Orchard (orch)

National Reference Metadata in ESS Standard for Orchard survey Quality Report Structure (esqrsor)

Compiling agency: Hungarian Central Statistical Office - HCSO


Eurostat metadata
Reference metadata
1. Contact
2. Statistical presentation
3. Statistical processing
4. Quality management
5. Relevance
6. Accuracy and reliability
7. Timeliness and punctuality
8. Coherence and comparability
9. Accessibility and clarity
10. Cost and Burden
11. Confidentiality
12. Comment
Related Metadata
Annexes (including footnotes)
 



For any question on data and metadata, please contact: Eurostat user support

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1. Contact Top
1.1. Contact organisation

Hungarian Central Statistical Office - HCSO
1.2. Contact organisation unit

Rural Development, Agriculture and Environment Statistics Department
1.5. Contact mail address

Központi Statisztikai Hivatal - HCSO

H-1024 Budapest Keleti Károly utca 5-7.


2. Statistical presentation Top
2.1. Data description

Main characteristics

2.1.1 Describe shortly the main characteristics of the statistics  

Structural orchard statistics provide data on the area, age and density of apple, pear, peach, nectarine, apricot, citrus fruit and olive orchards and vineyards producing table grapes. The statistics are collected as the census on orchards in 2017. The register informations on users of plantations are from administrative data source of SAPS (The Single Area Payment System). Data are collected at national level. The data collection concerns plantations being more than 1000 ha of area for any single fruit tree type.
2.1.2 Which of the fruit tree types are covered by the data collection?  Dessert apple trees
Dessert pear trees
Apricot trees
Dessert peach and nectarine trees
Peach and nectarine trees for industrial processing (including group of Pavie)
Other
2.1.3 If Other, please specify

Cherry, Sour cherry, Plum, Walnut, Elder


Reference period

2.1.4 Reference period of the data collection 

2017
2.1.5 When was the data collection done (month(s) and year)

February - April, in 2018


National legislation

2.1.6 Is there a national legislation covering these statistics?  Yes
If Yes, please answer all the following questions.   
2.1.7 Name of the national legislation 

- Hungarian act (CLV of 2016) on statistics;

- Government Decree 388/2017 (XII.13.) on National Data Collection Programme (OSAP) regarding obligatory data collection, Annex I OSAP 2419
2.1.8 Link to the national legislation 

http://www.ksh.hu/obligatory_data_collections

 

http://www.ksh.hu/docs/hun/info/adatgyujtes/2018/OSAP_MK17210.pdf
2.1.9 Responsible organisation for the national legislation 

Ministry of Justice
2.1.10 Year of entry into force of the national legislation 

2017
2.1.11 Please indicate which variables required under EU regulation are not covered by national legislation, if any.
2.1.12 Please indicate which national definitions differ from those in the EU regulation, if any. 

Density and age classes (see details in 2.4.2.)
2.1.13 Please indicate which additional variables have been collected if compared to Regulation (EU) 1337/2011, if any?

Data on the agricultural unit using plantations:

       – age, sex, education of the manager;

       – capacity of the storage used to store the fruit;

       – types of producer cooperations and marketing channels;

       – plans for future planting;

 

 Data on plantations:

       – varieties of stocks;

       – orchards under by organic production;

       – structure of original/replanted/perished area, area with missing trees;

       – yield;

       – cultivation method (tree crown types);

       – support system (trellis);

       – slope of the terrain;

       – estimated year of grubbing;

       – irrigation;

       – plantation status and tree vigour;

       – prevention, insurance.
2.1.14 Is there a legal obligation for respondents to reply?  Yes


Additional comments on data description
2.2. Classification system

Species and variety group classification, age and density classifications available in RAMON
2.3. Coverage - sector

Growing of perennial crops (NACE A01.2)
2.4. Statistical concepts and definitions

See: Orchard statistics Handbook

2.4.1 Do national crop item definitions differ from the definitions in the Handbook  (D-flagged data)? Yes
2.4.2 If Yes, please specify the items and the differences

Density and age classes are more detailed:

 

Age classes division
Apple, Pears Apple, Pears   Apricot Peach, Nectarine Apricot, Peach, Nectarine
HUN EU   HUN HUN EU
           
