1.1. Contact organisation
Republic of Croatia - Croatian Bureau of Statistics
1.2. Contact organisation unit
Spatial statistics Directorate/Agricultural, Production and Structural Statistics Department
1.3. Contact name
Confidential because of GDPR
1.4. Contact person function
Confidential because of GDPR
1.5. Contact mail address
Republic of Croatia - Croatian Bureau of Statistics
Ilica 3, 10000 Zagreb
Republic of Croatia
Spatial statistics Directorate/Agricultural, Production and Structural Statistics Department
Branimirova 19, 10000 Zagreb
Republic of Croatia
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
20 January 2025
2.2. Metadata last posted
6 March 2025
2.3. Metadata last update
20 January 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, small citrus fruit area, olives area, grapes for table use area, each one by age of plantation and density of trees.
3.5. Statistical unit
See sub-category below.
3.5.1. Definition of agricultural holding
The agricultural holding is a single unit, both technically and economically, that has a single management and that undertakes economic activities in agriculture in accordance with Regulation (EC) No 1893/2006 belonging to groups:
- A.01.1: Growing of non-perennial crops;
- A.01.2: Growing of perennial crops;
- A.01.3: Plant propagation;
- A.01.4: Animal production;
- A.01.5: Mixed farming or;
- The “maintenance of agricultural land in good agricultural and environmental condition” of group A.01.6 within the economic territory of the Union, either as its primary or secondary activity.
Regarding activities of class A.01.49, only the activities “Raising and breeding of semi-domesticated or other live animals” (with the exception of raising of insects) and “Bee-keeping and production of honey and beeswax” are included.
3.6. Statistical population
See sub-categories below.
3.6.1. Population covered by the core data sent to Eurostat (main frame and if applicable frame extension)
The thresholds of agricultural holdings are available in the annex.
Annexes:
3.6.1 Thresholds of agricultural holdings
3.6.1.1. Raised thresholds compared to Regulation (EU) 2018/1091
No3.6.1.2. Lowered and/or additional thresholds compared to Regulation (EU) 2018/1091
Yes3.6.2. Population covered by the data sent to Eurostat for the modules “Labour force and other gainful activities”, “Rural development” and “Machinery and equipment”
The same population of agricultural holdings defined in item 3.6.1.
3.6.3. Population covered by the data sent to Eurostat for the module “Animal housing and manure management”
Restricted from publication
3.6.4. Population covered by the data sent to Eurostat for the module “Irrigation”
The subset of agricultural holdings defined in item 3.6.2 with irrigable area.
3.6.5. Population covered by the data sent to Eurostat for the module “Soil management practices”
The subset of agricultural holdings defined in item 3.6.2 with arable land, permanent grassland and permanent crops.
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
Not applicable.
3.7.3. Criteria used to establish the geographical location of the holding
The main building for productionThe location where all agricultural activities are situated
The majority of the area of the holding
The most important parcel by physical size
The most important parcel by economic size
The residence of the farmer (manager) not further than 5 km straight from the farm
3.7.4. Additional information reference area
Not available.
3.8. Coverage - Time
Farm structure statistics in our country cover the period from 2007 onwards. Older time series are described in the previous quality reports (national methodological reports).
3.9. Base period
The 2023 data are processed (by Eurostat) with 2020 standard output coefficients (calculated as a 5-year average of the period 2018-2022). For more information, you can consult the definition of the standard output.
Two kinds of units are generally used:
- the units of measurement for the variables (area in hectares, livestock in (1000) heads or LSU (livestock units), labour force in persons or AWU (annual working units), standard output in Euro, places for animal housing etc.) and
- the number of agricultural holdings having these characteristics.
See sub-categories below.
5.1. Reference period for land variables
The use of land refers to the 12-month period ending on 1st June within the reference year 2023. In the case of successive crops from the same piece of land, the land use refers to a crop that is harvested during the reference year, regardless of when the crop in question is sown.
5.2. Reference period for variables on irrigation and soil management practices
For variables on irrigation and soil management practices, the reference period is a 12-month period ending on 1st June within the reference year 2023.
5.3. Reference day for variables on livestock and animal housing
The reference day 1st June within the reference year 2023 for livestock variables.
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 1st June within the reference year 2023.
5.6. Reference period for variables on rural development measures
The three-year period ending on 31 December 2023.
5.7. Reference day for all other variables
The reference day 1st June 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
Law on official statistics (Official Gazette, Nos 25/20 and 155/23).
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
2020
6.1.5. Legal obligations for respondents
Yes6.2. Institutional Mandate - data sharing
Law on official statistics gives Croatian Bureau of Statistics (CBS) rights to access data available in administrative sources and databases (IACS, vineyard register, records relating to rural development measures). Furthermore, there are written agreements with data providing and receiving agencies.
