Crop production (apro

Crop production (apro_cp)

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

Compiling agency: Luke, Natural Resources Institute Finland

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

Luke, Natural Resources Institute Finland

1.2. Contact organisation unit

Statistical Services

1.5. Contact mail address

Latokartanonkaari 9

FI-00790 HELSINKI

Finland

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).

In Finland is no production in nut trees, citrus fruit trees, vineyards and olive trees.

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 having Standard Output more than 2,000 €.

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?
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?
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

Survey
Administrative data

Sample survey on crop production

IACS

Final area under cultivation

Survey
Administrative data

Sample survey on crop production

IACS

Production

Survey
Administrative data
Expert estimate
Other

Sample survey on crop production

Expert estimate on preliminary crop production, August

Expert estimate on preliminary crop production, September

Nordic Sugar Group

IACS

Other data source - Nordic Sugar Group - total sugarbeet production for industrial sugar production

Yield

Survey
Expert estimate

Sample survey on crop production

Survey on preliminary crop production, August

Survey on preliminary crop production, September

Non-existing and non-significant crops

Administrative data

IACS

Table 2: Vegetables, melons and strawberries

Early estimates for harvested areas

Administrative data

IACS

Final harvested area

Census

Census on horticultural production (vegetables, melons and strawberries and permanent crops)

Production

Census

Census on horticultural production (vegetables, melons and strawberries and permanent crops)

Non-existing and non-significant crops

Administrative data

IACS

Table 3: Permanent crops

Early estimates for production area

Administrative data

IACS

Final production area

Census

Census on horticultural production (vegetables, melons and strawberries and permanent crops)

Production

Census

Census on horticultural production (vegetables, melons and strawberries and permanent crops)

Non-existing and non-significant crops

Administrative data

IACS

Table 4: Agricultural land use

Main area

Census
Survey
Administrative data

Sample survey on crop production

IACS

Census on horticultural production (vegetables, melons and strawberries and permanent crops)

Non-existing and non-significant crops

Administrative data

IACS

Total number of different data sources

Additional comments

For the total production of sugar beet in Finland, the amount of processed sugar beet is received from the sugar beet processing industry.

Non-existing and non-significant crops are same as in table Non significant crops 2022 in eDamis (Dataset CROPPROD_NSC_A)

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

Which method is used for calculating the yield for main arable crops?	production divided by harvested area
If another method, describe it.

Annexes:
Improving efficiency of the sample design in the Finnish horticultural survey,Impact of introducing Standard Output as a threshold and imputation methods

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	Sample survey on crop production	Agricultural and Horticultural Enterprise Register	Census on horticultural production (vegetables, melons and strawberries and permanent crops)	Expert estimate on preliminary crop production, August	Expert estimate on preliminary crop production, September
Planning (month-month/year)	5-10/2021	01-8/2022	05-08/2022	11/2021-4/2022	11/2021-4/2022
Preparation (month-month/year)	11/2021-8/2022	5-10/2022	09-11/2022	1-6/2022	1-7/2022
Data collection (month-month/year)	10-11/2022	11/2022-2/2023	10/2022-01/2023	8/2022	9/2022
Quality control (month-month/year)	11/2022-3/2023	11/2022-2/2023	01/-03/2023	8/2022	9/2022
Data analysis (month-month/year)	11/2022-3/2023	11/2022-3/2023	01-03/2023	8/2022	9/2022
Dissemination (month-month/year)	11/2022-4/2023	2-5/2023	03/2023	8/2022	9/2022
If there were delays, what were the reasons?

