National Institute of Statistics, Romania

General Direction of Economic Statistics - Department of Agricultural and Environmental Statistics

16 Libertatii Blvd., Bucharest 5, ROMANIA

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 2020 (the agricultural census), 2023 and 2026. The data are as comparable and coherent as possible with the other European countries.

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) 2018/1874.

The farm typology means a uniform classification of the holdings based on their type of farming and their economic size. Both are determined on the basis of the standard gross margin (SGM) (until 2007) or standard output (SO) (from 2010 onward) which is calculated for each crop and animal. The farm type is determined by the relative contribution of the different productions to the total standard gross margin or the standard output of the holding.

The territorial classification uses the NUTS classification to break down the regional data. The regional data is available at NUTS level 2.

The 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.

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 2020 are set in Commission Implementing Regulation (EU) 2018/1874.

The following groups of variables are collected in 2020:

• 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 "Animal housing and rural development module":  animal housing, nutrient use and manure on the farm, manure application techniques, facilities for manure.

For GAC2020, all variables for core and modules were collected for all agricultural holdings collected in the census.

See sub-category below.

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.

See sub-categories below.

Data collection was census type. All variables for core and modules were collected for all agricultural holdings collected in the census.

The thresholds of agricultural holdings are available in the annex.

The same population of agricultural holdings defined in item 3.6.1.

The above answer holds for the modules ‘Labour force and other gainful activities’ and ‘Rural development’. The module ‘Machinery and equipment’ is not collected in 2020.

The same population of agricultural holdings defined in item 3.6.1.

See sub-categories below.

The entire territory of Romania.

No

Not available.

Farm structure statistics in Romania cover the period from 2002 onwards. Older time series are described in the previous quality reports (national methodological reports).

The 2020 data are processed (by Eurostat) with 2017 standard output coefficients (calculated as a 5-year average of the period 2015-2019). For more information, you can consult the definition of the standard output.

Two kinds of units of measurement 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.

The use of land refers to 2020 as the crop reference year (October 1, 2019 - September 30, 2020).

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.

Reference period was crop year (October 1, 2019 - September 30, 2020).

The reference day is 31^st of December within the reference year 2020.

The 12-month period ending on 31^st of December 2020. This period includes the reference day used for livestock and animal housing.

Reference period  was crop year (October 1, 2019 - September 30, 2020).

The three-year period ending on December 31, 2020.

The reference day December 31, within the reference year 2020.

See sub-categories below.

• Law no. 226/2009 on the organization and functioning of official statistics in Romania, as subsequently amended and supplemented;
• Government’s Emergency Ordinance (GEO) No. 22/2020 on the GAC 2020, approved with amendments and completions by Law No. 177/2020;
• Government’s Decision No. 1056/2020 establishing the budget and categories of expenditures necessary to carry out the GAC 2020, as well as the measures regarding the implementation of some provisions of the GEO No. 22/2020;
• Decision No. 1 of 4 November 2020 issued by the Central Commission for the GAC 2020, on the revision of the calendar for the preparation and conduct of the General Agricultural Census;
• Decision No. 2 of 30 March 2021 issued by the Central Commission for the GAC 2020 regarding the start of data collection on 10 May 2021 instead of 1 May 2021.

See the annexes below.

2020, 2021

GEO no. 22 /2020, art 7 - provision for INS accesses to relevant data/information kept by National Agency for Cadastre and Real Estate Advertising, Agricultural Payments and Interventions Agency, National Sanitary Veterinary and Food Safety Authority - data/information were used for different stages of GAC2020 as establishing the list of agricultural holdings, validation/processing of collected data.

Also, protocols between INS and relevant institutions/ministries were set-up and covered GAC2020, too.

According to Law no. 226/2009 on the organisation and functioning of official statistics in Romania, as subsequently amended and supplemented, the individual data are confidential and could be used only for statistical purposes.

GEO no.22/2020 includes provisions on confidentiality in Chapter V Processing of data (art.16,17) and Chapter VI Confidentiality of statistical data.

Keeping the data confidentiality is mandatory for permanent and temporary staff; both categories sign a commitment to confidentiality when they are hired (see in the annex).

In addition, Norms of statistical data confidentiality are published in the Official Journal of Romania (see in the annex).

The "front cover" of the electronic questionnaire includes references to the Confidentiality of data and processing of personal data in accordance with GDPR. All enumerators were trained to present this information to farmers, before the beginning of the interviews.

See sub-categories below.

See sub-categories below.

The aggregated data does not allow the identification of an agricultural holding through dissemination. Output dissemination is available until counties level (NUTS 3).

