Orchard (orch)

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

Compiling agency: National Institute of Statistics


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

Download


1. Contact Top
1.1. Contact organisation

National Institute of Statistics
1.2. Contact organisation unit

Department of Agriculture and Environmental Statistics
1.5. Contact mail address

Libertatii No.16, Zip code 050706, Bucharest-Romania


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. 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
Dessert pear trees
Apricot trees
Dessert peach and nectarine trees
Table grape vines
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)

September 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 

Law No 348/2003 regarding fruit growing regulates the pomiculture development framework, the establishment and exploitation of fruit crops, fruit production and their turning to good account based on scientific research and expansion of technical progress under a common market system in the fruit sector, Law No 244/2002 on vines and wine production under a common viticulture market system and Law No 266/2002 on the production, processing, quality control and certification and marketing of seeds and planting stock, as well as the testing and registration of plant species.
2.1.8 Link to the national legislation 

http://www.madr.ro
2.1.9 Responsible organisation for the national legislation 

Ministry of Agriculture and Rural Development
2.1.10 Year of entry into force of the national legislation 

2002 and 2003
2.1.11 Please indicate which variables required under EU regulation are not covered by national legislation, if any.

- varieties for apples and pears;

- colors on peach trees, nectarine trees and vineyards for table grapes;

- consumption maturity on peach trees, nectarine trees and apricot trees;

- number of trees in orchards;

- number of vineyards for table grapes.
2.1.12 Please indicate which national definitions differ from those in the EU regulation, if any. 

Not applicable.
2.1.13 Please indicate which additional variables have been collected if compared to Regulation (EU) 1337/2011, if any?

Not applicable.
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? Yes
2.4.4 If yes, for which fruit tree types? Apple trees
Pear trees
Peach and nectarine 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.
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
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 applicable.
2.8. Coverage - Time

2012
2.9. Base period


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"

1
3.1.4 Total number of sources of the type "Administrative source"

0
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

The productive potential of fruit plantations and wine-growing plantations for the production of table grapes in 2017.
3.1.16 Name of Organisation responsible

National Institute of Statistics
3.1.17 Main scope

Determination of productive potential of fruit plantations and wine-growing plantations for mass table grapes production.
3.1.18 Target fruit tree types Dessert apple trees
Dessert pear trees
Apricot trees
Dessert peach and nectarine trees
Table grape vines
3.1.19 List used to build the frame

The Register of Agricultural Holdings was made up of data from the General Agricultural Census 2010 and is updated annually with surveys data and every 3 years with FSS data.
3.1.20 Any possible threshold values

Agricultural holdings cultivating at least 0.2 ha of each species of fruit trees or vineyards for table grapes.
3.1.21 Population size

151568
3.1.22 Sample size

16841
3.1.23 Sampling basis List
3.1.24 If Other, please specify
3.1.25 Type of sample design Stratified
Random
3.1.26 If Other, please specify
3.1.27 If Stratified, number of strata

116
3.1.28 If Stratified, stratification criteria Unit size
Unit location
Unit legal status
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
3.1.32 Name of Organisation responsible
3.1.33 Contact information (email and phone)
3.1.34 Main administrative scope
3.1.35 Target fruit tree types 
3.1.36 Geospatial Coverage
3.1.37 Update frequency
3.1.38 Legal basis
3.1.39 Are you able to access directly to the micro data?
3.1.40 Are you able to check the plausibility of the data, namely by contacting directly the units?
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?
3.1.42 Please list the main differences between the administrative source and the statistical definitions and concepts
3.1.43 Is a different threshold used in the administrative source and statistical data?
3.1.44 If Yes, please specify
3.1.45 Additional comments


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

The productive potential of fruit plantations and wine-growing plantations for the production of table grapes in 2017.
3.3.9 Methods of data collection Face-to-face interview
Other
3.3.10 If Other, please specify

Self-registration for legal units
3.3.11 If face-to-face or telephone interview, which method is used? Paper questionnaire
3.3.12 Data entry method, if paper questionnaires? Manual
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
3.3.16 Extraction date
3.3.17 How easy is it to get access to the data?
3.3.18 Data transfer method
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? Completeness
Outliers
Aggregates
Consistency
3.4.3 If Other, please specify
3.5. Data compilation
3.5.1 Describe the data compilation process

Two data processing modules were used:

- data entry and validation;

- elaboration of final tables and data analysis.
3.5.2 Additional comments
3.6. Adjustment

Not applicable.


