Orchard (orch)

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

Compiling agency: Statistics Austria Guglgasse 13, A-1110 Vienna

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

Statistics Austria

Guglgasse 13, A-1110 Vienna

1.2. Contact organisation unit

Directorate Spatial Statistics/Agriculture and Forestry/Plant Production

1.5. Contact mail address

Statistics Austria

Guglgasse 13, A-1110 Vienna

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 Dessert pear trees Apricot trees Dessert peach and nectarine trees Other
2.1.3 If Other, please specify	Cherries, Plums, Soft fruit, Elderberries, Nuts

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 - February 2018

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	BGBl II 247/2017: 247. Verordnung des Bundesministers für Land- und Forstwirtschaft, Umwelt und Wasserwirtschaft betreffend die Statistik über Erwerbsobstanlagen
2.1.8 Link to the national legislation	https://www.ris.bka.gv.at/eli/bgbl/II/2017/247
2.1.9 Responsible organisation for the national legislation	Federal Ministry for Sustainability and Tourism
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.	Not classes/groups were surveyed, but the single species and planting years, so all necessary aggregates for Reg. 1337/2011 could be built out of these raw data. For national publication partly other aggregates were used than for acquirements of Reg. 1337/2011
2.1.13 Please indicate which additional variables have been collected if compared to Regulation (EU) 1337/2011, if any?	additional species; organic farming; irrigation; use of hail net and weather protection; soft fruit under glass and percentage of pick-your-own; yield; marketing channels
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	Not applicable
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.	Topaz, Kronprinz Rudolf, Arlet, Rubinette, Summered, Rubens, Opal, Maschanzker, Minneiska (Swee Tango), Gloster
2.4.6 Other dessert pears n.e.c.	Uta, Novemberbirne, Gute Luise, Alexander Lucas, Cepuna (Migo), Clapps Liebling, Concorde, Carmen, Forellenbirne, Gellerts Butterbirne
2.4.7 Other oranges n.e.c.	Not applicable
2.4.8 Other small citrus fruits, including hybrids	Not applicable

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

Data on fruit plantations are available since 1973. However, due to different concepts and definitions the comparability among the different census is limited.

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	2
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"	1
3.1.5 Total number of sources of the type "Experts"	0
3.1.6 Total number of sources of the type "Other sources"	0

Census

These questions only apply to censuses. If there is more than one census, please describe the main census below and the additional ones in table 3.1 of the annexed Excel file
3.1.7 Name/Title	Census on fruit plantations 2017
3.1.8 Name of Organisation responsible	Statistics Austria
3.1.9 Main scope	Fruit growing agricultural holdings
3.1.10 Target fruit tree types	Dessert apple trees Dessert pear trees Apricot trees Dessert peach and nectarine trees Other
3.1.11 List used to build the frame	Agricultural Register (iFarm)
3.1.12 Any possible threshold values	0,15 ha total fruit area or 0,10 ha soft fruit area
3.1.13 Population size	3909 fruit growing holdings (eligible holdings); frame: 5175 holdings
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	IACS
3.1.32 Name of Organisation responsible	Agrarmarkt Austria
3.1.33 Contact information (email and phone)	+43 (1) 33151-0; oepul@ama.gv.at
3.1.34 Main administrative scope	Agricultural holdings
3.1.35 Target fruit tree types	Dessert apple trees Dessert pear trees Apricot trees Dessert peach and nectarine trees Other
3.1.36 Geospatial Coverage	Regional
3.1.37 Update frequency	Annual
3.1.38 Legal basis	National Regulation BGBl Nr. 376/1992
3.1.39 Are you able to access directly to the micro data?	Yes
3.1.40 Are you able to check the plausibility of the data, namely by contacting directly the units?	Yes
3.1.41 How would you assess the proximity of the definitions and concepts (including statistical units) used in the administrative source with those required in the EU regulation?	Very good
3.1.42 Please list the main differences between the administrative source and the statistical definitions and concepts	IACS contains just the area information of fruit species without differentiation in variety and planting year; extensive fruit areas partly included
3.1.43 Is a different threshold used in the administrative source and statistical data?	Yes
3.1.44 If Yes, please specify	only units with funding applications registered
3.1.45 Additional comments	administrative data only were used additionally to the census in order to help the respondents when filling in the questionnaire (prefilled parts) and for plausibility checks in the electronic questionnaire

