1.1. Contact organisation
Statistics Poland
1.2. Contact organisation unit
Agriculture and Environment Department
1.3. Contact name
Confidential because of GDPR
1.4. Contact person function
Confidential because of GDPR
1.5. Contact mail address
00-950 Warsaw, Poland, Al. Niepodległości 208
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
30 April 2025
2.2. Metadata last posted
5 May 2025
2.3. Metadata last update
30 April 2025
3.1. Data description
The data describe the structure of agricultural holdings providing the general characteristics of farms and farmers and information on their land, livestock and labour force. They also describe production methods, rural development measures and agro-environmental aspects that look at the impact of agriculture on the environment.
The data are used by public, researchers, farmers and policy-makers to better understand the state of the farming sector and the impact of agriculture on the environment. The data follow up the changes in the agricultural sector and provide a basis for decision-making in the Common Agricultural Policy (CAP) and other European Union policies.
The statistical unit is the agricultural holding (farm). The aggregated results are disseminated through statistical tables. The data are presented at different geographical levels and over periods.
The data collections are organised in line with Regulation (EU) 2018/1091 and have a new structure, consisting of a core data set and several modules. The regulation covers the data collections in 2019/2020 (the agricultural census), 2023 and 2026. The data are as comparable and coherent as possible with the other European countries.
3.2. Classification system
Data are arranged in tables using many classifications. Please find below information on most classifications.
The classifications of variables are available in Annex III of Regulation (EU) 2018/1091 and in Commission Implementing Regulation (EU) 2021/2286.
The farm typology means a uniform classification of the holdings based on their type of farming and their economic size. Both are determined on the basis of the standard gross margin (SGM) (until 2007) or standard output (SO) (from 2010 onward) which is calculated for each crop and animal. The farm type is determined by the relative contribution of the different productions to the total standard gross margin or the standard output of the holding.
The territorial classification uses the NUTS classification to break down the regional data. The regional data is available at NUTS level 2.
3.3. Coverage - sector
The statistics cover agricultural holdings undertaking agricultural activities as listed in item 3.5 below and meeting the minimum coverage requirements (thresholds) as listed in item 3.6 below.
3.4. Statistical concepts and definitions
The list of core variables is set in Annex III of Regulation (EU) 2018/1091.
The descriptions of the core variables as well as the lists and descriptions of the variables for the modules collected in 2023 are set in Commission Implementing Regulation (EU) 2021/2286.
The following groups of variables are collected in 2023:
- for core: location of the holding, legal personality of the holding, manager, type of tenure of the utilised agricultural area, variables of land, organic farming, irrigation on cultivated outdoor area, variables of livestock, organic production methods applied to animal production;
- for the module "Labour force and other gainful activities": farm management, family labour force, non-family labour force, other gainful activities directly and not directly related to the agricultural holding;
- for the module "Rural development": support received by agricultural holdings through various rural development measures;
- for the module “Irrigation”: availability of irrigation, irrigation methods, sources of irrigation water, technical parameters of the irrigation equipment, crops irrigated during a 12 months period;
- for the module “Soil management practices”: tillage methods, soil cover on arable land, crop rotation on arable land, ecological focus area;
- for the module “Machinery and equipment”: internet facilities, basic machinery, use of precision farming, machinery for livestock management, storage for agricultural products, equipment used for production of renewable energy on agricultural holdings;
- for the module “Orchards”: apples area, pears area, apricots 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 population of agricultural holdings defined in item 3.6.1 having irrigable area.
3.6.5. Population covered by the data sent to Eurostat for the module “Soil management practices”
The subset of population of agricultural holdings defined in item 3.6.1 having arable land.
3.6.6. Population covered by the data sent to Eurostat for the module “Orchard”
The subset of population of agricultural holdings defined in item 3.6.1 having 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
Not applicable.
3.7.3. Criteria used to establish the geographical location of the holding
The main building for productionThe most important parcel by physical size
3.7.4. Additional information reference area
Not available.
3.8. Coverage - Time
Farm structure statistics in PL covers the period from 2002 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)) and
- the number of agricultural holdings having the above-mentioned characteristics.
See sub-categories below.
5.1. Reference period for land variables
The use of land refers to 1 June of the reference year 2023, except cultivation of edible mushrooms which refers to the 12-month period from 2 June 2022 to 1 June 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 12-month period ending on 1 June within the reference year 2023.
5.3. Reference day for variables on livestock and animal housing
The reference day for livestock is 1 June within the reference year 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 12-month period ending on 1 June within the reference year 2023, except for the current economic activity which was related to the week from 26 May to 1 June 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
Generally, the reference day is 1 June within the reference year 2023. However, several variables require a different reference period due to their specificity. These include:
- edible mushroom cultivation,
- the fertilised area and the amount of manure as well as mineral, lime and lime-magnesium fertilisers used,
- number of plant protection treatments applied,
- using the support of qualified advisers when making decisions about the use of plant protection products,
- use of tractors and machines owned by farm as well as owned by other farms, cooperatives or service companies,
- economic activity (section X of the questionnaire) and structure of incomes, the reference period is the last 12 months ending on the reference date of the study, i.e. the period from 2 June 2022 to 1 June 2023, inclusive.
