Orchard (orch)

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

Compiling agency: ELSTAT (Hellenic Statistical Authority)

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

ELSTAT (Hellenic Statistical Authority)

1.2. Contact organisation unit

Primary Sector Statistics Division

Livestock and Crop Capital Statistics Section

1.5. Contact mail address

Pireos 46 & Eponiton

18510 – Piraeus

2. Statistical presentation

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2.1. Data description

Main characteristics

2.1.1 Describe shortly the main characteristics of the statistics	Statistics on orchards provide data on cultivated areas under fruit trees by type of trees, as well as by variety, density and age of the plantations. Data are collected through sample survey. The unit of the survey is the agricultural holding under the specific kind of fruit tree that is surveyed. The survey covers the whole of Greece.
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 Orange trees Small citrus fruit trees Lemon trees Olive trees Other
2.1.3 If Other, please specify	Cherry trees

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	Decision of the President of ELSTAT No 5675/Γ2−733/5.7.2017 (Government Gazette No 2412/Β/14.7.2017), on the “Approval, proclamation, assignment and distribution of costs for conducting the Livestock and Crop Surveys for the year 2017, as well as approval of using statistical representatives and determination of their remuneration for the year 2016”.
2.1.8 Link to the national legislation
2.1.9 Responsible organisation for the national legislation	ELSTAT (Hellenic Statistical Authority)
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?	Cherry trees
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.	SCARLET FYRIKI DELICIOUS PILAFA JEROMINE BELFORD OZARK GOLD SUMMER RED JONATHAN
2.4.6 Other dessert pears n.e.c.	KRYSTALLI KONTOULA SANTA MARIA BUTIRRA PRECOCE MORETTINI SISSY AXTSES (OF LESVOS) GRAND CHAMPION
2.4.7 Other oranges n.e.c.
2.4.8 Other small citrus fruits, including hybrids	NOVA COMMON ORTANIQUE ENCORE MANDARINE PAGE FORTUNA

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 (Greece).
2.7.2 Which special Member State territories are included?	Mount Athos (included in NUTS 1: EL5-VOREIA ELLADA)

2.8. Coverage - Time

From 1982 to 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"	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

Orchard Survey 2017

3.1.16 Name of Organisation responsible

Hellenic Statistical Authority (ELSTAT)

3.1.17 Main scope

The main objective of the survey is the collection of data on cultivated areas under fruit trees by type of trees (apple trees, pear trees, peach trees, apricot trees, cherry trees, orange trees, lemon trees, small citrus fruit trees and olive trees), as well as by variety, density and age of the plantations.

The survey unit is the agricultural holding, (a unified unit both in terms of technical and economic perspective, which is run by a unified management body and produces agricultural products). More specifically, the surveyed unit is an agricultural holding that cultivate fruit trees.

The survey covers the whole of the country (Greece) and the survey results are nationally published at the level of the Region (NUTS 2).

3.1.18 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

3.1.19 List used to build the frame

The Sampling Frame, which was used in this survey, was the Register of Agricultural Holdings of ELSTAT (Farm Register) as this resulted from the Agricultural Census of 2009-2010 and the relevant updating procedures hence.

The Farm Register is a statistical register generated and updated periodically during the Agricultural Censuses. Furthermore, the Farm Register is updated from administrative sources such as OPEKEPE (Payment and Control Agency for Guidance and Guarantee Community Aid), as well as other surveys conducted by ELSTAT such as the FSS (conducted every three years) and the specialized agricultural surveys (surveys on livestock and permanent crops).

3.1.20 Any possible threshold values

No threshold is applied. A holding under orchard trees in order to be included into the sampling frame of the survey, should cultivate more than 0.1 stemmas (1 stemma: Greek unit of land area equal to 1.000 m3 or 0.1ha).