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 NIS implements the quality management system based on the approach and elaboration of procedures and mechanisms in accordance with the EFQM/CAF excellence model for the continuous evaluation and in view of the quality improvement of the organizational system.
4.1.3 Has a peer review been carried out? Yes
4.1.4 If Yes, which were the main conclusions?

Assessing the current situation, the peer reviewers' opinion from 2015, is that the activities of the National Institute of Statistics and SSN National Statistical Sistem are largely in line with European Statistics Code of Practice.
4.1.5 What quality improvements are foreseen? Improve data validation
Further automation
Other
4.1.6 If Other, please specify

Use of administrative sources.
4.1.7 Additional comments
4.2. Quality management - assessment

Development since the last quality report

4.2.1 Overall quality Improvement
4.2.2 Relevance Improvement
4.2.3 Accuracy and reliability Improvement
4.2.4 Timeliness and punctuality Improvement
4.2.5 Comparability Improvement
4.2.6 Coherence Improvement
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

No
5.1.2 Please specify any plans to satisfy needs more completely in the future

No
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

98.2%
5.3.2 If not complete, which characteristics are missing?

Number of trees in orchards and number of vineyards for table grapes.
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

The productive potential of fruit plantations and wine-growing plantations for the production of table grapes in 2017.
6.2.2 Methods used to assess the sampling error Comparisons with other sources
6.2.3 If Other, please specify
6.2.4 Methods used to derive the extrapolation factor Basic weight
6.2.5 If Other, please specify
6.2.6 If coefficients of variation are calculated, please describe the calculation methods and formulas

SAS programming.
6.2.7 Sampling error - indicators

Please provide the coefficients of variation in %

  CV (%)
Dessert apple trees   0.44
Apple trees for industrial processing  
Dessert pear trees  2.67
Pear trees for industrial processing  
Apricot trees  0
Dessert peach and nectarine trees  0
Peach and nectarine trees for industrial processing (including group of Pavie)  
Orange trees  
Small citrus fruit trees  
Lemon trees  
Olive trees  
Table grape vines  3.64
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

The productive potential of fruit plantations and wine-growing plantations for the production of table grapes in 2017.
Over-coverage
6.3.1.15 Does the sample frame include wrongly classified units that are out of scope? No
6.3.1.16 What methods are used to detect the out-of scope units?

No
6.3.1.17 Does the sample frame include units that do not exist in practice? No
6.3.1.18 Over-coverage - rate

1.68%
6.3.1.19 Impact on the data quality Low
Under-coverage 
6.3.1.20 Does the sample frame include all units falling within the scope of this survey? No
6.3.1.21 If Not, which units are not included?

16
6.3.1.22 How large do you estimate the proportion of those units? (%)

1.0%
6.3.1.23 Impact on the data quality Low
Misclassification
6.3.1.24 Impact on the data quality None
Common units 
6.3.1.25 Common units - proportion

17.7%
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
Over-coverage
6.3.1.28 Does the administrative source include wrongly classified units that are out of scope?
6.3.1.29 What methods are used to detect the out-of scope units?
6.3.1.30 Does the administrative source include units that do not exist in practice?
6.3.1.31 Over-coverage - rate
6.3.1.32 Impact on the data quality
Under-coverage
6.3.1.33 Does the administrative source include all units falling within the scope of this survey?
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
Misclassification 
6.3.1.37 Impact on the data quality
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

The productive potential of fruit plantations and wine-growing plantations for the production of table grapes in 2017.
6.3.2.13 Is the questionnaire based on usual concepts for respondents? Yes
6.3.2.14 Number of surveys already performed with the current questionnaire?

