Crop production (apro

Crop production (apro_cp)

National Reference Metadata in ESS Standard for Quality Reports Structure (ESQRS)

Compiling agency: Statistics Austria

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

1.2. Contact organisation unit

Agriculture and Forestry

1.5. Contact mail address

1110 Wien, Guglgasse 13

2. Statistical presentation

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2.1. Data description

Annual crop statistics provide statistics on the area under main arable crops, vegetables and permanent crops and production and yield levels. The statistics are collected from a wide variety of sources: surveys, administrative sources, experts and other data providers. The data collection covers early estimates (before the harvest) and the final data. Data are collected mostly at national level but for some crops also regional data exist (NUTS1/2).

2.2. Classification system

Hierarchical crop classification system

2.3. Coverage - sector

Growing of non-perennial crops, perennial crops and plant propagation (NACE A01.1-01.3)

2.4. Statistical concepts and definitions

See: Annual crop statistics Handbook

2.5. Statistical unit

Utilised agricultural area cultivated by an agricultural holding.

2.6. Statistical population

All agricultural holdings growing crops.

2.7. Reference area

The entire territory of the country.

2.8. Coverage - Time

Crop year.

2.9. Base period

Not applicable.

3. Statistical processing

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3.1. Source data

	Source 1	Source 2	Source 3	Source 4
Have new data sources been introduced since the previous quality report?	NO
If yes, which new data sources have been introduced since the previous quality report?
Type of source?
To which Table (Reg 543/2009) do they contribute?
Have some data sources been dropped since the previous quality report?	NO
Which data sources have been dropped since the previous quality report?
Type of source?
Why have they been dropped?
Additional comments

Data sources: Please indicate the data sources which were used for the reference year on which is reported

Type

Name(s) of the sources

If other type, which kind of data source?

Table 1: Crops from arable land

Early estimates for areas

Expert estimate

Agricultural chambers

Final area under cultivation

Administrative data
Other

IACS - Area (Agrarmarkt Austria); Producer organisations

Production

Expert estimate
Other

Harvest reporters; Agricultural chambers

Producer organisations

Yield

Administrative data
Expert estimate

IACS - Yield (Agrarmarkt Austria)

Harvest reporters; Agricultural chambers

Non-existing and non-significant crops

Census
Administrative data

Agricultural Surveys (Orchard Survey/FSS); IACS - Area (Agrarmarkt Austria)

Table 2: Vegetables, melons and strawberries

Early estimates for harvested areas

Census
Administrative data
Expert estimate
Other

Field vegetable and horticultural survey/FSS

Agrarmarkt Austria

Agricultural chambers

Producer organisations

Final harvested area

Census
Administrative data
Expert estimate
Other

Field vegetable and horticultural survey/FSS

Agrarmarkt Austria

Agricultural chambers (and producer organisations)

Producer organisations

Production

Expert estimate
Other

Agricultural chambers

Producer organisations

Non-existing and non-significant crops

Census
Expert estimate
Other

Field vegetable and horticultural survey/FSS

Agricultural chambers

Producer organisations

Table 3: Permanent crops

Early estimates for production area

Census
Administrative data
Expert estimate

Orchard survey

Central wine register

Agricultural chambers

Final production area

Census
Administrative data
Expert estimate

Orchard survey

Central wine register, Agrarmarkt Austria

Agricultural chambers

Production

Administrative data
Expert estimate

Central wine register

Agricultural chambers; Harvest reporters

Non-existing and non-significant crops

Census
Expert estimate

Orchard survey

Agricultural chambers

Table 4: Agricultural land use

Main area

Census
Survey
Administrative data
Expert estimate

FSS

Agrarmarkt Austria

Agricultural chambers

Non-existing and non-significant crops

Census
Administrative data

FSS

Agrarmarkt Austria

Total number of different data sources

Additional comments

	Put x, if used
Surveyed: farmers report the humidy
Surveyed: farmers convert the production/yield into standard humidity	x
Surveyed: whole sale purchasers report the humidy
Surveyed: whole sale purchasers convert the production/yield into standard humidity	x
Surveyed by experts (e.g. test areas harvested and measured)
Estimated by experts	x
Other type
If other type, please explain
Additional information

Which method is used for calculating the yield for main arable crops?	yield is surveyed in the field and production volume is assessed on the basis of the yield
If another method, describe it.