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
- Act of 29 June 1995 on Official Statistics (Journal of Laws of 2023, item 773)
- Act of 10 May 2018 on the Protection of Personal Data (Journal of Laws of 2019, item 1781)
- Regulation of the Council of Ministers of 7 October 2022 on the Programme of Statistical Surveys of Official Statistics for 2023 (Journal of Laws of 2022, item 2453)
6.1.3. Link to national legal acts and other agreements
- Act on Official Statistics
- Act on the Protection of Personal Data
- Programme of Statistical Surveys of Official Statistics (PBSSP) 2023
6.1.4. Year of entry into force of national legal acts and other agreements
- Act on Official Statistics: 1995
- Act on the Protection of Personal Data: 2018
- PBSSP: 2023
6.1.5. Legal obligations for respondents
Yes6.2. Institutional Mandate - data sharing
Provisions on the use of administrative sources for the purposes of IFS 2023 can be found in the Regulation (EU) 2018/1091 of the European Parliament and of the Council, which is the EU legal basis for the IFS survey. The mentioned document lists the registers recommended for use, i.e. the integrated management and control system IACS, the animal identification and registration system, the register of organic farms and administrative sources related to specific rural development measures. In line with the adopted assumptions, for the purposes of IFS 2023, administrative sources were used to build and update the list of farms, replace statistical data, validate, impute and analyse data. The relation between Statistics Poland and data providers is regulated in the Regulation of the Council of Ministers of 7 October 2022 on the Programme of Statistical Surveys of Official Statistics for 2023 (Journal of Laws of 2022, item 2453), which includes arrangements, procedures or agreements to facilitate data sharing.
7.1. Confidentiality - policy
Pursuant to the provisions of the Act on Official Statistics (art. 10 and 12), all individual information and personal data collected and stored are covered by statistical confidentiality. The data obtained in the statistical survey can only be used exclusively for statistical studies, compilations and analyses and for creation by the public statistical services the frames for statistical surveys conducted by those services. Sharing or use of data collected for other purposes is prohibited under the sanction of criminal liability.
According to the above-mentioned Act, all persons performing work related to the IFS 2023 were obliged to comply with the statistical confidentiality and were allowed to perform the work after training and instruction about the nature of the statistical confidentiality, as well as, signing the written oath with the content specified in the Act on Official Statistics.
Similar as in the case of all statistical surveys conducted by the Statistics Poland (GUS), also in the course of collecting, storing, processing and disseminating data from the IFS 2023, the provisions of the Act on Official Statistics were strictly complied.
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)Secondary confidentiality rules
7.2.1.2. Methods to protect data in confidential cells
Table redesign (Collapsing rows and/or columns)Cell suppression (Completely suppress the value of some cells)
7.2.1.3. Description of rules and methods
Pursuant to the Act of 29 June 1995 on Official Statistics (Article 38 point 2):
1. It shall not be allowed to publish or disseminate individual data obtained in the statistical services of official statistics.
2. It shall not be allowed to publish or disseminate obtained in statistical surveys of official statistics statistical information which can be linked or can identify natural persons or individual data characterising business entities, especially if the aggregated data consist of less than three entities.
7.2.2. Microdata
See sub-categories below.
7.2.2.1. Use of EU methodology for microdata dissemination
Yes7.2.2.2. Methods of perturbation
Recoding of variablesRemoval of variables
Reduction of information
Merging categories
Rounding
Micro-aggregation
7.2.2.3. Description of methodology
Researchers can use anonymised microdata at a specially prepared computer station in the premises of Statistics Poland. Reports with aggregated data generated by user are checked by experts (statistical staff), responsible for enforcing statistical confidentiality according to the rules mentioned in item 7.2.1.1. The methodology is described in the dedicated section of Eurostat's website: microdata.
8.1. Release calendar
There is a release calendar for Integrated Farm Statistics.
8.2. Release calendar access
See stat website.
It concerns publications like Agriculture in 2024 and Characteristics of agricultural holdings in 2023.
8.3. Release policy - user access
The rules for disseminating the resulting statistical information are based on:
- the Act on Official statistics;
- Special Data Dissemination Standard (SDDS) Guidelines;
- the European Statistics Code of Practice.
Pursuant to the Act on Official Statistics, official statistics units disseminate the resulting statistical information in a professional manner (compliant with professional and ethical standards), ensuring equal treatment to all users, in accordance with applicable law. Official statistics provides equal, equal and simultaneous access to the resulting statistical information, in particular to the basic values and indicators, which the President of the Statistics Poland is obliged to publish on the basis of separate provisions of law.
In accordance with the dissemination policy in force at the Statistics Poland and the adopted schedule - the data obtained in the IFS 2023 (preliminary and final) will be made available in:
- national database systems: Local Data Bank, Domain Knowledge Base, Scientist position,
- Eurostat database - EU database on farm structure purposes,
- in the form of publications.
Individual orders for the results of the IFS 2023 will also be carried out on the basis of applications submitted by individual or institutional recipients. The results of the IFS 2023 from the national level to voivodships are presented according to the seat of the farm holder. The exception is the data on the typology of agricultural holdings, which are presented according to the seat of the farm.
8.3.1. Use of quality rating system
No8.3.1.1. Description of the quality rating system
Not applicable.
Every 10 years for census and additionally between censuses - every 3/4 years (2023, 2026) for other IFS data collections.
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
Yes, in English also10.2.2. Production of on-line publications
Yes, in English also10.2.3. Title, publisher, year and link
The following publications with the final results are planned to be released in 2025:
- Characteristics of agricultural holdings in 2023. The publication contains methodological information, together with basic definitions (in Polish and English), analysis of results, as well as tables presenting numerical data (in Polish and partly in English).
- The IFS 2023 data will be also available in comprehensive GUS publications, e.g. in the Statistical Yearbook of Poland, the Statistical Yearbook of Agriculture, and Agriculture in 2024. Some publications are released in paper form while all ones are available on-line.
10.3. Dissemination format - online database
See sub-categories below.
10.3.1. Data tables - consultations
Number of consultations on IFS data in the LDB from November 2024 to February 2025 amounted to 2 009. Knowledge Databases entry counter is currently under development.
