Crop production (apro_cp)

National Reference Metadata in ESS Standard for CROPS Reports Structure (ESQRSCP)

Compiling agency: Luke, Natural Resources Institute Finland  

Time Dimension: 2016-A0

Data Provider: FI7

Data Flow: CROPROD_ESQRSCP_A


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: EUROPEAN STATISTICAL DATA SUPPORT

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1. Contact Top
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 Top
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 Top
3.1. Source data

  Source 1 Source 2 Source 3 Source 4
Have new data sources been introduced since the previous Quality Report (2014)?       
If yes, which new data sources have been introduced since the previous quality report (2014)?
Type of source?
To which Table (Reg 543/2009) do they contribute?
Have some data sources been dropped since the previous Quality Report (2014)?      
Which data sources have been dropped since the previous quality report (2014)?
Type of source?
Why have they been dropped?
Additional comments


Data sources: Please indicate the data sources which were used for the reference year 2016

  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, July

Expert estimate on preliminary crop production, August

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

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

6

   
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 2016 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 as attachment: Improving efficiency of the sample design in the Finnish horticultural survey,Impact of introducing Standard Output as a threshold and imputation methods
   


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, July

 

Expert estimate on preliminary crop production, August

Planning (month-month/year)

5-10/2015

01-8/2016

05-08/2015

7-12/2015

7-12/2015

Preparation (month-month/year)

11/2015-8/2016

5-10/2016

09-11/2016

10/2015-6/2016

10/2015-7/2016

Data collection (month-month/year)

9-11/2016

11/2016-2/2017

10/2016-01/2017

7/2016

8/2016

Quality control (month-month/year)

11/2016-2/2017

11/2016-2/2017

01/-03/2017

7/2016

8/2016

Data analysis (month-month/year)

11/2016-2/2017

11/2016-3/2017

01-03/2017

7/2016

8/2016

Dissemination (month-month/year)

11/2016-4/2017

2-4/2017

03/2017

7/2016

8/2016

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)

G9100 Cereals harvested green includes grain cereals harvested before maturity. In HB guided to include C0000 Cereals for the production of grain.

 

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 triticale, buckwheat, maize, quinoa (Chenopodium quinoa), millet and other cereal n.e.c.

G9100 Other cereals harvested green includes wheat, barley, oats 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)

2/2017

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

http://stat.luke.fi/tiedonkeruu-satotutkimus_fi

Questionnaire

http://stat.luke.fi/sites/default/files/lomake_satotutkimus_2017.pdf

 

 

Home page

http://stat.luke.fi/tiedonkeruu-puutarhatutkimus_fi

Questionnaire

http://stat.luke.fi/sites/default/files/lomake_puutarhatutkimus_2017.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

IACS

Description

Mavi 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: http://www.mavi.fi/en/about-the-agency/Pages/default.aspx#sthash.ZTWuiAtN.dpuf

The data security system of the Finnish Agency for Rural Affairs (Mavi) fulfils the requirements of the ISO/IEC 27001:2005 standard.

The Information Security Management System (ISMS) certified on the basis of international ISO/IEC 27001 standard proves that Mavi manages its information in a way that keeps it accurate, easy to use and well protected.

- See more at: http://www.mavi.fi/en/about-the-agency/Pages/data-security.aspx#sthash.wXS66Xvy.dpuf

Data owner (organisation)

Agency for rural affairs

Update frequency Continuous
Reference date (month/year)

11/2016

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 ?
How were the data used?
Sample frame
Validation
Directly for estimates
Data used for other purposes, which?
Which variables were taken from administrative sources?

Cultivated and non-harvested area by crop

Were there any differences in the definition of the variables between the administrative source and those described in the Regulation? NO
Please describe the differences
What measures were taken to eliminate the differences?
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, July

Expert estimate on preliminary crop production, August

 

Data owner (organisation)

Luke

Luke

Update frequency (e.g. 1 year or 6 months) Yearly Yearly
Reference date (Month/Year  e.g. 1/16 - 8/16)

7/16

8/16

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 Top
4.1. Quality assurance

  Completeness Punctuality Accuracy Reliability Overall quality
How would you describe the overall quality development since 2014? 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:

http://stat.luke.fi/tilasto/4/laatuseloste/5615

2. Census on horticultural production (vegetables, melons and strawberries and permanent crops) and Agricultural and Horticultural Enterprise Register:

http://stat.luke.fi/tilasto/20/laatuseloste/1038

3. IACS and Agricultural and Horticultural Enterprise Register:

http://stat.luke.fi/tilasto/35/laatuseloste/3842

 

       
To which data source(s) is it linked?