3.2. Frequency of data collection

	Source 1	Source 2	Source 3	Source 4	Source 5	Source 6	Source 7	Source 8	Source 9
Name of data source	Agrarmarkt Austria/Area (IACS)	Agrarmarkt Austria/Yield	Harvest reporters	Agricultural chambers	Orchard survey	Central Wine register	Farm structure survey	Field Vegetable and horticultural survey
Planning (month-month/year)	April 2022	September 2022	January-March 2022	January-March 2022	see QR Orchard	January-February 2023	see QR FSS	February 2019 - October 2019
Preparation (month-month/year)	May 2022	September 2022	January-March 2022	January-March 2022	see QR Orchard	January-February 2023	see QR FSS	October 2019 - March 2020
Data collection (month-month/year)	May-November 2022	September-November 2022	May-December 2022	January-December 2022	see QR Orchard	February -March 2023	see QR FSS	March 2020 - February 2021
Quality control (month-month/year)	June-November 2022	September-November 2022	May-December 2022	January-December 2022	see QR Orchard	February -March 2023	see QR FSS	May 2022- Juli 2022
Data analysis (month-month/year)	June-November 2022	September-November 2022	May-December 2022	January-December 2022	see QR Orchard	February -March 2023	see QR FSS	Juni 2022 - November 2022
Dissemination (month-month/year)	June 2022-March 2023	September 2022-March 2023	May 2022-March 2023	January 2022-March 2023	see QR Orchard	March-May 2023	see QR FSS	Juli 2022 - December 2022
If there were delays, what were the reasons?								The survey was carried out in the frame of IFS, therefore quality control and dissemination was strongly connected with the IFS-workflow.

3.3. Data collection

Definitions	Question	In case yes, how do they differ?
Do national definitions differ from the definitions in Article 2 of Regulation (EC) No 543/2009?	NO
Are there differences between the national methodology and the methodology described in the Handbook concerning e.g. the item and aggregate calculations?	NO
Are special estimation/calculation methods used for main crops from arable land?	NO
Are special estimation/calculation methods used for vegetables or strawberries?	NO
Are special estimation/calculation methods used for permanent crops for human consumption?	NO
Are special estimation/calculation methods used for main land use?	NO
Do national crop item definitions differ from the definitions in the Handbook (D-flagged data)?	NO
In case yes, how do they differ? ( list all items and explanations)
In case data are delivered for one of the items below, describe the crop species included in the item:	C1900 - Other cereals n.e.c. (buckwheat, millet, etc.) R9000 - Other root crops n.e.c. I1190 -Other oilseed crops n.e.c I9000 - Other industrial crops n.e.c. G9900 - Other plants harvested green from arable land n.e.c. V1900 - Other brassicas n.e.c. V2900 - Other leafy or stalked vegetables n.e.c. V3900 - Other vegetables cultivated for fruit n.e.c. V4900 - Other root, tuber and bulb vegetables n.e.c. F3900 - Other berries n.e.c. H9000 - Other permanent crops for human consumption n.e.c. PECR9 - Other permanent crops	C1900 - Other cereals n.e.c.: Buckwheat, Millet, Amaranth, Quinoa, Canary seed R9000 - Other root crops n.e.c: Fodder beets, Carrots for stock feeding, Swedes I1190 -Other oilseed crops n.e.c: Pumpkins for oil, poppy, mustard, camelina, oil radish, saflor, hemp (corn) I9000 - Other industrial crops n.e.c: rolled lawn G9900 - Other plants harvested green from arable land n.e.c.: different fodder grasses, mixtures of vetches and cereals, others V1900 - Other brassicas n.e.c.: Chinese cabbage, Kohlrabi V2900 - Other leafy or stalked vegetables n.e.c: corn salad, rhubarb, parsley, chives V3900 - Other vegetables cultivated for fruit n.e.c.: sweet corn V4900 - Other root, tuber and bulb vegetables n.e.c.: fennel (bulb), horse-radish, black radish, hamburg parsley F3900 - Other berries n.e.c.: Elderberries, used mainly for food colouring, are not included here, but under H 9000: Other permanent crops for human consumption n.e.c. H9000 - Other permanent crops for human consumption n.e.c: Elderberries and Aronia, used mainly for food colouring PECR9 - Other permanent crops: Christmas trees, Gingko, Other