10.3.2. Accessibility of online database
Yes10.3.3. Link to online database
Statistics Poland - Knowledge Databases (Economy --> Agriculture)
Statistics Poland - Local Data Bank (Agriculture --> select required subdomain and indicate years)
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
No10.6.7. Title, publisher, year and link to methodological papers
Not applicable.
10.7. Quality management - documentation
Not available.
11.1. Quality assurance
See sub-categories below.
11.1.1. Quality management system
Yes11.1.2. Quality assurance and assessment procedures
Training coursesUse of best practices
Quality guidelines
Designated quality manager, quality unit and/or senior level committee
Compliance monitoring
Self-assessment
11.1.3. Description of the quality management system and procedures
For the IFS 2023, a data collection system with very detailed validations was developed. Surveillance of the interviewers was optimised by using the Corstat application.
The questionnaire included automatic validation, and quality management tools were implemented in the central database.
The data quality system included the following elements:
- validation at the level of questionnaire,
- Corstat - the management system of the survey,
- merit module with extended validation and checking by experts,
- validation at the level of data processing with the validation reports,
- comparison with trend and other administrative sources,
- documentation of validation algorithm.
11.1.4. Improvements in quality procedures
Activities in the field of improving the quality of research frames, among others:
- carrying out works aimed at improving the quality of the frame of agricultural surveys, including by recognising the possibility and advisability of using new sources of administrative data and the integration with the frame of social surveys.
- conducting work to ensure consistency between the sampling frame and administrative files.
11.2. Quality management - assessment
In general, the data quality is satisfactory.
12.1. Relevance - User Needs
The list of characteristics for the IFS 2023 is in accordance with the EU requirements, as well as the requirements and the needs of the domestic users. These needs were submitted primarily, by the Ministry of Agriculture and Rural Development (own analyses, shaping the agricultural policy), scientific institutes, producer’s organisations. IFS data are collected as well as for the needs of statistics: ensuring the data time series, livestock production forecasts, crops and harvest estimates, estimates of the number of workforce engaged in agriculture, analyses on population connected with agriculture and rural areas, calculation of the registered unemployment rate.
12.1.1. Main groups of variables collected only for national purposes
The list of characteristics surveyed exclusively for the national needs:
- sales of the agricultural products,
- consumption by the holder’s household more than 50% of the value of the final production of the holding,
- income structure of holder’s household,
- area of specific crops not specified in Regulation (EU) 2018/1091,
- number of livestock by groups (weight, use) not specified in Regulation (EU) 2018/1091,
- the amount of mineral, lime and lime-magnesium fertilisers used,
- the amount of used manure,
- number of plant protection treatments applied,
- using the support of qualified advisors when making decisions about the use of plant protection products,
- age of family members (excluding the farm manager),
- did the person live and form a household with the holder on 1 June,
- work on own holding in the current week (exclusively; mainly; additionally; worked only outside the holding),
- level of general education of the manager,
- number of working days worked on the holding connected with agricultural production in the period of the last 12 months by neighbour assistance, other persons working on the legal persons’ farms.
12.1.2. Unmet user needs
Main data need required by external institutions which was not included in the IFS 2023 was the data delivered on gminas/communes (NUTS 5 level).
12.1.3. Plans for satisfying unmet user needs
Consultations with national data users will be organised ahead of the next editions of the IFS structure surveys.
12.2. Relevance - User Satisfaction
There is no specific procedure to measure user satisfaction for the purposes of the IFS, however, positive information comes from data users like ministries and scientific institutes.
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 website: Circabc europa.
13.2.2. Reasons for non-compliant precision requirements in relation to Regulation (EU) 2018/1091
The national relative standard error for F1230 (apricots) is greater than required because the share of this crop in the national area of the UAA is around 0.1% and total area is just above the threshold of 1 000 ha.
Additional clarification: To determine the cases eligible for precision requirements, the weight of the core variables should be used for the estimation of the UAA area, which gives for MIRR module only national level (PL) as eligible. In a similar way, the weight of the core variables should be used for MORC module, then only eligible cases are for national level (PL) and for variables: F1230, F1110 (apples), F1120 (pears). Among these three cases, the only non-compliant case occurs for the variable F1230.
For PL2, PL4, and PL5 NUTS 1 regions, the prevalence of F1110 in the total utilised agricultural area of these regions is less than 1%, therefore these cases are not eligible, and relative standard error (RSE) values can be greater than the given threshold (7.5%). For PL21 or PL71 NUTS 2 regions, the prevalence of UAA_IB (UAA - irrigable) in the total utilised agricultural area of these regions is less than 4%, therefore these cases are not eligible.
In order to avoid high RSEs in the future surveys, the area of problematic phenomena and related number of holdings will be under detailed monitoring.
13.2.3. Reference on method of estimation
See the 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 europa.
The over-coverage rate is unweighted.
The over-coverage rate is calculated as the share of ineligible holdings to the holdings designated for the core data collection. The ineligible holdings include those holdings with unknown eligibility status that are not imputed nor re-weighted for (therefore considered ineligible).
The over-coverage rate is calculated over the holdings in the main frame and if applicable frame extension, for which core data are sent to Eurostat.
13.3.1.1.1. Types of holdings included in the frame but not belonging to the population of the core (main frame and if applicable frame extension)
Below thresholds during the reference periodTemporarily out of production during the reference period
Ceased activities
Merged to another unit
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
Using administrative sources played an important role in the updating of the list of farms. The IFS 2023 took into account the factual, not the legal status. Some farms were removed from the list during data collection and processing, because they did not meet the definition of an agricultural holding, i.e. they ceased agricultural activity or the activity was at a very low level (below the thresholds included in the definition of an agricultural holding). Roughly 130 000 farms were randomly selected including 120 000 for fulfilling IFS 2023 project requirements.