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 on fruit plantations 2017
3.3.2 Methods of data collection	Electronic questionnaire Telephone interview
3.3.3 If Other, please specify	Not applicable
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)	see Annex: Census_fruit_plantations_2017_AT.pdf
3.3.7 Additional comments	None

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	IACS
3.3.16 Extraction date	9. June 2017
3.3.17 How easy is it to get access to the data?	Access granted upon a simple request
3.3.18 Data transfer method	ACCESS-database via external server
3.3.19 Additional comments	Access to administrative sources in general is regulated by legal basis; for the census on fruit plantations additionally through specific national regulation for this census

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_fruit_plantatione_2017_AT

3.4. Data validation

3.4.1 Which kind of data validation measures are in place?	Automatic Manual
3.4.2 What do they target?	Completeness Outliers Consistency Aggregates
3.4.3 If Other, please specify	None

3.5. Data compilation

3.5.1 Describe the data compilation process

The census started with reference date 15. November 2017. For area related information the reference period was the calendar year 2017. All fruit growing farmers were obliged to fill in the electronic questionnaire. Some days before the reference date, all concerned farmers got a letter with comprehensive information and a handbook how to fill in the questionnaire. If they could not do this by themselves, they could give the required information by telephone interview. The interviewers were trained staff of Statistics Austria, who filled in the data directly in the electronic questionnaire. The questionnaire was personalized with personal login data and partly prefilled with administrative data, which always could be overwritten and were mainly used for automatic plausibility checks. The farmers had to fill in the questionnaires within 4 weeks, after getting the census information with the login data, or had to registrate for telephone interview two weeks after having got the census-information and by then were obliged to give the information by telephone interview within 10 weeks. A Hotline with trained staff of Statistics Austria was available within the whole census-period. For data processing data were imported into an ACCESS-application, where comprehensive plausibility checks were made: Comparison with earlier census, FSS and administrative sources, internal coherence. Imputation was used very scarce as due to automatic plausibility checks in the electronic questionnaire, it was not possible for the respondent to fill in incomplete data sets, so only in the case of clearly wrong data (e. g comma faults) or missing of whole data sets, imputation was necessary with the help of previous survey data, similar units or admin. source. If outliers or big deviations to previous data sets were detected, the respondent has often been contacted directly, to clarify the matter.

3.5.2 Additional comments

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?	Standard documentation, Feed back round with experts
4.1.3 Has a peer review been carried out?	Yes
4.1.4 If Yes, which were the main conclusions?	In cooperation with the Statistic Committee’s Quality Assurance Committee, feedback meetings on the quality of the various statistical products on the basis of the standard documentation are held regularly within the framework of Statistics Austria’s quality management programme. A feedback meeting on fruit plantations has been carried out in 2014 and will probably take place in 2019 for the actual census 2017.
4.1.5 What quality improvements are foreseen?	Improve data validation Further training
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	Improvement
4.2.3 Accuracy and reliability	Stable
4.2.4 Timeliness and punctuality	Stable
4.2.5 Comparability	Stable
4.2.6 Coherence	Improvement
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	no unmet user needs known
5.1.2 Please specify any plans to satisfy needs more completely in the future
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	2017
5.2.3 How satisfied were the users?	Satisfied
5.2.4 Additional comments	In autumn 2017 Statistics Austria conducted a user satisfaction survey. 108 experts participated in this survey on a voluntary basis and answered via a web based questionnaire. On the one hand the survey covered institutional aspects of general importance. On the other hand users had the possibility to evaluate important quality aspects such as frequency and modes of data usage, as well as the relevance and the general quality of the used data. The census on fruit plantations was not part of the satisfactory survey itself, but other related agricultural statistics, which contain results of fruit plantations survey as a base for further processing (SBS, EAA, Crop statistics). User consultation/contact specifically related to census on fruit plantations was ensured by meetings with relevant users before starting the census.