Population

Which measures were taken in order to make sure that the requirement stipulated in Art. 3.2 are met? (Statistics shall be representative of at least 95 % of the areas of each table in the Regulation).	Comparison with FSS census (Field crops) and other agricultural surveys (orchard survey, field vegetable and horticultural survey, vineyard survey) as well as with adm. data
Is the data collection based on holdings?	NO
If yes, how the holdings were identified?
If not, on which unit the data collection is based on?	regions
When was last update of the holding register? (month/year)	Regularly, on a daily basis, depending on the availability of sources for the update
Was a threshold applied?	NO
If yes, size of the excluded area	Area excluded on the basis of the threshold in % of the total area for that crop
Cereals for the production of grain (in %)	<1%
Dried pulses and protein crops (in %)	<1%
Root crops (in %)	<1%
Oilseeds (in %)	<1%
Other industrial crops (included all industrial crops besides oilseeds) (in %)	<1%
Plants harvested green from arable land (in %)	<1%
Total vegetables, melons and strawberries (in %)
Cultivated mushrooms (in %)
Total permanent crops (in %)
Fruit trees (in %)
Berries (in %)
Nut trees (in %)
Citrus fruit trees (in %)
Vineyards (in %)
Olive trees (in %)

Survey method (only for census and surveys)

	Survey 1	Survey 2	Survey 3	Survey 4	Survey 5	Survey 6	Survey 7
Name of the survey	Orchard survey	Farm structure survey	Field vegetable and horticultural survey
Which survey method was used?	On-line electronic questionnaire filled in by respondent	On-line electronic questionnaire filled in by respondent	On-line electronic questionnaire filled in by respondent
If 'other', please specify
Please provide a link to the questionnaire	see QR Orchard survey	see QR FSS	https://www.statistik.at/fileadmin/pages/155/AS2020_Ausfuellanleitung_Webfragebogen.pdf
Data entry method, if paper questionnaires?

Administrative data (This question block is only for administrative data)

	Admin source 1	Admin source 2	Admin source 3	Admin source 4	Admin source 5	Admin source 6
Name of the register	Agrarmarkt Austria / Area (IACS)	Agrarmarkt Austria/Yield survey	Central wine register
Description	Agricultural Area	Yield survey of certain main field crops	Wine harvest data (Area and production)
Data owner (organisation)	Agrarmarkt Austria	Agrarmarkt Austria	Federal Ministry of Agriculture, Forestry, Regions and Water Management
Update frequency	Once per year or more often	Once per year or more often	Once per year or more often
Reference date (month/year)	May/2022	August-November 2022	November 2022
Legal basis	National Reg.: BGBl. Nr. 376/1992	National Reg.: BGBl. Nr. 376/1992	National Reg.: BGBl. I Nr. 111/2009
Reporting unit	Holding	Holding	Holding
Identification variable (e.g. address, unique code, etc.)	Holding Number	Holding Number	Holding Number
Percentage of mismatches (%)	0% (use of regional aggregates)	0% (use of regional aggregates)	0% (use of regional aggregates)
How were the mismatches handled?
Degree of coverage (holdings, e.g. 80%)	90%		100%
Degree of completeness (variables, e.g. 60%)	99% for arable land		100%
If not complete, which other sources were used ?	FSS, Experts estimations
Were the data used for sample frame?	Directly for estimates	Directly for estimates	Directly for estimates
Data used for other purposes, which?
Which variables were taken from administrative sources?	Field crops Area perm crops area strawberries area	Yield of certain main field crops	Wine production and harvested area
Were there any differences in the definition of the variables between the administrative source and those described in the Regulation?	YES	NO	NO
Please describe the differences	main area
What measures were taken to eliminate the differences?	experts estimations
How was the reliability, accuracy and coherence (comparison to other available data) of the data originated from administrative data source (ante- and/or ex-post) checked?	comparison to FSS census
What were the possible limitations, drawbacks of using the data from administrative source(s)?