Age classes division
1 or less Y_LT5   1 or less 1 or less Y_LT5
2 years old Y_LT5   2 years old 2 years old Y_LT5
3 years old Y_LT5   3 years old 3 years old Y_LT5
4 years old Y_LT5   4 years old 4 years old Y_LT5
5–9 years old Y5-14   5–9 years old 5–9 years old Y5-14
10–14 years old Y5-14   10–14 years old 10–14 years old Y5-14
15–24 years old Y15-24   15–24 years old 15–24 years old Y_GE15
25 and over Y_GE25   25 and over 25 and over Y_GE15
           
Density classes division
< 150 LT400   < 150   LT600
150–249 LT400   150-249 < 250 LT600
250–299 LT400   250-299 250-299 LT600
300–399 LT400   300-399 300-399 LT600
400–499 400-1599   400-499 400-499 LT600
500–599 400-1599   500-599 500-599 LT600
600–799 400-1599   600-799 600–799 600-1199
800–999 400-1599   800-999 800–999 600-1199
1000–1999 400-1599    1000-1200 1000–1999 600-1199
1200–1399 400-1599   1200-1399  1200–1399 GE1200
1400–1599 400-1599    1400-1600 1400–1599 GE1200
1600–1799 1600-3199   1600 and more  1600 and more GE1200
1800–1999 1600-3199        
2000–2249 1600-3199        
2250–2499 1600-3199        
2500–3199 1600-3199        
3200 and more GE3200        

2.4.3 If the fruits for industrial processing are not separately surveyed, are they included in dessert fruit categories? Yes
2.4.4 If yes, for which fruit tree types? Apple trees
Pear trees
In case data are delivered for one of the items below, describe the 10 biggest varieties included in the item:   
2.4.5 Other dessert apples n.e.c.

Jonathan M.40

Jonathan Csány 1

Jonathan M.41

Rebella

Red Rome Van Well

Naményi Jonathán

Summerred

Watson Jonathan

Szatmárcsekei Jonathan

Kovelit
2.4.6 Other dessert pears n.e.c.

Clapp kedveltje

Kornélia

Nyári Kálmán körte

Hosui

Császár körte

Köcsög körte

Nijisseiki

Kétszertermő körte

Schweizerhose (Svájci csíkos)

Aromata de Bistrita
2.4.7 Other oranges n.e.c.
2.4.8 Other small citrus fruits, including hybrids
2.5. Statistical unit

Orchard area above 2500 m2 for which farmers applied SAPS in 2017. (The basic threshold was increased that this did not lead to the exclusion of more than an additional 5 % of the total planted area of the orchards.)
2.6. Statistical population

Users of the orchards who were applied for SAPS in 2017 for the planted orchard area.
2.7. Reference area
2.7.1 Geographical area covered

The entire territory of the country.
2.7.2 Which special Member State territories are included?
2.8. Coverage - Time

2001-2017
2.9. Base period


3. Statistical processing Top
3.1. Source data

Overall summary

3.1.1 Total number of different data sources used

2
The breakdown is as follows: 
3.1.2 Total number of sources of the type "Census"

1
3.1.3 Total number of sources of the type "Sample survey"

0
3.1.4 Total number of sources of the type "Administrative source"

1
3.1.5 Total number of sources of the type "Experts"

0
3.1.6 Total number of sources of the type "Other sources"

0


Census

These questions only apply to censuses. If there is more than one census, please describe the main census below and the additional ones in table 3.1 of the annexed Excel file 
3.1.7 Name/Title

OSAP 2419 Census on orchard plantations, 2017
3.1.8 Name of Organisation responsible

HCSO - Hungarian Central Statistical Office
3.1.9 Main scope

Characteristics of orchards
3.1.10 Target fruit tree types Dessert apple trees
Dessert pear trees
Apricot trees
Dessert peach and nectarine trees
Peach and nectarine trees for industrial processing (including group of Pavie)
Other
3.1.11 List used to build the frame

Administrative source of IACS register
3.1.12 Any possible threshold values

min. 0,25 ha planted area (plantations under this size were not included)
3.1.13 Population size

Users of plantations: 14877

Number of listed areas in SAPS register: 37273
3.1.14 Additional comments

In this orchard survey in 2017 the HCSO collected data of 9 fruit species (apple, pear, apricot, peach + cherry, sour cherry, plum, walnut and elderberry).

