Crop production (apro_cp)

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

Compiling agency: Ministry of Rural Development & Foods


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)



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1. Contact Top
1.1. Contact organisation

Ministry of Rural Development & Foods
1.2. Contact organisation unit

Directorate of Strategy, Rural Development, Evaluation & Documentation. 

 Division of Documentation & Agricultural Statistics

 
1.5. Contact mail address

2 Acharon Street, 101 76, Athens, Greece


2. Statistical presentation Top
2.1. Data description

Annual crop statistics provide data 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 opinion's registries (e.g. vineyard registry0 and other data providers. The data collection covers early estimates (before the harvest) and the final data. Data for Greece are collected at all level of regional aggregation (NUTS1,2,3) with the lowest level being NUTS-3 - prefecture.
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 Top
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?      
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

Expert’s opinions on agricultural sector.

  
Final area under cultivation Expert estimate

Expert’s opinions on agricultural sector.
Production Expert estimate

Expert’s opinions on agricultural sector.
Yield Expert estimate

Expert’s opinions on agricultural sector.
Non-existing and non-significant crops Expert estimate

Expert’s opinions on agricultural sector.
Table 2: Vegetables, melons and strawberries       
Early estimates for harvested areas Expert estimate

Expert’s opinions on agricultural sector.
Final harvested area Expert estimate

Expert’s opinions on agricultural sector.
Production Expert estimate

Expert’s opinions on agricultural sector.
Non-existing and non-significant crops Expert estimate

Expert’s opinions on agricultural sector.
Table 3: Permanent crops      
Early estimates for production area Expert estimate

Expert’s opinions on agricultural sector.
Final production area Expert estimate

Expert’s opinions on agricultural sector.
Production Administrative data

Olive Oil Tree Registry.

Vineyard Registry.

Data from industries processing fruits (e.g. Peaches)
Non-existing and non-significant crops Expert estimate

Expert’s opinions on agricultural sector.
Table 4: Agricultural land use      
Main area Expert estimate

Expert’s opinions on agricultural sector.
Non-existing and non-significant crops Expert estimate

Expert’s opinions on agricultural sector.
Total number of different data sources

4

  
Additional comments

Data are obtained primarily from the agencies located in the regional divisions of the country. They are based on the premise of the opinions of experts in the agricultural sectors. To be more precise, these include local agriculturists, cooperatives, local farmers, etc.  These data are cross-checked.

Our department collects data from regional administrative divisions, under the direct supervision of the local agriculturist, which cooperate with cooperatives, other governmental agencies and local farmers. These data are cross-checked with data from the Greek Payment Agency, the Hellenic Statistical Authority and other departments of the Ministry of Rural Development & Foods. 

Nevertheless, it should be noted, that there are cases in which the agencies in the administrative divisions do not sent the relevant data in time or do not respond at all. However, this constitutes a limited number of the total agencies (less than 10%), which usually do not carry a significant weight in the data at the national level. In this instance, we take into account the existing trend based on data of the previous years from the aforementioned agencies.  

 

Data source for the humidity

Put x, if used

Surveyed: farmers report the humidity

 

Surveyed: farmers convert the production/yield into standard humidity     

 

Surveyed: whole sale purchasers report the humidity

 X

Surveyed: whole sale purchasers convert the production/yield into standard humidity     

 

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? production divided by harvested area
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

Data are obtained primarily from the agencies located in the regional divisions of the country. They are based on the premise of the opinions of experts in the agricultural sectors. To be more precise, these include local agriculturists, cooperatives, local farmers, etc
Planning (month-month/year)

02/2022
Preparation (month-month/year)

03/2022
Data collection (month-month/year)

06/2022
Quality control (month-month/year)

07/2022
Data analysis (month-month/year)

07/2022
Dissemination (month-month/year)

08/2022
If there were delays, what were the reasons?

There are cases in which the agencies in the administrative divisions do not sent the relevant data in time or do not respond at all. However, this constitutes a limited number of the total agencies (less than 10%), which usually do not carry a significant weight in the data at the national level. In this instance, we take into account the existing trend based on data of the previous years from the aforementioned agencies. 

Our intention is the improvement in both the quality of data and punctuality in the transmission of the datasets. To that end we work closely with the Hellenic Statistical Authority.   

With respect improvements in timeliness, our department is in close contact with the regional administrative divisions and provides specific guidelines to and detailed information whenever this is necessary. 