In some special cases, neighboring intervals are joined to obtain larger ones, with more agricultural holdings, to avoid situations in which confidentiality could be affected.

See sub-categories below.

The access to microdata is permitted only for scientific purposes on the basis of a written commitment. The access is submitted under INS confidentiality rules, available to:

https://insse.ro/cms/en/content/nis-microdata-scientific-purposes

The release calendar is available on INS website (for press releases and publications, separately).

The release calendar is available on INS website (for press releases and publications, separately).

In line with the Community legal framework and the European Statistics Code of Practice, INS disseminate national statistics on INS's website (Principle 10 - Accessibility and clarity) respecting professional independence and in an objective, professional and transparent manner in which all users are treated equitably.

The calendar for official statistics compiled by the INS includes the exact date and time for all press releases, is flexible, has search facilities on topics and is regularly updated throughout the year.

The methodology is described in the EU handbook.

Every 10 years for censuses but every 3-4 years to all other IFS/FSS.

See sub-categories below.

https://insse.ro/cms/ro/content/recens%C4%83m%C3%A2ntul-general-agricol-runda-2020-%E2%80%93-date-provizorii

See sub-categories below.

GENERAL AGRICULTURAL CENSUS ROUND 2020 VOLUME

I - General data of the general agricultural census 2020, at national level (2022)

II - General data of the general agricultural census 2020, by macro-regions, development regions and counties (2022)

See sub-categories below.

Not applicable.

Not applicable.

See sub-category below.

Not available.

Not requested.

See sub-categories below.

Not requested.

Farm structure, National Reference Metadata in Single Integrated, Eurostat, 2022

Statistical Data and Metadata DB, INS, 2020

https://insse.ro/cms/files/RGA2020/aprilie2021/Manual-V1.2_1Metodologie-aprilie-rev.pdf

Not applicable.

INS' Quality declaration

Quality Guidelines for Official Statistics

National Methodological Report completed according to Eurostat requirements (IFS Quality report).

See sub-categories below.

INS is guided by the provisions of Law no. 226/2009 on the organisation and functioning of official statistics in Romania, as subsequently amended and supplemented. Statistical activities are performed in accordance with the Generic Statistical Business Process Model (GSBPM), according which the final phase of statistical activities is overall evaluation using information gathered in each phase or sub-process.
A peer review was conducted in 2015. For further details on quality assurance at INS Romania, please see the following link:
https://insse.ro/cms/en/content/quality-national-statistical-system

Certification according ISO 9001:2015 is ongoing.

See the Quality INS' Declaration and other relevant information at https://insse.ro/cms/en/content/quality-national-statistical-system

The use of the agricultural census results aims at substantiating the agricultural, regional, territorial cohesion, rural development, environment policies. Eurostat performs the consultations with EU and non-EU users of farm structure surveys.
At the national level, the main users agricultural census results include the Ministry of Agriculture and Rural Development, central and local public administration, National Academy of Agricultural and Forestry Sciences, other universities and scientific researchers.

The list of characteristics included in GAC2020 only for national purposes refer to:

- accounting records of the work on the holding (Y/N);

- own production sales - data from necessary for updating the weights used for the calculation of the price indices of agricultural products in 2020 base year.

- utilised agriculture area and arable land owned by foreigners (EU or non-EU citizens) - data needed for estimation of the area used from foreign property

- breakdown of the used agricultural area by the counties where is actually located.

All users' needs are met.

Not applicable.

There is no specific procedure to measure user satisfaction for the agricultural census.

Not applicable.

Information on low- and zero prevalence variables is available on: Eurostat's website.

Not applicable for Integrated Farm Statistics as the not collected variables, not-significant variables and not-existent variables are completed with 0.

See categories below.

See sub-categories below.

Not applicable. The data collection for 2020 reference year is based entirely on census.

Not applicable.

Not applicable.

See sub-categories below.

See sub-categories below.

The over-coverage rate is available in the annex. 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.

Not available.

Not requested.

See sub-categories below.

Not available.

Updated lists of agricultural holdings of census 2020 reference year was provided by local authorities. Integrated Administration and Control System (IACS) data for 2020 were also used to check and improve the accuracy of these lists.

Not available.

Not applicable.

Contact information is constantly updated based on information from the Statistical Business Register, IACS or direct from census questionnaires.

See sub-categories below.

To minimise errors of measurement, data collection questionnaire was developed by chapters (General information, Land use, Livestock etc) and some implemented measures were helpful in this context:

• The reference moment or period was specified on every chapter heading;
• The questionnaire included the arithmetical checks between rows;
• If the queries had to be ticked off, mention was made on the questionnaire if it was a single or multiple answering variant.