In 3.1.13 is the complete number of population size included the 9 species. See the number of units of the 4 fruit species shared in EUROSTAT dissemination in 10.4


Sample survey

These questions only apply to surveys. If there is more than one survey, please describe the main survey below and the additional ones in table 3.1 of the annexed Excel file 
3.1.15 Name/Title

 -
3.1.16 Name of Organisation responsible

 -
3.1.17 Main scope

-
3.1.18 Target fruit tree types
3.1.19 List used to build the frame

-
3.1.20 Any possible threshold values

-
3.1.21 Population size

-
3.1.22 Sample size

-
3.1.23 Sampling basis
3.1.24 If Other, please specify
3.1.25 Type of sample design
3.1.26 If Other, please specify
3.1.27 If Stratified, number of strata

-
3.1.28 If Stratified, stratification criteria
3.1.29 If Other, please specify

-
3.1.30 Additional comments


Administrative source

These questions only apply to administrative sources. If there is more than one administrative source, please describe the main source below and the additional ones in table 3.1 of the annexed Excel file 
3.1.31 Name/Title

Single Area Payment Scheme - SAPS
3.1.32 Name of Organisation responsible

Hungarian State Treasury
3.1.33 Contact information (email and phone)
3.1.34 Main administrative scope

The Single Area Payment System (SAPS) is a transitional, simplified income support scheme which was offered to the Member States who joined the EU in 2004 and 2007 (EU-12) as an option at the date of accession in order to facilitate the implementation of direct payments.
3.1.35 Target fruit tree types  Dessert apple trees
Dessert pear trees
Apricot trees
Peach and nectarine trees for industrial processing (including group of Pavie)
Dessert peach and nectarine trees
3.1.36 Geospatial Coverage National
Regional
3.1.37 Update frequency Continuous
3.1.38 Legal basis

International legal Acts:

Basic Act: Regulation (EC) No 73/2009.

Regulation (EC) No 1120/2009 concerns the implementing rules of the Single Payment Scheme including "Article 68" specific support.

Regulation (EC) No 1121/2009 concerns support schemes other than the Single Payment Scheme.

Regulation (EC) No 1122/2009 concerns Cross Compliance, modulation and IACS

National legal acts:

25/2011. (IV. 7.) VM rendelet az Európai Mezőgazdasági Garancia Alapból finanszírozott egységes területalapú támogatás (SAPS), valamint az ahhoz kapcsolódó kiegészítő nemzeti támogatások (top up) 2011. évi igénybevételével kapcsolatos egyes kérdésekről

24.) FVM rendelet az egységes területalapú támogatások és egyes vidékfejlesztési támogatások igényléséhez teljesítendő "Helyes Mezőgazdasági és Környezeti Állapot" fenntartásához szükséges feltételrendszer, valamint az állatok állategységre való átváltási arányának meghatározásáról
3.1.39 Are you able to access directly to the micro data? Yes
3.1.40 Are you able to check the plausibility of the data, namely by contacting directly the units? Yes
3.1.41 How would you assess the proximity of the definitions and concepts (including statistical units) used in the administrative source with those required in the EU regulation? Very good
3.1.42 Please list the main differences between the administrative source and the statistical definitions and concepts

There is no mentionable difference.
3.1.43 Is a different threshold used in the administrative source and statistical data? No
3.1.44 If Yes, please specify
3.1.45 Additional comments

Some farmers might not apply for SAPS for the planted area/new plantation.


Experts

If there is more than one Expert source, please describe the main one below and the additional ones in table 3.1 of the annexed Excel file 
3.1.46 Name/Title
3.1.47 Primary purpose
3.1.48 Target fruit tree types
3.1.49 Legal basis
3.1.50 Update frequency
3.1.51 Expert data supplier
3.1.52 If Other, please specify
3.1.53 How would you assess the quality of those data?
3.1.54 Additional comments


Other sources

If there is more than one other statistical activity, please describe the main one below and the additional ones in table 3.1 of the annexed Excel file 
3.1.55 Name/Title
3.1.56 Name of Organisation
3.1.57 Primary purpose
3.1.58 Target fruit tree types 
3.1.59 Data type
3.1.60 If Other, please specify
3.1.61 How would you assess the quality of those data?
3.1.62 Additional comments
3.2. Frequency of data collection

