Orchard (orch)

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

Compiling agency: Statiscal Office of Lithuania

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

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1.1. Contact organisation

Statiscal Office of Lithuania

1.2. Contact organisation unit

Agricultural and environmental statistics division

1.5. Contact mail address

29 Gedimino Ave, LT-01500, Vilnius, Lithuania

2. Statistical presentation

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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
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 - December 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	Order No DĮ-152 of the Director General of the Department of Statistics Lithuania (Statistics Lithuania) of 10 July 2017 concerning approval of the report on dessert apple trees (Annex 1 of the report of the area and the harvest of agricultural crops ŽŪ-29 (agricultural companies and enterprises), ŽŪŪ-29 (farmer's and family farms) (annual)
2.1.8 Link to the national legislation	https://www.e-tar.lt/portal/lt/legalAct/a3069f10656211e7b85cfdc787069b42
2.1.9 Responsible organisation for the national legislation	Statistics Lithuania
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.	None
2.1.12 Please indicate which national definitions differ from those in the EU regulation, if any.	None
2.1.13 Please indicate which additional variables have been collected if compared to Regulation (EU) 1337/2011, if any?	None
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.	Auksis, Bogatyr, Noris, Alva, Ligol, Delikates, Telisare, Lodel, Cortland, Geneva Early
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

2005-2017

2.9. Base period

Not applicable

3. Statistical processing

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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"	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"	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	Census of dessert apple trees 2017
3.1.8 Name of Organisation responsible	Statistics Lithuania
3.1.9 Main scope	Data on the area, age and density of dessert apple trees entirely or mainly for the market on national level.
3.1.10 Target fruit tree types	Dessert apple trees
3.1.11 List used to build the frame	Census of dessert apple trees 2007, 2012, FSS 2010, JACS Total survey (census) on crops production from arable land, vegetables, permanent crops and winter crops sowing area for agricultural companies and enterprises 2017 Sample survey on crops production from arable land, vegetables, permanent crops and winter crops sowing area for farmers’ and family farms 2017 Data of the Ministry of Agriculture of the Republic of Lithuania Data of Lithuanian Association of Business Orchards
3.1.12 Any possible threshold values	The threshold for the farms was at least 1 ha of utilized agricultural land. For those that had less than 1 ha of utilized agricultural land, the threshold for income from agricultural production was no less than EUR 1520 per year.
3.1.13 Population size	57
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
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	Census of dessert apple trees 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)	Annex 1
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
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:
Annex1-Dessert-apples-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 Aggregates Consistency Data flagging
3.4.3 If Other, please specify

3.5. Data compilation

3.5.1 Describe the data compilation process

The sources of statistical information was the statistical census on dessert apple trees (Annex 1 of the statistical survey on agricultural crop area, harvest and yield ŽŪ-29, ŽŪŪ-29), conducted by Statistics Lithuania.
Data were collected via Electronic statistical data preparation and transfer system e. Statistika gyventojams (for farmers’ and family farms), e-Statistics (for agricultural companies and enterprises), email, fax, mail.

Statistical data validation is carried out by Statistics Lithuania. Primary data entry control is performed using ORACLE software, which searches for errors at the data entry stage. Logical links and arithmetical errors are checked. The results are compared with the results of the previous years and data from administrative sources.

3.5.2 Additional comments

3.6. Adjustment

Data editing, weight adjustment for non-response are performed.

4. Quality management

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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 quality of statistical information and its production process is ensured by the provisions of the European Statistics Code of Practice. In 2007, a management system, conforming with the requirements of the international quality management system standard ISO 9001, was introduced at Statistics Lithuania. Data quality is in accordance with principles of accuracy and reliability, timeliness and punctuality, coherence and compatibility. The quality of the information obtained is analysed. Outliers are identified and analysed. In case of significant discrepancies, data providers are contacted, and the reasons for discrepancies are clarified. Additional statistical data control is exercised at the macrodata level. Statistical indicators are compared with the previous period’s data and other relevant indicators obtained from statistical surveys or administrative sources.
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?
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	Improvement
4.2.2 Relevance	Stable
4.2.3 Accuracy and reliability	Stable
4.2.4 Timeliness and punctuality	Stable
4.2.5 Comparability	Improvement
4.2.6 Coherence	Stable
4.2.7 Additional comments

5. Relevance

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5.1. Relevance - User Needs

5.1.1 If certain user needs are not met, please specify which and why	None
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?	Yes
If Yes, please answer all the following questions
5.2.2 Year of the user satisfaction survey	From 2005, user opinion surveys have been conducted on a regular basis. Official Statistics Portal traffic is monitored, website visitor opinion polls, general opinion poll on the products and services of Statistics Lithuania, target user group opinion polls and other surveys are conducted. In 2007, the compilation of a user satisfaction index was launched. The said surveys are aimed at the assessment of the overall demand for and necessity of statistical information in general and specific statistical indicators in particular.
5.2.3 How satisfied were the users?	Satisfied
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

