Crop production (apro_cp)

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

Compiling agency: Statistical Service of Cyprus (CYSTAT)


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 Top

 
1.1. Contact organisation

Statistical Service of Cyprus (CYSTAT)
1.2. Contact organisation unit

Agricultural Statistics Unit
1.5. Contact mail address

Statistical Service of Cyprus

Michael Karaoli str. 

1444 Nicosia, Cyprus


2. Statistical presentation Top
2.1. Data description

Crop statistics produced according to the Regulation (EC) No 543/2009 and the Annex of the Commission Delegated Regulation (EU) No 2015/1557 refer to the following types of annual data:

  • area under cultivation, harvested production, yield,  humidity and main area for cereals and for other main field crops (mainly dried pulses, root crops, fodder and industrial crops);
  • harvested area, harvested production and main area for vegetables;
  • production area, harvested production and main area for permanent crop.

The areas are expressed in 1000 hectares, the harvested quantities in 1000 tonnes and the yields in t/ha.   
2.2. Classification system

Hierarchical crop classification system.
2.3. Coverage - sector

Main crops from the utilised agricultural area and mushrooms.
2.4. Statistical concepts and definitions

See: Annual crop statistics Handbook
2.5. Statistical unit

An agricultural holding with utilised agricultural area for the production of a crop.
2.6. Statistical population

All agriculural holldings with utilised agricultural area. 
2.7. Reference area

Republic of Cyprus.
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? NO      
Which data sources have been dropped since the previous quality report?
Type of source?
Why have they been dropped?
Additional comments


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

  Type Name(s) of the sources If other type, which kind of data source?
Table 1: Crops from arable land      
Early estimates for areas Survey

Survey on Cereals, Survey on Vineyards, Survey on Crops

  
Final area under cultivation Survey

Survey on Cereals, Survey on Vineyards, Survey on Crops
Production Survey

Survey on Cereals, Survey on Vineyards, Survey on Crops
Yield Survey

Survey on Cereals, Survey on Vineyards, Survey on Crops
Non-existing and non-significant crops Survey

Survey on Cereals, Survey on Vineyards, Survey on Crops, IFS 2020
Table 2: Vegetables, melons and strawberries       
Early estimates for harvested areas Survey

Survey on Cereals, Survey on Vineyards, Survey on Crops
Final harvested area Survey

Survey on Cereals, Survey on Vineyards, Survey on Crops
Production Survey

Survey on Cereals, Survey on Vineyards, Survey on Crops
Non-existing and non-significant crops Survey

Survey on Cereals, Survey on Vineyards, Survey on Crops, IFS 2020
Table 3: Permanent crops      
Early estimates for production area Survey

Survey on Cereals, Survey on Vineyards, Survey on Crops
Final production area Survey

Survey on Cereals, Survey on Vineyards, Survey on Crops
Production Survey

Survey on the main crop products, Survey on vines
Non-existing and non-significant crops Survey

Survey on Cereals, Survey on Vineyards, Survey on Crops, IFS 2020
Table 4: Agricultural land use      
Main area Survey

Survey on Cereals, Survey on Vineyards, Survey on Crops
Non-existing and non-significant crops Survey

Survey on Cereals, Survey on Vineyards, Survey on Crops, IFS 2020
Total number of different data sources

4

  
Additional comments

 

  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 sown 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

SURVEY ON CEREALS 

SURVEY ON VINEYARDS

SURVEY ON CROPS
Planning (month-month/year)

(11-12/2022)

(3-4/2023)

(1-2/2023)
Preparation (month-month/year)

(1-2/2023)

(4-5/2023)

(2-3/2023)
Data collection (month-month/year)

(2-5/2023)

(6-10/2023)

(4-10/2023)
Quality control (month-month/year)

(2-5/2023)

(6-10/2023)

(4-10/2023)
Data analysis (month-month/year)

(10-11/2023)

(11-12/2023)

(11-12/2023)
Dissemination (month-month/year)

(3-6/2024)

(3-6/2024)

(3-6/2024)
If there were delays, what were the reasons?

Delays due to the preparation of IFS 2023.

Delays due to the preparation of IFS 2023.

Delays due to the preparation of IFS 2023.