Every 5 years
3.3. Data collection

Census

These questions only apply to censuses. If there is more than one census, please describe the main census below and the additional ones in table 3.3 of the annexed Excel file 
3.3.1 Name/Title

OSAP 2419 Census on orchard plantations, 2017
3.3.2 Methods of data collection Electronic questionnaire
Face-to-face interview
3.3.3 If Other, please specify
3.3.4 If face-to-face or telephone interview, which method is used? Electronic questionnaire
3.3.5 Data entry method, if paper questionnaires?
3.3.6 Please annex the questionnaire used (if very long: please provide the hyperlink)

http://www.ksh.hu/docs/hun/info/02osap/2018/kerdoiv/k182419.pdf
3.3.7 Additional comments


Sample survey

These questions only apply to surveys. If there is more than one survey, please describe the main survey below and the additional ones in table 3.3 of the annexed Excel file 
3.3.8 Name/Title
3.3.9 Methods of data collection
3.3.10 If Other, please specify

 
3.3.11 If face-to-face or telephone interview, which method is used?
3.3.12 Data entry method, if paper questionnaires?
3.3.13 Please annex the questionnaire used (if very long: please provide the hyperlink)
3.3.14 Additional comments


Administrative source

These questions only apply to administrative sources. If there is more than one administrative source, please describe the main source below and the additional ones in table 3.3 of the annexed Excel file 
3.3.15 Name/Title

Integrated Administration and Control System (IACS)
3.3.16 Extraction date

2018
3.3.17 How easy is it to get access to the data? Access granted upon a simple request
3.3.18 Data transfer method

digital - CD
3.3.19 Additional comments


Experts

If there is more than one other statistical activity, please describe the main one below and the additional ones in table 3.3 of the annexed Excel file 
3.3.20 Name/Title
3.3.21 Methods of data collection
3.3.22 Additional comments
3.4. Data validation
3.4.1 Which kind of data validation measures are in place? Automatic
3.4.2 What do they target? Consistency
Outliers
Aggregates
3.4.3 If Other, please specify
3.5. Data compilation
3.5.1 Describe the data compilation process

Data were collected on e-questionnaire.

There were two phases:

1. CAWI - computer assisted web interviewing

           (50,5% of respondants);

2. CAPI - computer assisted personal interviewing

      (if the respondant does not filled CAWI)

 

They could be filled by the users of plantations on internet (CAWI), or by the enumerators in the same application on tablet. Rightly after the questionners had been automatically checked and completed, the data were sent online to the central database. In the next step, after validation and correction of data, the database was ready to compilation. Figures for dissemination were prepared on PC, using office and database applications.
3.5.2 Additional comments
3.6. Adjustment

aggregations, classifications, cross-checkings, outlier cleanings


4. Quality management Top
4.1. Quality assurance
4.1.1 Is there a quality management system used in the organisation? Yes
4.1.2 If yes, how is it implemented?

Automatical check of data input;

~ 3% random checks on questionnaires by contacting suppliers;

Validation of figures manually by experts of HCSO.

 
4.1.3 Has a peer review been carried out? No
4.1.4 If Yes, which were the main conclusions?
4.1.5 What quality improvements are foreseen? Further automation
4.1.6 If Other, please specify
4.1.7 Additional comments
4.2. Quality management - assessment

Development since the last quality report

4.2.1 Overall quality Stable
4.2.2 Relevance Improvement
4.2.3 Accuracy and reliability Improvement
4.2.4 Timeliness and punctuality Improvement
4.2.5 Comparability Deterioration
4.2.6 Coherence Stable
4.2.7 Additional comments


5. Relevance Top
5.1. Relevance - User Needs
5.1.1 If certain user needs are not met, please specify which and why

 –
5.1.2 Please specify any plans to satisfy needs more completely in the future

 –
5.1.3 Additional comments
5.2. Relevance - User Satisfaction
5.2.1 Has a user satisfaction survey been conducted? No
If Yes, please answer all the following questions 
5.2.2 Year of the user satisfaction survey
5.2.3 How satisfied were the users?
5.2.4 Additional comments
5.3. Completeness
5.3.1 Data completeness - rate

100%
5.3.2 If not complete, which characteristics are missing?
5.3.3 Additional comments


6. Accuracy and reliability Top
6.1. Accuracy - overall
6.1.1 How good is the accuracy? Very good
6.1.2 What are the main factors lowering the accuracy? Measurement error
6.1.3 If Other, please specify
6.1.4 Additional comments
6.2. Sampling error

