdings and labour force.
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
In 2020, the reference day for all other variables is 1 December 2020, whereas in 2023, the reference day for all other variables is 31 October 2023.
15.2.7. Common land
See sub-categories below.
15.2.7.1. Changes in the methods to record common land since the last data transmission to Eurostat
There have been no changes15.2.7.2. Description of changes
Not applicable.
15.2.8. Explanations for major trends of main variables compared to the last data transmission to Eurostat
The main reason for the evolution over time of IFS variables, between 2020 and 2023, is in the weights. The final weights were obtained by calibrating to known totals from the latest available version of the farm register, in order to ensure internal consistency. In addition, Italy used a single weighting system for all variables within a unit.
This criterion does not guarantee good accuracy for all variables. Moreover, some of the most striking variations over time refer to rather low absolute values, and it should be noted that the comparison is between a census and a sample survey.
In 2023, there was also an increase in share of holdings having organic area; this finding was confirmed also by the Italian Ministry of Agriculture.
The time series analysis showed a decrease in the share of holdings benefitting from rural development measures. The trend is influenced by sample weights. Estimates are the sum of the product of individual farm data and their respective weights: farms with rural measures have lower average weights.
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
No micro-level data are available in other agricultural statistics for 2023.
15.3.4. Coherence at macro level with data collections in other domains in agriculture
See sub-categories below.
15.3.4.1. Analysis of coherence at macro level
Yes15.3.4.2. Results of analysis at macro level
Coherence cross-domain: IFS vs CROP PRODUCTION
The main reason for the discrepancies is in the weights. The final weights were obtained by calibrating to known totals from the latest available version of the farm register, in order to ensure internal consistency. In addition, we used a single weighting system for all variables within a unit. This criterion does not guarantee good accuracy for all variables. Moreover, some of the variations reported refer to rather low absolute values, and it should be noted that the comparison is between a census and a sample survey.
This justification pertains also to the cross-domain variations with animal and organic animal production.
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
We did not coordinate questionnaires of different data collections in agricultural statistics. This coordination would have avoided that farms are asked the same questions more than once.
16.2. Efficiency gains since the last data transmission to Eurostat
On-line surveysFurther training
16.2.1. Additional information efficiency gains
Not available.
16.3. Average duration of farm interview (in minutes)
See sub-categories below.
16.3.1. Core
30 minutes.
16.3.2. Module ‘Labour force and other gainful activities‘
15 minutes.
16.3.3. Module ‘Rural development’
Not relevant (data were collected from the administrative source).
16.3.4. Module ‘Animal housing and manure management’
Restricted from publication
16.3.5. Module ‘Irrigation’
10 minutes.
16.3.6. Module ‘Soil management practices’
5 minutes.
16.3.7. Module ‘Machinery and equipment’
10 minutes.
16.3.8. Module ‘Orchard’
15 minutes.
16.3.9. Module ‘Vineyard’
Restricted from publication
17.1. Data revision - policy
No data revision policy.
17.2. Data revision - practice
No data revisions for IFS 2023.
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
Farm Register.
18.1.1.3. Update frequency
Annual18.1.2. 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
Sampling design of the IFS survey is a one-stage stratified random sampling, with the strata defined by the combination of the modality of administrative regions (NUTS 2 level) and the classes of some dimensional variables (UAA, LSU). A fixed number of agricultural holdings is selected in each stratum without replacement and with equal probabilities. The optimal stratification and allocation in each stratum are determined by applying R package ‘SamplingStrata’ (Barcaroli G, Ballin M., 2022).
In the field of stratified sampling design, this package offers an approach for the determination of the best stratification of a sampling frame, the one that ensures the minimum sample cost under the condition to satisfy precision constraints in a multivariate and multidomain case. This approach is based on the use of the genetic algorithm: each solution (i.e. a particular partition in strata of the sampling frame) is considered as an individual in a population; the fitness of all individuals is evaluated applying the Bethel-Chromy algorithm to calculate the sampling size satisfying precision constraints on the target estimates. Functions in the package allows to: (a) analyse the obtained results of the optimisation step; (b) assign the new strata labels to the sampling frame; (c) select a sample from the new frame accordingly to the best allocation.
18.1.2.2.1. Name of sampling design
Stratified one-stage random sampling18.1.2.2.2. Stratification criteria
Unit sizeUnit location
18.1.2.2.3. Use of systematic sampling
No18.1.2.2.4. Full coverage strata
The only full coverage strata concern common land.
18.1.2.2.5. Method of determination of the overall sample size
The overall sample size was determined by the R package SamplingStrata.
18.1.2.2.6. Method of allocation of the overall sample size
Optimal allocation considering costs18.1.3. Core data collection on the frame extension
See sub-categories below.