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
Under-coverage rate is low (approx. 1.4%) as the quality of the survey frame is high (almost all units are registered in administrative registers).
13.3.1.3.2. Types of holdings belonging to the population of the core but not included in the frame (main frame and if applicable frame extension)
New birthsNew units derived from split
Units with outdated information in the frame (variables below thresholds in the frame but above thresholds in the reference period)
13.3.1.3.3. Actions to minimise the under-coverage error
In order to ensure the appropriate quality of the survey frame, it is regularly updated on the basis of many administrative sources as well as agricultural surveys.
13.3.1.3.4. Additional information under-coverage error
Not available.
13.3.1.4. Misclassification error
Yes13.3.1.4.1. Actions to minimise the misclassification error
There were cases when out-of-date information on the agricultural area of the holding in the survey frame caused its misclassification to inadequate stratum. These farms were captured after the survey as outliers in a given stratum. Misclassification error was taken into account in the weight correction algorithm.
13.3.1.5. Contact error
Yes13.3.1.5.1. Actions to minimise the contact error
In the process of preparing the survey frame for the IFS 2023, the following works were carried out to update the survey frame:
- correction and supplementation of the addresses existing in the survey frame (farm headquarters, user's headquarters and correspondence address) based on the NOBC database,
- update of x, y coordinates for address data - address of the user's seat and the seat of the farm.
Additionally, cross-check with administrative registers was applied. Information came also from respondents in the IFS 2023 questionnaire. The survey frame contains telephone numbers which increase chances of contacts with respondents.
13.3.1.6. Impact of coverage error on data quality
Low13.3.2. Measurement error
See sub-categories below.
13.3.2.1. List of variables mostly affected by measurement errors
The main source of measurement errors were respondents and interviewers. The scale of this type of errors is not fully known. Most of the errors were caught and corrected by a set of validations and by automatic corrections. There were cases when respondents discontinued the internet interview.
Additionally, there occurred errors at entering data by respondents in the internet method. Wide range of surveyed characteristics, difficult questions, as well as unclear definitions also caused measurement errors. Respondents and interviewers found the Labour force as well as Irrigation module as the most complicated section due to the:
- problem with collecting information on the number of regularly working involved in OGA (MOGA_NFAM_RH, SOGA_NFAM_RH).
- water volume measure units as well as irrigation methods and different reference periods within module.
- an issue with separation of the area of fresh vegetables growing in the crop rotation with agricultural crops (V0000_S0000TO) from growing in rotation with horticultural crops (V0000_S0000TK),
- too small or too large number of people working in agricultural production was introduced in relation to the size of the farm.
All detected errors were corrected automatically or by expert method at the data collection stage or during the dataset validation. The control algorithms in the form application and the validation rules are also improved.
While surveying additional comments and explanations were provided via Redmine app (the inner system for communication for statistician).
13.3.2.2. Causes of measurement errors
Complexity of variablesSensitivity of variables
Unclear questions
Respondents’ inability to provide accurate answers
13.3.2.3. Actions to minimise the measurement error
Pre-testing questionnairePre-filled questions
Explanatory notes or handbooks for enumerators or respondents
On-line FAQ or Hot-line support for enumerators or respondents
Training of enumerators
Other
13.3.2.4. Impact of measurement error on data quality
Low13.3.2.5. Additional information measurement error
Several validation processes were introduced during data collection and data processing in order to minimise the risk of measurement errors. The IFS 2023 electronic questionnaire were adapted to the type of farm, leading the respondent / interviewers along the “paths” of the interview by selecting the appropriate questions and setting the order in which they were asked. The definitions of the studied variables were additional guidance and clarifications. The questionnaire allowed for ongoing logical, accounting and scope control of entries. Errors were signalled showing the way for their corrections. It should be emphasised that each respondent was asked about full set of questions, because the number and type of collected variables depended on the specificity of a given farm. The use of an electronic questionnaire was extremely helpful, as it contained the necessary definitions and clarifications. In order to eliminate any errors connected with misunderstanding or misinterpretation of methodological principles or definitions, or with any extraordinary circumstances, interviewers could report any issues to IFS coordinators. Depending on their complexity, such issues could be then solved at the local or central level. With a view to minimising the number of errors made by interviewers, a number of training courses were conducted. A set of very detailed questionnaire completion guidelines was also developed.
13.3.3. Non response error
See sub-categories below.
13.3.3.1. Unit non-response - rate
See item 13.3.1.1.
The unit non-response rate is unweighted.
The unit non-response rate is calculated as the share of eligible non-respondent holdings to the eligible holdings. The eligible holdings include those holdings with unknown eligibility status which are imputed or re-weighted for (therefore considered eligible).
The unit non-response rate is calculated over the holdings in the main frame and if applicable frame extension, for which core data are sent to Eurostat.
13.3.3.1.1. Reasons for unit non-response
Failure to make contact with the unitRefusal to participate
Inability to participate (e.g. illness, absence)
Other
13.3.3.1.2. Actions to minimise or address unit non-response
Follow-up interviewsReminders
Weighting
13.3.3.1.3. Unit non-response analysis
The overall non-response rate was 1.4%. Due to the very low level of the overall non-response rate, there was no detailed analysis of non-responses, only the weight correction with calibration was performed.
