1.1. Contact organisation
Instituto Nacional de Estadística (INE)
1.2. Contact organisation unit
Sub-Directorate General for Economic Sectors Statistics
1.3. Contact name
Confidential because of GDPR
1.4. Contact person function
Confidential because of GDPR
1.5. Contact mail address
Avenida de Manoteras 50-52
Planta 3ª modulo 0364
28050 Madrid
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
11 April 2025
2.2. Metadata last posted
6 May 2025
2.3. Metadata last update
11 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.
The output of an agricultural characteristic is the monetary value of gross production at the farm gate price. Standard Output (SO) means the value of production corresponding to the average situation in a certain region for each agricultural characteristic. Output means the sum of the value of the main product(s) and the secondary product(s). The values shall be calculated by multiplying the output per unit by the farm gate price excluding VAT (Value Added Tax), product taxes and direct payments.
Standard outputs shall be determined using average basic data calculated over a reference period of five years. They are updated from time to time in line with economic trends.
The total standard output of the holding is the sum of the values obtained for each characteristic by multiplying the standard outputs per unit by the corresponding number of units.
Holdings are classified according to their economic size into different classes. The economic size of the holding shall be defined in terms of the total standard output of the holding, expressed in euro.
For the variables included in "Other poultry" (A5000X5100), ("Ducks" (A5210), "Geese" (A5220), "Turkeys" (A5230), "Ostrich" (A5410), "Guinea fowls and small poultry (including quails, pheasants and pigeons)" (A5240_5300)) the disaggregated standard output coefficients (SOCs) for each type of poultry are used, instead of using the SOC for other poultry.
Since our country has made the effort to create these coefficients, the 2020 SOCs of various disaggregated products were sent on a voluntary basis, they should be taken into account in the IFS. In this way, farms will be better classified by taking them into account and we will do so in our publications.
In relation to dairy cows (A2300F) and breeding female buffaloes (A2410), we have some farms where we have non-dairy buffaloes and dairy cows (which are not buffalo cows). The SO calculation is being done differently from Eurostat, as we have disaggregated information on breeding female buffaloes (dairy and non-dairy), and we have coefficients for both dairy and non-dairy cows.
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, nectarines area, apricots area, oranges area, small citrus fruit area, lemons area, olives area, grapes for table use area, grapes for raisins 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 national definition of an agricultural holding is a unit, from a technical and economic point of view, with a single address and which, within the Spanish economic territory, carries out agricultural activities, both as a main and secondary activity.
The economic activities are defined in accordance with Regulation (EC) No 1893/2006 as belonging to groupings:
- A.01.1: Non-perennial crops
- A.01.2: 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 a principal or secondary activity.
For the activities of class A.01.49, only the activities of ‘Raising of semi-domesticated or other live animals’ (with the exception of insect farming) and ‘Beekeeping and production of honey and beeswax’ are included.
In addition, the holding may have other complementary (non-agricultural) activities. This unit, being technically and economically unique, is characterised by a common use of labour and means of production (machinery, land, installations, fertilisers, etc.). This implies that, if the parcels of the holding are located in two or more municipalities, they may not be very far apart geographically.
The agricultural holding can therefore be defined as a unit of an agricultural nature (set of land and/or livestock), under a single management, located in a given geographical location, and using the same production methods.
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.
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
Canary Islands - Balearic Islands - Ceuta - Melilla
3.7.3. Criteria used to establish the geographical location of the holding
The most important parcel by physical sizeThe residence of the farmer (manager) not further than 5 km straight from the farm
3.7.4. Additional information reference area
To geolocate the agricultural holdings, proceed as follows:
- Regarding the information collected by administrative registry, if the coordinates are valid, the grid is created with them, otherwise the SIGPAC reference will be taken, provided that it is not duplicated with another holding; failing this, the cadastral reference will be taken, provided that it is not duplicated. If, after tracking these variables, no coordinates are obtained, they will be set to zero.
- For the information collected by questionnaire, the SIGPAC reference is looked at first, if it is valid, coordinates are taken and the grid is generated; if it is not valid, the cadastral reference of the land is taken, if it is valid, coordinates are taken and the grid is generated; if it is not valid, the cadastral reference of the livestock installations is taken, if it is valid, coordinates are taken and the grid is generated. If these three references are not valid, an auxiliary file is looked at with all the holdings with geolocation data (obtained from different administrative sources). If coordinate data are obtained, the grid is generated, and if not, the coordinates are set to zero.
For those holdings whose coordinates are missing, their geolocation will be imputed according to the holder's residence. For those farms that continue without georeferencing, the centroid of a grid of the municipality chosen probabilistically according to a weighting table that assigns greater weight to areas with a high density of farms will be established.
3.8. Coverage - Time
Farm structure statistics in our country cover the period from 1962 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 of the variables (area in hectares, livestock in (1000) heads or LSU (livestock units), labour force in persons or AWU (annual work unit), standard production in euros, volume of water used in irrigation in cubic metres, age of planting of fruit trees in years, tractor power in kilowatts, storage of agricultural products in cubic metres), and
- the number of agricultural holdings with these characteristics.
The unit of measurement used for the area of the agricultural holding and crops is the hectare, except in the case of cultivated mushrooms where it is the square metre.
Livestock data can be expressed in number of heads of animals of the different types of livestock. Heads of livestock are given on a reference day within the reference period (30 September 2023), as the number of livestock in a year may fluctuate.
Livestock units (LSU) is a standard unit of measurement that allows the aggregation of various categories of livestock of different species and ages according to convention, through the use of specific coefficients established on the basis of nutritional or feed requirements of each type of animal, to allow a comparison. The coefficients used are adopted in accordance with Annex I of Regulation (EU) 2018/1091.
Farm labour data are expressed either in number of working days, in percentage of working time or in annual work unit (AWU); one AWU is equivalent to the work done by one person on a full-time basis over one year. The annual work unit (AWU) is equivalent to the work done by a full-time person over a year, i.e. the total hours worked divided by the average annual hours worked in full-time jobs in the country.
Data on volume of water used for irrigation are expressed in cubic metres. Similarly, data on storage of agricultural products are also expressed in cubic metres.