Enumerators were trained to understand and respect some obligations that contributed to the reduction of measurement errors, as:

• The obligation to decline the official quality as enumerator by showing the personal identification card when first visiting an agricultural holding;
• Interviewing the most competent person from the agricultural holding, preferably the head of agriculture holding;
• Avoiding the interview in front of people that do not belong to the concerned holding by explaining the information is confidential and to be used only for statistical purposes;
• To get precise and sincere replies, the questions were formulated clearly and politely;
• If the questions had several answering variants, the interview was presented a full list of them so he/she may choose the correct one;
• Taking down the replies as they were provided by the interviewee;
• Coming back to certain questions where the answer did not meet the arithmetical checks or if they did not correlate.

Due to the above measures, no major measurement errors were scored.

In addition, the data collection was done electronic only and was monitored using Survey Solutions software  (CAPI method). Data collection using the CAPI method has as main advantage the assurance of a good quality of the collected data by implementing some sets of correlations and validations at the level of the questionnaire, active in real time (during the data collection).

Improvements in quality procedures due to electronic data collection (CAPI method) led to the minimisation of measurement errors: testing the electronic questionnaire in a pilot census and the resulting improvements, the implementation of the Survey Solution workflow helped to reduce measurement errors.

See sub-categories below.

The unit non-response rate is in the annex of 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.

The updated agricultural list of holdings was pre-loaded into electronic questionnaires (developed with World Bank Survey Solutions software).
Non-responses were monitored during the collection period through the facilities provided by the Survey Solutions software. The unresolved ones were treated during the processing period, by comparing the variables of respondents and non-respondents available in administrative sources, or collected in previous rounds or other annual surveys on crop and animals.

Survey Solution, as CAPI method, improve quality of collected data through a series of built-in checks. This method has enable us to validate data in real time because the platform’s programming can allow for automated skip patterns, display error messages whenever unexpected values are entered by the interviewer, and follow other validation rules.

Interviewers (enumerators) see in the questionnaire: 1.Question to be answered. 2. Question that is not to be answered (skipped due to questionnaire logic). 3. Question that has been answered incorrectly (also shows instruction and an error message).

Once the interviewers have left for fieldwork, Survey Solutions has quality control functions that can be utilised by field supervisors and office-based staff (headquarters). Quality control performed by field supervisors is case-by-case checking, which is designed to mimic (electronically) the process of manual checking during paper-based data collection. In this approach, the interviewer completes a questionnaire, then passes the form to their supervisor for review. After checking for mistakes, the supervisor then sends back the forms to the interviewer to make corrections where necessary. Relevant in this case is that application indicate if there are questions without answer and supervisor must reject the questionnaire to the interviewer in order to provide answers to all questions. Supervisors and headquarter cannot approve questionnaires containing not-answered questions.

At questionnaire level, there is the following colour code available for all Survey Solution roles: read for sections with errors, green for complete sections and blue for incomplete sections. Also, each questionnaire has counter of answered, unanswered and erroneous questions.

In conclusion, no item non-response is registered at the end of data collection process.

Not applicable.

See answer to the item 13.3.3.2.

See sub-categories below.

To minimise processing errors the information system is extensively tested and manual actions are minimised as much as possible. All corrections are made using scripts (no manual adjustments) and before data is released extensive checks and analyses are performed.

 Corrections of data were done as follows:

• via the logical controls included in the questionnaires (see Survey Solutions software functions) - corrections done by Interviewers (enumerators) based on answers of respondents.
• analysis of extreme values during data collection (see Surveys Solution software functionalities) as well as during data processing (Visual Fox Pro, R software) - corrections done by statisticians and IT teams in central and territorial statistical offices.

Not available.

Not applicable.

See sub-categories below.

15 months

24 months, according to the time table attached in item 18.

See sub-categories below.

See sub-categories below.

Not requested.

The actual publication date coincides with the target date for data publication. 

See sub-categories below.

Not applicable, because there are no mirror flows in Integrated Farm Statistics.

See sub-categories below.

The definition of agricultural holdings is in accordance with Regulation (EU) 2018/1091.

Not applicable.

See sub-categories below.

GAC 2020 data collection is census type. All variables for core and modules were collected for all census agricultural holdings.

Exceptions: The households that did not exceed a minimum threshold were not agricultural holdings and, consequently, they were not registered: they had as utilised agricultural area only the kitchen garden (an area equal or smaller than 15 ares) and grew only few poultry (less than 10) for own consumption.

No threshold was applied, census-type survey.

Not applicable.

See sub-categories below.