13.3.3.2. Item non-response - rate
The electronic questionnaire did not allow omitting mandatory questions or leaving them unanswered. Due to this feature, item non-response for particular questions was marginal. Incomplete questionnaires occurred in the case of online self-interviews. They were forwarded for completion to the face to face and telephone methods (both in an electronic form). In a small number of cases, the missing data was supplemented based on existing data in the questionnaire. Generally, in the face to face and telephone methods, apart from infrequent cases, fully completed forms were obtained.
13.3.3.2.1. Variables with the highest item non-response rate
Generally, there were no significant non-responses for any of the surveyed variables.
13.3.3.2.2. Reasons for item non-response
Interview interruption13.3.3.2.3. 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
An important issue in the context of the non-response rate reduction was to popularise survey information among farmers, including its objectives and significance. Before start of the IFS 2023, the Letter of the President of the Statistics Poland was distributed to all respondents, requesting them to participate in the survey and assuring that any data collected will be subject to strict statistical confidentiality. An important role was played by the Call Centre, where the respondents might obtain detailed information or clarify any doubts by phone. A proper training of interviewers, who were able not only to effectively motivate the users to participate in the survey, but also to deal with troublesome respondents, also led to a lower number of refusals. In the case of absence of a holder, the interviewers could conduct an interview with any other adult member of the holder’s household.
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
Nearest neighbour imputationPrevious data for the same unit
13.3.4.3. Actions to correct or minimise processing errors
The main IFS dataset was covered by automatic, logical and counting controls and it was verified in terms of the scope. The control algorithms also included the principles of validation required by Eurostat. Control rules were established for each section of the questionnaire. The sections were verified in a specified order. Having controlled and possibly adjusted a given module, a subsequent module was verified. Upon completion of the control process, reports were generated, containing information on the validation principles applied and errors identified. The erroneous records, were analysed by experts who, based on their specialised knowledge and information available, identified the errors and decided how they should be treated (accepted or corrected and the way of correction).
13.3.4.4. Tools and staff authorised to make corrections
Staff - Agriculture and Environment Department, Social Surveys and Labour Market Department and Innovation Department experts, IT specialists.
Tools - SQL, SAS, R.
13.3.4.5. Impact of processing error on data quality
Low13.3.4.6. Additional information processing error
Among sources of processing errors, there were mistakes in algorithms for validation as well as wrong implementation of the algorithms. Wrong algorithms and errors in implementation were verified and corrected.
13.3.5. Model assumption error
Not applicable.
14.1. Timeliness
See sub-categories below.
14.1.1. Time lag - first result
Preliminary results were released in July 2024, i.e. 7 months from the last day of the reference year.
14.1.2. Time lag - final result
Final results were released in April 2025, i.e. 16 months from the last day of the reference year.
14.2. Punctuality
See sub-categories below.
14.2.1. Punctuality - delivery and publication
See sub-categories below.
14.2.1.1. Punctuality - delivery
Not requested.
14.2.1.2. Punctuality - publication
Data were published according to the planned timetable.
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
The target population (main frame) consisted of agricultural holdings meeting the same physical thresholds as listed in Annex II of Regulation (EU) 2018/1091, according to Article 3(2) of the same Regulation. It means that the smallest units were not sent to Eurostat.
15.1.3.2. Differences between the national thresholds and the thresholds used for the data sent to Eurostat
Differences exist. According to Article 3(2) of Regulation (EU) 2018/1091, we sent to Eurostat the holdings of area 5 ha of UAA and more. For national purposes, we also collected data for farms below 5 ha of UAA.
15.1.3.3. Reasons for differences
Due to specificity of Polish agriculture, for national purposes, it is necessary to gather the data for farms below 5 ha of UAA.
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
There are no deviations from 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
No use of different LSU coefficients.
15.1.4.1.5. Livestock included in “Other livestock n.e.c.”
For national needs, equine data were collected as a separate variable. However, for the EU database on farm structure purposes, equine animals are counted among the item "Other livestock (A0030)".
15.1.4.2. Reasons for deviations
Maintenance of time series. Equine data are collected and published at national level as a separate variable in order to maintain the national time series.
15.1.5. Reference periods/days
See sub-categories below.
15.1.5.1. Deviations from Regulation (EU) 2018/1091
No deviations.
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
Administrative sources15.1.6.5. Description of methods to record data on common land
For the survey purposes, common land holdings were especially created at the NUTS 4 level. Common land consists of meadows and pastures. Number of especially created common land units is 130.
15.1.6.6. Possible problems in relation to the collection of data on common land and proposals for future data collections
We did not experience problems to collect data on common land.
15.1.7. National standards and rules for certification of organic products
See sub-categories below.
15.1.7.1. Deviations from Council Regulation (EC) No 834/2007
Only the EU standards and rules specified in Council Regulation (EC) No 834/2007 are applied in Poland (there are no national standards and rules).
15.1.7.2. Reasons for deviations
Not applicable.
15.1.8. Differences in methods across regions within the country
No differences.
15.2. Comparability - over time
See sub-categories below.
15.2.1. Length of comparable time series
1
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 2020, the threshold of 1 ha of UAA was used (the main frame plus frame extension).
In IFS 2023, the threshold of 5 ha of UAA was used (the main frame without frame extension).
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
Not applicable.
15.2.6. Reference periods/days
See sub-categories below.
15.2.6.1. Changes since the last data transmission to Eurostat
There have been no changes15.2.6.2. Description of changes
Not applicable.
15.2.7. Common land
See sub-categories below.
15.2.7.1. Changes in the methods to record common land since the last data transmission to Eurostat
There have been some changes but not enough to warrant the designation of a break in series15.2.7.2. Description of changes
In 2020, common land data were derived from administrative sources at the NUTS 5 level, based on the fact that the data could be disaggregated. In 2023, data were collected at the NUTS 4 level. This explains the decrease in the number of common land units between 2020 and 2023.