See sub-categories below.
5.1. Reference period for land variables
The use of land refers to 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.
For the variables related to land, the reference period is the 2023 agricultural campaign, from 1 October 2022 to 30 September 2023.
5.2. Reference period for variables on irrigation and soil management practices
The 12-month reference period for the variables on irrigation and soil management practices, which coincides with the 2023 agricultural campaign, starts on 1 October 2022 and ends on 30 September 2023.
5.3. Reference day for variables on livestock and animal housing
For variables on livestock the reference date is 30 September 2023. 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 reference period is the 2023 agricultural campaign, from 1 October 2022 to 30 September 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 30 September 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
1) Law 12/1989, of 9 May, on the Public Statistical Function (also named LFEP)
2) Royal Decree 1110/2020, of 15 December, on the National Statistical Plan 2021-2024
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
1) 1989
2) 2021
6.1.5. Legal obligations for respondents
Yes6.2. Institutional Mandate - data sharing
Data exchanges between INE and the other statistical services of the State (ministerial departments, autonomous bodies and public entities of the State Administration), as well as between these and the statistical services of the Autonomous Communities for the development of the statistics entrusted to them, are regulated in the LFEP. The LFEP also establishes the mechanisms for statistical coordination between administrations, as well as the conclusion of cooperation agreements when deemed appropriate.
The IFS 2023 data for NUTS 2 region ES21 have been obtained in collaboration with the Basque Statistical Institute (EUSTAT), in accordance with the agreement signed between INE and EUSTAT.
7.1. Confidentiality - policy
The statistical data provided to the National Statistics Institute is protected by statistical secrecy. Statistical secrecy is a guarantee and trust mechanism for respondents that implies the protection of the data that is obtained for statistical purposes.
Chapter III of the aforementioned Public Statistical Function Law (LFEP) regulates all aspects of statistical secrecy.
Therefore, the INE adopts the necessary logical, physical and administrative measures to ensure that the protection of confidential data is effective, from data collection to publication.
A legal clause is included in the information collection questionnaires informing about the protection of the data collected.
In the information processing phases, directly identifiable data are removed and additional measures are taken to ensure the security and integrity of the information. The LFEP obliges statistical services to "adopt the necessary organisational and technical measures to protect the information". Specifically, the security policy applied at INE follows the standards of the Spanish national security framework.
In the publication of the results tables, the detail of the information is analysed in order to prevent confidential data from being deduced from the statistical units, applying direct and indirect anonymisation techniques.
The Farm Statistics is a statistical operation included in the National Statistical Plan, so it is subject to the LFEP, so its data are protected by the statistical secrecy in all phases of its elaboration.
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
No methods applied7.2.1.3. Description of rules and methods
Not applicable.
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 INE provides users with information on microdata, aggregating the data in a way that preserves the direct or indirect identification of the statistical unit. The variable that is most often aggregated to preserve the statistical unit's confidentiality is the regional variable. Access to the aggregated microdata is available on the INE website.
We also grant access to our microdata, showing all its variables without aggregation, solely for scientific purposes while maintaining the same security as proposed by the EU methodology, described in the dedicated section of Eurostat's website.
In cases where microdata are requested without any type of aggregation for scientific purposes, access is provided through a secure environment, where the user works with the information and performs analyses, without being able to download the microdata.
The form to access this information is as follows: INE webiste.
8.1. Release calendar
There is a calendar of structural statistics in which the publication of the Integrated Farm Statistics 2023 is included.
The publication of the IFS 2023 data at national level was on 26 February 2025.
8.2. Release calendar access
8.3. Release policy - user access
In accordance with the publication schedule, data are transmitted simultaneously to all interested parties (media), typically accompanied by a press release.
At the same time, the data are published on the INE website at 11 AM.
Tailor-made requests are also sent to registered users.
Some users, such as the Autonomous Communities if required, may receive information under embargo, as specified in the European Statistics Code of Practice.
8.3.1. Use of quality rating system
No8.3.1.1. Description of the quality rating system
Not applicable.
Farm statistics are disseminated every 10 years for censuses and every 3-4 years between censuses for sample-based data collections.
10.1. Dissemination format - News release
See sub-categories below.
10.1.1. Publication of news releases
Yes10.1.2. Link to news releases
The results of IFS 2023 are generally disseminated with press releases that can be consulted both in the menu corresponding to the operation and in the press releases section.
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
The number of accesses for Farm Statistics data queries has been 156 956 in 2023, and 123 312 for the period 1 January 2024 - 1 December 2024.
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
No10.6.3. Title, publisher, year and link to national reference metadata
Not applicable.
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
Yes10.6.7. Title, publisher, year and link to methodological papers
IFS 2023 - Technical project - May 2023:
Link to the methodology:
Methodology page (for IFS 2023)
Link to the Standardised Methodological Report:
Standardised Methodological Report
10.7. Quality management - documentation
The quality assurance framework for INE statistics is based on the European Statistics Code of Practice (ESSCoP).
A series of measures have been implemented that contribute to guaranteeing the quality of the process and the results. Among them are the following:
- Both the questionnaire and the definitions are agreed in a working group with the participation of experts.
- Data collection through a multichannel media. We note CATI, CAWI, CAPI applications with implementation of errors and warnings of incompatibility or incongruity between the survey responses in order to perform a first debugging.
- Editing and imputation after the collection of information.
11.1. Quality assurance
See sub-categories below.
11.1.1. Quality management system
No11.1.2. Quality assurance and assessment procedures
Other11.1.3. Description of the quality management system and procedures
The quality of the process has been ensured through the analysis, integration, standardisation and exhaustiveness (completeness) of all the agricultural files referred to in the Regulation (EU) 2018/1091, together with a sample data collection, to cover the entire target population of the IFS 2023.
Since the beginning of the process, regular meetings have been held with experts, in which the coverage has been studied and the coherence of the IFS 2023 data with other sources available in the Ministry of Agriculture has been analysed.
11.1.4. Improvements in quality procedures
There has been no improvement in quality compared to IFS 2020.
11.2. Quality management - assessment
Not available.