Sample survey

These questions only apply to surveys. If there is more than one survey, please describe the main survey below and the additional ones in table 6.2 of the annexed Excel file 
6.2.1 Name/Title
6.2.2 Methods used to assess the sampling error
6.2.3 If Other, please specify
6.2.4 Methods used to derive the extrapolation factor
6.2.5 If Other, please specify
6.2.6 If coefficients of variation are calculated, please describe the calculation methods and formulas
6.2.7 Sampling error - indicators
6.2.8 Additional comments
6.3. Non-sampling error
6.3.1. Coverage error

Census

These questions only apply to censuses. If there is more than one census, please describe the main census below and the additional ones in table 6.3 of the annexed Excel file 
6.3.1.1 Name/Title

OSAP 2419 Census on orchard plantations, 2017
Over-coverage
6.3.1.2 Does the sample frame include wrongly classified units that are out of scope? No
6.3.1.3 What methods are used to detect the out-of scope units?

Not applicable.
6.3.1.4 Does the sample frame include units that do not exist in practice? No
6.3.1.5 Over-coverage - rate

0%
6.3.1.6 Impact on the data quality None
Under-coverage
6.3.1.7 Does the sample frame include all units falling within the scope of this survey? No
6.3.1.8 If Not, which units are not included?

Only orchards with area less than 2500 m2 were excluded. There might have been also some areas for that no SAPS applications were made.
6.3.1.9 How large do you estimate the proportion of those units? (%)

~ 2-3 %
6.3.1.10 Impact on the data quality None
Misclassification
6.3.1.11 Impact on the data quality None
Common units
6.3.1.12 Common units - proportion

Not applicable
6.3.1.13 Additional comments


Sample survey

These questions only apply to surveys. If there is more than one survey, please describe the main survey below and the additional ones in table 6.3 of the annexed Excel file 
6.3.1.14 Name/Title
Over-coverage
6.3.1.15 Does the sample frame include wrongly classified units that are out of scope?
6.3.1.16 What methods are used to detect the out-of scope units?
6.3.1.17 Does the sample frame include units that do not exist in practice?
6.3.1.18 Over-coverage - rate
6.3.1.19 Impact on the data quality
Under-coverage 
6.3.1.20 Does the sample frame include all units falling within the scope of this survey?
6.3.1.21 If Not, which units are not included?
6.3.1.22 How large do you estimate the proportion of those units? (%)
6.3.1.23 Impact on the data quality
Misclassification
6.3.1.24 Impact on the data quality
Common units 
6.3.1.25 Common units - proportion
6.3.1.26 Additional comments


Administrative data

These questions only apply to administrative sources. If there is more than one administrative source, please describe the main source below and the additional ones in table 6.3 of the annexed Excel file 
6.3.1.27 Name/Title of the administrative source

Integrated Administration and Control System - IACS
Over-coverage
6.3.1.28 Does the administrative source include wrongly classified units that are out of scope? No
6.3.1.29 What methods are used to detect the out-of scope units?

Not applicable.
6.3.1.30 Does the administrative source include units that do not exist in practice? No
6.3.1.31 Over-coverage - rate

0%
6.3.1.32 Impact on the data quality None
Under-coverage
6.3.1.33 Does the administrative source include all units falling within the scope of this survey? No
6.3.1.34 If Not, which units are not included?

Areas for that SAPS were not submitted in 2017.
6.3.1.35 How large do you estimate the proportion of those units? (%)

2%
6.3.1.36 Impact on the data quality Low
Misclassification 
6.3.1.37 Impact on the data quality None
6.3.1.38 Additional comments
6.3.2. Measurement error

Census

These questions only apply to censuses. If there is more than one census, please describe the main census below and the additional ones in table 6.3 of the annexed Excel file 
6.3.2.1 Name/Title

OSAP 2419 Census on orchard plantations, 2017
6.3.2.2 Is the questionnaire based on usual concepts for respondents? Yes
6.3.2.3 Number of censuses already performed with the current questionnaire?

0
6.3.2.4 Preparatory testing of the questionnaire? Yes
6.3.2.5 Number of units participating in the tests? 