7.1. Confidentiality - policy
Statistical data collected for IFS2023, according to the Law on official statistics (Official Gazette, Nos 25/20 and 155/23) is confidential and its purpose is restricted exclusively to statistical usage. Authorised interviewers are obliged to respect these restrictions. The results will be published in a cumulative form which prevents displaying data on individuals.
Regulation (EC) No 223/2009 on European statistics (recital 24 and Article 20(4)) of 11 March 2009 (OJ L 87, p. 164), stipulates the need to establish common principles and guidelines ensuring the confidentiality of data used for the production of European statistics and the access to those confidential data with due account for technical developments and the requirements of users in a democratic society.
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)Dominance rule (The n largest contributions make up for more than k% of the cell total)
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)
Rounding: controlled, deterministic or random (Round each cell value to a pre-specified rounding base)
7.2.1.3. Description of rules and methods
In the ongoing CBS restructuring, it is foreseen to place the focal point for ensuring confidentiality, including provision of guidance, recommending appropriate methodologies and periodical examination of methods used for data protection, within the Statistical Business Register, Classifications, Sampling, Statistical Methods and Analyses Department. A filter is applied during the table compilation using the following processes:
• dominance treatment: if any holdings account for at least 85% of the value, this value is put to zero;
• small number of units: if a value is calculated from less than 3 holdings, this value is put to zero;
• rounding: the values are rounded to the closer multiple of 10.
• secondary confidentiality: in order to prevent the possibility of calculating data protected through primary confidentiality methods based on other published data included in the aggregate, secondary confidentiality methods are applied by protecting aggregates within hierarchical tables. If, either vertically or horizontally, within the same hierarchical level, a protected value can be calculated through subtraction of unprotected variables from the total aggregate, an additional data point is protected. This is usually the data point with the lowest frequency of occurrence within the aggregate itself. All data identified for protection, whether through primary or secondary methods, are replaced with the letter 'z' instead of numerical values during publication.
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 information is provided in the Ordinance on Conditions and Terms of Using Confidential Data for Scientific Purposes (Official Gazette, No. 137/13) which defines in detail conditions, modalities and measures for protecting confidential information (research proposal submitted by independent researchers or research entities referred to in Article 2 of the Ordinance; access to confidential data on the basis of research proposals submitted and approved; confidential declaration has to be signed by any individual researcher using confidential data; special contract has to be concluded inter CBS and independent researcher or research entity; access to confidential data may be granted only for the period of the duration of the research project, max 5 years; obligations for taking all legal, administrative, technical and organisational safeguards of the confidential data for scientific purpose which have been granted; confidential data must be destroyed when the research project is finished; after expiry of the research project, the researchers or research entity are obliged to provide CBS with references to all reports that have been produced using the data; termination access to data; etc.).
Each usage of confidential information is regulated through a specific contract with CBS, which strictly regulates this issue.
8.1. Release calendar
Notifications about the dissemination of statistics are published in the calendar of statistical data issues. This calendar contains the review of publications planned to be issued in current year and by the end of May for next year, which depends on when the processing of a particular statistical survey can be finished and on whether it is feasible to make a particular kind of publication or not. All calendars are publicly available.
8.2. Release calendar access
DZS website - Calendar of statistical data issues and publishing programme.
8.3. Release policy - user access
Data are disseminated according to a predefined calendar and are simultaneously available to all users on the pages of CBS.
8.3.1. Use of quality rating system
No8.3.1.1. Description of the quality rating system
Not applicable.
Every 3-4 years.
10.1. Dissemination format - News release
See sub-categories below.
10.1.1. Publication of news releases
Yes10.1.2. Link to news releases
More information at this DZS webiste.
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
We do not monitor and record the number of consultations of data tables in the field of farm structure.
10.3.2. Accessibility of online database
Yes10.3.3. Link to online database
Database with IFS2023 data is available on the website of CBS: Statistical database (field 'Farm structure survey')
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
CBS accepted Total quality management (TQM) approach as the general model for quality management, quality assessment and quality improvement. To support implementation of this model the basic strategic document is developed where the following main cornerstones of the TQM model are explained and described:
- High quality statistical processes and products;
- User satisfaction;
- Professional orientation of the employees;
- Efficiency of the processes;
- Reduction of the response burden.
For each of these general aims, concrete actions are foreseen and plans for their implementation described.
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
11.1.3. Description of the quality management system and procedures
In order to establish a comprehensive system of quality, the Croatian Bureau of Statistics applies the model of Total Quality Management, which also contains the Code of Practice of European Statistics. This model offers a possibility of continuous improvement for each business process. It focuses not only on products and services, but also to users and their satisfaction, the active participation of employees, long-term business success and social benefit. The communication is recognised as a key element of all statistical processes that affect the business success.
Additionally, training courses for interviewers are organised at the county level and include detailed theoretical and practical exercises. These sessions are aligned with current legislation and the guidelines provided in the handbook, ensuring interviewers are well-prepared to collect data accurately and consistently. The training emphasises both the technical and methodological aspects of data collection, enhancing the quality of statistical processes.