 
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: P9000 - Other dry pulses and protein crops n.e.c.
I1190 -Other oilseed crops n.e.c
G2900 - Other leguminous plants harvested green n.e.c.
G9100- Other cereals harvested green (excluding green maize)
G9900 - Other plants harvested green from arable land n.e.c.
V2900 - Other leafy or stalked vegetables n.e.c.
V3900 - Other vegetables cultivated for fruit n.e.c.
V4900 - Other root, tuber and bulb vegetables n.e.c.
V5900 - Other fresh pulses n.e.c.
F1190 - Other pome fruits n.e.c.
F1290 - Other stone fruits n.e.c.
F2900 - Other fruits from subtropical and tropical climate zones n.e.c.
F4900 - Other nuts n.e.c.
T2900 - Other small citrus fruits (including hybrids) n.e.c.
H9000 - Other permanent crops for human consumption n.e.c.
PECR9 - Other permanent crops

P9000: Louvana, Lentils,Cowpeas

I1190: Sesame, Groundnuts

G2900: Any other leguminous plants harvested green excluding Lucerne ( information also collected from FSS)

G9100: Cereals harvested green mainly wheat, barley, oats, annual sorghum

G9900: Annual raygrasses

V2900: Parsley, coriander and other leafy vegetables

V3900: Okra

V4900: Colocase

V5900: Broadbeans fresh , Cowpeas fresh

F1190: Quinces

F1290: Loquats

F2900: Pomegranates + Other tropical fruits

F4900: Pistachios

T2900: Mandarin and small citrus hybrids

H9000: Carobs

PERC9: Other permanent crops like Christmas trees etc.


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).

All surveys were conducted as sample surveys with the method of stratified systematic random sampling. The stratification variable was the cultivated area of each type of crop (e.g. vineyards) or group of crops (vegetables, melons and strawberries). At the end of each survey, all row data are weighted using extrapolation factors that are calculated based on the sampling design. The estimated relative standard error for each variable surveyed is minimal due the large sample size taken. This ensures that all statistics are representative of at least 95% of the areas of each table in the Regulation.  
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?
When was last update of the holding register? (month/year)

2022
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

SURVEY ON CEREALS

SURVEY ON VINEYARDS

SURVEY ON CROPS
Which survey method was used? Telephone interview, electronic questionnaire Telephone interview, electronic questionnaire Telephone interview, electronic questionnaire
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
Description
Data owner (organisation)
Update frequency
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?
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
Data owner (organisation)
Update frequency (e.g. 1 year or 6 months)
Reference date (Month/Year  e.g. 1/22 - 8/22)
Legal basis
Use purpose of the estimates?
What kind of expertise the experts have?
What kind of estimation methods were used?
Were there any differences in the definition of the variables between the experts' estimates and those described in the Regulation?
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? YES
If yes, which kind of dataset? Previous results
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 the previous quality report? Improvement Improvement Improvement Improvement Improvement
Is there a quality management process in place for crop statistics? YES        
If, yes, what are the components?

Statistics are produced in accordance with the provisions of the European Statistics Code of Practice and in line with the principles governing its implementation. 

The quality of statistics is assessed according to five quality criteria: relevance, accuracy, timeliness and punctuality, accessibility and clarity, coherence and comparability. The statistics are assessed taking into account the Regulation (EC) No 543/2009  and the Handbook on Annual Crop Statistics  on the methodology for collecing data on annual crop (Annex 1). On the basis of the above criteria, crop statistics are assessed as being of good quality.

        
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? Systematic validation improvements        
If, other, please specify        
Additional comments        






 
4.2. Quality management - assessment

The statistics are assessed as being of good quality. Eurostat also monitors regularly the quality of crop statistics provided by the Member States concerning the availability, the completeness and the punctuality of the statistics provided.


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? YES
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

 Α user satisfaction survey is carried out on an annual basis, but is not specific to crop statistics. It does not allow for adequate conclusions to be made on crop statistics.






 
5.3. Completeness

Most of the requested data are available.
5.3.1. Data completeness - rate

See the European level Quality Report


6. Accuracy and reliability Top
6.1. Accuracy - overall

Coverage errors, measurement errors, non-response errors, processing errors are considered to be small but nevertheless actions are undertaken to reduce these different types of errors as much as possible.
6.2. Sampling error

Sampling method and sampling error

  Survey 1 Survey 2 Survey 3 Survey 4 Survey 5 Survey 6 Survey 7
Name

Survey on Cereals

Survey on Vineyards

Survey on Crops
Sampling basis? Area Area Area
If 'other', please specify
Sampling method? Random
Stratified		 Random
Stratified		 Random
Stratified
If stratified, number of strata?

5 strata

5 strata

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

7912

8068

29650
Size of sample

6000

4101

8269
Which methods were used to assess the sampling error?  Relative standard error Relative standard error Relative standard error
If other, which?
Which methods were used to derive the extrapolation factor?  Basic weight
Non-response		 Basic weight
Non-response		 Basic weight
Non-response
If other, which?
If CV (co-efficient of variation) was calculated, please describe the calculation methods and formulas

Not available

Not available

Not available
If the results were compared with other sources, please describe the results
Which were the main sources of errors?


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
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

Surveys for the reference year 2022 are still in progress and therefore the over-coverage error is not available yet.