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?	Very good
6.1.2 What are the main factors lowering the accuracy?	Other
6.1.3 If Other, please specify	Detailed information for special parameters not always available at the respondents
6.1.4 Additional comments	Partly difficult for the respondent to give exact information for example of planting year and number of trees, so these figures are sometimes estimated by the respondents

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 on fruit plantations 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?	check as good as possible in the forefield with other data sources and previous census; holdings with fruit production not for market could not completely be excluded from the frame beforehand - not relevant units were detected while doing the census and excluded before data processing.
6.3.1.4 Does the sample frame include units that do not exist in practice?	Yes
6.3.1.5 Over-coverage - rate	30%
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?	Yes
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	None
Common units
6.3.1.12 Common units - proportion
6.3.1.13 Additional comments	Threshold applied; Holdings with fruit production not for market could not completely be excluded from the frame beforehand

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	IACS
Over-coverage
6.3.1.28 Does the administrative source include wrongly classified units that are out of scope?	Yes
6.3.1.29 What methods are used to detect the out-of scope units?	administrative data only were used additionally to the census in order to help the respondents when filling in the questionnaire (prefilled parts) and for plausibility checks in the electronic questionnaire - so the respondent was able to tell if the unit was out of scope (under giving good reason, e.g. extensive use for self consumption)
6.3.1.30 Does the administrative source include units that do not exist in practice?	No
6.3.1.31 Over-coverage - rate
6.3.1.32 Impact on the data quality	None
Under-coverage
6.3.1.33 Does the administrative source include all units falling within the scope of this survey?	No
6.3.1.34 If Not, which units are not included?	holdings without funding application
6.3.1.35 How large do you estimate the proportion of those units? (%)	20% (9% related to area)
6.3.1.36 Impact on the data quality	Low
Misclassification
6.3.1.37 Impact on the data quality	None
6.3.1.38 Additional comments

6.3.2. Measurement error

Census

These questions only apply to censuses. If there is more than one census, please describe the main census below and the additional ones in table 6.3 of the annexed Excel file
6.3.2.1 Name/Title	Census on fruit plantations 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?	0
6.3.2.4 Preparatory testing of the questionnaire?	Yes
6.3.2.5 Number of units participating in the tests?	15
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?	Yes
6.3.2.8 Are there pre-filled questions?	Yes
6.3.2.9 Percentage of pre-filled questions out of total number of questions	5% (raw estimation)
6.3.2.10 Other actions taken for reducing the measurement error?	interactive plausibility checks and direct help in the questionnaire
6.3.2.11 Additional comments	Because of the interactivity of the questionnaire the number of questions was different for the single respondent, dependent on the cultivated fruit species; so the percentage of prefilled questions cannot be given exactly.

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 on fruit plantations 2017
6.3.3.2 Unit non-response - rate	0,2%
6.3.3.3 How do you evaluate the recorded unit non-response rate in the overall context?	Very low
6.3.3.4 Measures taken for minimising the unit non-response	Follow-up interviews Reminders Legal actions
6.3.3.5 If Other, please specify
6.3.3.6 Item non-response rate	<1% (estimated)
6.3.3.7 Item non-response rate - Minimum	0%
6.3.3.8 Item non-response rate - Maximum	see comment
6.3.3.9 Which items had a high item non-response rate?	see comment
6.3.3.10 Additional comments	due to plausibility checks in the questionnaire, it was not possible for the respondent to fill in incomplete data sets (e.g. fruit variety without planting year), so the item non response was very low

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 on fruit plantations 2017
6.3.4.2 Imputation - rate	<1% (estimated)
6.3.4.3 Imputation - basis	Similar units Same unit in previous data Other sources
6.3.4.4 If Other, please specify	administrative data (IACS)
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

Not applicable

6.4. Seasonal adjustment

Not applicable

6.5. Data revision - policy

Press release of preliminary main results (selected figures) for early information; detailed publication of final data later

6.6. Data revision - practice

6.6.1 Data revision - average size	not quantifiable; preliminary results (selected figures) were published before finishing all plausibility checks
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?	Press release of preliminary main results (selected figures) for early information; detailed publication of final data later
6.6.4 How do you evaluate the impact of the revisions?	Not important
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?	April 2018
7.1.2 When were the final results for the reference period published?	June 2018; Detailed results September 2018
7.1.3 Reasons for possible long production times?	Difficulties to get back all forms (questionnaires) in time

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

Not applicable

8.2. Comparability - over time

8.2.1 Length of comparable time series	1994-2017
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	Until 2007 the net area was counted; beginning with 2012 the gross area in adaption to administrative source (IACS) was counted and the treshold raised; change of threshold in 2017 compared to 2012, the threshold was lowered because more fruit species with smaller areas were counted. For direct comparability of the results 2017 to 2012 and 2007, adaptations (recalculation with adapted treshold) have been made for specific tables (Annex with main results) in the national publication to ensure best possible comparability.