11.1.4. Improvements in quality procedures
We started preparing files for import into the Generic Statistical Business Process Model (GSBPM) application from 2019 onwards. Descriptions of business sub-processes for IFS have been entered according to the GSBPM model, with the aim of further harmonising processes with European and international standards in the field of quality. This process improves tools for monitoring the quality of statistical products and supports the implementation of the general Peer Review recommendation.
11.2. Quality management - assessment
In general, the data quality is good.
12.1. Relevance - User Needs
Ministry of Agriculture, Faculty of Agriculture, Government of the Republic of Croatia, researchers and the general public for the purpose of forming economic policy and allocating state budget resources.
12.1.1. Main groups of variables collected only for national purposes
Some of the characteristics were added to the questionnaire for national purposes only:
• holder's name and surname,
• areas under triticale (included in other cereals),
• areas under secondary crops,
• address of the holder,
• number of trees in extensive orchards and olive groves and number of vines in vineyards – needed for calculation of production,
• all spices of vegetables are added in open fields, in glasshouses and in kitchen gardens.
The characteristics surveyed only for national purposes are used in EAA, for updating farm register and for calculating standard output.
12.1.2. Unmet user needs
There is no information about unmet user needs.
12.1.3. Plans for satisfying unmet user needs
Not applicable.
12.2. Relevance - User Satisfaction
CBS conducts user satisfaction surveys. The quality of statistical processes and final results, that is, statistical products and services, can best be assessed through a user satisfaction survey. The purpose of the user satisfaction survey is to find out the level of user satisfaction with the quality of the products and services of the CBS. At the same time, users have the opportunity to express their needs and remarks, thus participating in improving the overall quality of the CBS. The results of the survey are important for future planning of the activities of the CBS, for decision-making and identifying priorities, and are considered an important source of information about the opinions, wishes, needs and expectations of users. The first user satisfaction survey of the CBS was conducted in 2013, the second one in 2015, and the last one at the end of 2022.
12.2.1. User satisfaction survey
Yes12.2.2. Year of user satisfaction survey
2022
12.2.3. Satisfaction level
SatisfiedAnnexes:
12.2.3. Results of the user satisfaction survey
12.3. 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
There is an expected increase of the variability of the characteristics following changes since the last update of the sampling frame.
To address this issue in the future, the following measures will be considered:
- Increasing the sample size for the regions with high expected variability to reduce variability and improve precision.
- Updating and refining the sampling frame to ensure it more accurately reflects the population and its characteristics.
13.2.3. Reference on method of estimation
The estimation of RSEs was performed using the SAS PROC SURVEYMEANS procedure. We computed standard errors and coefficients of variation utilising standard SAS programs commonly employed in most CBS surveys.
13.2.4. Impact of sampling error on data quality
None13.3. Non-sampling error
See sub-categories below.
13.3.1. Coverage error
See sub-categories below.
13.3.1.1. Over-coverage - rate
The over-coverage rate is available on Eurostat’s website, at the website: Circabc website.
The over-coverage rate is unweighted.
The over-coverage rate is calculated as the share of ineligible holdings to the holdings designated for the core data collection. The ineligible holdings include those holdings with unknown eligibility status that are not imputed nor re-weighted for (therefore considered ineligible).
The over-coverage rate is calculated over the holdings in the main frame and if applicable frame extension, for which core data are sent to Eurostat.
13.3.1.1.1. Types of holdings included in the frame but not belonging to the population of the core (main frame and if applicable frame extension)
Below thresholds during the reference periodTemporarily out of production during the reference period
Ceased activities
Merged to another unit
Duplicate units
13.3.1.1.2. Actions to minimize the over-coverage error
Removal of ineligible units from the records, leaving unchanged the weights for the other units13.3.1.1.3. Additional information over-coverage error
Not available.
13.3.1.2. Common units - proportion
Not requested.
13.3.1.3. Under-coverage error
See sub-categories below.
13.3.1.3.1. Under-coverage rate
Under-coverage is very low as the frame for survey was statistical Register of agricultural holdings (SRAH) that has been updated with all available administrative sources.
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)
Units with outdated information in the frame (variables below thresholds in the frame but above thresholds in the reference period)Other
13.3.1.3.3. Actions to minimise the under-coverage error
Further updating of SRAH.
13.3.1.3.4. Additional information under-coverage error
Other types of holdings not included in the frame refer to business entities that have declared they are no longer engaged in agricultural activities, as well as those that are inactive or in bankruptcy.
13.3.1.4. Misclassification error
No13.3.1.4.1. Actions to minimise the misclassification error
Not applicable.
13.3.1.5. Contact error
Yes13.3.1.5.1. Actions to minimise the contact error
Contact information is constantly updated. Information comes from the SBR, IACS or direct information from respondents in the census questionnaire.
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
Characteristics that are complicated for both respondents and interviewers are related to labour force, animal housing and manure management.