12.1. Relevance - User Needs
The data are used by the public, researchers, farmers and policy makers to better understand the state of the agricultural sector and the impact of agriculture on the environment. The data track changes in the agricultural sector and provide a basis for decision-making in the Common Agricultural Policy (CAP) and other EU policies.
The variables investigated, and their description, are reduced to the list proposed in Commission Implementing Regulation (EU) 2021/2286.
12.1.1. Main groups of variables collected only for national purposes
At the request of the Ministry of Agriculture, the following variables have been added, for the preparation and comparison of statistics carried out by this body:
- In the list of other gainful activities related to the holding, another category, ‘Leasing of land for hunting activities (hunting and mountaineering)’, has been included for the compilation of the Economic Accounts for Agriculture.
- In relation to the irrigation methods used in the campaign, a greater breakdown has been collected in the categories of sprinkler irrigation and localised irrigation, in order to have this information available for analysis.
- The volume of water used has been collected disaggregated by irrigation water source.
- The square metres where the solar panels for photovoltaic energy production are located are requested, if such panels are available. It is also asked whether the aim of the production of this energy is to supply the irrigation systems.
12.1.2. Unmet user needs
In the preparatory work for the survey, when the information collection questionnaire was being developed, our main users were consulted and were provided with a draft of the questionnaire so that they could inform us of any shortcomings or missing variables. All proposals have been taken into account in the survey, and variables have been included at the request of the Ministry of Agriculture.
For the other users, the proposals received through the User Consultations, which arrive via e-mail, are taken into account, without there being a special form.
12.1.3. Plans for satisfying unmet user needs
Not applicable.
12.2. Relevance - User Satisfaction
A user satisfaction survey has been conducted in 2019.
12.2.1. User satisfaction survey
Yes12.2.2. Year of user satisfaction survey
2019
12.2.3. Satisfaction level
Satisfied12.3. 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 europa.
13.2.2. Reasons for non-compliant precision requirements in relation to Regulation (EU) 2018/1091
All accuracy requirements set out in Annex V of the Regulation (EU) 2018/1091 are met, except:
- Module LAFO and variable J1000_3000TE (Permanent grassland excluding rough grazings) in NUTS 2 region ES70, the RSE exceeds 5%.
- Module MORC:
- Variable T2000 (Small citrus fruits) in NUTS 2 region ES52, the RSE exceeds 7.5%.
- Variable T3000 (Lemons and acid limes) in NUTS 2 region ES62, the RSE exceeds 7.5%.
- Variables O1000 (Olives) and T1000 (Oranges) in NUTS 1 region ES7, the RSE exceeds 7.5%.
- Variable T2000 (Small citrus fruits) in NUTS 1 region ES5, the RSE exceeds 7.5%.
- Variables F1110 (Apples), F1120 (Pears), F1210 (Peaches), F1220 (Nectarines), F1230 (Apricots), W1200 (Grapes for table use) and W1300 (Grapes for raisins) at country level, the RSE exceeds 7.5%.
The main reason is that the variability of the net sample (respondents' holdings) exceeded that of the gross sample.
Future steps to avoid non-compliance precision requirements are to improve the quality of the framework auxiliary variables and to increase the sample size.
13.2.3. Reference on method of estimation
The variance estimation method does not take into account the effect of calibration on the variance.
The formula specified in the attachment is applied.
Annexes:
13.2.3. Methodology used to calculate relative standard errors
13.2.4. Impact of sampling error on data quality
Unknown13.3. 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)
Temporarily out of production during the reference periodCeased activities
13.3.1.1.2. Actions to minimize the over-coverage error
Other13.3.1.1.3. Additional information over-coverage error
Types of holdings included in the frame but not belonging to the population
We note that more than 48% are due to cease their activity, and approximately 27% are temporarily out of production
Actions to minimise the over-coverage error
To update data using the most recent administrative registers:
- Agricultural Register
- Tax Register
- Population Register
13.3.1.2. Common units - proportion
Not requested.
13.3.1.3. Under-coverage error
See sub-categories below.
13.3.1.3.1. Under-coverage rate
For the core data, we utilised the IACS 2023 administrative register and the Livestock Register 2023 for birth records. To address potential under-coverage of farms not listed in these registers, we implemented a stratified sampling approach. Specifically, a sample was drawn from the 2020 target population frame, and an additional independent sample was drawn from an updated 2022 frame. While a precise under-coverage rate cannot be calculated, we performed extensive analyses and comparisons of the estimated main crop and livestock variables with other agricultural data sources and historical trends. These analyses indicate that the residual under-coverage is likely minimal, given the comprehensive nature of our sampling and validation procedures.
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
Update the administrative registers for all holdings identified during fieldwork that were not previously registered.
13.3.1.3.4. Additional information under-coverage error
All holdings belonging to the population of the core have been included in the frame; but it is possible that some birth in 2023 year that is not in the 2023 agricultural register, has not been included; we think that these cases have been minimal.
We have realised periodical meetings with different experts to analyse the coverage, comparing IFS 2023 data with other agricultural sources. The conclusions are that the data are consistent taking into account the different methodologies.
13.3.1.4. Misclassification error
No13.3.1.4.1. Actions to minimise the misclassification error
Misclassification errors have been minimised by having up-to-date records.
13.3.1.5. Contact error
No13.3.1.5.1. Actions to minimise the contact error
Contact errors have been minimised by having up-to-date records.
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
We consider that the variables are not affected by measurement errors.
13.3.2.2. Causes of measurement errors
Not applicable13.3.2.3. Actions to minimise the measurement error
Explanatory notes or handbooks for enumerators or respondentsTraining of enumerators
Other
13.3.2.4. Impact of measurement error on data quality
None13.3.2.5. Additional information measurement error
To avoid measurement errors, the questionnaire and the collection method have been improved with the experience gained from previous censuses and surveys.
To ensure data consistency and minimise errors, an application (IRIA) developed by INE has been used which integrates all phases of data collection and editing. All questionnaires (postal mail, CAWI, CATI, CAPI) were recorded with IRIA.