15.2.8. Explanations for major trends of main variables compared to the last data transmission to Eurostat
In terms of overall number of holdings, there has been a shrink between 2023 compared to 2020. 2023 data are only referred to main frame, excluding frame extension which is instead included in 2020 data.
In terms of evolution of the main aggregates over time, these comments can be made:
C1500T Grain maize and corn-cob-mix – outdoor. The area is growing systematically, the interest in cultivation rises due to the use of grain for animal feed.
I1130T Soya – outdoor. The area is growing systematically, the interest in cultivation rises due to the use of grain for animal feed and also as an alternative to imported soy.
I1140T Linseed (oil flax) – outdoor. Decreased over time.
I2200T Hemp – outdoor. Decreased over time.
I5000T Aromatic, medicinal and culinary plants – outdoor. Declining interest in outdoor cultivation due to unfavourable climatic conditions and low profitability.
I6000T + I9000T Energy crops n.e.c. – outdoor + Other industrial crops n.e.c. – outdoor. Increase in interest in cultivation due to its use as an alternative source of heat energy.
G9100T + G9900T Other cereals and other plants harvested green – outdoor. Increase in interest in cultivation due to use as animal feed in the spring and summer.
Q0000T Fallow land – outdoor. The area of follow land is decreasing due to greater interest in other crops (maize, rape).
PECRS Permanent crops – under glass or high accessible cover. Low prevalence.
SRCAA – Short rotation coppice areas. Increase in interest in cultivation due to its use as an alternative source of heat energy.
U1000 – Cultivated mushrooms. Decreased over time.
UAAT_IB Irrigable utilised agricultural area – outdoor. Decreased interest results from a decrease in profitability resulting from the high costs of using irrigation, fertilisation and plant protection.
I6000T Energy crops n.e.c. – outdoor. Increase in interest in cultivation due to its use as an alternative source of heat energy.
Holdings breakdown by LSU class: reduction of the share of holdings with LSU >=0 and less than 5, and the parallel increase of the share of farms without livestock as a consequence of livestock’s production concentration.
Holdings breakdown by SO_EURO class: there has been a decrease in the number of holdings having SO_EURO less than 3000 and an increase of upper classes of SO_EURO, also this was due to concentration.
With regards to the holdings breakdown by farm typology, there has been an increase for FT15 and a parallel decrease of FT16.
15.2.9. Maintain of statistical identifiers over time
No15.3. Coherence - cross domain
See sub-categories below.
15.3.1. Coherence - sub annual and annual statistics
Not applicable to Integrated Farm Statistics, because there are no sub annual data collections in agriculture.
15.3.2. Coherence - National Accounts
Not applicable, because Integrated Farm Statistics have no relevance for national accounts.
15.3.3. Coherence at micro level with data collections in other domains in agriculture
See sub-categories below.
15.3.3.1. Analysis of coherence at micro level
Yes15.3.3.2. Results of analysis at micro level
Regarding characteristics, outliers and illogical values were compared with data obtained from other or previous agricultural surveys, as well as with administrative data. There were some differences in the area of agricultural land between the IFS 2023 and the administrative data. The found errors were corrected. The final analysis showed the consistency of the data.
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
Major discrepancies were found for PECR, ARAT, ARA99T, G0000, J0000, K0000 and UAA (with IFS values lower than crops statistics).
E0000, ARAT_ORG and L0000 had IFS values higher than those of crops production statistics.
Coherence cross-domain: IFS vs ANIMAL PRODUCTION
Values recorded in IFS 2023 for A2130, A2300G, A3110, were higher than those of animal production statistics.
Values recorded in IFS 2023 for A2230, A3100, A3120, were lower than those of animal production statistics.
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 data of the IFS 2023 was selected so that the data of other surveys did not overlap. Additionally, to decrease the cost and burden of respondents, one integrated questionnaire was developed to collect core data and data from modules. This solution allowed to avoid the situation that respondents have to answer the same questions two or more times as well as to reduce number of the phone calls or visits of the interviewers. In order to reduce costs, all farms could provide their data electronically. Additionally, administrative data were used as much as possible.
16.2. Efficiency gains since the last data transmission to Eurostat
Further automationIncreased use of administrative data
Further 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
Not available.
16.3.2. Module ‘Labour force and other gainful activities‘
Not available.
16.3.3. Module ‘Rural development’
Not available.
16.3.4. Module ‘Animal housing and manure management’
Restricted from publication
16.3.5. Module ‘Irrigation’
Not available.
16.3.6. Module ‘Soil management practices’
Not available.
16.3.7. Module ‘Machinery and equipment’
Not available.
16.3.8. Module ‘Orchard’
Not available.
16.3.9. Module ‘Vineyard’
Restricted from publication
17.1. Data revision - policy
There is no data revision policy for IFS. However, in case of an unplanned revision due to editorial mistakes, corrections are published immediately upon completion, along with an explanation of the reasons.
17.2. Data revision - practice
Since 2002, there has been no need to conduct unplanned revisions for IFS/FSS.
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
Survey frame.
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
The sampling design was based on a single-stage stratified random sampling of holdings with take-all strata for core data (main frame) and all modules. Experience from previous editions of farm structure surveys was taken into account, and the issues required by Eurostat for the IFS (in accordance with the Regulation (EU) 2018/1091 of the European Parliament and the Council).