18.1.3.1. Coverage of agricultural holdings
Not applicable18.1.3.2. Sampling design
Not applicable.
18.1.3.2.1. Name of sampling design
Not applicable18.1.3.2.2. Stratification criteria
Not applicable18.1.3.2.3. Use of systematic sampling
Not applicable18.1.3.2.4. Full coverage strata
Not applicable.
18.1.3.2.5. Method of determination of the overall sample size
Not applicable.
18.1.3.2.6. Method of allocation of the overall sample size
Not applicable18.1.4. Module “Labour force and other gainful activities”
See sub-categories below.
18.1.4.1. Coverage of agricultural holdings
Sample18.1.4.2. Sampling design
See item 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
18.1.4.2.3. Use of systematic sampling
No18.1.4.2.4. Full coverage strata
The only full coverage strata concern common land.
18.1.4.2.5. Method of determination of the overall sample size
The overall sample size was determined by the R package SamplingStrata.
18.1.4.2.6. Method of allocation of the overall sample size
Optimal allocation considering costs18.1.4.2.7. If sampled from the core sample, the sampling and calibration strategy
Not applicable18.1.5. Module “Rural development”
See sub-categories below.
18.1.5.1. Coverage of agricultural holdings
Sample18.1.5.2. Sampling design
See item 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
18.1.5.2.3. Use of systematic sampling
No18.1.5.2.4. Full coverage strata
The only full coverage strata concern common land.
18.1.5.2.5. Method of determination of the overall sample size
The overall sample size was determined by the R package SamplingStrata.
18.1.5.2.6. Method of allocation of the overall sample size
Optimal allocation considering costs18.1.5.2.7. If sampled from the core sample, the sampling strategy and calibration strategy
Not applicable18.1.6. Module “Animal housing and manure management module”
Restricted from publication
18.1.6.1. Coverage of agricultural holdings
Restricted from publication
18.1.6.2. Sampling design
Restricted from publication
18.1.6.2.1. Name of sampling design
Restricted from publication
18.1.6.2.2. Stratification criteria
Restricted from publication
18.1.6.2.3. Use of systematic sampling
Restricted from publication
18.1.6.2.4. Full coverage strata
Restricted from publication
18.1.6.2.5. Method of determination of the overall sample size
Restricted from publication
18.1.6.2.6. Method of allocation of the overall sample size
Restricted from publication
18.1.6.2.7. If sampled from the core sample, the sampling strategy and calibration strategy
Restricted from publication
18.1.7. Module ‘Irrigation’
See sub-categories below.
18.1.7.1. Coverage of agricultural holdings
Sample18.1.7.2. Sampling design
See item 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
18.1.7.2.3. Use of systematic sampling
No18.1.7.2.4. Full coverage strata
The only full coverage strata concern common land.
18.1.7.2.5. Method of determination of the overall sample size
The overall sample size was determined by the R package SamplingStrata.
18.1.7.2.6. Method of allocation of the overall sample size
Optimal allocation considering costs18.1.7.2.7. If sampled from the core sample, the sampling strategy and calibration strategy
Not applicable18.1.8. Module ‘Soil management practices’
See sub-categories below.
18.1.8.1. Coverage of agricultural holdings
Sample18.1.8.2. Sampling design
See item 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
18.1.8.2.3. Use of systematic sampling
No18.1.8.2.4. Full coverage strata
The only full coverage strata concern common land.
18.1.8.2.5. Method of determination of the overall sample size
The overall sample size was determined by the R package SamplingStrata.
18.1.8.2.6. Method of allocation of the overall sample size
Optimal allocation considering costs18.1.8.2.7. If sampled from the core sample, the sampling strategy and calibration strategy
Not applicable18.1.9. Module ‘Machinery and equipment’
See sub-categories below.
18.1.9.1. Coverage of agricultural holdings
Sample18.1.9.2. Sampling design
See item 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
18.1.9.2.3. Use of systematic sampling
No18.1.9.2.4. Full coverage strata
The only full coverage strata concern common land.
18.1.9.2.5. Method of determination of the overall sample size
The overall sample size was determined by the R package SamplingStrata.
18.1.9.2.6. Method of allocation of the overall sample size
Optimal allocation considering costs18.1.9.2.7. If sampled from the core sample, the sampling strategy and calibration strategy
Not applicable18.1.10. Module ‘Orchard’
See sub-categories below.
18.1.10.1. Coverage of agricultural holdings
Sample18.1.10.2. Sampling design
See item 18.1.2.2.