During the collection and recording phases of the mailed questionnaires the data were checked, with a quality control of the recording and a control of the data supplied. In addition, CAWI, CATI and CAPI have their own checks in IRIA.
IRIA detects errors in the internal consistency of the questionnaires (partial absence of data in a questionnaire, inconsistent data between different variables and control of the extent and existence of quantitative variables). It also detects and lists outlier controls, such as crops that are not common in certain regions.
Post-recording editing was carried out centrally by the Responsible Department with the help of an external company.
13.3.3. Non response error
See sub-categories below.
13.3.3.1. Unit non-response - rate
See item 13.3.1.1.
The unit non-response rate is unweighted.
The unit non-response rate is calculated as the share of eligible non-respondent holdings to the eligible holdings. The eligible holdings include those holdings with unknown eligibility status which are imputed or re-weighted for (therefore considered eligible).
The unit non-response rate is calculated over the holdings in the main frame and if applicable frame extension, for which core data are sent to Eurostat.
13.3.3.1.1. Reasons for unit non-response
Failure to make contact with the unitRefusal to participate
13.3.3.1.2. Actions to minimise or address unit non-response
Weighting13.3.3.1.3. Unit non-response analysis
Non-responding eligible units are defined as those selected from the sampling frame that were contacted but for which a completed questionnaire was not obtained due to refusal or non-contact. Reweighting is applied to compensate for this non-response, except for units within 'certainty' strata. Stratification for reweighting is based on the intersection of region, technical economic orientation, and size group. Given this stratification, we conclude that the non-response bias is minimal.
13.3.3.2. Item non-response - rate
The variables relating to the farm manager (year of birth of farm manager, sex of farm manager, working hours by farm manager - % band Annual work units (AWU), year classified as a farm manager, agricultural training of the farm manager and vocational training by farm manager in last 12 months) are empty in some farms, so non-response is assumed in these variables.
The unweighted non-response rate for these variables is as follows:
- Year of birth of farm manager = 4 379/626 047 = 0.0070 → 0.70%
- Sex of farm manager = 3 272/626 047 = 0.0052 → 0.52%
- Working hours by farm manager - % band Annual work units (AWU) = 25 098/626 047 = 0.0401 → 4.01%
- Year classified as a farm manager = 26 827/626 047 = 0.0429 → 4.29%
- Agricultural training of the farm manager = 3 601/626 047 = 0.0058 → 0.58%
- Vocational training by farm manager in last 12 months = 19 672/626 047 = 0.0314 → 3.14%
13.3.3.2.1. Variables with the highest item non-response rate
- Y_BIRTH_MAN (Year of birth of farm manager),
- SEX_MAN (Sex of farm manager),
- WH_MAN_AWU_PC (Working hours by farm manager - % band Annual work units (AWU)),
- Y_FARM_MAN (Year classified as a farm manager),
- TNG_MAN (Agricultural training of the farm manager), and
- VT_MAN_M12 (Vocational training by farm manager in last 12 months),
- FA9 (Other areas on the farms).
13.3.3.2.2. Reasons for item non-response
Farmers do not know the answer13.3.3.2.3. Actions to minimise or address item non-response
Imputation13.3.3.3. Impact of non-response error on data quality
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
Imputation methodsData processing
13.3.4.2. Imputation methods
Mean imputation13.3.4.3. Actions to correct or minimise processing errors
To minimise processing errors, validation checks were implemented during both data collection and processing. These checks included consistency analyses between core and modules variables, as well as verification that aggregated variables matched their constituent sums. For instance, UAA was checked against the sum of its components: arable land, woody crops, permanent pastures, orchards, and greenhouses.
13.3.4.4. Tools and staff authorised to make corrections
The Responsible Department, the IT Support Unit and the Sampling Unit are authorised to make corrections. The tools used to carry out these corrections are SAS programming and a custom-designed application for the uploading of all survey information, in which a manual and macro-debugging procedures have been carried out.
13.3.4.5. Impact of processing error on data quality
Unknown13.3.4.6. Additional information processing error
Automatic imputation: a process of automatic imputation of the information was carried out, using a specific program.
13.3.5. Model assumption error
Not applicable.
14.1. Timeliness
See sub-categories below.
14.1.1. Time lag - first result
No interim results have been published.
14.1.2. Time lag - final result
The time lag of the final results of the survey is 14 months, from 31 December 2023 to 26 February 2025, the publication date.
14.2. Punctuality
See sub-categories below.
14.2.1. Punctuality - delivery and publication
See sub-categories below.
14.2.1.1. Punctuality - delivery
Not requested.
14.2.1.2. Punctuality - publication
In time.
15.1. Comparability - geographical
See sub-categories below.
15.1.1. Asymmetry for mirror flow statistics - coefficient
Not applicable, because there are no mirror flows in Integrated Farm Statistics.
15.1.2. Definition of agricultural holding
See sub-categories below.
15.1.2.1. Deviations from Regulation (EU) 2018/1091
No deviations.
15.1.2.2. Reasons for deviations
Not applicable.
15.1.3. Thresholds of agricultural holdings
See sub-categories below.
15.1.3.1. Proofs that the EU coverage requirements are met
With the data from the 2020 Agricultural Census and the updated agricultural registers, we have calculated the percentage of the total utilised agricultural and livestock area of the holdings that meet the thresholds listed in Annex II of the Regulation (EU) 2018/1091.
We checked that with these thresholds, the required data cover 98% of the total utilised agricultural area and 98% of the livestock units in Spain.
| Total | Covered by the thresholds | Attained coverage | Minimum requested coverage | |
|---|---|---|---|---|
| 1 | 2 | 3=2*100/1 | 4 | |
| UAA excluding kitchen gardens | 23 508 180 | 23 422 106 | 99.63% | 98% |
| LSU | 14 734 429 | 14 705 193 | 99.80% | 98% |
For IFS 2023, all holdings that meet at least one of the physical thresholds listed in Annex II to Regulation (EU) 2018/1091 have been covered.
15.1.3.2. Differences between the national thresholds and the thresholds used for the data sent to Eurostat
There are no differences.
15.1.3.3. Reasons for differences
Not applicable.
