Orchard (orch)

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

Compiling agency: REPUBLIC OF CROATIA - CROATIAN BUREAU OF STATISTICS Address: Ilica 310000 ZagrebRepublic of Croatiaphone: (+385 1) 48 06 111 www.dzs.hr


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

REPUBLIC OF CROATIA - CROATIAN BUREAU OF STATISTICS

Address:

Ilica 3
10000 Zagreb
Republic of Croatia
phone: (+385 1) 48 06 111

www.dzs.hr
1.2. Contact organisation unit

AGRICULTURE, FORESTRY, FISHERIES AND ENVIRONMENT DIRECTORATE/Agriculture, Forestry and Fisheries Production Statistics Department/Crop Production Statistics and Register of Agricultural Holdings Unit, Branimirova 19, 10 000 Zagreb, Phone:+385 (0) 1 4893-583, Fax: +385 (0) 1 4893-588
1.5. Contact mail address

Branimirova 19, 10 000 Zagreb, Republic of Croatia


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 from surveys and administrative data sources. Data are collected on national level and for some variables also at NUTS1 level. The data collection concerns countries with 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
Small citrus fruit trees
Olive trees
2.1.3 If Other, please specify


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)

June 2017


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 

The Official Statistics Act (the Official Gazette Nos. 103/03, 75/09 and 59/12)
2.1.8 Link to the national legislation 

http://www.dzs.hr/default_e.htm
2.1.9 Responsible organisation for the national legislation 

CBS
2.1.10 Year of entry into force of the national legislation 

2003
2.1.11 Please indicate which variables required under EU regulation are not covered by national legislation, if any.

The all variables requested under EU regulation are covered by national legislation.
2.1.12 Please indicate which national definitions differ from those in the EU regulation, if any. 

There is no difference in national definitions from those in the EU regulation.
2.1.13 Please indicate which additional variables have been collected if compared to Regulation (EU) 1337/2011, if any?

No additional variables.
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)? No
2.4.2 If Yes, please specify the items and the differences
2.4.3 If the fruits for industrial processing are not separately surveyed, are they included in dessert fruit categories? No
2.4.4 If yes, for which fruit tree types?
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.

Most of other dessert apples are unclassified varieties (almost 35% oout of total dessert apples).

The other varieties are: Florina (11.8%), Gold Rush (10.4%),Topaz (5,9%),Enterprise (4,7%), Gloster (4.4%), Bohnapfel (4,3%), Kolačara (2.8%), Mutzu (2.5%) and Summerred (1.9 %).
2.4.6 Other dessert pears n.e.c.
2.4.7 Other oranges n.e.c.
2.4.8 Other small citrus fruits, including hybrids

The biggest varieties included in Other small citrus fruits are:

- Kowano wase

- Zorica rana

- Chahara

- Unshiu owari

- Okitsu

 

They represented more than 85% of total varieties.
2.5. Statistical unit

Utilised agricultural area used for the cultivation of permanent crops mentioned in point 2.1, cultivated by an agricultural holding producing entirely or mainly for the market.
2.6. Statistical population

All agricultural holdings growing entirely or mainly for the market permanent crops mentioned in point 2.1.
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?

Not relevant.
2.8. Coverage - Time

Orchard survey data is available for the following years: 2012 and 2017
2.9. Base period

Not applicable.


3. Statistical processing Top
3.1. Source data

Overall summary

3.1.1 Total number of different data sources used

1
The breakdown is as follows: 
3.1.2 Total number of sources of the type "Census"

0
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
3.1.8 Name of Organisation responsible
3.1.9 Main scope
3.1.10 Target fruit tree types
3.1.11 List used to build the frame
3.1.12 Any possible threshold values
3.1.13 Population size
3.1.14 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 3.1 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

The Register of the Paying Agency for Agriculture, Fisheries and Rural Development
3.1.32 Name of Organisation responsible

Paying Agency for Agriculture, Fisheries and Rural Development
3.1.33 Contact information (email and phone)

phone: 0038516002700

e-mail: info@apprrr.hr
3.1.34 Main administrative scope

Register was established among others needs for The Agency of the Republic of Croatia, for the implementation of programs of the agricultural policy reform, alignment to the EU common agricultural policy and payment of SAPARD pre-accession assistance.  Register is also part of Integrated Administration and Control System (IACS).
3.1.35 Target fruit tree types  Dessert apple trees
Dessert pear trees
Apricot trees
Dessert peach and nectarine trees
Orange trees
Small citrus fruit trees
Lemon trees
Olive trees
Other
3.1.36 Geospatial Coverage National
3.1.37 Update frequency Continuous
3.1.38 Legal basis

OJ of the Republic of Croatia No. 17/18
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

No differences.
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

Information on holdings below the threshold.