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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
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	Census of dessert apple trees 2017
Over-coverage
6.3.1.2 Does the sample frame include wrongly classified units that are out of scope?	Yes
6.3.1.3 What methods are used to detect the out-of scope units?	Over-coverage units were eliminated during the data collection.
6.3.1.4 Does the sample frame include units that do not exist in practice?	Yes
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?	During the survey on crops production from arable land, vegetables, permanent crops and winter crops sowing area 2017 some changes in the farms type of farming were obtained. Therefore those farms were added to the frame.
6.3.1.9 How large do you estimate the proportion of those units? (%)	5.5%
6.3.1.10 Impact on the data quality	Low
Misclassification
6.3.1.11 Impact on the data quality	Low
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
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	Census of dessert apple trees 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?	3
6.3.2.4 Preparatory testing of the questionnaire?	No
6.3.2.5 Number of units participating in the tests?
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?	No
6.3.2.8 Are there pre-filled questions?	No
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?	In connection with the data entry, a thorough data checking of each questionnaire was done. The data on area of apples were compared with the corresponding data of the report on the area and the harvest of agricultural crops ŽŪŪ-29. If there were any uncertainties, the interviewers were asked to clarify the data or to give complementary information. The data were compared with data of previous survey and of purchase of agriculture production.
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	Census of dessert apple trees 2017
6.3.3.2 Unit non-response - rate	9%
6.3.3.3 How do you evaluate the recorded unit non-response rate in the overall context?	Low
6.3.3.4 Measures taken for minimising the unit non-response	Follow-up interviews Reminders
6.3.3.5 If Other, please specify
6.3.3.6 Item non-response rate	0%
6.3.3.7 Item non-response rate - Minimum	0%
6.3.3.8 Item non-response rate - Maximum	0%
6.3.3.9 Which items had a high item non-response rate?	None
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	Census of dessert apple trees 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

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?
6.6.4 How do you evaluate the impact of the revisions?
6.6.5 Additional comments

7. Timeliness and punctuality

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7.1. Timeliness

7.1.1 When were the first results for the reference period published?	June 2018
7.1.2 When were the final results for the reference period published?	June 2018
7.1.3 Reasons for possible long production times?	No delays

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

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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	2005-2017
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, Price statistics

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
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

Logical comparisons have been made. Other national data sources include total area of apple tress (dessert apple and apple for industrial processing). According purchase statistics dessert apples accounted 18 per cent of purchased apples.

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

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9.1. Dissemination format - News release

9.1.1 Do you publish a news release?	No
9.1.2 If Yes, please provide a link

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?	No
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	Not available
9.3.2 Is an on-line database accessible to users?	Yes
9.3.3 Please provide a link	https://osp.stat.gov.lt/pradinis

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	http://estatistika.stat.gov.lt/statistiniu-ataskaitu-formos.html

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

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10.1 Efficiency gains if compared to the previous quality report	On-line surveys Increased use of administrative data
10.2 If Other, please specify
10.3 Burden reduction measures since the previous quality report	Easier data transmission
10.4 If Other, please specify

11. Confidentiality

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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	In the process of statistical data collection, processing and analysis and dissemination of statistical information, Statistics Lithuania fully guarantees the confidentiality of the data submitted by respondents (households, enterprises, institutions, organisations and other statistical units), as defined in the Confidentiality Policy Guidelines of Statistics Lithuania.

11.2. Confidentiality - data treatment

11.2.1 Describe the procedures for ensuring confidentiality during dissemination

Regulation (EC) No 223/2009 of the European Parliament and of the Council of 11 March 2009 on European statistics provides a reference framework for European statistics on permanent crops. In particular, that Regulation requires conformity with principles of professional independence, impartiality, objectivity, reliability, statistical confidentiality and cost-effectiveness.

Data confidentiality is laid down by Description of Statistical Disclosure Control Methods, approved by Order No DĮ-124 of 27 May 2008 of the Director General of Statistics Lithuania; Paragraph 6 of the Rules for the Secure Management of Electronic Information in the Statistical Information System, approved by Order No DĮ-76 of 12 March 2008 of the Director General of Statistics Lithuania

For reason of confidentiality, the data were aggregate before publication in accordance with the breakdowns of species by groups, density classes and age classes set out in Annex I of Regulation (EC) No 1337/2011, taking due account of the protection of confidential data.

11.2.2 Additional comments

12. Comment

Related metadata

Annexes