Expert estimations (This question block is only for expert estimates)

	Expert estimate 1	Expert estimate 2	Expert estimate 3	Expert estimate 4	Expert estimate 5	Expert estimate 6
Name of the estimation	Harvest reporters	Agricultural chambers/Producer organisations
Data owner (organisation)	Statistics Austria	Agricultural chambers/Producer organisations
Update frequency (e.g. 1 year or 6 months)	Yearly	Yearly
Reference date (Month/Year e.g. 1/22 - 8/22)	5/22-11/22	01/22-11/22
Legal basis	BGBl. II 83/2012	BGBl. II 83/2012
Use purpose of the estimates?	per hectare yields of field crops, fruits, wine, especially concerning forecasts	per hectare yields and area of vegetables, fruits, certain field crops
What kind of expertise the experts have?	mainly practical experience (farmers)	agricultural experts by education and/or practice
What kind of estimation methods were used?	mainly surveying of own field crops, checking of plant density and development; questionning of other farmers	questioning of farmers and producer organisations; use of administrative data
Were there any differences in the definition of the variables between the experts' estimates and those described in the Regulation?	NO	NO
If yes, please describe the differences
What measures were taken to eliminate the differences?
How were the reliability, accuracy and coherence (comparison to other available data) of the data originated from experts' estimates (ante- and/or ex-post)checked?	comparison of forecasts to final results, as far as they stem from other data source	comparison to other data sources as far as possible, and yearly discussion in working groups
What were the possible limitations, drawbacks of using the data from expert estimate(s)?
Additional comments

3.4. Data validation

Which kind of data validation measures are in place?	Automatic and Manual
What do they target?	Completeness Outliers Aggregate calculations
Is the data cross-validated against an other dataset?	YES
If yes, which kind of dataset?	Previous results Other dataset
If other, please describe	agricultural census

3.5. Data compilation

Not Applicable.

3.6. Adjustment

Not applicable.

4. Quality management

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4.1. Quality assurance

	Completeness	Punctuality	Accuracy	Reliability	Overall quality
How would you describe the overall quality development since the previous quality report?	Improvement	Improvement	Stable	Stable	Improvement
Is there a quality management process in place for crop statistics?	YES
If, yes, what are the components?	Quality report; Feed back discussion with experts and main users; refereeing system (for articles); working groups
Is there a quality report available?	YES
If yes, please provide a link(s)	https://www.statistik.at/fileadmin/shared/QM/Standarddokumentationen/RW/std_r_ernteerhebung.pdf
To which data source(s) is it linked?	Crop statistics; Ochard survey; FSS
Has a peer-review been carried out for crop statistics?	YES
If, yes, which were the main conclusions?
What quality improvement measures are planned for the next 3 years?	Systematic validation improvements Further automation
If, other, please specify
Additional comments

4.2. Quality management - assessment

See the European level Quality Report.

5. Relevance

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5.1. Relevance - User Needs

Are there known unmet user needs?	YES
Describe the unmet needs	Regional data
Does the Regulation 543/2009 meet the national data needs?	NO
Does the ESS agreement meet the national needs?	NO
If not, which additional data are collected?	more single products, more regional data, earlier availability of certain data
Additional comments

5.2. Relevance - User Satisfaction

Have any user satisfaction surveys been done?	YES
If yes, how satisfied the users were?	Satisfied
Additional comments

5.3. Completeness

See the European level Quality Report

5.3.1. Data completeness - rate

See the European level Quality Report

6. Accuracy and reliability

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6.1. Accuracy - overall

See the European level Quality Report.