13.3.2.2. Causes of measurement errors
Complexity of variablesUnclear questions
Respondents’ inability to provide accurate answers
13.3.2.3. Actions to minimise the measurement error
Pre-testing questionnairePre-filled questions
Explanatory notes or handbooks for enumerators or respondents
On-line FAQ or Hot-line support for enumerators or respondents
Training of enumerators
13.3.2.4. Impact of measurement error on data quality
Low13.3.2.5. Additional information measurement error
Statistics correct possible errors of measurement by using the logic-numeric control. We are trying to avoid the measurement error by training of interviewers and supervisors, control data and process validation. The CAPI application contains logic-numeric controls which warning interviewer on possible measurement caused errors. Also, the set of basic checks are implemented in CAWI application in order to reduce measurement errors. After data entry, extreme values of variables are checked and corrected if necessary. Remaining errors are mostly detected throw data processing and corrected accordingly.
13.3.3. Non response error
See sub-categories below.
13.3.3.1. Unit non-response - rate
See item 13.3.1.1.
The unit non-response rate is unweighted.
The unit non-response rate is calculated as the share of eligible non-respondent holdings to the eligible holdings. The eligible holdings include those holdings with unknown eligibility status which are imputed or re-weighted for (therefore considered eligible).
The unit non-response rate is calculated over the holdings in the main frame and if applicable frame extension, for which core data are sent to Eurostat.
13.3.3.1.1. Reasons for unit non-response
Failure to make contact with the unitRefusal to participate
Inability to participate (e.g. illness, absence)
13.3.3.1.2. Actions to minimise or address unit non-response
Follow-up interviewsReminders
Imputation
Weighting
13.3.3.1.3. Unit non-response analysis
The unit non-response rate was very low and no special analysis was made.
13.3.3.2. Item non-response - rate
The item non-response is very low but the item non-response rate is not calculated.
13.3.3.2.1. Variables with the highest item non-response rate
Not available.
13.3.3.2.2. Reasons for item non-response
Skip of due questionFarmers do not know the answer
13.3.3.2.3. Actions to minimise or address item non-response
Follow-up interviewsImputation
13.3.3.3. Impact of non-response error on data quality
Low13.3.3.4. Additional information non-response error
Imputation methods were applied to address both unit non-response and item non-response. Administrative data sources, such as the Unique Register of domestic animals, orchard register, olive register, vineyard register, organic farming register, rural development measures and IACS, were used for deductive imputation. This approach allowed the derivation of missing values for many characteristics based on logical relationships and available information from these registers.
13.3.4. Processing error
See sub-categories below.
13.3.4.1. Sources of processing errors
Data processing13.3.4.2. Imputation methods
Previous data for the same unitOther
13.3.4.3. Actions to correct or minimise processing errors
Within data validation tool exist a lot of numeric-logical controls and active signals that practically prevent the creation of processing errors.
13.3.4.4. Tools and staff authorised to make corrections
Only employees from the Agricultural, Production and Structural Statistics Department, who were directly involved in data processing, were authorised to make corrections. Tools - SQL and SAS systems.
13.3.4.5. Impact of processing error on data quality
Low13.3.4.6. Additional information processing error
Imputation was applied to units whose land or livestock were recorded in available administrative registers. The primary data used for imputation were the crop and livestock information (for the same unit) obtained from these administrative registers.
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 on 2 January 2024, i.e. 2 days from the last day of the reference year.
14.1.2. Time lag - final result
| Last day of the reference period | 31 December 2023 |
|---|---|
| Day of publication of final results | 31 December 2024 |
| Difference number of months | 12 |
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 are 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
The definition of agricultural holdings is in accordance with Regulation (EU) 2018/1091.
15.1.2.2. Reasons for deviations
Not applicable.
15.1.3. Thresholds of agricultural holdings
See sub-categories below.
15.1.3.1. Proofs that the EU coverage requirements are met
| Total | Covered by the thresholds | Attained coverage | Minimum requested coverage | |
| 1 | 2 | 3=2*100/1 | 4 | |
| UAA excluding kitchen gardens | 1 507 438.46 | 1 504 485.626 | 99.8% | 98% |
| LSU | 680 912.49 | 680 687.885 | 99.967% | 98% |
All holdings meeting at least one of the physical thresholds listed in Annex II of Regulation (EU) 2018/1091, as outlined in Article 3(2) of the same Regulation, have been covered.
15.1.3.2. Differences between the national thresholds and the thresholds used for the data sent to Eurostat
No differences between the national thresholds and the thresholds for the data sent to Eurostat.
15.1.3.3. Reasons for differences
Not applicable.
15.1.4. Definitions and classifications of variables
See sub-categories below.
15.1.4.1. Deviations from Regulation (EU) 2018/1091 and EU handbook
There are no deviations from Regulation (EU) 2018/1091 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 website: Circabc website.