5
6.3.2.6 Explanatory notes/handbook for surveyors/respondents?  Yes
6.3.2.7 On-line FAQ or Hot-line support for surveyors/respondents? Yes
6.3.2.8 Are there pre-filled questions? Yes
6.3.2.9 Percentage of pre-filled questions out of total number of questions

10%
6.3.2.10 Other actions taken for reducing the measurement error?

Automatic checking rules running in the electronic questionnaire;

Random sample checking data on questionnaire.
6.3.2.11 Additional comments


Sample survey

These questions only apply to surveys. If there is more than one survey, please describe the main survey below and the additional ones in table 6.3 of the annexed Excel file 
6.3.2.12 Name/Title
6.3.2.13 Is the questionnaire based on usual concepts for respondents?
6.3.2.14 Number of surveys already performed with the current questionnaire?
6.3.2.15 Preparatory testing of the questionnaire?
6.3.2.16 Number of units participating in the tests? 
6.3.2.17 Explanatory notes/handbook for surveyors/respondents? 
6.3.2.18 On-line FAQ or Hot-line support for surveyors/respondents?
6.3.2.19 Are there pre-filled questions?
6.3.2.20 Percentage of pre-filled questions out of total number of questions
6.3.2.21 Other actions taken for reducing the measurement error?
6.3.2.22 Additional comments
6.3.3. Non response error

Census

These questions only apply to censuses. If there is more than one census, please describe the main census below and the additional ones in table 6.3 of the annexed Excel file 
6.3.3.1 Name/Title of the survey

OSAP 2419 Census on orchard plantations, 2017
6.3.3.2 Unit non-response - rate

0,5%
6.3.3.3 How do you evaluate the recorded unit non-response rate in the overall context? Very low
6.3.3.4 Measures taken for minimising the unit non-response Legal actions
Reminders
6.3.3.5 If Other, please specify

Price game
6.3.3.6 Item non-response rate

-
6.3.3.7 Item non-response rate - Minimum

-
6.3.3.8 Item non-response rate - Maximum

-
6.3.3.9 Which items had a high item non-response rate? 

-
6.3.3.10 Additional comments


Sample survey

These questions only apply to surveys. If there is more than one survey, please describe the main survey below and the additional ones in table 6.3 of the annexed Excel file 
6.3.3.11 Name/Title of the survey

 

 
6.3.3.12 Unit non-response - rate
6.3.3.13 How do you evaluate the recorded unit non-response rate in the overall context?
6.3.3.14 Measures taken for minimising the unit non-response
6.3.3.15 If Other, please specify
6.3.3.16 Item non-response rate
6.3.3.17 Item non-response rate - Minimum
6.3.3.18 Item non-response rate - Maximum
6.3.3.19 Which items had a high item non-response rate? 
6.3.3.20 Additional comments
6.3.4. Processing error

Census

These questions only apply to censuses. If there is more than one census, please describe the main census below and the additional ones in table 6.3 of the annexed Excel file 
6.3.4.1 Name/Title

OSAP 2419 Census on orchard plantations, 2017
6.3.4.2 Imputation - rate

0%
6.3.4.3 Imputation - basis
6.3.4.4 If Other, please specify
6.3.4.5 Additional comments


Sample survey

These questions only apply to surveys. If there is more than one survey, please describe the main survey below and the additional ones in table 6.3 of the annexed Excel file 
6.3.4.6 Name/Title
6.3.4.7 Imputation - rate
6.3.4.8 Imputation - basis
6.3.4.9 If Other, please specify
6.3.4.10 How do you evaluate the impact of imputation on Coefficients of Variation?
6.3.4.11 Additionnal comments
6.3.5. Model assumption error

-
6.4. Seasonal adjustment

-
6.5. Data revision - policy

The data were checked under and after the data collection by the experts of the statistical office.
6.6. Data revision - practice
6.6.1 Data revision - average size

-
6.6.2 Were data revisions due to conceptual changes (e.g. new definitions)  carried out since the last quality report? No
6.6.3 What was the main reason for the revisions?
6.6.4 How do you evaluate the impact of the revisions?
6.6.5 Additional comments


7. Timeliness and punctuality Top
7.1. Timeliness
7.1.1 When were  the first  results for the reference period published?

05/2018
7.1.2 When were  the final results for the reference period published?