15.1.4. Definitions and classifications of variables
See sub-categories below.
15.1.4.1. Deviations from Regulation (EU) 2018/1091 and EU handbook
Data are collected, sent to Eurostat and published in accordance with the definitions and classification of variables according to Regulation (EU) 2018/1091, Commission Implementing Regulation (EU) 2021/2286 and the EU handbook.
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 set out in Regulation (EU) 2018/1091 are used.
15.1.4.1.5. Livestock included in “Other livestock n.e.c.”
There are no differences between the types of livestock included under the heading 'Other livestock n.e.c.' and the types of livestock included according to the EU handbook.
15.1.4.2. Reasons for deviations
Not applicable.
15.1.5. Reference periods/days
See sub-categories below.
15.1.5.1. Deviations from Regulation (EU) 2018/1091
Data are collected, sent to Eurostat and published in accordance with the reference periods/days set out 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 the land of agricultural holdings renting or being allotted the land based on written or oral agreements.Common land is included in the land of agricultural holdings based on a statistical model.
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
Agricultural holdings are asked whether they use common grazing land. If they do, these communal pastures are allocated to the individual farms, without counting these pastures in the virtual farms that own this communal land.
In the process of data collection, common land was collected, but in the process of purification, all the remaining communal pastures were distributed among the livestock farms in the area. The common land has been distributed among the livestock farms in the same location (municipality or province), following a methodology analogous to that indicated in Annex II of the IFS 2023 handbook.
For livestock farms with grazing livestock (cattle, sheep, and goats) and without permanent grassland, by municipality, the communal pasture area was distributed among the pure livestock farms without pasture. The distribution was made taking into account the ratio of pastures to livestock units in each municipality, and then, per holding, the pasture area was imputed according to the livestock units of each farm.
where PASTOS stands for communal pasture, UGP stands for grazing livestock units, coci stands for a coefficient, pm/PM designates a municipality while pmi designates a farm in the municipality.
15.1.6.6. Possible problems in relation to the collection of data on common land and proposals for future data collections
Not applicable.
15.1.7. National standards and rules for certification of organic products
See sub-categories below.
15.1.7.1. Deviations from Council Regulation (EC) No 834/2007
There are no deviations in the national standards and rules for the 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
For all variables except AWU, the number of comparable items in a time series since their last break is 13 (from 1987 to 2023), while for AWU this indicator is 12 (from 1993 to 2023).
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
Compared with IFS 2020, some thresholds are raised for NUTS 2 region ES11 in IFS 2023.
In IFS 2020, the thresholds for NUTS 2 region ES11 were lower in order to ensure the minimum coverage of 98% UAA and LSU in NUTS 2 region ES11.
In IFS 2023, this extended framework has not been done, so the thresholds are the same for all autonomous communities.
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 no changes15.2.6.2. Description of changes
Not applicable.
15.2.7. Common land
See sub-categories below.
15.2.7.1. Changes in the methods to record common land since the last data transmission to Eurostat
There have been 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
Since IFS 2023 does not cover frame extension as it did in IFS 2020, only partial comparison can be achieved; nevertheless some remarkable comments can be made by comparing 2023 with 2020 data:
- By observing the total number of holdings by legal status it can be noticed that the number of holdings having family management (FARM_FAM, FARM_SPOU) sharply decreased from 2020 to 2023, as well as the FARM_NFAM holdings. On the contrary, the number of holdings classified as PER_LEG_EG + PER_LEG_NEG and HLD_GRP increased.
- When it comes to crops, what could be pointed out is an increase of N0000T and N0000S, and a decrease of F2000T and PECRS.
- The number of holdings with smaller UAA size have decreased generating an increase of the median UAA of Spanish holdings.
- With regards to the work time of the holding managers, the share of managers working 100% of the working days has increased in 2023 compared to 2020.
15.2.9. Maintain of statistical identifiers over time
Yes15.3. Coherence - cross domain
See sub-categories below.
15.3.1. Coherence - sub annual and annual statistics
Not applicable to Integrated Farm Statistics, because there are no sub annual data collections in agriculture.
15.3.2. Coherence - National Accounts
Not applicable, because Integrated Farm Statistics have no relevance for national accounts.
15.3.3. Coherence at micro level with data collections in other domains in agriculture
See sub-categories below.
15.3.3.1. Analysis of coherence at micro level
Yes15.3.3.2. Results of analysis at micro level
The results were continuously evaluated during the editing.
During centralised editing, the application indicated from which source the holding was included in the population frame of holdings and which collection method was used. The data that the holding had in the frame was displayed. This allowed the editor to compare the information at the micro level.
In addition, a comparison was made at the provincial level between the 2016 survey information, the Annual Cultivated Areas and Annual Cultivated Productions data according to Regulation (EC) No 543/2009, the 2020 census data and the 2023 survey information.
Differences have been found for grassland data with the information from the Ministry of Agriculture (from the Annual Cultivated Areas and Annual Cultivated Productions data), due to different classification and definition methodologies. For the rest of the crops the data were more or less consistent.
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 data compared come from different sources, with different regulations and specific methodologies, which do not always coincide:
- Different concepts as in the variable kitchen gardens (K0000T). For the IFS, kitchen gardens with an area larger than 0.05 hectares are considered as vegetable area.
- Different target population. The IFS population is determined by the thresholds set out in Annex III of Regulation (EU) 2018/1091.
- Different collection methods.
- Different observation and survey unit. In the IFS the UAA of the agricultural holding is collected.
- Different way of collecting livestock. In the IFS the stock is collected at a fixed date, for 2023 it was 30 September 2023, which explains the variations between the data. However, each source follows and respects its own methodology.
- For the IFS, the reference period is the 2023 crop year, which runs from 1 October 2022 to 30 September 2023; for CROPS it is the year in which harvesting begins.
- For IFS, the study population is the farms that meet the thresholds set in the regulation; for CROPS, the population is all areas, not subject to thresholds.
- Different methodologies are applied in each case (for example for C000T, E0000T, G0000T, P0000T, PECRT, Q0000T, R1000T, UAA, V0000_S0000, W1000T). The farms surveyed by the IFS must meet at least one of the thresholds set out in Annex II of Regulation (EU) 2018/1091, while the CROPS data do not have this restriction.