The number of working hours and days in a year for a full-time job correspond to one annual working unit (AWU) in the country. One annual work unit corresponds to the work performed by one person who is occupied on an agricultural holding on a full-time basis. Annual working units are used to calculate the farm work on the agricultural holdings.
15.1.4.1.2. Point chosen in the Annual work unit (AWU) percentage band to calculate the AWU of holders, managers, family and non-family regular workers
See item 15.1.4.1.1.
15.1.4.1.3. AWU for workers of certain age groups
See item 15.1.4.1.1.
15.1.4.1.4. Livestock coefficients
No national livestock coefficients are used.
We have used the same livestock coefficients as those set in Regulation (EU) 2018/1091.
15.1.4.1.5. Livestock included in “Other livestock n.e.c.”
No deviations.
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
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 the land of agricultural holdings renting or being allotted the land based on written or oral agreements.Common land is included in separate records representing virtual entities without managers.
15.1.6.4. Source of collected data on common land
SurveysAdministrative sources
15.1.6.5. Description of methods to record data on common land
In IFS2023, the land used as common land was directly attached to farms and also collected as common land units at regional level. The common land is mainly in state ownership. The obtained area of common land used by the farm was mainly added to the rough grazing area of the farm. A separate questionnaire for common land was not used in FSS questionnaire.
Concerning data coming from administrative source (Ministry of Agriculture), the area of common land is recorded in a special unit in the dataset at level of NUTS3 regions (15 units). In terms of tenure classification is treated as common land.
The area of permanent grassland in state owned is on around 1 million hectares in Croatia based on cadastral data (but not all area is used) and in common land units utilised area of grasslands is presented.
15.1.6.6. Possible problems in relation to the collection of data on common land and proposals for future data collections
It was not possible to allocate the common land on farms with grazing livestock because data on lower NUTS were not available. For future data collections, HR will explore the possibility of allocating common land by each holding to improve data accuracy. This will depend on the availability of relevant data and methodological advancements.
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
No deviations.
15.1.7.2. Reasons for deviations
Not applicable.
15.1.8. Differences in methods across regions within the country
There are no differences in methods across regions within the country.
15.2. Comparability - over time
See sub-categories below.
15.2.1. Length of comparable time series
5 years.
15.2.2. Definition of agricultural holding
See sub-categories below.
15.2.2.1. Changes since the last data transmission to Eurostat
There have been no changes15.2.2.2. Description of changes
There are no changes as both 2020 and 2023 are data collection years covered by the same Regulation (EU) 2018/1091.
15.2.3. Thresholds of agricultural holdings
See sub-categories below.
15.2.3.1. Changes in the thresholds of holdings for which core data are sent to Eurostat since the last data transmission
There have been no changes15.2.3.2. Description of changes
Not applicable.
15.2.4. Geographical coverage
See sub-categories below.
15.2.4.1. Change in the geographical coverage since the last data transmission to Eurostat
There have been no changes15.2.4.2. Description of changes
Not applicable.
15.2.5. Definitions and classifications of variables
See sub-categories below.
15.2.5.1. Changes since the last data transmission to Eurostat
There have been 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
Evolution of the holdings by legal status, in the 2023 vs 202 comparison
it has been noted that the share of FARM_FAM holdings decreased considerably while at the same time, the share of FARM_SPOU holdings increased sharply.
Evolution of the crops in the 2023 vs 202 comparison
with regards to cereals it was recorded a sharp increase in C1200T, I1120,V0000_S0000TK, G9100T, E0000T, PCERS and W1190T. Products like I3000T, I1110T, I1190T, J3000TE sought a remarkable decrease in hectares, in the time frame analysed.
Evolution of the livestock in the 2023 vs 202 comparison
Number of livestock of A2300G and A2139 remarkably increased in the time period, whereas A2230, A3110 and A5000X5100 sharply declined
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
IFS micro level data were compared with “Annual Crop Statistics” and “Animal Production Statistics”. There were differences related to the animal production statistics occurred due to the different reference dates.
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
Cross-domain comparison of IFS vs Organic crop production
When comparing the data with the year 2022, we noticed that in 2023 was a significant drop in areas, namely for UAAX and ARA. The following are explanations for these discrepancies:
- a new five-year period began, leading to the exit of many entities from the organic agriculture system.
- at the same time, the number of new organic entities entering the system is significantly lower than in previous years.
- changes to organic legislation have made compliance more complex.
- subsidies for pastures, arable crops, walnuts, and hazelnuts have been significantly reduced. Additionally, nearly identical support can now be accessed through other interventions/operations from the strategic plan, leading many producers to shift focus.
- producers who have secured buyers outside of Croatia continue to grow grains. Others, however, are influenced by fluctuating purchase prices and often choose to sow or reseed with alternative crops, most commonly grasses (perennial crops)
Cross-domain comparison of IFS vs Organic animal production
A similar trend was observed in organic livestock production, specifically with sheep, where an important decrease was recorded. This was caused by:
- many entities involved in sheep farming leave the organic system.