A generalisation of the classical stratified sampling scheme in the form of balanced sampling, the so-called CUBE method (described e.g. in: Yves Tillé (2011), Ten years of balanced sampling with the cube method: An appraisal, Survey Methodology 37, issue 2, 215-226) was used to draw the sample within core population parts. This approach makes it possible to obtain (approximately) consistent estimators for the given characteristics on the basis of the drawn sample with the corresponding values resulting from the frame (for each NUTS 2 region). The theory suggests that an appropriate selection of additional characteristics, well correlated with the survey variables, yields a significant improvement in the efficiency of the sampling scheme. The following characteristics were selected as additional variables for the CUBE method: area of agricultural land, area under cereals, area under rape, area under permanent crops, area under permanent grassland, numbers of cattle, cows, pigs and poultry (in large units), area under apples and area under pears.
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
Unit legal status
Other
Annexes:
18.1.2.2.2. Stratification criteria
18.1.2.2.3. Use of systematic sampling
No18.1.2.2.4. Full coverage strata
The sampling scheme took into account a complete survey of certain types of agricultural holdings.
The farms included in categories A, E and F were surveyed on a 100% basis.
In addition, take-all strata were:
- Category B: last stratum (i.e. h=06) in each region which consists of such sampling units for which the value of at least one of the variables adopted as the stratification basis is above the specified threshold.
- Category C: last stratum (i.e. h=09).
- Category D: last stratum (i.e. h=12, and included farms which in sampling frame had minimum 30 head of cattle or 100 head of pigs).
- Category G: last stratum (i.e. h=17).
For further details, see the annex in point 18.1.2.2.2 Stratification criteria.
18.1.2.2.5. Method of determination of the overall sample size
The size of the sample was decided in accordance with financial and organisational possibilities and the precision requirements provided in the Regulation (EU) 2018/1091. The number of agricultural holdings in the population and in the sample by category is provided in the following table:
| Category of holding | Population | Sample |
|---|---|---|
| Poland | 1 334 892 | 128 128 |
| A | 25 694 | 25 694 |
| B | 861 902 | 58 192 |
| C | 4 298 | 1 089 |
| D | 64 035 | 8 408 |
| E | 20 071 | 20 071 |
| F | 5 998 | 5 998 |
| G | 352 894 | 8 676 |
18.1.2.2.6. Method of allocation of the overall sample size
Neymann allocationAnnexes:
18.1.2.2.6. Method of allocation of the overall sample size
18.1.3. 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 the point 18.1.2.2.
18.1.4.2.1. Name of sampling design
Stratified one-stage random sampling18.1.4.2.2. Stratification criteria
Unit sizeUnit location
Unit specialization
Unit legal status
Other
18.1.4.2.3. Use of systematic sampling
No18.1.4.2.4. Full coverage strata
See the point 18.1.2.2.4.
18.1.4.2.5. Method of determination of the overall sample size
See the point 18.1.2.2.5.
18.1.4.2.6. Method of allocation of the overall sample size
Neymann allocation18.1.4.2.7. If sampled from the core sample, the sampling and calibration strategy
Not applicable18.1.5. Module “Rural development”
See sub-categories below.
18.1.5.1. Coverage of agricultural holdings
Sample18.1.5.2. Sampling design
See the point 18.1.2.2.
18.1.5.2.1. Name of sampling design
Stratified one-stage random sampling18.1.5.2.2. Stratification criteria
Unit sizeUnit location
Unit specialization
Unit legal status
Other
18.1.5.2.3. Use of systematic sampling
No18.1.5.2.4. Full coverage strata
See the point 18.1.2.2.4.
18.1.5.2.5. Method of determination of the overall sample size
See the point 18.1.2.2.5.
18.1.5.2.6. Method of allocation of the overall sample size
Neymann allocation18.1.5.2.7. If sampled from the core sample, the sampling strategy and calibration strategy
Not applicable18.1.6. Module “Animal housing and manure management module”
Restricted from publication
18.1.6.1. Coverage of agricultural holdings
Restricted from publication
18.1.6.2. Sampling design
Restricted from publication
18.1.6.2.1. Name of sampling design
Restricted from publication
18.1.6.2.2. Stratification criteria
Restricted from publication
18.1.6.2.3. Use of systematic sampling
Restricted from publication
18.1.6.2.4. Full coverage strata
Restricted from publication
18.1.6.2.5. Method of determination of the overall sample size
Restricted from publication
18.1.6.2.6. Method of allocation of the overall sample size
Restricted from publication
18.1.6.2.7. If sampled from the core sample, the sampling strategy and calibration strategy
Restricted from publication
18.1.7. Module ‘Irrigation’
See sub-categories below.
18.1.7.1. Coverage of agricultural holdings
Sample18.1.7.2. Sampling design
See the point 18.1.2.2.
18.1.7.2.1. Name of sampling design
Stratified one-stage random sampling18.1.7.2.2. Stratification criteria
Unit sizeUnit location
Unit specialization
Unit legal status
Other
18.1.7.2.3. Use of systematic sampling
No18.1.7.2.4. Full coverage strata
See the point 18.1.2.2.4.
18.1.7.2.5. Method of determination of the overall sample size
See the point 18.1.2.2.5.
18.1.7.2.6. Method of allocation of the overall sample size
Neymann allocation18.1.7.2.7. If sampled from the core sample, the sampling strategy and calibration strategy
Not applicable18.1.8. Module ‘Soil management practices’
See sub-categories below.
18.1.8.1. Coverage of agricultural holdings
Sample18.1.8.2. Sampling design
See the point 18.1.2.2.
18.1.8.2.1. Name of sampling design
Stratified one-stage random sampling18.1.8.2.2. Stratification criteria
Unit sizeUnit location
Unit specialization
Unit legal status
Other
18.1.8.2.3. Use of systematic sampling
No18.1.8.2.4. Full coverage strata
See the point 18.1.2.2.4.
18.1.8.2.5. Method of determination of the overall sample size
See the point 18.1.2.2.5.
