Orchard (orch)

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

Compiling agency: Central Statistical Bureau of Latvia


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

Central Statistical Bureau of Latvia
1.2. Contact organisation unit

Agricultural Statistics Section
1.5. Contact mail address

Lacplesa Street 1, Riga, Latvia, LV-1301


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. The statistics are collected from survey. Data are collected on national level. The data collection concerns area with more than 1000 ha of apple tree.
2.1.2 Which of the fruit tree types are covered by the data collection?  Dessert apple 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)

November 2017


National legislation

2.1.6 Is there a national legislation covering these statistics?  No
If Yes, please answer all the following questions.   
2.1.7 Name of the national legislation 
2.1.8 Link to the national legislation 
2.1.9 Responsible organisation for the national legislation 
2.1.10 Year of entry into force of the national legislation 
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. 
2.1.13 Please indicate which additional variables have been collected if compared to Regulation (EU) 1337/2011, if any?
2.1.14 Is there a legal obligation for respondents to reply? 


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.
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?
2.8. Coverage - Time

2012 - 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"

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

Survey on apple trees 2017
3.1.16 Name of Organisation responsible

The CSB of Latvia
3.1.17 Main scope

 All economically active holdings growing apple trees excluding  holdings below a threshold of 0,2 ha of apple trees

exclude holdings below a threshold of 0,2 ha of each permanent crop 
3.1.18 Target fruit tree types Dessert apple trees
3.1.19 List used to build the frame

Statistical Farm Register updated on the base of the information from IACS

 
3.1.20 Any possible threshold values

More than 0.2 ha
3.1.21 Population size

5247 holdings
3.1.22 Sample size

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

63
3.1.28 If Stratified, stratification criteria Unit location
Unit specialization
Unit size
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

Survey on apple trees 2017
3.3.9 Methods of data collection Electronic questionnaire
Postal questionnaire
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? 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


Annexes:
Questionnaire of Survey on apple trees 2017
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
Consistency
3.4.3 If Other, please specify
3.5. Data compilation
3.5.1 Describe the data compilation process

Extrapolation of the data of holdings surveyed to the all holdings is taken into account how many similar holdings represent the sampled holdings as well as actual response rate in each of the survey areas
3.5.2 Additional comments
3.6. Adjustment

None


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?

Activities of the Total Quality Management System to identify statistical and organizational processes and develop their descriptions in compliance with requirements of the quality management system. Components are fundamental processes such as project preparation, data collection, data processing, data analysis, data dissemination and support processes as metadata and documentation of processes. Quality Management System is maintained and updated electronically in QPR portal.
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? Improve data validation
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 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
5.1.2 Please specify any plans to satisfy needs more completely in the future

Will depend on the outcomes of the IFS Regulation
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? Good
6.1.2 What are the main factors lowering the accuracy? Non-response error
Sampling error
Coverage 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

Survey on apple trees 2017
6.2.2 Methods used to assess the sampling error Relative standard error
6.2.3 If Other, please specify
6.2.4 Methods used to derive the extrapolation factor Non-response
6.2.5 If Other, please specify
6.2.6 If coefficients of variation are calculated, please describe the calculation methods and formulas

Variance estimation is done by the ultimate cluster method (Hansen, Hurwitz and Madow, 1953) and the residual estimation from the regression model to take weight calibration into account. Direct estimator of variance for totals is used. Software R package vardpoor is used for variance estimation.

See Formulas applied for estimating variance of estimates of totals in Annex 6.2. Formulas applied for estimating variance of estimates of totals.
6.2.7 Sampling error - indicators

Please provide the coefficients of variation in %

  CV (%)
Dessert apple trees  2.5
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


Annexes:
Formulas applied for estimating variance of estimates of totals
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

Survey on apple trees 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?

None
6.3.1.17 Does the sample frame include units that do not exist in practice? No
6.3.1.18 Over-coverage - rate

8.9 %
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? Yes
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 Unknown
Common units 
6.3.1.25 Common units - proportion

587
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

Survey on apple trees 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? 

3
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
6.3.2.21 Other actions taken for reducing the measurement error?

No
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

Survey on apple trees 2017
6.3.3.12 Unit non-response - rate

5%
6.3.3.13 How do you evaluate the recorded unit non-response rate in the overall context? Very low
6.3.3.14 Measures taken for minimising the unit non-response Follow-up interviews
Reminders
6.3.3.15 If Other, please specify
6.3.3.16 Item non-response rate

0 %
6.3.3.17 Item non-response rate - Minimum

0 %
6.3.3.18 Item non-response rate - Maximum

0 %
6.3.3.19 Which items had a high item non-response rate? 

Density
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

Survey on apple trees 2017
6.3.4.7 Imputation - rate

None
6.3.4.8 Imputation - basis Other
6.3.4.9 If Other, please specify

None
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

Not applicable
6.4. Seasonal adjustment

Not applicable
6.5. Data revision - policy

None
6.6. Data revision - practice
6.6.1 Data revision - average size

None
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?

None
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?

08/2018
7.1.2 When were  the final results for the reference period published?

08/2018
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

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

From 2007
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? Other
Annual crop statistics 2017
IACS
8.3.2 If Other, please specify

Administrative data - IACS, Annual Crop Survey 2017
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  -4.2     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        
Lemon trees        
Olive trees        
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 applicable
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

https://www.csb.gov.lv/en/statistics/statistics-by-theme/agriculture/agricultural-prices/search-in-theme/2406-apple-tree-plantations-2017
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? Yes
9.2.4 If Yes, is there an English version? Yes
9.2.5 Please provide a link

Will be available in 2019
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? Yes
9.3.3 Please provide a link

 

https://www.csb.gov.lv/en/statistics/statistics-by-theme/agriculture/crop/tables

 
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? Yes
9.6.2 Please provide a link

https://www.csb.gov.lv/en/statistics/statistics-by-theme/agriculture/crop/tables 
9.6.3 Are methodological papers available? Yes
9.6.4 Please provide a link

 

https://www.csb.gov.lv/en/statistics/statistics-by-theme/agriculture/crop/tables 
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

100%
9.7.2 Metadata - consultations

Not available
9.7.3 Is a quality report available? Yes
9.7.4 Please provide a link

CSB homepage is partly under transformation and all thus items aren't accessible at the moment


10. Cost and Burden Top
10.1 Efficiency gains if compared to the previous quality report On-line surveys
Further automation
10.2 If Other, please specify
10.3 Burden reduction measures since the previous quality report Less respondents
More user-friendly questionnaires
Easier data transmission
Multiple use of the collected data
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

No confidential data during dissemination
11.2. Confidentiality - data treatment
11.2.1 Describe the procedures for ensuring confidentiality during dissemination

No confidential data durind dissemination
11.2.2 Additional comments


12. Comment Top


Related metadata Top


Annexes Top
ESQRS_ANNEX_LV