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?	YES	G9100 Cereals harvested green includes grain cereals harvested before maturity. In HB guided to include C0000 Cereals for the production of grain.
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)?	YES
In case yes, how do they differ? ( list all items and explanations)	C0000 - Cereals for the production of grain (including seed) G9100 - Other cereals harvested green (excluding green maize) F1120 - Pears F1121 - Pears for fresh consumption V4210 - Onions	G9100 Cereals harvested green includes grain cereals harvested before maturity. In HB guided to include C0000 Cereals for the production of grain. F1120 Pears jas F1121 Pears for fresh consumption: Area of Pears is from IACS register. The production for Pears is expert estimation. All pears are for fresh consumption. V4210 Onions: Onions includes shallots.
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.) G9100- Other cereals harvested green (excluding green maize) V9000 - Other fresh vegetables n.e.c. ARA99 - Other arable land crops n.e.c.	C1900 Other cereals n.e.c. includes buckwheat, maize, quinoa (Chenopodium quinoa), millet and other cereal n.e.c. G9100 Other cereals harvested green includes wheat, barley, oats, rye, triticale and mixed cereals harvested as greenfodder (whole crop or grain) and whole crop cereals for silage V9000 the fresh vegetables n.e.c. includes sweet corn (maize) ARA99 Other arable land crops n.e.c. includes all the arable crops included to table 1 n.e.c. with area less than 500 hectares/crop (e.g. rolled lawn, hemp, sunflower seed).

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 between IACS and statistics
Is the data collection based on holdings?	YES
If yes, how the holdings were identified?	Unique statistical farm identifier
If not, on which unit the data collection is based on?	Almost 100% of the holdings can be identified from the Administrative sources For provisional production estimates the unit is municipality.
When was last update of the holding register? (month/year)	2/2022
Was a threshold applied?	YES
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 %)	0.5%
Dried pulses and protein crops (in %)	0.5%
Root crops (in %)	1%
Oilseeds (in %)	0.5%
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 %)	1%
Cultivated mushrooms (in %)	0,1%
Total permanent crops (in %)	2%
Fruit trees (in %)	1% (total fruit tree area)
Berries (in %)	5% (total area in berry cultivation)
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	Sample survey on crop production	Census on horticultural production (vegetables, melons and strawberries and permanent crops)
Which survey method was used?	On-line electronic questionnaire filled in by respondent Telephone interview, electronic questionnaire	On-line electronic questionnaire filled in by respondent Telephone interview, electronic questionnaire
If 'other', please specify
Please provide a link to the questionnaire	Home page https://www.luke.fi/fi/tilastot/kyselyt-ja-tiedonkeruut/maataloussektorin-kyselyt-ja-tutkimukset/satotilastokysely	Home page https://www.luke.fi/fi/tilastot/kyselyt-ja-tiedonkeruut/maataloussektorin-kyselyt-ja-tutkimukset/puutarhatilastokysely
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	IACS
Description	Finnish Food Authority is responsible for the use of funds of the European Agricultural Guarantee Fund and the European Agricultural Fund for Rural Development in Finland. - See more at:https://www.ruokavirasto.fi/en/about-us/what-is-the-finnish-food-authority/finnish-food-authority-as-paying-agency/ The data security system of the Finnish Food Authority fulfils the requirements of the ISO/IEC 27001:2013 standard.
Data owner (organisation)	Finnish Food Authority
Update frequency	Continuous
Reference date (month/year)	11/2022
Legal basis
Reporting unit	Holding
Identification variable (e.g. address, unique code, etc.)	Farm code
Percentage of mismatches (%)	less tha 1%
How were the mismatches handled?
Degree of coverage (holdings, e.g. 80%)	99-100%
Degree of completeness (variables, e.g. 60%)	100%
If not complete, which other sources were used ?
Were the data used for sample frame?	Sample frame Validation Directly for estimates
Data used for other purposes, which?
Which variables were taken from administrative sources?	Cultivated area per crop
Were there any differences in the definition of the variables between the administrative source and those described in the Regulation?	YES
Please describe the differences	Area for cereals and pulses in IACS includes both area cereals harvested as dry and green.
What measures were taken to eliminate the differences?	Harvested area as green per crops is collected in sample survey on crop production.
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?
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	Expert estimate on preliminary crop production, August	Expert estimate on preliminary crop production, September
Data owner (organisation)	Luke	Luke
Update frequency (e.g. 1 year or 6 months)	Yearly	Yearly
Reference date (Month/Year e.g. 1/22 - 8/22)	8/22	9/22
Legal basis
Use purpose of the estimates?	Estimate the yield	Estimate the yield
What kind of expertise the experts have?	Experts for crop production at ProAgria Rural Advisory Centres	Experts for crop production at ProAgria Rural Advisory Centres
What kind of estimation methods were used?	Experts etimate the average yield per crop at the municipality level	Experts etimate the average yield per crop at the municipality level
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?
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?	NO
If yes, which kind of dataset?
If other, please describe