6.2. Sampling error

Sampling method and sampling error

	Survey 1	Survey 2	Survey 3	Survey 4	Survey 5	Survey 6	Survey 7
Name
Sampling basis?
If 'other', please specify
Sampling method?
If stratified, number of strata?
If stratified, stratification basis?
If 'other', please specify
Size of total population
Size of sample
Which methods were used to assess the sampling error?
If other, which?
Which methods were used to derive the extrapolation factor?
If other, which?
If CV (co-efficient of variation) was calculated, please describe the calculation methods and formulas
If the results were compared with other sources, please describe the results
Which were the main sources of errors?

Sampling error - indicators

Not applicable

Coefficient of variation (CV) for the area (on the MS level)

	Survey 1	Survey 2	Survey 3	Survey 4	Survey 5	Survey 6	Survey 7
Name of the survey
Cereals for the production of grain (in %)
Dried pulses and protein crops (in %)
Root crops (in %)
Oilseeds (in %)
Other industrial crops (included all industrial crops besides oilseeds) (in %)
Plants harvested green from arable land (in %)
Total vegetables, melons and strawberries (in %)
Cultivated mushrooms (in %)
Total permanent crops (in %)
Fruit trees (in %)
Berries (in %)
Nut trees (in %)
Citrus fruit trees (in %)
Vineyards (in %)
Olive trees (in %)
Additional comments	No sample survey

6.3. Non-sampling error

6.3.1. Coverage error

Over-coverage - rate

Not applicable

Common units - proportion

Not applicable

	Data source 1	Data source 2	Data source 3	Data source 4	Data source 5	Data source 6	Data source 7	Data source 8	Data source 9
Name of the data source	Orchard survey	Farm structure survey	Field vegetable and horticultural survey
Error type	Under-coverage Over-coverage		Under-coverage
Degree of bias caused by coverage errors	Low		Low
What were the reasons for coverage errors?	see QR Orchard survey	see QR FSS	Threshold applied
Which actions were taken for reducing the error or to correct the statistics?	see QR Orchard survey	see QR FSS	Evaluation with farm register, FSS, admin. sources
Additional comments	see QR Orchard survey	see QR FSS

6.3.2. Measurement error

	Data source 1	Data source 2	Data source 3	Data source 4	Data source 5	Data source 6	Data source 7	Data source 8	Data source 9
Name of the data source	Orchard survey	Farm structure survey	Field vegetable and horticultural survey
Was the questionnaire based on usual concepts for respondents?	YES	YES	YES
Number of surveys already performed with the current questionnaire (or a slightly amended version of it)?	see QR Orchard survey	see QR FSS	2
Preparatory (field) testing of the questionnaire?	YES	YES	YES
Number of units participating in the tests?	see QR Orchard survey	see QR FSS	4
Explanatory notes/handbook for surveyors/respondents?	YES	YES	YES
On-line FAQ or Hot-line support for surveyors/respondents?	YES	YES	YES
Were pre-filled questionnaires used?	YES	YES	NO
Percentage of pre-filled questions out of total number of questions	see QR Orchard survey	see QR FSS
Were some actions taken for reducing the measurement error or to correct the statistics?	YES	YES	YES
If yes, describe the actions and their impact	see QR Orchard survey	see QR FSS	Survey method was conceptuated as similar as possible to the previous survey (e.g. questionnaire)