09/2018
7.1.3 Reasons for possible long production times?
7.2. Punctuality
7.2.1 Were data released nationally according to a pre-announced schedule (Release Calendar)? Yes
7.2.2 If Yes, were data released on the target date? Yes
7.2.3 If No, reasons for delays?
7.2.4 Number of days between the national release date of data and the target date


8. Coherence and comparability Top
8.1. Comparability - geographical

To be assessed by Eurostat
8.1.1. Asymmetry for mirror flow statistics - coefficient

-
8.2. Comparability - over time
8.2.1 Length of comparable time series

16 years from the basic census in 2001 (2001, 2007, 2012, 2017)
8.2.2 Have there been major breaks in the time series? Yes
8.2.3 If Yes, please specify the year of break and the reason

In 2017 there were differrent methodology and thresold.
8.2.4 Additional comments
8.3. Coherence - cross domain
8.3.1 With which other national data sources have the data been compared? Annual crop statistics 2017
8.3.2 If Other, please specify
8.3.3 Describe briefly the results of comparisons

Results should be expressed as percentage deviation from the corresponding areas in the orchard survey.

 

Annual Crop Statistics (2017) *

 FSS (2016) IACS Other source
Dessert apple trees  na  -0,8  0,7  
Apple trees for industrial processing  na  -  -  
Dessert pear trees  na  +29  0,8  
Pear trees for industrial processing na  -  -  
Apricot trees na  -2  0,7  
Dessert peach and nectarine trees na  -  -  
Peach and nectarine trees for industrial processing (including group of Pavie)  na  +13  1,1  
Orange trees        
Small citrus fruit trees        
Lemon trees        
Olive trees        
Table grape vines        

 

Annual Crop statistics 2017 will be available later.
8.3.4 If no comparisons have been made, explain why
8.3.5 Additional comments

The reasons of the differences are the different methodology and thresold.
8.4. Coherence - sub annual and annual statistics

-
8.5. Coherence - National Accounts

-
8.6. Coherence - internal

-


9. Accessibility and clarity Top
9.1. Dissemination format - News release
9.1.1 Do you publish a news release? Yes
9.1.2 If Yes, please provide a link

http://www.ksh.hu/sajtoszoba_kozlemenyek_tajekoztatok_2018_02_08
9.2. Dissemination format - Publications
9.2.1 Do you produce a paper publication? No
9.2.2 If Yes, is there an English version?
9.2.3 Do you produce an electronic publication? Yes
9.2.4 If Yes, is there an English version? No
9.2.5 Please provide a link

http://www.ksh.hu/docs/hun/agrar/gyum2017/gyumolcs2017_adatok.xlsx

 

http://www.ksh.hu/elemzesek/gyumolcs2017_elozetes/index.html

 

 
9.3. Dissemination format - online database
9.3.1 Data tables - consultations

-
9.3.2 Is an on-line database accessible to users? No
9.3.3 Please provide a link
9.4. Dissemination format - microdata access
9.4.1 Are micro-data accessible to users? Yes
9.4.2 Please provide a link

It will be shared locally, on PC workstation, in the "Research Room" of HCSO
9.5. Dissemination format - other

online, on the website of HCSO
9.6. Documentation on methodology
9.6.1 Are national reference metadata files available? Yes
9.6.2 Please provide a link

http://www.ksh.hu/apps/meta.objektum?p_lang=EN&p_menu_id=110&p_almenu_id=101&p_ot_id=100&p_obj_id=OMG&p_session_id=48032580
9.6.3 Are methodological papers available? Yes
9.6.4 Please provide a link

http://www.ksh.hu/elemzesek/gyumolcs2017_elozetes/index.html

(Módszertan, Fogalmak)
9.6.5 Is a handbook available? Yes
9.6.6 Please provide a link

http://www.ksh.hu/docs/hun/agrar/html/fogalomtar.html
9.7. Quality management - documentation
9.7.1 Metadata completeness - rate

100%
9.7.2 Metadata - consultations
9.7.3 Is a quality report available? No
9.7.4 Please provide a link


10. Cost and Burden Top
10.1 Efficiency gains if compared to the previous quality report Further automation
Increased use of administrative data
10.2 If Other, please specify
10.3 Burden reduction measures since the previous quality report Easier data transmission
More user-friendly questionnaires
Other
10.4 If Other, please specify

Year of Survey

 Number of units in the survey

 Sample area size, ha

 2007

12 185* 

48 786

 2012

 9 203*

 36 632

 2017

 9 459**

 36 291

  * sample unit: plantation

** sample unit: user of plantation (farm)

 

Instead of the earlier practice in sample surveys, to collect data on plantations on the field by enumerators, in 2017 the users of plantations were contacted personally. There was no need to move and travel directly to the area of plantations. One respondent - the user of plantations - provided the data about more plantation units.