3.1.21 Population size

Population size (number of holdings):

Dessert apple trees: 12,054

Dessert pear trees: 7,376

Apricot trees: 8,163

Dessert peach and nectarine trees: 20,428

Orange trees: 52,568

Lemon trees: 21,877

Small citrus fruit trees: 16,592

Olive trees: 448,873

3.1.22 Sample size

Sample size (number of holdings):

Dessert apple trees: 551

Dessert pear trees: 500

Apricot trees: 452

Dessert peach and nectarine trees: 907

Orange trees: 1,025

Lemon trees: 580

Small citrus fruit trees: 586

Olive trees: 1,676

3.1.23 Sampling basis

List

3.1.24 If Other, please specify

3.1.25 Type of sample design

Random
Stratified

3.1.26 If Other, please specify

3.1.27 If Stratified, number of strata

In each region (NUTS2), the holdings were stratified into L=10 size classes (with the exception of olive trees stratified into L= 13) according to their size, determined by their area under the specific kind of fruit tree in the agricultural register.

Size class	Area under fruit trees (in ha)	Area under olive trees (in ha)
1	0.01 - 0.19	0.01 - 0.19
2	0.2 - 0.39	0.2 - 0.39
3	0.4 - 0.59	0.4 - 0.59
4	0.6 - 0.99	0.6 - 0.99
5	1 - 1.99	1 - 1.99
6	2 - 2.99	2 - 2.99
7	3 - 4.99	3 - 4.99
8	5 - 6.99	5 - 6.99
9	7 - 9.99	7 - 9.99
10	10 +	10 - 14.99
11		15 - 21.99
12		22 - 34.99
13		35+

Holdings with fruit trees belonging to the last size class -10^th for all fruit trees except olive trees and 13^th for olive trees- are surveyed exhaustively.

3.1.28 If Stratified, stratification criteria

Unit size
Unit location

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	Orchard survey 2017
3.3.9 Methods of data collection	Face-to-face interview
3.3.10 If Other, please specify
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)	See annex
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:
3.3.13 - Orchard Survey 2017 Questionnaire

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	See annex
3.5.2 Additional comments

Annexes:
3.5.1 Data compilation process

3.6. Adjustment

Not applicable

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 Hellenic Statistical Authority pursues its mission by following in all areas the highest European and international standards of statistical practice, as well as by unswervingly observing the rules and responsibilities it is committed to. The quality policy of ELSTAT is available on the portal of ELSTAT: http://www.statistics.gr/documents/20181/2571f853-1e37-46da-9387-595bbe2a162b The guiding principles and best practices for ensuring quality in the various stages of the statistical production process in ELSTAT, are included in the “Quality Guidelines”, available on the portal of ELSTAT: http://www.statistics.gr/documents/20181/1609796/ELSTAT_Quality_Instructions_EN.pdf/4095e67c-2fe4-450b-8a95-18bc992a83c6
4.1.3 Has a peer review been carried out?	Yes
4.1.4 If Yes, which were the main conclusions?	The Peer Reviewers’ recommendations and ELSTAT improvement actions in response to the recommendations, are available on Eurostat website: https://ec.europa.eu/eurostat/documents/64157/4372828/2015-EL-improvement-actions/479f2d0c-afb6-4a6c-b04a-4ae90d34f0c3
4.1.5 What quality improvements are foreseen?	Other
4.1.6 If Other, please specify	Mainly issues related to human recourses and the acess to administrative data.
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	Improvement
4.2.4 Timeliness and punctuality	Improvement
4.2.5 Comparability	Improvement
4.2.6 Coherence	Improvement
4.2.7 Additional comments	Although discrepancies still exist, there has been an improvement in comparability with ACS results, specifically in the case of lemon trees, apple trees and orange trees.

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
5.1.2 Please specify any plans to satisfy needs more completely in the future
5.1.3 Additional comments	According to ELSTAT’s general policy the user needs are expressed in user conferences conducted at regular intervals. ELSTAT also records the user needs through the everyday communication between the institution and the users. ELSTAT compiles its annual programs as well as the 3-year program of the Hellenic Statistical System setting as a goal the satisfaction of users needs. Main users of agricultural surveys data are: National Accounts Division of ELSTAT, Ministry of Rural Development and Food, Universities, Research centers, European and International Organizations.