18.1.8.2.6. Method of allocation of the overall sample size
Neymann allocation18.1.8.2.7. If sampled from the core sample, the sampling strategy and calibration strategy
Not applicable18.1.9. Module ‘Machinery and equipment’
See sub-categories below.
18.1.9.1. Coverage of agricultural holdings
Sample18.1.9.2. Sampling design
See the point 18.1.2.2.
18.1.9.2.1. Name of sampling design
Stratified one-stage random sampling18.1.9.2.2. Stratification criteria
Unit sizeUnit location
Unit specialization
Unit legal status
Other
18.1.9.2.3. Use of systematic sampling
No18.1.9.2.4. Full coverage strata
See the point 18.1.2.2.4.
18.1.9.2.5. Method of determination of the overall sample size
See the point 18.1.2.2.5.
18.1.9.2.6. Method of allocation of the overall sample size
Neymann allocation18.1.9.2.7. If sampled from the core sample, the sampling strategy and calibration strategy
Not applicable18.1.10. Module ‘Orchard’
See sub-categories below.
18.1.10.1. Coverage of agricultural holdings
Sample18.1.10.2. Sampling design
See the point 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
Unit specialization
Unit legal status
Other
18.1.10.2.3. Use of systematic sampling
No18.1.10.2.4. Full coverage strata
See the point 18.1.2.2.4.
18.1.10.2.5. Method of determination of the overall sample size
See the point 18.1.2.2.5.
18.1.10.2.6. Method of allocation of the overall sample size
Neymann allocation18.1.10.2.7. If sampled from the core sample, the sampling strategy and calibration strategy
Not applicable18.1.11. Module ‘Vineyard’
Restricted from publication
18.1.11.1. Coverage of agricultural holdings
Restricted from publication
18.1.11.2. Sampling design
Restricted from publication
18.1.11.2.1. Name of sampling design
Restricted from publication
18.1.11.2.2. Stratification criteria
Restricted from publication
18.1.11.2.3. Use of systematic sampling
Restricted from publication
18.1.11.2.4. Full coverage strata
Restricted from publication
18.1.11.2.5. Method of determination of the overall sample size
Restricted from publication
18.1.11.2.6. Method of allocation of the overall sample size
Restricted from publication
18.1.11.2.7. If sampled from the core sample, the sampling strategy and calibration strategy
Restricted from publication
18.1.12. Software tool used for sample selection
R and SAS
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
None18.1.14. 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 questionnaires in annex.
Annexes:
18.3.3. Questionnaire in Polish
18.3.3. Questionnaire in English
18.4. Data validation
See sub-categories below.
18.4.1. Type of validation checks
Data format checksCompleteness checks
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 local departments
Staff from central department
18.4.3. Tools used for data validation
Validation rules were implemented in the questionnaires and within data processing software. For the purposes of IFS survey data processing, the Statistical data processing system (SPDS) was used, which replaced several dozen previously used applications managing the processing of data obtained in individual statistical surveys. The system operates in a uniform environment and provides all the necessary functions, which include import of collected statistical data, editing individual entries, validation of processed data, sharing data on the basis of which preliminary reports will be generated, auto-correction, editing information on the fulfilment of reporting obligations, defining reports and control tables, generating reports and control tables, data storage and transfer to external subsystems (statistical data warehouse). Each stage of data processing ends with the generation of a validation report and control tables.
18.5. Data compilation
After data collection and pre-processing, data is accessed by staff from the Agriculture and Environment Department as well as the Social Surveys and Labour Market Department for final data compilation, which includes checks and analyses at different levels (micro, macro).
18.5.1. Imputation - rate
Not applicable.
18.5.2. Methods used to derive the extrapolation factor
Design weightNon-response adjustment
Calibration
Other
Annexes:
18.5.2. Methods used to derive the extrapolation factor
18.6. Adjustment
Covered under Data compilation.
18.6.1. Seasonal adjustment
Not applicable to Integrated Farm Statistics, because it collects structural data on agriculture.
See sub-categories below.
19.1. List of abbreviations
AWU – Annual Working Unit
CAP – Common Agriculture Policy
EU – European Union
FSS – Farm Structure Survey
GUS – Statistics Poland
IACS – Integrated Administration and Control System
IFS – Integrated Farm Statistics
LDB – Local Data Bank
LSU – Livestock Unit
MIRR – Irrigation module
MOGA_NFAM_RH – Non-family labour force regularly working on the holding and having other gainful activities (related to the agricultural holding) as their main activity
MORC – Orchards module
NOBC – Address identification system for streets, properties, buildings and dwellings
NUTS – Nomenclature of Territorial Units for Statistics
OGA – Other gainful activities
PBSSP – Programme of Statistical Surveys of Official Statistics
RSE – Relative Standard Error
SDDS – Special Data Dissemination Standard
SGM – Standard Gross Margin
SO – Standard Output
SOGA_NFAM_RH – Non-family labour force regularly working on the holding and having other gainful activities (related to the agricultural holding) as their secondary activity
SPDS – Statistical data processing system
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.
30 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, apricots area, each one by age of plantation and density of trees.
See sub-category below.
See sub-categories below.
See sub-categories below.
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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)) and
- the number of agricultural holdings having the above-mentioned characteristics.
After data collection and pre-processing, data is accessed by staff from the Agriculture and Environment Department as well as the Social Surveys and Labour Market Department for final data compilation, which includes checks and analyses at different levels (micro, macro).
See sub-categories below.
Every 10 years for census and additionally between censuses - every 3/4 years (2023, 2026) for other IFS data collections.
See sub-categories below.
See sub-categories below.
See sub-categories below.