18.1.10.2.1. Name of sampling design
Stratified one-stage random sampling18.1.10.2.2. Stratification criteria
Unit sizeUnit location
18.1.10.2.3. Use of systematic sampling
No18.1.10.2.4. Full coverage strata
The only full coverage strata concern common land.
18.1.10.2.5. Method of determination of the overall sample size
The overall sample size was determined by the R package SamplingStrata.
18.1.10.2.6. Method of allocation of the overall sample size
Optimal allocation considering costs18.1.10.2.7. If sampled from the core sample, the sampling strategy and calibration strategy
Not applicable18.1.11. Module ‘Vineyard’
Restricted from publication
18.1.11.1. Coverage of agricultural holdings
Restricted from publication
18.1.11.2. Sampling design
Restricted from publication
18.1.11.2.1. Name of sampling design
Restricted from publication
18.1.11.2.2. Stratification criteria
Restricted from publication
18.1.11.2.3. Use of systematic sampling
Restricted from publication
18.1.11.2.4. Full coverage strata
Restricted from publication
18.1.11.2.5. Method of determination of the overall sample size
Restricted from publication
18.1.11.2.6. Method of allocation of the overall sample size
Restricted from publication
18.1.11.2.7. If sampled from the core sample, the sampling strategy and calibration strategy
Restricted from publication
18.1.12. Software tool used for sample selection
For the sample selection a routine in R was written.
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.
Annexes:
18.3.3. Questionnaire in Italian
18.3.3. Questionnaire in English
18.4. Data validation
See sub-categories below.
18.4.1. Type of validation checks
Data format checksRange checks
Comparisons with previous rounds of the data collection
18.4.2. Staff involved in data validation
SupervisorsStaff from local departments
Staff from central department
18.4.3. Tools used for data validation
Software such as Excel and SAS.
18.5. Data compilation
See in annex.
Annexes:
18.5. Data compilation
18.5.1. Imputation - rate
Please find the imputation rate for main variables in the annex.
Annexes:
18.5.1. Imputation - rate
18.5.2. Methods used to derive the extrapolation factor
Calibration18.6. 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
AGEA – Agency for the Disbursement in Agriculture
ATC – Division for Agricultural statistics and surveys
AWU – Annual working unit
CAP – Common Agricultural Policy
CREA – Council for Agricultural Research and Analysis of Agricultural Economics
ES – European Statistics
EU – European Union
FADN – Farm Accountancy Data Network
IACS – Integrated Administration and Control System
IFS – Integrated Farm Statistics
ISMEA – Institute of Agricultural Food Market Services
ISPRA – National Institute for Environmental Protection and Research
Istat – National Statistical Institute
LSU – Livestock unit
MASAF – Ministry of Agriculture
MORC – Module on Orchard
NUTS – Nomenclature of territorial units for statistics
OGA – Other gainful activities
PDO – Protected Designation of Origin
PEC – Certified Electronic Mail
PGI – Protected Geographical Indication
PSN – National Statistical Plan
QRCA – Quality Report Card for Administrative data
RSE – Relative standard error
SGM – Standard gross margin
SIQual – Quality Information System
Sistan – National Statistical System
SO – Standard output
UAA – Utilised agricultural area
19.2. Additional comments
No additional comments.
The data describe the structure of agricultural holdings providing the general characteristics of farms and farmers and information on their land, livestock and labour force. They also describe production methods, rural development measures and agro-environmental aspects that look at the impact of agriculture on the environment.
The data are used by public, researchers, farmers and policy-makers to better understand the state of the farming sector and the impact of agriculture on the environment. The data follow up the changes in the agricultural sector and provide a basis for decision-making in the Common Agricultural Policy (CAP) and other European Union policies.
The statistical unit is the agricultural holding (farm). The aggregated results are disseminated through statistical tables. The data are presented at different geographical levels and over periods.
The data collections are organised in line with Regulation (EU) 2018/1091 and have a new structure, consisting of a core data set and several modules. The regulation covers the data collections in 2019/2020 (the agricultural census), 2023 and 2026. The data are as comparable and coherent as possible with the other European countries.
12 November 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;
- 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, nectarines area, apricots area, oranges area, small citrus fruit area, lemons area, olives area, grapes for table use area, each one by age of plantation and density of trees.
See sub-category below.
See sub-categories below.
See sub-categories below.
See sub-categories below.
See categories below.
Two kinds of units are generally used:
- the units of measurement for the variables (area in hectares, livestock in (1000) heads or LSU (livestock units), labour force in persons or AWU (annual working units), standard output in Euro) and
- the number of agricultural holdings having these characteristics.
See in annex.
Annexes:
18.5. Data compilation
See sub-categories below.
At national level, IFS data are disseminated every 3 years.
See sub-categories below.
See sub-categories below.
See sub-categories below.