 
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.)
P9000 - Other dry pulses and protein crops n.e.c.
I1190 -Other oilseed crops n.e.c
G9100- Other cereals harvested green (excluding green maize)
V2900 - Other leafy or stalked vegetables n.e.c.
V3900 - Other vegetables cultivated for fruit n.e.c.
V5900 - Other fresh pulses n.e.c.
U1900 - Other cultivated mushrooms n.e.c.
F1290 - Other stone fruits n.e.c.
F3900 - Other berries n.e.c.
F4900 - Other nuts n.e.c.
T1900 - Other oranges n.e.c.
T9000 - Other citrus fruits n.e.c.
(H9000) - Other permanent crops for human consumption n.e.c.
PECR9 - Other permanent crops

P9000 - Other dry pulses and protein crops n.e.c. The varieties included are the following (scientific names in brackets): Vetch (vicia), Rovi plant (Vicia ervilia), Dry Peas (Dry Pisum),  Lentils (Lens culinaris), Cheakpeas (Cicer arietinum), Peanuts (Arachis), Dry Beans (Phaseolus vulgaris), Black Beans (Vigna Beans), Fava Santorinis (Pisum sativum Santorinis), Fava (Pisum sativum). It is worth mentioned that Santorini (or Thira) is an island in which this specific variety is first cultivated. This particular variety is cultivated also in several areas of Greece (islands and mainland).
T1900 - Other oranges n.e.c. This dataset, includes varieties of oranges that cannot be classified in the current classification. However, it is very difficult to provide detailed data for each different variety. Usually, the sources of our primary data classify the data under the label “Other Varieties of Oranges” while specifying a particular variety is rather rare.

 I1110- Rape & Turnip Rape Seeds are included in Energy Crops n.e.c. (I6000)


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).
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?

Nuts-3 regions
When was last update of the holding register? (month/year)
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 %)
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 %)


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
Which survey method was used?
If 'other', please specify
Please provide a link to the questionnaire
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

Olive Oil Tree Registry

Vineyard Registry

Subsidies Payments Registry
Description
Data owner (organisation)

Ministry of Rural Development & Foods

Ministry of Rural Development & Foods

Greek Payment Agency
Update frequency Once per year or more often Once per year or more often Once per year or more often
Reference date (month/year)
Legal basis
Reporting unit
Identification variable (e.g. address, unique code, etc.)
Percentage of mismatches (%)
How were the mismatches handled?
Degree of coverage (holdings, e.g. 80%)
Degree of completeness (variables, e.g. 60%)
If not complete, which other sources were used ?
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?
Were there any differences in the definition of the variables between the administrative source and those described in the Regulation?
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

local agriculturists
Data owner (organisation)

regional administrative divisions
Update frequency (e.g. 1 year or 6 months)
Reference date (Month/Year  e.g. 1/22 - 8/22)

Year
Legal basis

Regualtion 543/2009
Use purpose of the estimates?

For official use of the NUTS-3 agencies 
What kind of expertise the experts have?

Agronomists
What kind of estimation methods were used?

Cooperation with cooperatives, other governmental agencies and local farmers
Were there any differences in the definition of the variables between the experts' estimates and those described in the Regulation? 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?

Data are cross-checked with data from the Greek Payment Agency, the Hellenic Statistical Authority and other departments of the Ministry of Rural Development & Foods
What were the possible limitations, drawbacks of using the data from expert estimate(s)?

Subjective factor 
Additional comments

Any changes to the existing system are planned inn corporation with Hellenic Statistical Authority in order to improve the quality of the data and the punctuality.  


 
3.4. Data validation

Which kind of data validation measures are in place? 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

Data are cross-checked with data from the Greek Payment Agency, the Hellenic Statistical Authority and other departments of the Ministry of Rural Development & Foods






 
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 the previous quality report?
Is there a quality management process in place for crop statistics? NO        
If, yes, what are the components?        
Is there a quality report available? NO        
If yes, please provide a link(s)        
To which data source(s) is it linked?
       
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? Other        
If, other, please specify

Our intention is the improvement in both the quality of data and punctuality in the transmission of the datasets. To that end we work closely with the Hellenic Statistical Authority and the regional administrative divisions.

        
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? YES
Does the ESS agreement meet the national needs?
If not, which additional data are collected?
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
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            




 
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
Error type
Degree of bias caused by coverage errors
What were the reasons for coverage errors?
Which actions were taken for reducing the error or to correct the statistics?
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
Was the questionnaire based on usual concepts for respondents?
Number of surveys already performed with the current questionnaire (or a slightly amended version of it)?
Preparatory (field) testing of the questionnaire?
Number of units participating in the tests? 
Explanatory notes/handbook for surveyors/respondents? 
On-line FAQ or Hot-line support for surveyors/respondents?
Were pre-filled questionnaires used?
Percentage of pre-filled questions out of total number of questions
Were some actions taken for reducing the measurement error or to correct the statistics?
If yes, describe the actions and their impact