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?	Stable	Stable	Stable	Stable	Stable
Is there a quality management process in place for crop statistics?	YES
If, yes, what are the components?	The producers of Official Statistics of Finland have approved a common quality assurance in which they commit to common quality criteria and quality assurance measures. The quality criteria of Official Statistics of Finland are compatible with the European Statistics Code of Practice. The good practices followed in the statistics are presented in Statistics Finland's Quality Guidelines for Official Statistics handbook.
Is there a quality report available?	YES
If yes, please provide a link(s)	1. Sample survey on crop production and Nordic Sugar Group: https://www.luke.fi/sites/default/files/2022-08/Satotilasto%20Laaturaportti_EN_0.pdf 2. Census on horticultural production (vegetables, melons and strawberries and permanent crops) and Agricultural and Horticultural Enterprise Register: https://www.luke.fi/sites/default/files/2023-01/Puutarhatilastot%20Laaturaportti_EN.pdf 3. IACS and Agricultural and Horticultural Enterprise Register: https://www.luke.fi/sites/default/files/2023-01/K%C3%A4yt%C3%B6ss%C3%A4%20oleva%20maatalousmaa%20Laaturaportti_EN.pdf
To which data source(s) is it linked?	1. Table 1 2. Table 2 and 3 3. Table 4 (UAA)
Has a peer-review been carried out for crop statistics?	NO
If, yes, which were the main conclusions?
What quality improvement measures are planned for the next 3 years?	Systematic validation improvements Further automation Other
If, other, please specify	Possible evaluating and updating the sample survey (for example sample size)
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?	NO
Describe the unmet needs
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?	Stocks of cereals on farms. Potted vegetables in greenhouses, beetroots and some other root vegetables, ornamentals in greenhouse Production under lights (artificial lightning) Not every year, but according to needs of research: food loss in primary production (cereals, potatoes, strawberry, carrot, cabbage)
Additional comments

5.2. Relevance - User Satisfaction

Have any user satisfaction surveys been done?	NO
If yes, how satisfied the users were?
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	Sample survey on crop production
Sampling basis?	Other
If 'other', please specify	Farm and Horticultural Enterprise Register
Sampling method?	Stratified
If stratified, number of strata?	492
If stratified, stratification basis?	Location Size Specialisation
If 'other', please specify
Size of total population	43540
Size of sample	6086
Which methods were used to assess the sampling error?	Relative standard error
If other, which?
Which methods were used to derive the extrapolation factor?	Basic weight Non-response
If other, which?
If CV (co-efficient of variation) was calculated, please describe the calculation methods and formulas	The results were estimated with SAS software. Variances were estimated using the CLAN-macro developed by Statistics Sweden.
If the results were compared with other sources, please describe the results
Which were the main sources of errors?	Stratification could have been more specified and different allocation methods could have been tried (now used Neyman allocation)

Sampling error - indicators

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	Sample survey on crop production
Cereals for the production of grain (in %)	See "Additional comments"
Dried pulses and protein crops (in %)	See "Additional comments"
Root crops (in %)	See "Additional comments"
Oilseeds (in %)	See "Additional comments"
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	Sowned area based on IACS e.g. census (no need CVs). Harvested area = sowned area - non-harvested area, based on Sample survey on crop production.

6.3. Non-sampling error

See 6.3.1 - 6.6

Non-sampling errors were minor, even almost non-existent.

6.3.1. Coverage error

Over-coverage - rate

About 1 %

Reasons:

- Farm may have finished the farm-keeping after the sample has drawn.

- Farm has not cultivation asked. Farmer has updated figures in register after the sample has drawn

Common units - proportion

Sowned area is from administration source (IACS) and might become more accurate in survey (farmer corrects the value, quite rare). Harvested area is gathered by Sample survey on crop production.