- several farmers abandoned organic livestock registration during inspections to avoid reporting irregularities.
- farmers reducing herd sizes through sales or slaughter, sometimes on a large scale.
- in some cases, entire flocks were sold in 2023, and sheep farming was abandoned altogether.
- some farmers opted to rejuvenate their herds, leading to the sale of older sheep in larger numbers. On the other hand, the number of cattle and dairy cows increased in 2023, as confirmed by control bodies. These factors explain the observed discrepancies and the significant shifts in organic agricultural data between 2022 and 2023.
Cross-domain comparison of IFS vs Animal production
Differences in data across individual categories primarily result from the survey terms and the population size on the reference date. The largest discrepancies, due to varying survey dates, are especially noticeable in the categories of other sheep and goats. This is largely because the majority of animals in these categories are young sheep and goats, whose populations are more substantial during the first half of the year. The decline in the number of dairy cows (A2300F) is indicative of a long-term trend, which is most pronounced in the HR02 region, recognized as the most significant for this category.
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
Within the framework of the IFS2023, the regular annual Survey on Areas Sown and Survey on early crops and fruits was carried out. With this kind of organisation we carried out only one survey and reduced the response burden on farmers. On the other hand, we have to provide results for the Survey on Areas Sown much earlier than for the IFS, which means more burdens for the CBS.
The biggest burden is on biggest units for which we have full coverage in the sample and for all cycles of surveys while for the smaller units the Classifications, Sampling, Statistical Methods and Analyses Department controlled that the same unit is not included in the sample in consecutive number of times.
16.2. Efficiency gains since the last data transmission to Eurostat
Further automationIncreased use of administrative data
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 relevant (data were collected from the administrative source).
16.3.4. Module ‘Animal housing and manure management’
Restricted from publication
16.3.5. Module ‘Irrigation’
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
Revision Policy of the Croatian Bureau of Statistics is based on the principles of the European Statistics Code of Practice.
Revision policy of the Croatian Bureau of Statistics distinguishes three types of revisions: regular revisions, major revisions and unscheduled revisions.
Unplanned revision of the IFS2023 may be carried out. In any case it is necessary to clarify the reasons for a revision (mistake in data sources or calculations or due to the unexpected changes in the methodology or data sources).
17.2. Data revision - practice
Data revision is not planned so far.
17.2.1. Data revision - average size
Not requested.
18.1. Source data
See sub-categories below.
18.1.1. Sampling design & Procedure frame
See sub-categories below.
18.1.1.1. Type of frame
List frame18.1.1.2. Name of frame
Statistical Register of Agricultural holdings.
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
Stratified one-stage random sampling was conducted for both core and modules on the main frame. The stratification was based on NUTS2 regions and size classifications. There is complete coverage for strata related to organic producers and units from the largest size class. For the remaining size classes, optimal allocation was utilised.
18.1.2.2.1. Name of sampling design
Stratified one-stage random sampling18.1.2.2.2. Stratification criteria
Unit sizeUnit location
18.1.2.2.3. Use of systematic sampling
No18.1.2.2.4. Full coverage strata
All organic producers and those in the largest size class were included in the complete enumeration.
18.1.2.2.5. Method of determination of the overall sample size
After stratification, we determined the number of agricultural holdings to select from each stratum in the core sample using optimal (Neymann) allocation. The sample size was 30 000.
18.1.2.2.6. Method of allocation of the overall sample size
Neymann allocation18.1.3. Core data collection on the frame extension
See sub-categories below.
18.1.3.1. Coverage of agricultural holdings
Sample18.1.3.2. Sampling design
Stratified one-stage random sampling was conducted for both core and modules on the frame extension. The stratification was based on NUTS2 regions and size classifications. There is complete coverage for strata related to organic producers and units from the largest size class. For the remaining size classes, optimal allocation was utilised.
18.1.3.2.1. Name of sampling design
Stratified one-stage random sampling18.1.3.2.2. Stratification criteria
Unit sizeUnit location
18.1.3.2.3. Use of systematic sampling
No18.1.3.2.4. Full coverage strata
All organic producers and those in the largest size class were included in the complete enumeration.
18.1.3.2.5. Method of determination of the overall sample size
After stratification, we determined the number of agricultural holdings to select from each stratum in the core sample using optimal (Neymann) allocation. The sample size was 1 981.
18.1.3.2.6. Method of allocation of the overall sample size
Neymann allocation18.1.4. Module “Labour force and other gainful activities”
See sub-categories below.
18.1.4.1. Coverage of agricultural holdings
Sample18.1.4.2. Sampling design
Stratified one-stage random sampling was conducted for both core and modules on the main frame. The stratification was based on NUTS2 regions and size classifications. There is complete coverage for strata related to organic producers and units from the largest size class. For the remaining size classes, optimal allocation was utilised.