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	In order to fulfil the need of Greek users, ELSTAT carries out annually, since 2011, a User Satisfaction Survey. The data from this survey are posted on the portal of ELSTAT. The last available data concern year 2016: http://www.statistics.gr/en/user-satisfaction-survey
5.2.3 How satisfied were the users?	Highly 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?	None
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?	Coverage error 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

Orchard survey 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

Basic weight
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

See annex

6.2.7 Sampling error - indicators

Please provide the coefficients of variation in %

	CV (%)
Dessert apple trees	0.76
Apple trees for industrial processing
Dessert pear trees	0.90
Pear trees for industrial processing
Apricot trees	0.95
Dessert peach and nectarine trees	0.53
Peach and nectarine trees for industrial processing (including group of Pavie)
Orange trees	0.64
Small citrus fruit trees	0.89
Lemon trees	1.23
Olive trees	0.40
Table grape vines

6.2.8 Additional comments

Annexes:
6.2.6 Calculation of coefficients of variation

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

Orchard survey 2017

Over-coverage

6.3.1.15 Does the sample frame include wrongly classified units that are out of scope?

Yes

6.3.1.16 What methods are used to detect the out-of scope units?

These units are detected when the survey is contacted. In this case, when feasible, the initial sample holding is replaced by a holding from the “additional sample” according to the relevant rules that are given to interviewers.

6.3.1.17 Does the sample frame include units that do not exist in practice?

Yes

6.3.1.18 Over-coverage - rate

	OVER COVERAGE RATE
Dessert apple trees	10.1%
Dessert pear trees	13.1%
Apricot trees	13.2%
Dessert peach and nectarine trees	9.3%
Orange trees	8.9%
Small citrus fruit trees	8.2%
Lemon trees	11.8%
Olive trees	6.0%

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?

6.3.1.21 If Not, which units are not included?

As reported in item 3.1.19, the Farm Register is updated periodically from administrative sources and surveys. New holdings may not be included at a certain point of time. During the compilation of FSS 2016, 3,467 holdings were surveyed for the first time coming from OPEKEPE. Out of these, 1,635 holdings were recorded with cultivated areas under fruit trees. These holdings were not included in the orchard survey 2017 sample frame, since at the sampling stage of the orchard survey (September 2017) ELSTAT farm register was not yet updated on the basis of FSS 2016 results.

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

The proportion of units not included in the Farm Register, although difficult to be measured, is considered very low.

6.3.1.23 Impact on the data quality

Low

Misclassification

6.3.1.24 Impact on the data quality

Low

Common units

6.3.1.25 Common units - proportion

Not applicable

6.3.1.26 Additional comments

Over-coverage stems from the fact that there are units accessible via the frame but they do not belong to the target population. In orchard survey(s), the over-coverage has to do mainly with holdings that were included in the farm register, they were selected in the sample, but they did not actually exist at the time of the survey (namely, holdings that do not operate permanently or temporarily, holdings fully turned over and merged with another holding or double entries).

Misclassification stems from the fact that the auxiliary information provided by the frame may be inaccurate for some population units (e.g. wrong size). Due to misclassification problems, a number of sampling units may change design stratum after the data collection. In the orchard survey, these units were allocated to the new strata (post-stratification), changing their initial selection probabilities.