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	-13%		-6%
Apple trees for industrial processing
Dessert pear trees	-26%		-20%
Pear trees for industrial processing
Apricot trees	-22%		-21%
Dessert peach and nectarine trees
Peach and nectarine trees for industrial processing (including group of Pavie)	-25%		-13%
Orange trees
Small citrus fruit trees
Lemon trees
Olive trees
Table grape vines

8.3.4 If no comparisons have been made, explain why

FSS 2016: only total fruit area available, not single species; comparison only possible for total fruit area

8.3.5 Additional comments

Crop statistics: production area; IACS: only units with funding application included

8.4. Coherence - sub annual and annual statistics

Not applicable

8.5. Coherence - National Accounts

the results are used as base for nat. acc.

8.6. Coherence - internal

Due to comprehensive plausibility checks internal coherence is ensured.

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.statistik.at/web_de/presse/116868.html

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?	Yes
9.2.4 If Yes, is there an English version?	Yes
9.2.5 Please provide a link	http://www.statistik.at/web_de/statistiken/wirtschaft/land_und_forstwirtschaft/agrarstruktur_flaechen_ertraege/obst/index.html

9.3. Dissemination format - online database

9.3.1 Data tables - consultations	Consultations vary depending on publication mediums and over time.
9.3.2 Is an on-line database accessible to users?	Yes
9.3.3 Please provide a link	http://www.statistik.at/web_de/statistiken/wirtschaft/land_und_forstwirtschaft/agrarstruktur_flaechen_ertraege/obst/index.html; work in progress

9.4. Dissemination format - microdata access

9.4.1 Are micro-data accessible to users?	Yes
9.4.2 Please provide a link	http://www.statistik.at/web_de/services/mikrodaten_fuer_forschung_und_lehre/index.html

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	work in progress (publication 2019)
9.6.3 Are methodological papers available?	Yes
9.6.4 Please provide a link	work in progress (publication 2019)
9.6.5 Is a handbook available?	Yes
9.6.6 Please provide a link	http://www.statistik.at/web_de/statistiken/wirtschaft/land_und_forstwirtschaft/agrarstruktur_flaechen_ertraege/obst/index.html: work in progress (publication 2019)

9.7. Quality management - documentation

9.7.1 Metadata completeness - rate	work in progress
9.7.2 Metadata - consultations	work in progress
9.7.3 Is a quality report available?	Yes
9.7.4 Please provide a link	http://www.statistik.at/web_de/ueber_uns/aufgaben_und_grundsaetze/qualitaet/index.html; http://www.statistik.at/web_de/statistiken/wirtschaft/land_und_forstwirtschaft/agrarstruktur_flaechen_ertraege/obst/index.html: work in progress (publication 2019)

10. Cost and Burden

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10.1 Efficiency gains if compared to the previous quality report	On-line surveys Further training Further automation Increased use of administrative data
10.2 If Other, please specify
10.3 Burden reduction measures since the previous quality report	More user-friendly questionnaires Easier data transmission Multiple use of the collected data
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 handling of confidential data relating to individuals and organisations is regulated by the strict confidentiality provisions of the Austrian Federal Statistics Act 2000, BGBl. I No 163/1999 , BGBl. I No 71/2003, BGBl. I No 92/2007, BGBl. No 125/2009, BGBl. I No 111/2010 and BGBl. I No 40/2014).

11.2. Confidentiality - data treatment

11.2.1 Describe the procedures for ensuring confidentiality during dissemination	Confidential data suppressed in the online database; certain cross tables enabled
11.2.2 Additional comments

12. Comment

Related metadata

Annexes