They had the possibility to fill in the questionnaire online. The response rate was extremely high, more than 50% - this is an unique success of HCSO, comparing with other censuses .

The rest of the respondents were interviewed by enumerators.


11. Confidentiality Top
11.1. Confidentiality - policy
11.1.1 Are confidential data transmitted to Eurostat? Yes
11.1.2 If yes, are they confidential in the sense of Reg. (EC) 223/2009? Yes
11.1.3 Describe the data confidentiality policy in place

Data less then 3 respondents were not published;

Merging categories were applied.

 

The protection of personal data and the publicity of data of public interest are regulated by the following Acts in Hungary: 

- Act CLV of 2016 on Official statistics,

- Act CXII of 2011 on Informational Self-Determination and on Freedom of Information.

Besides the above mentioned legal acts, internal regulations on confidentiality exist within the HCSO. On the top of the regulations concerning data protection stands the Confidentiality Policy of the HCSO, which contains the most important principles regarding statistical confidentiality. The HCSO Regulation on Data Protection sets forth more detailed rules and as a framework regulation is complemented by several other internal regulations. The access to statistical data is regulated in a separate internal regulation (Regulation 18/2014 on the rules of data access) which contains the rules on the six data access channels of the HCSO.

In virtue of the Act CXII of 2011 on Informational Self-Determination and on Freedom of Information and the Act XLVI CLV of 2016 on Official statistics all individual data are qualified as confidential and are treated as such. Survey data are validated and checked exclusively by the staff of HCSO and enumerators are responsible for preventing unauthorized access to the completed questionnaires.

Confidentiality Policy of the HCSO: http://www.ksh.hu/docs/bemutatkozas/eng/avpol_web_eng.pdf

HCSO Regulation on Data Protection: http://www.ksh.hu/docs/szolgaltatasok/adatigenyles/hcso_regulation_on_data_protection.pdf
11.2. Confidentiality - data treatment
11.2.1 Describe the procedures for ensuring confidentiality during dissemination

The dissemination contains data for NUTS 3 and 5km x 5 km grid level with a respect of confidentiality rules.
11.2.2 Additional comments


12. Comment Top

For the better understanding of data the HCSO publishes the documentation of many different statistical domains including EAA documentation. It comprises the definitions of concepts, the data sources used in a statistical domain, the methods applied, data quality aspects, the most frequently used classifications and other metadata. Metadata and documentations are permanently updated in accordance with changes.

Statistical domains: main metadata of statistics (purpose, content, legal base, data production methodology, data quality, concepts, classifications, data sources, forms of publications)

Concepts and definitions: glossary of statistics, explanation of and cross-references between concepts, changes of definitions over time

Classifications: main classifications used in statistics, their structure, with explanation of items, tables of correspondence between different classifications and the different versions of each classification

Data sources: registers, data surveys, and data received from other sources, serving as a basis for producing statistics.

- Data collections: most important metadata of data collections and surveys, which are the data sources of a statistical domain (enactor, frequency, scope of data suppliers, deadline for data receipt, etc.). Data collections: data collected directly from the observation units for statistical purposes; statistical data collections ordered by the HCSO are listed here.
- Administrative data sources: description of administrative data sources used as data sources for a statistical domain (data provider, frequency, scope of data, etc.). Administrative data: data generated during the implementation of the statutory administrative tasks of the administrative organization (e.g. public registers). Administrative data received by the HCSO are displayed here.
- Non-administrative data sources: description of non-administrative data sources forming the data source of the statistical domain (data provider, frequency, scope of data, etc). Data sources not required by law or created for non-administrative purposes as well as statistical data collections ordered by organizations of the official statistical service other than the HCSO if these are taken over by the HCSO are displayed here.
- Registers: descriptions of HCSO registers and attributes of register units serving as a basis for a statistical domain


Related metadata Top


Annexes Top
ESQRS_ANNEX_HU