 
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
Unit level non-response rate (in %)
Item level non-response rate (in %)              
               - Min% / item
               - Max% / item
               - Overall%
Was the non-response been treated ?
Which actions were taken to reduce the impact of non-response?
Which items had a high item-level non-response rate? 
Which methods were used for handling missing data?
(several answers allowed)
In case of imputation which was the basis?
In case of imputation, which was the imputation rate (%)?
Estimated degree of bias caused by non-response?
Which tools were used for correcting the data?
Which organisation did the corrections?
Additional comments




 
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

Harmonization with data from other sources is conducted in conjunction with the Hellenic Statistical Authority in cases when data need to be revised due to lags from obtaining data from various sources.  
6.6. Data revision - practice

Harmonization with data from other sources
6.6.1. Data revision - average size

2-3 revisions per year.


7. Timeliness and punctuality Top
7.1. Timeliness

Time lag - first result


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?

One

One

One

One

One

 

One

 

One

 

One

 

One

 

One

 

One

 

One

 

One

 

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

One
When was the first  forecasting published for the crop year on which is reported? (day/month/year)
When were the final results published for the crop year on which is reported? (day/month/year)
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 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 in the previous 5 years? NO NO YES NO
If yes, to which were they related? Items
If other, which?
Which items were affected? F2000 - Fruits from subtropical and tropical climate zones
F2900 - Other fruits from subtropical and tropical climate zones n.e.c.
H9000 - Other permanent crops for human consumption n.e.c.		 H9000 - Other permanent crops for human consumption n.e.c.
Year of break (number)

2021

2021
Impact on comparability Low Low
Additional comments

Pomegranates were included in H9000 and now are included in F2900, Other fruits from subtropical and tropical climate zones n.e.c. However, this does not affect significantly the overall data set, since the particular item is of very low importance for the country.

Pomegranates were included in H9000 and now are included in F2900, Other fruits from subtropical and tropical climate zones n.e.c. However, this does not affect significantly the overall data set, since the particular item is of very low importance for the country.





 
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?  Other
If others, which?

Data are cross-checked with the Greek Payment Agency, the Hellenic Statistical Authority and other departments of the Ministry of Rural Development & Foods.
If no comparisons have been made, why not?


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    

Not significant difference 

Data are compared with figures from the Greek Payment Agency & the Hellenic Statistical Authority. Data are generally, in excess from those of the former source, given that there are areas/products which do not fall into the payment schemes. Differences with the data from Hellenic Statistical Authority, given that this source conducts mainly surveys, uses different definitions and methodologies.
Dried pulses and protein crops    

Not significant difference 

Data are compared with figures from the Greek Payment Agency & the Hellenic Statistical Authority. Data are generally, in excess from those of the former source, given that there are areas/products which do not fall into the payment schemes. Differences with the data from Hellenic Statistical Authority, given that this source conducts mainly surveys, uses different definitions and methodologies.
Root crops    

Not significant difference 

Data are compared with figures from the Greek Payment Agency & the Hellenic Statistical Authority. Data are generally, in excess from those of the former source, given that there are areas/products which do not fall into the payment schemes. Differences with the data from Hellenic Statistical Authority, given that this source conducts mainly surveys, uses different definitions and methodologies.
Oilseeds    

Not significant difference 

Data are compared with figures from the Greek Payment Agency & the Hellenic Statistical Authority. Data are generally, in excess from those of the former source, given that there are areas/products which do not fall into the payment schemes. Differences with the data from Hellenic Statistical Authority, given that this source conducts mainly surveys, uses different definitions and methodologies.
Other industrial crops (than oilseeds)    

Not significant difference 

Data are compared with figures from the Greek Payment Agency & the Hellenic Statistical Authority. Data are generally, in excess from those of the former source, given that there are areas/products which do not fall into the payment schemes. Differences with the data from Hellenic Statistical Authority, given that this source conducts mainly surveys, uses different definitions and methodologies.
Plants harvested green    

Not significant difference 

Data are compared with figures from the Greek Payment Agency & the Hellenic Statistical Authority. Data are generally, in excess from those of the former source, given that there are areas/products which do not fall into the payment schemes. Differences with the data from Hellenic Statistical Authority, given that this source conducts mainly surveys, uses different definitions and methodologies.
Total vegetables, melons and strawberries    

Not significant difference 

Data are compared with figures from the Greek Payment Agency & the Hellenic Statistical Authority. Data are generally, in excess from those of the former source, given that there are areas/products which do not fall into the payment schemes. Differences with the data from Hellenic Statistical Authority, given that this source conducts mainly surveys, uses different definitions and methodologies.
Vegetables and melons    