1. Sample survey on crop production and Nordic Sugar Group:

http://stat.luke.fi/tilasto/4/laatuseloste/5615

2. Census on horticultural production (vegetables, melons and strawberries and permanent crops) and Agricultural and Horticultural Enterprise Register:

http://stat.luke.fi/tilasto/20/laatuseloste/1038

3. IACS and Agricultural and Horticultural Enterprise Register:

http://stat.luke.fi/tilasto/35/laatuseloste/3842

       
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
       
If, other, please specify        
Additional comments        
4.2. Quality management - assessment

See the European level Quality Report.


5. Relevance Top
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?

Potted vegetables in greenhouses, beetroots and some other root vegetables, ornamentals in greenhouse

Production under lights (artificial lightning)

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 Top
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?

500

If stratified, stratification basis? Location
Size
Specialisation
If 'other', please specify
Size of total population

49707

Size of sample

6616

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 Unknown 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.

By using the most updated information of IACS.

Additional comments




 

6.3.1.1. 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

6.3.1.2. 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.

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)?

22

22

Preparatory (field) testing of the questionnaire? NO NO
Number of units participating in the tests? 
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 min/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

6.1 %

- unit non-responses (406 farms)

- total sample size (6616 farms)


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 %)

6.1

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

6.3.3.1. Unit non-response - rate

6.1 %

- unit non-responses (406 farms)

- total sample size (6616 farms)

6.3.3.2. Item non-response - rate

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

6.3.4. Processing error

There were numerous controls and checks during the survey (WEB- and CATI -based survey).Checks and controls resulted in either a warning or an error notification (=error). Due this controls and 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 Top
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?

4

4

4

4

2

1

1

1

1

1

1

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

2

2

2

2

0

0

0

0

0

0

0

When was  the first  forecasting published for crop year 2016? (day/month/year) 22/07/2016 22/07/2016 22/07/2016 22/07/2016 23/02/2017 23/02/2017
When were the final results published for crop year 2016? (day/month/year) 23/02/2017 23/02/2017 23/02/2017 23/02/2017 23/02/2017 23/02/2017 28/03/2017 28/03/2017 28/03/2017 28/03/2017 28/03/2017
Additional comments
7.1.1. Time lag - first result

0 days

7.1.2. Time lag - final result
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 Top
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 since 2013? 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

  Data source
With which other data sources the crop statistics data have been compared? 
If others, which?

 

If no comparisons have been made, why not?

FSS 2016 data is not yet available - comparison will be done later.


Results of comparisons FSS 2016 (if available) Vineyard survey 2015 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

 

 

 

Berries

 

 

 

Nut trees  
Citrus fruit trees  
Vineyards
Olive trees  
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 Top
9.1. Dissemination format - News release

9.2. Dissemination format - Publications

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

9.3.1. Data tables - consultations

Not applicable

9.4. Dissemination format - microdata access

Availability Links
NO
9.5. Dissemination format - other

Free : http://stat.luke.fi/en/uusi-etusivu

Additionnal content : http://www.maataloustilastot.fi/en/information-service_en

9.6. Documentation on methodology

  Availability Links
Methodological report None
Quality Report National language

http://stat.luke.fi/laatuselosteet

http://stat.luke.fi/sv/kvalitetsbeskrivningar

 

 

 

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 reference year (2013) Further automation
Increased use of administrative data
Staff further training
Other
If other, which?

Improving efficiency of the sample design in the Census on horticultural production survey - introducing Standard Output as a threshold and imputation methods (please, look the attachment!)

Burden reduction measures since the previous reference year  More user-friendly questionnaires
Easier data transmission
Multiple use of the collected data
If other, which?

Improving efficiency of the sample design in the Census on horticultural production survey - introducing Standard Output as a threshold and imputation methods (please, look the attachment!)


11. Confidentiality Top
Restricted from publication
11.1. Confidentiality - policy
Restricted from publication
11.2. Confidentiality - data treatment
Restricted from publication


12. Comment Top

Agricultural and Horticultural Enterprise Register is an annual compilation of data on all the farms engaging in agricultural or horticultural production in Finland with an economic size of at least EUR 2,000. The majority of the content of the register is based on official information gathered from farmers during their dealings with Finland’s agricultural industry administration. This data is supplemented with information received from other surveys and the register update questionnaire sent directly to farmers every few years.

 


Related metadata Top


Annexes Top
Improving efficiency of the sample design in the Finnish horticultural survey,Impact of introducing Standard Output as a threshold and imputation methods
List of all data sources_Finland
List of sorces from Finland