Coherence cross-domain: IFS vs ORGANIC CROP PRODUCTION
The difference in the organic UAA is mainly due to organic permanent grassland, where different collection methodologies are used. The IFS takes into account the admissibility coefficient of permanent grassland, so the area of permanent grassland that is not really, is deducted.
Coherence cross-domain: IFS vs ANIMAL PRODUCTION
Differences in livestock data are due to:
- Data compared come from different sources, with different regulations and specific methodologies, which do not always coincide:
- Different target population. The IFS population is determined by the thresholds set out in Annex III of Regulation (EU) 2018/1091.
- Different collection methods.
- Different observation and survey unit. In the IFS the UAA of the agricultural holding is collected.
- Different way of collecting livestock. In the IFS the stock is collected at a fixed date, for 2023 it was 30 September 2023, which explains the variations between the data.
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
The collection of the 2023 survey has not been coordinated with any survey.
The collection questionnaires have been sent to the Ministry of Agriculture, so that it can be informed and check the need to introduce new variables.
16.2. Efficiency gains since the last data transmission to Eurostat
Increased use of administrative data16.2.1. Additional information efficiency gains
As in the 2020 census, administrative information was used in the 2023 survey. For core data, approximately 70% of the farms' data came from administrative sources. The rural development module relied entirely on administrative sources.
16.3. Average duration of farm interview (in minutes)
See sub-categories below.
16.3.1. Core
The average duration for collecting the core variables from the farm is 5.2 minutes.
16.3.2. Module ‘Labour force and other gainful activities‘
The average duration for collecting the labour force and other gainful activities variables from the farm is 6.4 minutes.
16.3.3. Module ‘Rural development’
Not relevant, the collection of the rural development variables has been carried out by administrative registers.
16.3.4. Module ‘Animal housing and manure management’
Restricted from publication
16.3.5. Module ‘Irrigation’
The average duration for collecting the irrigation variables from the farm is 4.0 minutes.
16.3.6. Module ‘Soil management practices’
The average duration for collecting the soil management practices variables from the farm is 2.5 minutes.
16.3.7. Module ‘Machinery and equipment’
The average duration for collecting the machinery and equipment variables from the farm is 4.3 minutes.
16.3.8. Module ‘Orchard’
The average duration for collecting the orchard variables from the farm is 2.8 minutes.
16.3.9. Module ‘Vineyard’
Restricted from publication
17.1. Data revision - policy
The INE has a policy that regulates the basic aspects of the revision of statistical data, guaranteeing the transparency of the processes and the quality of the products. This policy is described in this document approved by the Board of Directors at the meeting held on 13 March 2015.
This general policy establishes the criteria to be followed for the different types of revisions: routine - in the case of statistics that by their nature are revised on a regular basis; major revisions, due to methodological changes or changes in basic statistical reference sources; and extraordinary revisions (for example, those due to an error in statistics already published).
17.2. Data revision - practice
If errors are detected and it is necessary to modify the data, an explanatory note would be added to the revised data (published tables and microdata) to inform users that the data has been modified.
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
We have used the administrative registers referred to in Article 4(2) of the Regulation (EU) 2018/1091. We also have used the IFS 2020 data and the Business 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
- For the holdings that applied for CAP aid, i.e. their information was obtained from the administrative registers, they were surveyed in their entirety, a census was carried out for this subset.
- For the units to which a questionnaire had to be sent, i.e. direct collection, a stratified random design was used.
The strata were formed by crossover region (NUTS 2), technical-economic orientation and size, measured by utilised agricultural area, labour land and livestock units.
18.1.2.2.1. Name of sampling design
Stratified one-stage random sampling18.1.2.2.2. Stratification criteria
Unit sizeUnit location
Unit specialization
18.1.2.2.3. Use of systematic sampling
No18.1.2.2.4. Full coverage strata
We created take-all strata by selecting the largest holdings within each NUTS 2 region.
18.1.2.2.5. Method of determination of the overall sample size
We determined the overall sample size as a result of optimum allocation. The precision requirements of Annex V of the Regulation (EU) 2018/1091 were used to calculate the sample size. We also increased the result of the optimum allocation by preventing the non-response.
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
A stratified random design was used.
The strata were formed by crossover region (NUTS 2), technical-economic orientation and size, measured by utilised agricultural area, labour land and livestock units.
18.1.4.2.1. Name of sampling design
Stratified one-stage random sampling18.1.4.2.2. Stratification criteria
Unit sizeUnit location
Unit specialization
18.1.4.2.3. Use of systematic sampling
No18.1.4.2.4. Full coverage strata
We created take-all strata by selecting the largest holdings within each NUTS 2 region.
18.1.4.2.5. Method of determination of the overall sample size
We determined the overall sample size as a result of optimum allocation. The precision requirements of Annex V of the Regulation (EU) 2018/1091 were used to calculate the sample size. We also increased the result of the optimum allocation by preventing the non-response.
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
Multi-phase sampling where core collected variables are used to calibrate the module data18.1.5. Module “Rural development”
See sub-categories below.
18.1.5.1. Coverage of agricultural holdings
Sample18.1.5.2. Sampling design
A stratified random design was used.
The strata were formed by crossover region (NUTS 2), technical-economic orientation and size, measured by utilised agricultural area, labour land and livestock units.
18.1.5.2.1. Name of sampling design
Stratified one-stage random sampling18.1.5.2.2. Stratification criteria
Unit sizeUnit location
Unit specialization
18.1.5.2.3. Use of systematic sampling
No18.1.5.2.4. Full coverage strata
We created take-all strata by selecting the largest holdings within each NUTS 2 region.
18.1.5.2.5. Method of determination of the overall sample size
We determined the overall sample size as a result of optimum allocation. The precision requirements of Annex V of the Regulation (EU) 2018/1091 were used to calculate the sample size. We also increased the result of the optimum allocation by preventing the non-response.
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
A stratified random design was used.
The strata were formed by crossover region (NUTS 2), technical-economic orientation and size, measured by irrigation land.