Not significant difference 

Data are compared with figures from the Greek Payment Agency & the Hellenic Statistical Authority. Data are generally, in excess from those of the former source, given that there are areas/products which do not fall into the payment schemes. Differences with the data from Hellenic Statistical Authority, given that this source conducts mainly surveys, uses different definitions and methodologies.
Strawberries    

Not significant difference 

Data are compared with figures from the Greek Payment Agency & the Hellenic Statistical Authority. Data are generally, in excess from those of the former source, given that there are areas/products which do not fall into the payment schemes. Differences with the data from Hellenic Statistical Authority, given that this source conducts mainly surveys, uses different definitions and methodologies.
Cultivated mushrooms  

Not significant difference 

Data are compared with figures from the Greek Payment Agency & the Hellenic Statistical Authority. Data are generally, in excess from those of the former source, given that there are areas/products which do not fall into the payment schemes. Differences with the data from Hellenic Statistical Authority, given that this source conducts mainly surveys, uses different definitions and methodologies.
Total permanent crops  

Not significant difference 

Data are compared with figures from the Greek Payment Agency & the Hellenic Statistical Authority. Data are generally, in excess from those of the former source, given that there are areas/products which do not fall into the payment schemes. Differences with the data from Hellenic Statistical Authority, given that this source conducts mainly surveys, uses different definitions and methodologies.
Fruit trees

Not applicable.

Not significant difference 

Data are compared with figures from the Greek Payment Agency & the Hellenic Statistical Authority. Data are generally, in excess from those of the former source, given that there are areas/products which do not fall into the payment schemes. Differences with the data from Hellenic Statistical Authority, given that this source conducts mainly surveys, uses different definitions and methodologies.
Berries  

Not significant difference 

Data are compared with figures from the Greek Payment Agency & the Hellenic Statistical Authority. Data are generally, in excess from those of the former source, given that there are areas/products which do not fall into the payment schemes. Differences with the data from Hellenic Statistical Authority, given that this source conducts mainly surveys, uses different definitions and methodologies.
Nut trees  

Not significant difference 

Data are compared with figures from the Greek Payment Agency & the Hellenic Statistical Authority. Data are generally, in excess from those of the former source, given that there are areas/products which do not fall into the payment schemes. Differences with the data from Hellenic Statistical Authority, given that this source conducts mainly surveys, uses different definitions and methodologies.
Citrus fruit trees

Not applicable.

Not significant difference 

Data are compared with figures from the Greek Payment Agency & the Hellenic Statistical Authority. Data are generally, in excess from those of the former source, given that there are areas/products which do not fall into the payment schemes. Differences with the data from Hellenic Statistical Authority, given that this source conducts mainly surveys, uses different definitions and methodologies.

A final remark for “T1900 Other oranges n.e.c.” dataset; this dataset includes varieties of oranges that cannot be classified in the current classification. However, it is very difficult to provide detailed data for each different variety. Usually, the sources of our primary data (e.g. farmers, Hellenic payment agency, etc) classify the data under the label “Other Varieties of Oranges” while specifying a particular variety is rather rare.
Vineyards

Not significant differences

 

Not significant difference 

Data are compared with figures from the Greek Payment Agency & the Hellenic Statistical Authority. Data are generally, in excess from those of the former source, given that there are areas/products which do not fall into the payment schemes. Differences with the data from Hellenic Statistical Authority, given that this source conducts mainly surveys, uses different definitions and methodologies.
Olive trees

Not applicable.

Not significant difference 

Data are compared with figures from the Greek Payment Agency & the Hellenic Statistical Authority. Data are generally, in excess from those of the former source, given that there are areas/products which do not fall into the payment schemes. Differences with the data from Hellenic Statistical Authority, given that this source conducts mainly surveys, uses different definitions and methodologies.
If there were considerable differences, which factors explain them?

Overall, differences with other sources vary and the % of difference is not stable.   

  





 
8.4. Coherence - sub annual and annual statistics

Not applicable.
8.5. Coherence - National Accounts

8.6. Coherence - internal

Not applicable.


9. Accessibility and clarity Top
9.1. Dissemination format - News release

Availability Links
NO






 
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 NO
Website





 
9.4. Dissemination format - microdata access

Availability Links
NO






 
9.5. Dissemination format - other

Not applicable.
9.6. Documentation on methodology

  Availability Links
Methodological report None
Quality Report English
Metadata
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 Top

Efficiency gains if compared to the previous quality report?
If other, which?
Burden reduction measures since the previous reference year 
If other, which?

The cost is relatively low, since the data are received from the regional administrative units of the country. 
Guidelines and explanations related to the data required by the Reg. 543/2009 were sent to the regional administrative units of the country.






 


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


12. Comment Top

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Related metadata Top


Annexes Top