There are no deviations in definitions and classifications of variables.

The information is available in the annex. 
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.

The information is available in the annex of item 15.1.4.1.1. 

The information is available in the annex of item 15.1.4.1.1. 

No use of different LSU coefficients.

No deviations.

Not applicable.

See sub-categories below.

No deviations.

Not applicable.

Not applicable.

Common land is included in the land of entities complying with definition of agricultural holdings, having own managers.

We do not experience problems to collect data on common land.

See sub-categories below.

No deviations.

Not applicable.

No differences.

See sub-categories below.

Since 2002.

See sub-categories below.

Regulation (EU) 2018/1091 newly considers agricultural holdings with only fur animals. However, in our country, fur animals is a non-significant variable.

See sub-categories below.

Not applicable.

See sub-categories below.

Not applicable.

See sub-categories below.

Legal personality of the agricultural holding

In IFS, there is a new class (“shared ownership”) for the legal personality of the holding compared to FSS 2016, which triggers fluctuations of holdings in the classes of sole holder holdings and group holdings.

Other livestock n.e.c.

In FSS 2016, deer were included in this class, but in IFS they are classified separately.

Also in FSS 2016, there was a class for the collection of equidae. That has been dropped and equidae are included in IFS in "other livestock n.e.c."

Livestock units

In FSS 2016, turkeys, ducks, geese, ostriches and other poultry were considered each one in a separate class with a coefficient of 0.03 for all the classes except for ostriches (coefficient 0.035). In IFS 2020, the coefficients were adjusted accordingly, with turkeys remaining at 0.03, ostriches remaining at 0.35, ducks adjusted to 0.01, geese adjusted to 0.02 and other poultry fowls n.e.c. adjusted to 0.001.

Organic animals

While in FSS only fully compliant (certified converted) animals were included, in IFS both animals under conversion and fully converted are to be included.

See sub-categories below.

In FSS 2016, the reference period for manure management was October 1, 2015 – September 30, 2016.

In IFS 2020, for the variables on manure management, the reference period is the 12-month period ending on December 31, 2020.

According to Regulation (EU) 2018/1091 art. 10 point (c) The variables on manure management shall refer to a 12-month period including reference day for livestock (December 31, 2020 in the Romanian case). This specification was not mentioned in Regulation 1166/2008. We consider that this shift has no impact on the data.

See sub-categories below.

According to the 2016 methodology, common land units did not have manager data while according to the 2020 methodology, common land units have manager data, and in both years the common land units comply with the definition of agricultural holdings.

Time series discrepancies between 2016 and 2020:

- A2220 and A2230 discrepancies come from closed age category of heifers; 

- A3110 and A3120 the decrease is due by the fact that the livestock were affected by swine fever.

- C1120T it is known that the areas cultivated increased but these are small in comparison with other crops.

- C2000T it is known that in general the areas cultivated decreased.

- F1100T, F1200T, F3000T, F400T, V0000_S0000S, the areas cultivated are increased as the demands for fruits and vegetables increased and the farmers are turning to these crops.

- W1110T+W1120T the increased is determined by subsidies for the conversion of hybrid vineyards into vineyards for quality wines.

- J1000T the decrease is caused by decreased of the common land which is represented by permanent grassland.

- MOGA_NFAM_RH the decrease is caused by decrease of the number of holdings.

- OGA_NRH  the sharp reduction in Romania in 2020 can be explained by the decrease of the number of the holdings especially the small ones for which the managers had in the preceding years other gainful activities not related to the farm activities.

See sub-categories below.

Not applicable to Integrated Farm Statistics, because there are no sub annual data collections in agriculture.

Not applicable, because Integrated Farm Statistics have no relevance for national accounts.

See sub-categories below.

Results are coherent at micro level for annual crop statistics and animal production statistics. The differences have diminished.

See sub-categories below.

Discrepancies  between APRO and IFS statistics:

- The main differences in crops regional statistics come especially from the small cultivated areas. IFS2020 was exhaustively survey and the annual crop statistics was a survey based on sample, and the agricultural holdings with this kind of crops were not exhaustively surveyed. 

The data in ACS were revised and were retransmitted in June [2022]. 

Discrepancies between Animal production and IFS 2020 statistics:

- For the goats, the discrepancies at the national level come from the following reasons:

  - At IFS 2020, the reference moment is different from the Livestock survey,

  - IFS 2020 was an exhaustive survey unlike the Livestock survey which was a sample-based survey.

   - Goat livestock have registered a downward trend in use, a fact also evident in the 2021 livestock survey.