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

The Register of the Paying Agency for Agriculture, Fisheries and Rural Development.
3.3.16 Extraction date

01.06.2017
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

By FTP solution (FileZilla).
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? Manual
Automatic
3.4.2 What do they target? Outliers
Consistency
3.4.3 If Other, please specify
3.5. Data compilation
3.5.1 Describe the data compilation process

Not available
3.5.2 Additional comments
3.6. Adjustment

Not available.


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?

The main tool for the systematic quality assessment and quality management is the database on quality information (DBBQI). The DBQI has in first stage the Basic analytical tool for comparative analyses of quality indicators and later will contain Advanced analytical tool for comparative analyses of quality indicators.
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

Data is collected from reliable sources applying high standards with regard to the methodology and ensuring a high degree of comparability.
4.2. Quality management - assessment

Development since the last quality report

4.2.1 Overall quality Stable
4.2.2 Relevance Stable
4.2.3 Accuracy and reliability Stable
4.2.4 Timeliness and punctuality Stable
4.2.5 Comparability Stable
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

Not available
5.1.2 Please specify any plans to satisfy needs more completely in the future

None
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?

There were all of requested variables delivered to Eurostat. Not delivered were only variables bellow threshold.
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

Please provide the coefficients of variation in %

  CV (%)
Dessert apple trees  
Apple trees for industrial processing  
Dessert pear trees  
Pear trees for industrial processing  
Apricot trees  
Dessert peach and nectarine trees  
Peach and nectarine trees for industrial processing (including group of Pavie)  
Orange trees  
Small citrus fruit trees  
Lemon trees  
Olive trees  
Table grape vines  
6.2.8 Additional comments
6.3. Non-sampling error

See sections below.
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
Over-coverage
6.3.1.2 Does the sample frame include wrongly classified units that are out of scope?
6.3.1.3 What methods are used to detect the out-of scope units?
6.3.1.4 Does the sample frame include units that do not exist in practice?
6.3.1.5 Over-coverage - rate
6.3.1.6 Impact on the data quality
Under-coverage
6.3.1.7 Does the sample frame include all units falling within the scope of this survey?
6.3.1.8 If Not, which units are not included?
6.3.1.9 How large do you estimate the proportion of those units? (%)
6.3.1.10 Impact on the data quality
Misclassification
6.3.1.11 Impact on the data quality
Common units
6.3.1.12 Common units - proportion
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

The Register of the Paying Agency for Agriculture, Fisheries and Rural Development.
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?

The out-of-scope units are detected by implementing data processing procedure (validation rules, call backs, etc.)
6.3.1.30 Does the administrative source include units that do not exist in practice? No
6.3.1.31 Over-coverage - rate

Not applicable.
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? Yes
6.3.1.34 If Not, which units are not included?
6.3.1.35 How large do you estimate the proportion of those units? (%)
6.3.1.36 Impact on the data quality None
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
6.3.2.2 Is the questionnaire based on usual concepts for respondents?
6.3.2.3 Number of censuses already performed with the current questionnaire?
6.3.2.4 Preparatory testing of the questionnaire?
6.3.2.5 Number of units participating in the tests? 
6.3.2.6 Explanatory notes/handbook for surveyors/respondents? 
6.3.2.7 On-line FAQ or Hot-line support for surveyors/respondents?
6.3.2.8 Are there pre-filled questions?
6.3.2.9 Percentage of pre-filled questions out of total number of questions
6.3.2.10 Other actions taken for reducing the measurement error?
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
6.3.3.2 Unit non-response - rate
6.3.3.3 How do you evaluate the recorded unit non-response rate in the overall context?
6.3.3.4 Measures taken for minimising the unit non-response
6.3.3.5 If Other, please specify
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
6.3.4.2 Imputation - rate
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

Revision policy of the Croatian Bureau of Statistics distinguishes three types of revisions: regular revisions, major revisions and unscheduled revisions.

Unplanned revision of the orchard survey 2017 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).
6.6. Data revision - practice
6.6.1 Data revision - average size

Not available.
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?

Not relevant.
6.6.4 How do you evaluate the impact of the revisions? Not important
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?

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

September 2018
7.1.3 Reasons for possible long production times?