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

Survey on Cereals

Survey on Vineyards

Survey on Crops
Error type Under-coverage
Over-coverage
Contact errors		 Under-coverage
Over-coverage
Contact errors		 Under-coverage
Over-coverage
Contact errors
Degree of bias caused by coverage errors None None None
What were the reasons for coverage errors?

Under-coverage errors include new units that were not included in the frame but were surveyed because of either demergers from other units or real birth. During the data collection some holdings were demerged to several holdings, thus creating new units to be surveyed. 

Over-coverage errors occurred in the cases where the holdings ceased their activities because of two main reasons: 1) the change of the use of the holding (the land was no longer agricultural but became land plots for building) and 2) the agricultural activities were abandoned.

Contact errors occured when the holders were unable to be reached, because of either wrong contact information or they moved and could not be located.  

Under-coverage errors include new units that were not included in the frame but were surveyed because of either demergers from other units or real birth. During the data collection some holdings were demerged to several holdings, thus creating new units to be surveyed. 

Over-coverage errors occurred in the cases where the holdings ceased their activities because of two main reasons: 1) the change of the use of the holding (the land was no longer agricultural but became land plots for building) and 2) the agricultural activities were abandoned.

Contact errors occured when the holders were unable to be reached, because of either wrong contact information or they moved and could not be located.  

Under-coverage errors include new units that were not included in the frame but were surveyed because of either demergers from other units or real birth. During the data collection some holdings were demerged to several holdings, thus creating new units to be surveyed. 

Over-coverage errors occurred in the cases where the holdings ceased their activities because of two main reasons: 1) the change of the use of the holding (the land was no longer agricultural but became land plots for building) and 2) the agricultural activities were abandoned.

Contact errors occured when the holders were unable to be reached, because of either wrong contact information or they moved and could not be located.  
Which actions were taken for reducing the error or to correct the statistics?

Statistics were corrected by removing any holdings that caused a coverage error from the frame and then recalculating the weights.

Statistics were corrected by removing any holdings that caused a coverage error from the frame and then recalculating the weights.

Statistics were corrected by removing any holdings that caused a coverage error from the frame and then recalculating the weights.
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

Survey on Cereals

Survey on Vineyards

Survey on Crops
Was the questionnaire based on usual concepts for respondents? YES YES YES
Number of surveys already performed with the current questionnaire (or a slightly amended version of it)?

10

10

10
Preparatory (field) testing of the questionnaire? NO NO NO
Number of units participating in the tests? 

0

0

0
Explanatory notes/handbook for surveyors/respondents?  YES YES YES
On-line FAQ or Hot-line support for surveyors/respondents? NO NO NO
Were pre-filled questionnaires used? NO NO NO
Percentage of pre-filled questions out of total number of questions

0

0

0
Were some actions taken for reducing the measurement error or to correct the statistics? YES YES YES
If yes, describe the actions and their impact

When cases of measurement errors were found, they were corrected immediately, therefore by the end of the survey the measurement errors in the data were minimized.

Errors were checked by checking the data against the prior information available in the existing register and in many cases by contacting the holder again through the telephone. The need for such corrections was minimal. Follow-up interviews were carried out during the data collection if needed. 

The analytical checking process in conjunction with the intensive callback strategy minimized almost entirely missing and inaccurate data as well as the number of lost cases. This led to the elimination of any measurement errors and therefore no correction or imputations was necessary.

Many checks are carried out at microlevel and are compared against the results of previous years in order to ensure the quality of the data.  

When cases of measurement errors were found, they were corrected immediately, therefore by the end of the survey the measurement errors in the data were minimized.

Errors were checked by checking the data against the prior information available in the existing register and in many cases by contacting the holder again through the telephone. The need for such corrections was minimal. Follow-up interviews were carried out during the data collection if needed. 

The analytical checking process in conjunction with the intensive callback strategy minimized almost entirely missing and inaccurate data as well as the number of lost cases. This led to the elimination of any measurement errors and therefore no correction or imputations was necessary.

Many checks are carried out at microlevel and are compared against the results of previous years in order to ensure the quality of the data.  

When cases of measurement errors were found, they were corrected immediately, therefore by the end of the survey the measurement errors in the data were minimized.

Errors were checked by checking the data against the prior information available in the existing register and in many cases by contacting the holder again through the telephone. The need for such corrections was minimal. Follow-up interviews were carried out during the data collection if needed. 

The analytical checking process in conjunction with the intensive callback strategy minimized almost entirely missing and inaccurate data as well as the number of lost cases. This led to the elimination of any measurement errors and therefore no correction or imputations was necessary.

Many checks are carried out at microlevel and are compared against the results of previous years in order to ensure the quality of the data.  