- The discrepancies recorded at the level of the regions, come from the fact that, at the IFS 2020, the data was recorded according to the location of the agricultural holding. If an agricultural holding had the animals in several development regions, the livestock was recorded in a single place, where the agricultural holding is located, unlike the Livestock survey, where the animal numbers were recorded at the level of each development region.

- For the region RO 32, Bucharest Ilfov where differences are larger, the livestock mentioned represents less than 1% of the livestock at the national level.

The data are internally consistent. This is ensured by the application of a wide range of validation rules.

See sub-categories below.

The coordination is made by the roles of Survey Solutions software used for GAC2020 data collection:

- interviewers (enumerators) allocated following sectorisation process to the sectors considering the location of the farms (face-to-face data collection, CAPI method)

- supervisors (chief enumerators) - ensure coordination, guidance and control of subordinate interviewer's activities (including validation of electronic questionnaires)

- headquarter (coordinators) - ensure coordination, guidance and control of interviewers and supervisors activities

- observer - monitors the activity of any interviewer, supervisor or headquarters

Since around 99% of the agriculture holdings belongs to natural persons, there are few cases where the respondents have to answer multiple questionnaires with the same kind of questions.

Not available.

See sub-categories below.

Not available.

Not available.

Not available.

Not available.

As a rule, data in IFS/FSS are not subject to revisions.

No revision was done.

Not requested.

See sub-categories below.

See sub-categories below.

Administrative Farm Register, IACS

See sub-categories below.

Not applicable for 2020.

Not applicable for 2020.

Not applicable for 2020.

See sub-categories below.

Not applicable.

Not applicable

Not applicable

See sub-categories below.

Not applicable.

Not applicable.

Not applicable.

See sub-categories below.

Not applicable.

Not applicable.

Not applicable.

See sub-categories below.

Not applicable.

Not applicable.

Not applicable.

Not applicable.

See sub-categories below.

The information is available on Eurostat's website.

INS Romania does not use administrative data sources.

The information on innovative approaches is available on Eurostat's website.

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.

See sub-categories below.

Please find the questionnaire in annex.

For GAC2020, Romania used Survey Solutions, a CAPI software developed by World Bank, which combines rich data capture functionality on tablets with powerful tools for survey management and data aggregation, reducing the time lag between data collection and data analysis and major improving data quality.  Survey Solutions also collects massive amounts of auxiliary data (known as paradata) on the interview process, which allows the calculation of a large number of indicators to assess the quality of data collected both in real time and on the basis of data exports with a lower or higher periodicity, depending on the chosen analysis plan. For GAC2020 was used both to assess real-time data quality and on the basis of daily data exports.

Survey Solutions has four levels of quality control for ensuring quality of data: automatic validations, supervisor data verification, headquarter data verification, and optional external validation.

1) Automatic rule-based validation helps notify the interviewers about the data problems immediately, still during the interview when they are easiest to fix.

2) Supervisor validation allows benefiting from supervisors’ intuition and knowledge of the area of data collection, and helps in verifying the interviewers follow the data collection protocol.

3) Headquarter validation allows headquarter users to centrally monitor the quality of the incoming data, adherence to the established procedures, identify the problems appearing in the field, reject questionnaires approved by supervisors, which still don’t satisfy the requirements.

4) Optional external validation allows exporting the data and utilising external tools (or external data sources) not available in Survey Solutions to validate the survey data at regular intervals (daily in case of Romania). This allows searching for errors across all the interviews, for example to identify outliers. Each of these layers of defence allows improvements in data quality. But they are most effective in their combination. In addition, the use of Survey Solutions CAPI simplifies navigation in the questionnaire, automatically hides questions to be skipped, and provides proper input controls corresponding to the question types further reducing the possibility for user mistakes and improving the quality of the data.

See sub-categories below.

At questionnaire level, validation condition are included for each variable in the electronic questionnaire (Survey Solution).
Validation between different chapters of the questionnaires -customised IT application, in house.

Not applicable.

Imputation rate=1.98%

Covered under Data compilation.

Not applicable to Integrated Farm Statistics, because it collects structural data on agriculture.

See sub-categories below.

CAP – Common Agricultural Policy

CAPI –  Computer Assisted Personal Interview

FSS – Farm Structure Survey

IACS – Integrated Administration and Control System

IFS – Integrated Farm Statistics

LSU – Livestock units

NACE – Nomenclature of Economic Activities

NUTS – Nomenclature of territorial units for statistics

SO – Standard output

UAA – Utilised agricultural area

AWU - Annual working units

GAC2020 - General Agricultural Census, 2020 reference year

GEO - Government’s Emergency Ordinance

INS - National Institute of Statistics

No additional comments.