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	Orchard survey 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?	7
6.3.2.15 Preparatory testing of the questionnaire?	No
6.3.2.16 Number of units participating in the tests?
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?	No
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?	Measurement errors occur during the data collection and make the recorded values of variables to be different than the true ones. Their causes are commonly categorized as: Survey instrument: Questionnaire or other measuring instrument used for data collection may lead to recording of wrong values; Respondent: Respondents may, consciously or unconsciously, provide erroneous data; Interviewer: Interviewers may influence the answers given by respondents. In the orchard survey, the data collection method used was face-to-face interview completing paper questionnaires. The collection method applied ensured the high quality of the information gathered, since the interviewers assisted the respondents, and carefully checked the filled in questionnaires, before leaving the holding. The interviewers participated in the survey were private collaborators. Before the initiation of the survey, the interviewers attended a training seminar. The scope of the seminar was to enable the interviewers to: a) fully understand the definitions of the survey characteristics in order to avoid the respondent bias, (b) correctly fill in the questionnaire, and (c) efficiently check for errors by applying logical checks. The structure and the size of the questionnaire were designed to be user-friendly for the interviewers and the questions were formulated in a clear and simple language, using appropriate vocabulary. Additionally, documents containing useful instructions were compiled, analyzing all the questions of the questionnaire. This activity aimed at collecting fully filled in questionnaires, with no missing variables. The support and supervision of the data collection and the data processing were decentralized in the regional offices of ELSTAT. In regional offices the staff was involved in coding, checking for the detection of measurement errors, logical checks and comparisons of the survey data with other sources of statistical information.
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

Orchard survey 2017

6.3.3.12 Unit non-response - rate

	UNIT NON RESPONSE RATE
Dessert apple trees	15.9%
Dessert pear trees	11.0%
Apricot trees	10.4%
Dessert peach and nectarine trees	12.9%
Orange trees	10.5%
Small citrus fruit trees	9.1%
Lemon trees	10.6%
Olive trees	13.1%

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
Other

6.3.3.15 If Other, please specify

Methods employed towards reducing the non-response rate were:

Updating the Farm Register so as to have valid contact information;
Contacting the interviewees prior to the actual interview to ensure their presence;
Training of the enumerators on the personal interview procedure;
Contacting non-respondents by telephone at a later date, and if possible completing the interview, especially for large holdings and holdings belonging to exhaustively surveyed strata.

For cases where the holder refused to provide information, the interviewer had instructions to insist and inform the holder about the Greek Statistical Law that obliges the surveyed person to provide the required statistical information.

In cases in which it was impossible to collect statistical information from certain sampling units (no response, permanent absence of the holder etc.) the original sample holding was replaced by a holding from the “additional sample” according to the relevant rules that were given to interviewers.

6.3.3.16 Item non-response rate

There was no item non-response, because even in some very rare cases where a field in the questionnaire was not filled in, the personnel of ELSTAT contacted the farm owner in order to eliminate item non-response.

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?

None

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

Orchard survey 2017

6.3.4.7 Imputation - rate

	IMPUTATION RATE
Dessert apple trees	1.9%
Dessert pear trees	2.2%
Apricot trees	2.4%
Dessert peach and nectarine trees	3.1%
Orange trees	0.9%
Small citrus fruit trees	2.1%
Lemon trees	2.4%
Olive trees	0.4%

6.3.4.8 Imputation - basis

Same unit in previous data
Other sources

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

In case of difficulties (no response, permanent absence of the holder etc.) the original sample holding was replaced by a holding from the “additional sample” according to the relevant rules that were given to interviewers. When this was not feasible, to address the problem of non response a limited number of imputations was applied.

6.3.5. Model assumption error

Not applicable

6.4. Seasonal adjustment

Not applicable

6.5. Data revision - policy

The revision policy of the Hellenic Statistical Authority (ELSTAT) defines standard rules and principles for data revisions, in accordance with the European Statistics Code of Practice and the principles for a common revision policy for European Statistics contained in the Annex of the European Statistical System (ESS) guidelines on revision policy.

(http://www.statistics.gr/documents/20181/a49dca9a-dacf-4b52-b5df-b156216cb354)

6.6. Data revision - practice

6.6.1 Data revision - average size	No revisions were made.
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?	29/11/2018
7.1.2 When were the final results for the reference period published?	29/11/2018
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

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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	1982-
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	The survey data is available for the following years: 1982, 1987, 1992, 1997, 2002, 2007, 2012 and 2017. In addition, for the period 1982-1997 except of the basic survey conducted every five years, a special survey was conducted every year in order to study specific features. The survey results are available electronically for the years 1997-2012, while for the previous years the results are available in paper form.