18.1.7.2.1. Name of sampling design
Stratified one-stage random sampling18.1.7.2.2. Stratification criteria
Unit sizeUnit location
Unit specialization
18.1.7.2.3. Use of systematic sampling
No18.1.7.2.4. Full coverage strata
We created take-all strata by selecting the largest holdings within each NUTS 2 region.
18.1.7.2.5. Method of determination of the overall sample size
We determined the overall sample size as a result of optimum allocation. The precision requirements of Annex V of the Regulation (EU) 2018/1091 were used to calculate the sample size. We also increased the result of the optimum allocation by preventing the non-response.
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
Positive coordination18.1.8. Module ‘Soil management practices’
See sub-categories below.
18.1.8.1. Coverage of agricultural holdings
Sample18.1.8.2. Sampling design
A stratified random design was used.
The strata were formed by crossover region (NUTS 2), technical-economic orientation and size, measured by arable land.
18.1.8.2.1. Name of sampling design
Stratified one-stage random sampling18.1.8.2.2. Stratification criteria
Unit sizeUnit location
Unit specialization
18.1.8.2.3. Use of systematic sampling
No18.1.8.2.4. Full coverage strata
We created take-all strata by selecting the largest holdings within each NUTS 2 region.
18.1.8.2.5. Method of determination of the overall sample size
We determined the overall sample size as a result of optimum allocation. The precision requirements of Annex V of the Regulation (EU) 2018/1091 were used to calculate the sample size. We also increased the result of the optimum allocation by preventing the non-response.
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
Positive coordination18.1.9. Module ‘Machinery and equipment’
See sub-categories below.
18.1.9.1. Coverage of agricultural holdings
Sample18.1.9.2. Sampling design
A stratified random design was used.
The strata were formed by crossover region (NUTS 2), technical-economic orientation and size, measured by utilised agricultural area, labour land and livestock units.
18.1.9.2.1. Name of sampling design
Stratified one-stage random sampling18.1.9.2.2. Stratification criteria
Unit sizeUnit location
Unit specialization
18.1.9.2.3. Use of systematic sampling
No18.1.9.2.4. Full coverage strata
We created take-all strata by selecting the largest holdings within each NUTS 2 region.
18.1.9.2.5. Method of determination of the overall sample size
We determined the overall sample size as a result of optimum allocation. The precision requirements of Annex V of the Regulation (EU) 2018/1091 were used to calculate the sample size. We also increased the result of the optimum allocation by preventing the non-response.
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
Multi-phase sampling where core collected variables are used to calibrate the module data18.1.10. Module ‘Orchard’
See sub-categories below.
18.1.10.1. Coverage of agricultural holdings
Sample18.1.10.2. Sampling design
A stratified random design was used.
The strata were formed by crossover region (NUTS 2), technical-economic orientation and size, measured by target fruit land.
18.1.10.2.1. Name of sampling design
Stratified one-stage random sampling18.1.10.2.2. Stratification criteria
Unit sizeUnit location
Unit specialization
18.1.10.2.3. Use of systematic sampling
No18.1.10.2.4. Full coverage strata
We created take-all strata by selecting the largest holdings within each NUTS 2 region.
18.1.10.2.5. Method of determination of the overall sample size
We determined the overall sample size as a result of optimum allocation. The precision requirements of Annex V of the Regulation (EU) 2018/1091 were used to calculate the sample size. We also increased the result of the optimum allocation by preventing the non-response.
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
Positive coordination18.1.11. Module ‘Vineyard’
Restricted from publication
18.1.11.1. Coverage of agricultural holdings
Restricted from publication
18.1.11.2. Sampling design
Restricted from publication
18.1.11.2.1. Name of sampling design
Restricted from publication
18.1.11.2.2. Stratification criteria
Restricted from publication
18.1.11.2.3. Use of systematic sampling
Restricted from publication
18.1.11.2.4. Full coverage strata
Restricted from publication
18.1.11.2.5. Method of determination of the overall sample size
Restricted from publication
18.1.11.2.6. Method of allocation of the overall sample size
Restricted from publication
18.1.11.2.7. If sampled from the core sample, the sampling strategy and calibration strategy
Restricted from publication
18.1.12. Software tool used for sample selection
The software tool used for sample selection was SAS (tailor-made programmes).
18.1.13. Administrative sources
See sub-categories below.
18.1.13.1. Administrative sources used and the purposes of using them
The information is available on Eurostat’s website, at the link: Additional data - Eurostat (europa.eu).
18.1.13.2. Description and quality of the administrative sources
See the Excel file in the annex.
Annexes:
18.1.13.2 Description and quality of administrative sources
18.1.13.3. Difficulties using additional administrative sources not currently used
Other18.1.14. Innovative approaches
The information on the innovative approaches and the quality methods applied is available on Eurostat’s website, at the link: Additional data - Eurostat (europa.eu).
18.2. Frequency of data collection
Agricultural surveys are conducted every 3-4 years in the periods between agricultural censuses, which are conducted every 10 years.
18.3. Data collection
See sub-categories below.
18.3.1. Methods of data collection
Postal, non-electronic versionFace-to-face, electronic version
Telephone, non-electronic version
Use of Internet
18.3.2. Data entry method, if paper questionnaires
Manual18.3.3. Questionnaire
Please find the questionnaires in annex.
Data collection on common land is carried out in item 5.3 of the core questionnaire.
Annexes:
18.3.3 Questionnaire in Spanish - Core
18.3.3 Questionnaire in Spanish - Modules
18.3.3 Questionnaire in English - Core
18.3.3 Questionnaire in English - Modules
18.4. Data validation
See sub-categories below.
18.4.1. Type of validation checks
Data format checksCompleteness checks
Routing checks
Range checks
Relational checks
Comparisons with previous rounds of the data collection
Comparisons with other domains in agricultural statistics
18.4.2. Staff involved in data validation
InterviewersSupervisors
Staff from central department
18.4.3. Tools used for data validation
The IRIA (Integration of Information Collection and Administration) software was the tool used during data validation.