	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	Sample survey of crop production		Census on horticultural production (vegetables, melons and strawberries and permanent crops)
Error type	Over-coverage		Misclassification
Degree of bias caused by coverage errors	Low		Low
What were the reasons for coverage errors?	- Farm may have finished the farm-keeping after the sample has drawn. - Farm has not cultivation asked. Farmer has updated figures in register after the sample has drawn.		Saving errors in IACS (quite rare).
Which actions were taken for reducing the error or to correct the statistics?	By using the most updated information of IACS.		By using the most updated information of IACS.
Additional comments

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	Sample survey on crop production		Census on horticultural production (vegetables, melons and strawberries and permanent crops)
Was the questionnaire based on usual concepts for respondents?	YES		YES
Number of surveys already performed with the current questionnaire (or a slightly amended version of it)?	The survey has remained fairly unchanged since 1995, and the first web survey was conducted in 2010. Year 2022 was the first time with new web application.		The first time with the new web application The survey has remained fairly unchanged since 1984, and the first web survey was conducted in 2008.
Preparatory (field) testing of the questionnaire?	YES		YES
Number of units participating in the tests?	4		4
Explanatory notes/handbook for surveyors/respondents?	YES		YES
On-line FAQ or Hot-line support for surveyors/respondents?	YES		YES
Were pre-filled questionnaires used?	YES		YES
Percentage of pre-filled questions out of total number of questions	16.6		15
Were some actions taken for reducing the measurement error or to correct the statistics?	YES		YES
If yes, describe the actions and their impact	WEB- and CATI -survey included logical checks and max -checks - above actions improved the quality of data (e.g. no outliers or extraordinary values)		WEB- and CATI -survey included logical checks and min/max -checks - above actions improved the quality of data (e.g. no outliers or extraordinary values)

6.3.3. Non response error

Unit non-response - rate

Item non-response - rate

Due the controls and checks in WEB- and CATI -survey, the rate was non-existent.

	Survey 1	Survey 2	Survey 3	Survey 4	Survey 5	Survey 6	Survey 7
Name	Sample survey on crop production	Census on horticultural production (vegetables, melons and strawberries and permanent crops)
Unit level non-response rate (in %)	9.5	3
Item level non-response rate (in %)
- Min% / item	*) see 6.3.3.2
- Max% / item	*) see 6.3.3.2
- Overall%	*) see 6.3.3.2
Was the non-response been treated ?	NO	NO
Which actions were taken to reduce the impact of non-response?	Non-responses were not used in estimation.
Which items had a high item-level non-response rate?	Due the checks in WEB- and CATI -survey, the rate was non-existent.
Which methods were used for handling missing data? (several answers allowed)	Follow-up interviews Reminders Weighting	Imputations
In case of imputation which was the basis?		Imputation based on similar units
In case of imputation, which was the imputation rate (%)?		5
Estimated degree of bias caused by non-response?	Unknown	Unknown
Which tools were used for correcting the data?	WEB-application, SAS	WEB-application, SAS
Which organisation did the corrections?	Luke	Luke
Additional comments	1) Web-survey and reminders 2) farmers, which did not answered web -survey --> follow-up interviews	1) Web-survey and reminders 2) farmers, which did not answered web -survey --> follow-up interviews

6.3.4. Processing error

There were numerous controls during the survey (WEB- and CATI -based survey).Controls resulted an error notification (=error). Due this checks data processing errors are extremely unlike.

6.3.4.1. Imputation - rate

In Sample survey on crop production:

Imputation was not used. Unit non-responses did not used in estimation.

In Census on horticultural production:

donor imputation of the nearest neighbourhood, estimation methods based on register information

6.3.5. Model assumption error

Not applicable.

6.4. Seasonal adjustment

Not applicable.

6.5. Data revision - policy

Revision policy follows the national guidelines for Official Statistis of Finland (FOS). The OSF quality criteria are compatible with the quality criteria of the European Statistical System (ESS).

6.6. Data revision - practice

Statistics are corrected according to the revision practice of Official Statistics of Finland. Practices are guided by the recommendations of the Advisory Board of Official Statistics of Finland:

Information on a significant error is disseminated at least to the same audiences and with the same visibility as the original data. However, if the erroneous information has received much publicity, the error may require publishing of a separate correction release to larger audiences and with more visibility than originally.

Information on the error must remain permanently visible, with the exception of tables in databases.

Notifications of significant errors and the time of correction are added to the web page on changes in these statistics.

6.6.1. Data revision - average size

Not applicable.