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
IACS

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	-2.4	-3.2	7.6
Apple trees for industrial processing
Dessert pear trees	-23.3	-5.7	-14.5
Pear trees for industrial processing
Apricot trees	-49.2	-44.5	-56.0
Dessert peach and nectarine trees	-22.6	-16.2	32.3
Peach and nectarine trees for industrial processing (including group of Pavie)
Orange trees	-2.9	-0.9	21.2
Small citrus fruit trees	-34.6	-19.9	-26.7
Lemon trees	-1.9	-8.7	43.2
Olive trees	-42.7	-2.4	-4.7
Table grape vines

The frame for both FSS and Orchard Survey is the statistical farm register maintained by ELSTAT. This register has resulted from the agricultural census (2010) and is updated on the basis of the crop and livestock surveys and the FSS.

The differences recorded in the results of the two surveys can be attributed to:

The different sampling design of the two surveys. Namely, the significance of the specific characteristics in the FSS sampling procedure is rather small; thus the extrapolation factors are quite different between the two surveys. Therefore even if the raw data for the same sample unit are the same, the resulting values might be quite different.
FSS 2016 has covered a number of holdings coming from Payment and Control Agency for Guidance and Guarantee Community Aid - OPEKEPE (IACS), which have not been covered by the Orchard Survey 2017, since at the sampling stage of the orchard survey (September 2017) ELSTAT farm register was not yet updated on the basis of FSS 2016 results.

The data of the Ministry of Rural Development and Food (responsible for the ACS) are estimates obtained primarily from the agencies located in the regional divisions of the country. They are based on the premise of the opinions of experts in the agricultural sectors. To be more precise, these include local agriculturists, cooperatives, local farmers, etc. The responsible department collects data from regional administrative divisions, under the direct supervision of the local agriculturist, which cooperate with cooperatives, other governmental agencies and local farmers.

The IACS data are based on the farmers’ declarations. These declarations come only from the farmers who are beneficiaries of subsidies. Small size holdings are excluded.

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 relevant

8.5. Coherence - National Accounts

Since the survey is conducted every five years, the results can be used by National Accounts only to cross-check the annual data sources used.

8.6. Coherence - internal

All correlating variables are coherent with each other.

9. Accessibility and clarity

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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.statistics.gr/el/statistics/-/publication/SPG63/2012

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?
9.2.5 Please provide a link

9.3. Dissemination format - online database

9.3.1 Data tables - consultations	3,308 consultations in 2017, including consultations of metadata.
9.3.2 Is an on-line database accessible to users?	Yes
9.3.3 Please provide a link	http://www.statistics.gr/el/statistics/-/publication/SPG63/2012

9.4. Dissemination format - microdata access

9.4.1 Are micro-data accessible to users?	Yes
9.4.2 Please provide a link	Users can have access to micro-data, upon request, after submitting an application to: http://www.statistics.gr/en/scientific_provision_data and following the approval of the Statistical Confidentiality Committee. For confidentiality reasons, access to microdata is permitted only under strict conditions and with respect of the relevant process.

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	http://www.statistics.gr/el/statistics/-/publication/SPG63/2012
9.6.3 Are methodological papers available?	Yes
9.6.4 Please provide a link	http://www.statistics.gr/el/statistics/-/publication/SPG63/2012
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	3,308 consultations in 2017, including consultations of metadata.
9.7.3 Is a quality report available?	Yes
9.7.4 Please provide a link	http://www.statistics.gr/el/statistics/-/publication/SPG63/2012

10. Cost and Burden

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10.1 Efficiency gains if compared to the previous quality report	None
10.2 If Other, please specify
10.3 Burden reduction measures since the previous quality report	None
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?	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	The issues concerning the observance of statistical confidentiality by the Hellenic Statistical Authority (ELSTAT) are arranged by articles 7, 8 and 9 of the Law 3832/2010 as in force, by Articles 8, 10 and 11(2) of the Regulation on Statistical Obligations of the agencies of the Hellenic Statistical System and by Articles 10 and 15 of the Regulation on the Operation and Administration of ELSTAT. More precisely: ELSTAT disseminates the statistics in compliance with the statistical principles of the European Statistics Code of Practice and in particular with the principle of statistical confidentiality. http://www.statistics.gr/en/statistical-confidentiality?inheritRedirect=true