6.3.3. Non response error

Unit non-response - rate

Not applicable

Item non-response - rate

Not applicable

	Survey 1	Survey 2	Survey 3	Survey 4	Survey 5	Survey 6	Survey 7
Name	Orchard survey	Farm structure survey	Field vegetable and horticultural survey
Unit level non-response rate (in %)	see QR orchard survey	see QR FSS	0,1%
Item level non-response rate (in %)
- Min% / item	see QR orchard survey	see QR FSS	0%
- Max% / item	see QR orchard survey	see QR FSS	low (exact figure not known)
- Overall%	see QR orchard survey	see QR FSS	low (exact figure not known)
Was the non-response been treated ?	YES	YES	YES
Which actions were taken to reduce the impact of non-response?	see QR orchard survey	see QR FSS	Integrated plausibility checks in the electronic questionnaire; impossibility of sending the questionnaire when obligatory values were missing; Hotline-support
Which items had a high item-level non-response rate?	see QR orchard survey	see QR FSS
Which methods were used for handling missing data? (several answers allowed)	Follow-up interviews Reminders Imputations	Follow-up interviews Reminders Imputations	Follow-up interviews Reminders Imputations
In case of imputation which was the basis?	Imputation based on the same unit in previous data Imputation based on similar units Imputation based on other sources	Imputation based on the same unit in previous data Imputation based on similar units Imputation based on other sources	Imputation based on the same unit in previous data Imputation based on other sources
In case of imputation, which was the imputation rate (%)?	low (1% estimated)	see QR FSS	low (around 0,6%)
Estimated degree of bias caused by non-response?	Insignificant	Insignificant	Insignificant
Which tools were used for correcting the data?	see QR orchard survey	see QR FSS	electronic plausibility application
Which organisation did the corrections?	see QR orchard survey	see QR FSS	STAT
Additional comments	see QR orchard survey	see QR FSS

6.3.4. Processing error

Not applicable.

6.3.4.1. Imputation - rate

Not applicable.

6.3.5. Model assumption error

Not applicable.

6.4. Seasonal adjustment

Not applicable.

6.5. Data revision - policy

Not applicable.

6.6. Data revision - practice

Not applicable.

6.6.1. Data revision - average size

Not applicable.

7. Timeliness and punctuality

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7.1. Timeliness

Time lag - first result

Not applicable

Time lag - final result

Not applicable

Cereals

Dried pulses and protein crops

Root crops

Oilseeds

Other industrial crops

Plants harvested green

Vegetables and melons

Strawberries

Cultivated mushrooms

Fruit trees

Berries

Nut trees

Citrus fruit trees

Vineyards

Olive trees

How many main data releases there are yearly in the national crop statistics for the following types of crops?

How many of them are forecasts (releases before the harvest)?

When was the first forecasting published for the crop year on which is reported? (day/month/year)

27/06/2022

02/09/2022

03/08/2022

27/06/2022

03/08/2022

11/08/2022

03/08/2022

15/06/2022

03/08/2022

31/08/2022

When were the final results published for the crop year on which is reported? (day/month/year)

21/12/2022

02/12/2022

25/11/2022

02/03/2023

Additional comments

First area estimations in January 2022

First area estimations in April 2022

First area estimations (rape) in January 2022

First area estimations in April 2022

7.2. Punctuality

See the European level Quality Report

7.2.1. Punctuality - delivery and publication

See the European level Quality Report

8. Coherence and comparability

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8.1. Comparability - geographical

Not applicable.

8.1.1. Asymmetry for mirror flow statistics - coefficient

Not applicable.

8.2. Comparability - over time

Length of comparable time series

Not applicable

	Crops from arable land (Table 1)	Vegetables, melons and strawberries (Table 2)	Permanent crops (Table 3)	Agricultural land use (Table 4)
Have there been major breaks in the time series in the previous 5 years?	NO	NO	YES	NO
If yes, to which were they related?			Other
If other, which?			New basis for extensive fruit trees (IFS 2020): Due to actualisation of tree figures in 2022, based on farm structure survey 2020, the production amounts (production in total) are not directly comparable to the previous year.
Which items were affected?			H0000 - Permanent crops for human consumption F0000 - Fruits, berries and nuts (excluding citrus fruits, grapes and strawberries) F1100 - Pome fruits F1110 - Apples F1111 - Apples for fresh consumption F1112 - Apples for processing F1120 - Pears F1121 - Pears for fresh consumption F1122 - Pears for processing F1200 - Stone fruits F1210_1220 - Peaches and nectarines F1210 - Peaches F1230 - Apricots F1240 - Cherries F1241 - Sour cherries F1242 - Sweet cherries F1250 - Plums F4000 - Nuts F4100 - Walnuts
Year of break (number)			2022
Impact on comparability			Moderate
Additional comments