7. Timeliness and punctuality

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7.1. Timeliness

Time lag - first result

0 days

Time lag - final result

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)

19/08/2022

24/11/2022

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

23/02/2022

23/02/2023

16/02/2023

16/02/2022

Additional comments

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	NO	NO
If yes, to which were they related?
If other, which?
Which items were affected?
Year of break (number)
Impact on comparability
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?
If others, which?
If no comparisons have been made, why not?	IACS is one of the data sources.

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
Dried pulses and protein crops
Root crops
Oilseeds
Other industrial crops (than oilseeds)
Plants harvested green
Total vegetables, melons and strawberries
Vegetables and melons
Strawberries
Cultivated mushrooms
Total permanent crops
Fruit trees		Not applicable.
Berries
Nut trees
Citrus fruit trees		Not applicable.
Vineyards
Olive trees		Not applicable.
If there were considerable differences, which factors explain them?

8.4. Coherence - sub annual and annual statistics

Not applicable.

8.5. Coherence - National Accounts

Statistial unit, coverage, reference period, classification and geographical coverage are equal.

8.6. Coherence - internal

Not applicable.

9. Accessibility and clarity

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9.1. Dissemination format - News release

Availability

Links

YES

20/6/2022:

https://www.luke.fi/en/news/springsown-wheat-offsets-winter-damage-to-autumnsown-wheat

19/8/2022:

https://www.luke.fi/en/blogs/can-we-have-domestic-cereals-this-year

23/9/2022:

https://www.luke.fi/en/news/cereal-harvest-estimated-at-36-million-tons-the-return-to-average-harvest-levels-after-two-weaker-years-is-a-relief

24/11/2022:

https://www.luke.fi/en/news/the-2022-cereal-harvest-returns-to-normal-following-the-crop-failure-year-growth-of-one-million-tons

13/12/2022:

https://www.luke.fi/en/news/springsown-wheat-offsets-winter-damage-to-autumnsown-wheat

16/2/2023:

https://www.luke.fi/en/news/exceptional-year-in-greenhouse-vegetable-production

23/2/2023:

https://www.luke.fi/en/news/recordbreaking-pea-harvest-on-finnish-fields-in-2022-cereal-harvest-slightly-larger-than-average

Release calender:

https://www.luke.fi/en/julkistamiskalenteri

9.2. Dissemination format - Publications

	Availability	Links
Publications	None
Publications in English	None

9.3. Dissemination format - online database

Data tables - consultations

Not applicable

	Availability	Links
On-line database accessible to users	YES
Website	National language English	http://statdb.luke.fi/PXWeb/pxweb/en/LUKE/?rxid=001bc7da-70f4-47c4-a6c2-c9100d8b50db

9.4. Dissemination format - microdata access

Availability	Links
NO

9.5. Dissemination format - other

Free :https://www.luke.fi/en/statistics

Additionnal content : https://www.luke.fi/en/statistics/information-service

9.6. Documentation on methodology

	Availability	Links
Methodological report	None
Quality Report	National language English	https://www.luke.fi/fi/tilastot/satotilasto/satotilaston-laatuseloste https://www.luke.fi/sv/tilastot/satotilasto/skordestatistik https://www.luke.fi/sites/default/files/2022-08/Satotilasto%20Laaturaportti_EN_0.pdf https://www.luke.fi/sites/default/files/2023-01/Puutarhatilastot%20Laaturaportti_FI.pdf https://www.luke.fi/sites/default/files/2023-01/Puutarhatilastot%20Laaturaportti_SV.pdf https://www.luke.fi/sites/default/files/2023-01/K%C3%A4yt%C3%B6ss%C3%A4%20oleva%20maatalousmaa%20Laaturaportti_FI.pdf https://www.luke.fi/sites/default/files/2023-01/K%C3%A4yt%C3%B6ss%C3%A4%20oleva%20maatalousmaa%20Laaturaportti_SV.pdf https://www.luke.fi/sites/default/files/2023-01/K%C3%A4yt%C3%B6ss%C3%A4%20oleva%20maatalousmaa%20Laaturaportti_EN.pdf
Metadata	None
Additional comments	None

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

Top

Efficiency gains if compared to the previous quality report?	Staff further training Other
If other, which?	The new web survey for horticultural statistics, look the comments
Burden reduction measures since the previous reference year
If other, which?