11.2. Confidentiality - data treatment

11.2.1 Describe the procedures for ensuring confidentiality during dissemination

ELSTAT protects and does not disseminate data it has obtained or it has access to, which enable the direct or indirect identification of the statistical units that have provided them by the disclosure of individual information directly received for statistical purposes or indirectly supplied from administrative or other sources. ELSTAT takes all appropriate preventive measures so as to render impossible the identification of individual statistical units by technical or other means that might reasonably be used by a third party. Statistical data that could potentially enable the identification of the statistical unit are disseminated by ELSTAT if and only if:

a) these data have been treated, as it is specifically set out in the Regulation on Statistical Obligations of the agencies of the Hellenic Statistical System (ELSS), in such a way that their dissemination does not prejudice statistical confidentiality or

b) the statistical unit has given its consent, without any reservations, for the disclosure of data.

The confidential data that are transmitted by ELSS agencies to ELSTAT are used exclusively for statistical purposes and the only persons who have the right to have access to these data are the personnel engaged in this task and appointed by an act of the President of ELSTAT.

ELSTAT may grant researchers conducting statistical analyses for scientific purposes access to data that enable the indirect identification of the statistical units concerned. The access is granted provided the following conditions are satisfied:

a) an appropriate request together with a detailed research proposal in conformity with current scientific standards have been submitted;
b) the research proposal indicates in sufficient detail the set of data to be accessed, the methods of analyzing them, and the time needed for the research;
c) a contract specifying the conditions for access, the obligations of the researchers, the measures for respecting the confidentiality of statistical data and the sanctions in case of breach of these obligations has been signed by the individual researcher, by his/her institution, or by the organization commissioning the research, as the case may be, and by ELSTAT.

Issues referring to the observance of statistical confidentiality are examined by the Statistical Confidentiality Committee (SCC) operating in ELSTAT. The responsibilities of this Committee are to make recommendations to the President of ELSTAT on:

• the level of detail at which statistical data can be disseminated, so as the identification, either directly or indirectly, of the surveyed statistical unit is not possible;

• the anonymization criteria for the microdata provided to users;

• the granting to researchers access to confidential data for scientific purposes.

The staff of ELSTAT, under any employment status, as well as the temporary survey workers who are employed for the collection of statistical data in statistical surveys conducted by ELSTAT, who acquire access by any means to confidential data, are bound by the principle of confidentiality and must use these data exclusively for the statistical purposes of ELSTAT. After the termination of their term of office, they are not allowed to use these data for any purpose.

Violation of data confidentiality and/or statistical confidentiality by any civil servant or employee of ELSTAT constitutes the disciplinary offence of violation of duty and may be punished with the penalty of final dismissal.

ELSTAT, by its decision, may impose a penalty amounting from ten thousand (10,000) up to two hundred thousand (200,000) euros to anyone who violates the confidentiality of data and/or statistical confidentiality. The penalty is always imposed after the hearing of the defense of the person liable for the breach, depending on the gravity and the repercussions of the violation. Any elapse constitutes an aggravating factor for the assessment of the administrative sanction.

11.2.2 Additional comments

12. Comment

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Since the national press release for the results of Orchard Survey 2017 is scheduled for the 29th November 2018 and thus data tables and metadata are not yet uploaded on the web, the links provided in item 9.-Accessibility and Clarity refer to the previous Orchard Survey (2012).

As reported in item 2.4, apples, pears, peaches and nectarines for industrial processing are not separately surveyed because our Farm Register does not include information concerning the use. However, in the case of peach and nectarine trees, from the varieties declared in the questionnaires it became possible to present the results separately for the two categories. This was feasible because there are specific varieties intended only for industrial processing.

In case of orange trees the national harvest time definitions are as follows:

	Early	Medium	Late
Navel	October-January	December-March	January-May
Blancas	December-March	x	March-September

Related metadata

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Annexes

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ESQRS_ANNEX_EL