18.1.4.2.1. Name of sampling design
Stratified one-stage random sampling18.1.4.2.2. Stratification criteria
Unit sizeUnit location
18.1.4.2.3. Use of systematic sampling
No18.1.4.2.4. Full coverage strata
All organic producers and those in the largest size class were included in the complete enumeration.
18.1.4.2.5. Method of determination of the overall sample size
After stratification, we determined the number of agricultural holdings to select from each stratum in the core sample using optimal (Neymann) allocation. The sample size was 30 000.
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
Stratified one-stage random sampling was conducted for both core and modules on the main frame. The stratification was based on NUTS2 regions and size classifications. There is complete coverage for strata related to organic producers and units from the largest size class. For the remaining size classes, optimal allocation was utilised.
18.1.5.2.1. Name of sampling design
Stratified one-stage random sampling18.1.5.2.2. Stratification criteria
Unit sizeUnit location
18.1.5.2.3. Use of systematic sampling
No18.1.5.2.4. Full coverage strata
All organic producers and those in the largest size class were included in the complete enumeration.
18.1.5.2.5. Method of determination of the overall sample size
After stratification, we determined the number of agricultural holdings to select from each stratum in the core sample using optimal (Neymann) allocation. The sample size was 30 000.
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
Stratified one-stage random sampling was conducted for both core and modules on the main frame. The stratification was based on NUTS2 regions and size classifications. There is complete coverage for strata related to organic producers and units from the largest size class. For the remaining size classes, optimal allocation was utilised.
18.1.7.2.1. Name of sampling design
Stratified one-stage random sampling18.1.7.2.2. Stratification criteria
Unit sizeUnit location
18.1.7.2.3. Use of systematic sampling
No18.1.7.2.4. Full coverage strata
All organic producers and those in the largest size class were included in the complete enumeration.
18.1.7.2.5. Method of determination of the overall sample size
After stratification and selection of the core sample, all holdings in the module sample are included in the core sample. The sample size was 4 596.
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
Stratified one-stage random sampling was conducted for both core and modules on the main frame. The stratification was based on NUTS2 regions and size classifications. There is complete coverage for strata related to organic producers and units from the largest size class. For the remaining size classes, optimal allocation was utilised.
18.1.8.2.1. Name of sampling design
Stratified one-stage random sampling18.1.8.2.2. Stratification criteria
Unit sizeUnit location
18.1.8.2.3. Use of systematic sampling
No18.1.8.2.4. Full coverage strata
All organic producers and those in the largest size class were included in the complete enumeration.
18.1.8.2.5. Method of determination of the overall sample size
After stratification and selection of core sample, all holdings in the module sample are included in the core sample. Sample size was 23 464.
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
Stratified one-stage random sampling was conducted for both core and modules on the main frame. The stratification was based on NUTS2 regions and size classifications. There is complete coverage for strata related to organic producers and units from the largest size class. For the remaining size classes, optimal allocation was utilised.
18.1.9.2.1. Name of sampling design
Stratified one-stage random sampling18.1.9.2.2. Stratification criteria
Unit sizeUnit location
18.1.9.2.3. Use of systematic sampling
No18.1.9.2.4. Full coverage strata
All organic producers and those in the largest size class were included in the complete enumeration.
18.1.9.2.5. Method of determination of the overall sample size
After stratification and selection of core sample, all holdings in the module sample are included in the core sample. Sample size was 30 000.
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
Stratified one-stage random sampling was conducted for both core and modules on the main frame. The stratification was based on NUTS2 regions and size classifications. There is complete coverage for strata related to organic producers and units from the largest size class. For the remaining size classes, optimal allocation was utilised.
18.1.10.2.1. Name of sampling design
Stratified one-stage random sampling18.1.10.2.2. Stratification criteria
Unit sizeUnit location
18.1.10.2.3. Use of systematic sampling
No18.1.10.2.4. Full coverage strata
All organic producers and those in the largest size class were included in the complete enumeration.
18.1.10.2.5. Method of determination of the overall sample size
After stratification and selection of core sample, all holdings in the module sample are included in the core sample. Sample size was 5 005.
18.1.10.2.6. Method of allocation of the overall sample size
Neymann allocation18.1.10.2.7. If sampled from the core sample, the sampling strategy and calibration strategy
Not applicable18.1.11. Module ‘Vineyard’
Restricted from publication
18.1.11.1. Coverage of agricultural holdings
Restricted from publication
18.1.11.2. Sampling design
Restricted from publication
18.1.11.2.1. Name of sampling design
Restricted from publication
18.1.11.2.2. Stratification criteria
Restricted from publication
18.1.11.2.3. Use of systematic sampling
Restricted from publication
18.1.11.2.4. Full coverage strata
Restricted from publication
18.1.11.2.5. Method of determination of the overall sample size
Restricted from publication
18.1.11.2.6. Method of allocation of the overall sample size
Restricted from publication
18.1.11.2.7. If sampled from the core sample, the sampling strategy and calibration strategy
Restricted from publication
18.1.12. Software tool used for sample selection
The software tool used for sample selection was SAS procedure PROC SURVEYSELECT.