2
6.3.2.15 Preparatory testing of the questionnaire? Yes
6.3.2.16 Number of units participating in the tests? 

20
6.3.2.17 Explanatory notes/handbook for surveyors/respondents?  Yes
6.3.2.18 On-line FAQ or Hot-line support for surveyors/respondents? Yes
6.3.2.19 Are there pre-filled questions? No
6.3.2.20 Percentage of pre-filled questions out of total number of questions

0
6.3.2.21 Other actions taken for reducing the measurement error?

Questionnaire correlations (among indicators, among chapters, control totals) and control tables.
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

The productive potential of fruit plantations and wine-growing plantations for the production of table grapes in 2017.
6.3.3.12 Unit non-response - rate

1.8%
6.3.3.13 How do you evaluate the recorded unit non-response rate in the overall context? Low
6.3.3.14 Measures taken for minimising the unit non-response Follow-up interviews
Imputation
Weighting
6.3.3.15 If Other, please specify
6.3.3.16 Item non-response rate

1.8%
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

The productive potential of fruit plantations and wine-growing plantations for the production of table grapes in 2017.
6.3.4.7 Imputation - rate

1.8%
6.3.4.8 Imputation - basis Similar units
6.3.4.9 If Other, please specify
6.3.4.10 How do you evaluate the impact of imputation on Coefficients of Variation? Not important
6.3.4.11 Additionnal comments
6.3.5. Model assumption error

Not applicable.
6.4. Seasonal adjustment

Not applicable.
6.5. Data revision - policy

Not applicable.
6.6. Data revision - practice
6.6.1 Data revision - average size

Not applicable.
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 applicable.
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?

28 September 2018 press release
7.1.2 When were  the final results for the reference period published?

26 November 2018 publication
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

0 days


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

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
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  -0.98%  -0.07%    
Apple trees for industrial processing        
Dessert pear trees  +3.67%  -9.90%    
Pear trees for industrial processing        
Apricot trees  +11.93%  -16.38%    
Dessert peach and nectarine trees  +4.56%  -9.99%    
Peach and nectarine trees for industrial processing (including group of Pavie)        
Orange trees        
Small citrus fruit trees        
Lemon trees        
Olive trees        
Table grape vines  -22.21%  +5.21%    
8.3.4 If no comparisons have been made, explain why
8.3.5 Additional comments
8.4. Coherence - sub annual and annual statistics

ACS 2017 and FSS 2016.
8.5. Coherence - National Accounts

Not applicable.
8.6. Coherence - internal

Not applicable.


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.insse.ro
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? No
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
9.3. Dissemination format - online database
9.3.1 Data tables - consultations

No
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
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? Yes
9.6.6 Please provide a link

 
9.7. Quality management - documentation
9.7.1 Metadata completeness - rate

98.2%
9.7.2 Metadata - consultations
9.7.3 Is a quality report available? No
9.7.4 Please provide a link


Annexes:
Questionnaire
Methodological guide


10. Cost and Burden Top
10.1 Efficiency gains if compared to the previous quality report Increased use of administrative data
10.2 If Other, please specify

 
10.3 Burden reduction measures since the previous quality report Less respondents
10.4 If Other, please specify

 


11. Confidentiality Top
11.1. Confidentiality - policy
11.1.1 Are confidential data transmitted to Eurostat? No
11.1.2 If yes, are they confidential in the sense of Reg. (EC) 223/2009?
11.1.3 Describe the data confidentiality policy in place
11.2. Confidentiality - data treatment
11.2.1 Describe the procedures for ensuring confidentiality during dissemination

The disseminated data resulted from the processing of individual aggregated data. There were no cases where aggregated data should result from the summing up of less than three agricultural holdings.
11.2.2 Additional comments


12. Comment Top

 


Related metadata Top


Annexes Top
ESQRS_ANNEX_RO