Two types of validation can be distinguished:
- Validation during data collection: there were controls included in the IRIA application in each of the collection phases (CAWI, postal collection, CATI, CAPI). The controls were presented to the interviewers during the interview itself or at the end of the interview, depending on the type of error. In the case of postal collection, where the questionnaire was recorded once it had arrived by post, the controls were detailed at the end of the interview and resolved by telephone calls. Subsequently, the survey inspectors had to accept or reject each of the enumerators' questionnaires, depending on the types of errors they contained and the comments included in them. At the next level, the survey inspector carried out an overall inspection of the information collected.
- Validation in Central Services: once the questionnaires were marked as clean in the collection phase, the Responsible Department carried out a validation of the information, guided mainly by the identification of the observations to be dealt with, depending on the coherence in the evolution of the estimated data, with regard to the results available from previous surveys or from the census. Likewise, a follow-up was carried out of the incidences in the collection.
18.5. Data compilation
The LAFO module population is slightly larger than the CORE population because LAFO uses a sample-based estimate, whereas CORE uses census data for 70% of farms (those applying for CAP support) and sample-based estimates for the remaining 30%.
We apply calibration techniques, using the SAS CALMAR macro, in cases where correlations exist between the core and module variables.
Thus, for the:
- LAFO module: small farms are calibrated by the labour force of the farm manager and in general, for each of the UAA sizes, it is calibrated by the number of census holdings, hectares of cultivated area and pasture.
- MIRR module: it is calibrated, in each of the UAA sizes, by the census holdings belonging to this population, and at NUTS 2 level, it is calibrated by the irrigable area of the frame.
- MSMP module: it is calibrated by the arable land of the frame.
- ORCH module: it is calibrated by the fruit trees of the frame.
18.5.1. Imputation - rate
The unweighted imputation rates are as follows for the non-response variables:
- Y_BIRTH_MAN (Year of birth of farm manager) = 0.70%
- SEX_MAN (Sex of farm manager) = 0.52%
- WH_MAN_AWU_PC (Working hours by farm manager - % band Annual work units (AWU)) = 4.01%
- Y_FARM_MAN (Year classified as a farm manager) = 4.29%
- TNG_MAN (Agricultural training of the farm manager) = 0.58%
- VT_MAN_M12 (Vocational training by farm manager in last 12 months) = 3.14%
- FA9 (Other areas on the farms) = 1.14%
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
AWU – Annual Working Unit
CALMAR – Calibration Programme
CAP – Common Agricultural Policy
CAPI – Computer Assisted Personal Interview
CATI – Computer Assisted Telephone Interview
CAWI – Computer Assisted Web Interview
CORE – General, crops and livestock variables of Annex III of Regulation (EU) 2018/1091
EC – European Community
ESSCoP – European Statistics Code of Practice
EU – European Union
EUSTAT – Basque Statistical Institute
FEGA – Spanish Agricultural Guarantee Fund
IACS – Integrated Administration and Control System
IFS – Integrated Farm Statistics
INE – Instituto Nacional de Estadística / National Statistical Institute
IRIA – Integration of Information Collection and Administration
LAFO – Labour force and other gainful activities
LFEP – Public Statistical Function Law
LSU – Livestock unit
MIRR – Irrigation module
MORC – Orchards module
MSMP – Soil management practices module
NUTS – Nomenclature of territorial units for statistics
ORCH – Orchards
REGA – Register of livestock farms
RSE – Relative Standard Error
SGM – Standard Gross Margin
SIGPAC – Geographical Information System for Agricultural Parcels
SO – Standard Output
SOC – Standard Output Coefficient
TIN – Tax Identification Number
UAA – Utilised agricultural area
VAT – Value Added Tax
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.
11 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, nectarines area, apricots area, oranges area, small citrus fruit area, lemons area, olives area, grapes for table use area, grapes for raisins 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 of the variables (area in hectares, livestock in (1000) heads or LSU (livestock units), labour force in persons or AWU (annual work unit), standard production in euros, volume of water used in irrigation in cubic metres, age of planting of fruit trees in years, tractor power in kilowatts, storage of agricultural products in cubic metres), and
- the number of agricultural holdings with these characteristics.
The unit of measurement used for the area of the agricultural holding and crops is the hectare, except in the case of cultivated mushrooms where it is the square metre.
Livestock data can be expressed in number of heads of animals of the different types of livestock. Heads of livestock are given on a reference day within the reference period (30 September 2023), as the number of livestock in a year may fluctuate.
Livestock units (LSU) is a standard unit of measurement that allows the aggregation of various categories of livestock of different species and ages according to convention, through the use of specific coefficients established on the basis of nutritional or feed requirements of each type of animal, to allow a comparison. The coefficients used are adopted in accordance with Annex I of Regulation (EU) 2018/1091.
Farm labour data are expressed either in number of working days, in percentage of working time or in annual work unit (AWU); one AWU is equivalent to the work done by one person on a full-time basis over one year. The annual work unit (AWU) is equivalent to the work done by a full-time person over a year, i.e. the total hours worked divided by the average annual hours worked in full-time jobs in the country.
Data on volume of water used for irrigation are expressed in cubic metres. Similarly, data on storage of agricultural products are also expressed in cubic metres.
The LAFO module population is slightly larger than the CORE population because LAFO uses a sample-based estimate, whereas CORE uses census data for 70% of farms (those applying for CAP support) and sample-based estimates for the remaining 30%.
We apply calibration techniques, using the SAS CALMAR macro, in cases where correlations exist between the core and module variables.
Thus, for the:
- LAFO module: small farms are calibrated by the labour force of the farm manager and in general, for each of the UAA sizes, it is calibrated by the number of census holdings, hectares of cultivated area and pasture.
- MIRR module: it is calibrated, in each of the UAA sizes, by the census holdings belonging to this population, and at NUTS 2 level, it is calibrated by the irrigable area of the frame.
- MSMP module: it is calibrated by the arable land of the frame.
- ORCH module: it is calibrated by the fruit trees of the frame.
See sub-categories below.
Farm statistics are disseminated every 10 years for censuses and every 3-4 years between censuses for sample-based data collections.
See sub-categories below.
See sub-categories below.
See sub-categories below.