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
Postal, non-electronic versionPostal, electronic version (email)
Face-to-face, electronic version
Telephone, electronic version
Use of Internet
18.3.2. Data entry method, if paper questionnaires
Manual18.3.3. Questionnaire
Please find the questionnaire in annex.
Annexes:
18.3.3. Questionnaire (business entities) in Croatian
18.3.3. Questionnaire (business entities) in English
18.3.3. Questionnaire (private family farms) in Croatian
18.3.3. Questionnaire (private family farms) in English
18.3.3. Methodological guidance for private family farms
18.3.3. Methodological guidance for business entities
18.4. Data validation
See sub-categories below.
18.4.1. Type of validation checks
Completeness checksRange checks
Comparisons with previous rounds of the data collection
18.4.2. Staff involved in data validation
InterviewersSupervisors
Staff from local departments
Staff from central department
Other
18.4.3. Tools used for data validation
Validation rules were used in the questionnaires and within special data processing tool. Additional validations were done through special queries.
18.5. Data compilation
Design weights are defined as the inverse of the selection probabilities for the units. We utilised the Horvitz-Thomson estimator (regular design weight) as our estimation method, and we also multiplied this estimator by response weights that we calculated based on the respondents' provided statuses. Thus, the final weight was the product of these two components, and we employed them during the estimation procedure. To address unit non-response, we adjusted the weights accordingly. The non-response rates were calculated at the region level (NUTS2) for each stratum, with each stratum belonging to a specific region. If a farm changed strata during the survey based on area size, it retained its initial weight, which was then adjusted for non-response according to the stratum from which it was originally selected.
The non-response weight was calculated using the following formula:
NON-RESPONSE WEIGHT = (X1 + X3 + X4) / (X1 + X4).
Where:
- X1 represents the number of respondents,
- X3 represents the number of farms that did not want to participate or were unreachable,
- X4 represents the number of respondents whose owner information or address was incorrect.
This adjustment for non-response was specifically conducted for the sample survey of private family farms.
18.5.1. Imputation - rate
The imputation rate is 11.37%. Imputation is done for unit non-response and includes all corresponding variables from the administrative sources. Imputation rate for item non-response was not calculated.
18.5.2. Methods used to derive the extrapolation factor
Design weightNon-response adjustment
18.6. Adjustment
Covered under Data compilation.
18.6.1. Seasonal adjustment
Not applicable to Integrated Farm Statistics, because it collects structural data on agriculture.
See sub-categories below.
19.1. List of abbreviations
AWU – Annual working unit
CAP – Common Agricultural Policy
CAPI – Computer Assisted Personal Interview
CAWI – Computer Assisted Web Interview
CBS – Croatian Bureau of Statistics
EAA – Economic accounts for Agriculture
EU – European Union
FSS – Farm Structure Survey
GSBPM – Generic Statistical Business Process Model
IACS – Integrated Administration and Control System
IFS – Integrated Farm Statistics
LSU – Livestock unit
NUTS – Nomenclature of territorial units for statistics
PAAFRD – Paying Agency for Agriculture, Fisheries and Rural Development
RSE – Relative standard error
SBR – Statistical business register
SGA – State Geodetic Administration
SGM – Standard gross margin
SO – Standard output
SRAH – Register of agricultural holdings
TQM – Total quality management
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.
20 January 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, small citrus fruit area, olives area, grapes for table use area, each one by age of plantation and density of trees.
See sub-category below.
See sub-categories below.
See sub-categories below.
See sub-categories below.
See categories below.
Two kinds of units are generally used:
- the units of measurement for the variables (area in hectares, livestock in (1000) heads or LSU (livestock units), labour force in persons or AWU (annual working units), standard output in Euro, places for animal housing etc.) and
- the number of agricultural holdings having these characteristics.
Design weights are defined as the inverse of the selection probabilities for the units. We utilised the Horvitz-Thomson estimator (regular design weight) as our estimation method, and we also multiplied this estimator by response weights that we calculated based on the respondents' provided statuses. Thus, the final weight was the product of these two components, and we employed them during the estimation procedure. To address unit non-response, we adjusted the weights accordingly. The non-response rates were calculated at the region level (NUTS2) for each stratum, with each stratum belonging to a specific region. If a farm changed strata during the survey based on area size, it retained its initial weight, which was then adjusted for non-response according to the stratum from which it was originally selected.
The non-response weight was calculated using the following formula:
NON-RESPONSE WEIGHT = (X1 + X3 + X4) / (X1 + X4).
Where:
- X1 represents the number of respondents,
- X3 represents the number of farms that did not want to participate or were unreachable,
- X4 represents the number of respondents whose owner information or address was incorrect.
This adjustment for non-response was specifically conducted for the sample survey of private family farms.
See sub-categories below.
Every 3-4 years.
See sub-categories below.
See sub-categories below.
See sub-categories below.