 Workload, incompletness of data in administrative source
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

0


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

Since 2012.
8.2.2 Have there been major breaks in the time series? No
8.2.3 If Yes, please specify the year of break and the reason
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
FSS 2016
Other
8.3.2 If Other, please specify

Administrative data
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  -7,8%  -20,6%  -7,8%  
Apple trees for industrial processing        
Dessert pear trees        
Pear trees for industrial processing        
Apricot trees        
Dessert peach and nectarine trees        
Peach and nectarine trees for industrial processing (including group of Pavie)        
Orange trees        
Small citrus fruit trees  -14,6%  -15,4%  -4,1%  
Lemon trees        
Olive trees  -8,9%  -8,9%  -1,8%  
Table grape vines        
8.3.4 If no comparisons have been made, explain why
8.3.5 Additional comments
8.4. Coherence - sub annual and annual statistics

Not available.
8.5. Coherence - National Accounts

Not available.
8.6. Coherence - internal

Not available.


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

https://www.dzs.hr/Hrv_Eng/publication/2018/01-01-31_01_2018.htm

 

 

 
9.2. Dissemination format - Publications
9.2.1 Do you produce a paper publication? Yes
9.2.2 If Yes, is there an English version? Yes
9.2.3 Do you produce an electronic publication? No
9.2.4 If Yes, is there an English version?
9.2.5 Please provide a link
9.3. Dissemination format - online database
9.3.1 Data tables - consultations

Not available
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? No
9.4.2 Please provide a link
9.5. Dissemination format - other

Not available
9.6. Documentation on methodology
9.6.1 Are national reference metadata files available? No
9.6.2 Please provide a link
9.6.3 Are methodological papers available? No
9.6.4 Please provide a link
9.6.5 Is a handbook available? No
9.6.6 Please provide a link
9.7. Quality management - documentation
9.7.1 Metadata completeness - rate

Not available
9.7.2 Metadata - consultations

Not available.
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
10.2 If Other, please specify
10.3 Burden reduction measures since the previous quality report Multiple use of the collected data
Less variables surveyed
10.4 If Other, please specify


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

Statistical data collected in this survey, according to the Law on official statistics (NN, br. 103/03., 75/09. i 59/12.) is confidential and its purpose is restricted exclusively to statistical usage.Authorized interviewers are obligated 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.
11.2. Confidentiality - data treatment
11.2.1 Describe the procedures for ensuring confidentiality during dissemination

Threshold combined with dominance rules.

National Statistics Act (OJ HR No. 103/03 and 75/09.) defines Statistical confidentiality. Results are published subject to the minimum number of statistical units (3 units). To avoid indirect identification results are systematically hidden.

In article 65 of National statistics Act states that Producers of official statistics may, on the basis of a written request, provide individual statistical data without identifier for the purpose of performing the activities of scientific research. The request referred to in paragraph 1 of this Article shall state the purpose of the use of the statistical data.

A special contract shall be concluded on the use of the statistical data referred to in paragraph 1 of this Article, according to which the user shall be held financially and criminally responsible to use statistical data only for the purpose stated in the request, and shall not provide these data for inspection or use to unauthorised persons, and shall destroy such data after use.
11.2.2 Additional comments


12. Comment Top

Croatia used administrative data source Register of the Paying Agency for Agriculture, Fisheries and Rural Development.

Register is also part of Integrated Administration and Control System (IACS).

The threshold is coherent with statistical needs.

Data from Register of producers in plantations of fruit trees covers all statistics for statistical survey Census of fruit and olive plantations.

 

Variables covered with register:

 

The register covers data on:

 

1- agricultural holder

 

2- agricultural holding

 

3- plantations of fruit trees and olives:

 

a) land use

 

- ID number of agricultural holding 

- ID number of parcel 

- numbers of parcels 

- area of parcel 

- area of orchards 

b) technological characteristics

 

- fruit and olive species and varieties 

- number of trees 

- rootstock 

- planting year

- distance in row and between row

In 2009, Croatia carried out a basic survey on orchards for the first time, however some data requested in the basic orchard survey were already collected in 2003 Agricultural Census and after in annual surveys on areas of crops carried out each year in June.

The basic survey on orchards ( PO-VOČE a, b) was carried out on the bases of Law on official statistics ( OJ, No.103/3) and Law on changes and amendments of law of official statistics (O.J. No.75/09) and based on the Annual plan of statistical surveys of the Republic of Croatia for year 2009 ( O.J. No.80/09).The survey was harmonized with the methodology of EU and provided all data requested by EU as well as some additional data related to additional national needs in respect to orchards. The observation units were:

1. on agricultural holding

  • Farming at least0,1 hectaresof orchards including olives
  • and/or producing fruit and olives mainly or solely for the market

2. on fruit and olive plantations

  • gross area of orchard by species
  • on fruit species
  • fruit varieties
  • year of planting of orchard
  • number of trees
  • distance between the trees in the rows and between the rows
  • production of fruit and olives
  • fruit processing and marketing

 Also, we planned to have methodologycal report and database for Orchard census 2012 and 2017  in December 2018.


Related metadata Top


Annexes Top
ESQRS_ANNEX_HR