 
6.3.3. Non response error

Unit non-response - rate

Surveys are still in progress and the non-response rate is not available yet.


Item non-response - rate

not applicable


  Survey 1 Survey 2 Survey 3 Survey 4 Survey 5 Survey 6 Survey 7
Name

Survey on cereals

Survey on vineyards

Survey on crops
Unit level non-response rate (in %)

Estimated to be 0.8%

Estimated to be 0.5%

Estimated to be 0.3%
Item level non-response rate (in %)              
               - Min% / item
               - Max% / item
               - Overall%
Was the non-response been treated ? YES YES YES
Which actions were taken to reduce the impact of non-response?

The non-response was measured and weights were recalculated.

The non-response was measured and weights were recalculated.

The non-response was measured and weights were recalculated.
Which items had a high item-level non-response rate? 

Not applicable.

Not applicable.

Not applicable.
Which methods were used for handling missing data?
(several answers allowed)		 Follow-up interviews
Reminders		 Follow-up interviews
Reminders		 Follow-up interviews
Reminders
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? Insignificant Insignificant Insignificant
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

CYSTAT's Revision Policy describes the general rules and principles governing the procedure of revising data published by CYSTAT. As part of this policy, CYSTAT publishes a list of scheduled revisions on an annual basis, which can be found on the release calendar page.

https://www.cystat.gov.cy/en/StaticPage?id=1072

The same practice is applied both to data released nationally and to data transmitted to Eurostat. When revised data are transmitted to Eurostat, these are flagged accordingly and accompanied by the necessary explanations.

Cystat maintains and releases a revision calendar for scheduled revisions on its website, so that users are informed in advance when revised data are released.

 
6.6. Data revision - practice

Not applicable.
6.6.1. Data revision - average size

Not applicable.


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?

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1
How many of them are forecasts (releases before the harvest)?

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0
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

Data are transmitted to Eurostat on time.
7.2.1. Punctuality - delivery and publication

Deadlines are respected. All data deliveries are on time according to the EU Regulation.


8. Coherence and comparability Top

Agricultural land use data are also collected in IFS. Differences may occur in the cross-domain comparability as a result of different definitions and data collection methods. Checks are also performed before the data are sent to Eurostat to eliminate possible consistency errors.
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

2 reference periods (from 2020 onwards)


  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? YES NO NO NO
If yes, to which were they related? Methods
If other, which?
Which items were affected? G9900 - Other plants harvested green from arable land n.e.c.
Year of break (number)

2020
Impact on comparability Low
Additional comments

Break in the series exists for ‘other plants harvested green from arable land nec’ (code G9900) due to the fact that for previous years information was collected with cereals harvested green as a total area but from 2020 onwards, this item is collected separately in order to be in line with the definitions and guidelines provided by IFS.    





 
8.2.1. Length of comparable time series

2 reference periods (from 2020 onwards)
8.3. Coherence - cross domain

With which other data sources the crop statistics data have been compared?  None
If others, which?
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    

 
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

Not applicable.
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

Summary tables in Excel format at www.cystat.gov.cy
Publications in English None

Summary tables in Excel format at www.cystat.gov.cy






 
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
YES

There is no microdata access to outside users.

Under the provisions of the Statistics Law, CYSTAT may release microdata for the sole use of scientific research. Applicants have to submit the request form "APPLICATION FOR DATA FOR RESEARCH PURPOSES" giving thorough information on the project for which micro-data are needed.The application is evaluated by CYSTAT’s Confidentiality Committee and if the application is approved, a charge is fixed according to the volume and time consumed for preparation of the data. Micro-data may then be released after an anonymisation process which ensures no direct identification of the statistical units but, at the same time, ensures usability of the data.

The link for the application is: https://www.cystat.gov.cy/en/DataRequestContactForm?fid=7






 
9.5. Dissemination format - other

Free : www.cystat.gov.cy

 
9.6. Documentation on methodology

  Availability Links
Methodological report None
Quality Report English

www.cystat.gov.cy
Metadata

 
Additional comments  






 
9.7. Quality management - documentation

The quality of statistics in CYSTAT is managed in the framework of the European Statistics Code of Practice which sets the standards for developing, producing and disseminating European Statistics as well as the ESS Quality Assurance Framework (QAF). CYSTAT endorses the Quality Declaration of the European Statistical System. In addition, CYSTAT is guided by the requirements provided for in Article 11 of the Statistics Law No. 25(I) of 2021 as well as Article 12 of Regulation (EC) No 223/2009 on European statistics, which sets out the quality criteria to be applied in the development, production and dissemination of European statistics.

Links to all of the above documents:
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? Further automation
If other, which?
Burden reduction measures since the previous reference year  Less respondents
If other, which?






 


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


12. Comment Top


Related metadata Top


Annexes Top