8.2.1. Length of comparable time series

Not applicable

8.3. Coherence - cross domain

With which other data sources the crop statistics data have been compared?	IACS Other
If others, which?	FSS 2020, Vineyard-survey 2020
If no comparisons have been made, why not?	Comparison with price statistics and national accounts not senseful

Differences between ACS and other data sources (%)

Results of comparisons	IFS 2020	Vineyard survey 2020	IACS	Other source(s)	In case of other sources, which?
Cereals			0%
Dried pulses and protein crops			0%
Root crops			+0.3%
Oilseeds			0%
Other industrial crops (than oilseeds)			0%
Plants harvested green			0%
Total vegetables, melons and strawberries			+10,9%
Vegetables and melons			+11,0%
Strawberries			+9,3%
Cultivated mushrooms
Total permanent crops	-14,6%		-7,3%
Fruit trees	-24,6%	Not applicable	-15,0%
Berries	-14,9%		-7,4%
Nut trees	-89,9%
Citrus fruit trees		Not applicable.
Vineyards	-8,1%	-3,7%	-4,5%
Olive trees		Not applicable.
If there were considerable differences, which factors explain them?	different reference year (FSS): 2020; definition differs (main area; permanent crop includes extensively cultivated fruit areas and not (yet) productive area).	Different reference years; definition differs Vineyard survey: total area (incl young plantations without harvest)	IACS reference year: 2022; definition differs (main area, inkl. non productive areas; permanent crop may include extensively cultivated fruit areas), partly undercoveraged. ACS: Vegetable area incl. successive cropping

8.4. Coherence - sub annual and annual statistics

Not applicable.

8.5. Coherence - National Accounts

Not applicable.

8.6. Coherence - internal

Not applicable.

9. Accessibility and clarity

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9.1. Dissemination format - News release

Availability	Links
YES	https://www.statistik.at/en/medien/press-releases

9.2. Dissemination format - Publications

	Availability	Links
Publications	Electronic	https://www.statistik.at/statistiken/land-und-forstwirtschaft/pflanzenbau
Publications in English	Electronic	https://www.statistik.at/en/statistics/agriculture-and-forestry/crop-production-and-farming

9.3. Dissemination format - online database

Data tables - consultations

Not applicable

	Availability	Links
On-line database accessible to users	YES	https://www.statistik.at/en/databases/statcube-statistical-database
Website	National language English	https://www.statistik.at/statistiken/land-und-forstwirtschaft https://www.statistik.at/en/statistics/agriculture-and-forestry

9.4. Dissemination format - microdata access

Availability	Links

9.5. Dissemination format - other

Not applicable.

9.6. Documentation on methodology

	Availability	Links
Methodological report	National language English	https://www.statistik.at/fileadmin/shared/QM/Standarddokumentationen/RW/std_r_ernteerhebung.pdf https://www.statistik.at/fileadmin/shared/QM/Standarddokumentationen/RW_en/engl_std_r_ernteerhebung.pdf
Quality Report	National language English	https://www.statistik.at/fileadmin/shared/QM/Standarddokumentationen/RW/std_r_ernteerhebung.pdf https://www.statistik.at/fileadmin/shared/QM/Standarddokumentationen/RW_en/engl_std_r_ernteerhebung.pdf
Metadata	National language English	https://www.statistik.at/fileadmin/shared/QM/Standarddokumentationen/RW/std_r_ernteerhebung.pdf https://www.statistik.at/fileadmin/shared/QM/Standarddokumentationen/RW_en/engl_std_r_ernteerhebung.pdf
Additional comments

9.7. Quality management - documentation

Not applicable.

9.7.1. Metadata completeness - rate

Not applicable.

9.7.2. Metadata - consultations

Not applicable.

10. Cost and Burden

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Efficiency gains if compared to the previous quality report?	Further automation Increased use of administrative data Staff further training
If other, which?
Burden reduction measures since the previous reference year	More user-friendly questionnaires Easier data transmission Multiple use of the collected data
If other, which?

11. Confidentiality

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11.1. Confidentiality - policy

Restricted from publication

11.2. Confidentiality - data treatment

Restricted from publication

12. Comment

Top

Restricted from publication

Related metadata

Top

Annexes

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