Crop production (apro_cp)

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

Compiling agency: Statistical Service of Cyprus (CYSTAT)

Time Dimension: 2016-A0

Data Provider: CY1

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

Statistical Service of Cyprus (CYSTAT)

1.2. Contact organisation unit

Agriculture

1.5. Contact mail address

Statistical Service of Cyprus

CY-1444

Nicosia

Cyprus


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).

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

Government controlled areas of the 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 (2014)?  NO      
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)? NO      
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

Survey on Cereals, FSS, Survey on the main crop products

Final area under cultivation Survey

Survey on Cereals, FSS, Survey on the main crop products

Production Survey

Survey on Cereals, Survey on the main crop products

Yield Survey

Survey on Cereals, Survey on the main crop products

Non-existing and non-significant crops Survey

Survey on Cereals, FSS, Survey on the main crop products

Table 2: Vegetables, melons and strawberries       
Early estimates for harvested areas Survey

Survey on the main crop products, FSS

Final harvested area Survey

Survey on the main crop products, FSS

Production Survey

Survey on the main crop products

Non-existing and non-significant crops Survey

Survey on the main crop products, FSS

Table 3: Permanent crops      
Early estimates for production area Survey

Survey on the main crop products, Survey on vines, FSS

Final production area Survey

Survey on the main crop products, Survey on vines, FSS

Production Survey

Survey on the main crop products, Survey on vines

Non-existing and non-significant crops Survey

Survey on the main crop products, Survey on vines, FSS

Table 4: Agricultural land use      
Main area Survey

Surveys on cereals, vines, the main crop products, FSS

Non-existing and non-significant crops Survey

Surveys on cereals, vines, the main crop products, FSS

Total number of different data sources

4

   
Additional comments

please find in point 12 explanations about the way FSS was used as data source as mentioned in question 3.1

 

  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 VINES

SURVEY ON THE MAIN CROP PRODUCTS

Planning (month-month/year)

(10-12/2016)

(10-12/2016)

(12/2016-2/2017)

Preparation (month-month/year)

(1-2/2017)

(1-2/2017)

(3-4/2017)

Data collection (month-month/year)

(3-6/2017)

(3-6/2017)

(5-7/2017)

Quality control (month-month/year)

(6-7/2017)

(6-7/2017)

(7-8/2017)

Data analysis (month-month/year)

(8-9/2017)

(8-9/2017)

(8-9/2017)

Dissemination (month-month/year)

(9/2017)

(9/2017)

(9/2017)

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? 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.
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
F1290 - Other stone fruits n.e.c.

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

(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)

2016

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 VINES

SURVEY ON THE MAIN CROP PRODUCTS

Which survey method was used? Telephone interview, paper questionnaire
Face-to-face interview
Telephone interview, paper questionnaire
Face-to-face interview
Telephone interview, paper questionnaire
Face-to-face interview
If 'other', please specify
Please provide a link to the questionnaire
Data entry method, if paper questionnaires? Manual Manual Manual


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 ?
How were the data used?
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. 1/16 - 8/16)
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? 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? Improvement Improvement Improvement Improvement Improvement
Is there a quality management process in place for crop statistics? YES        
If, yes, what are the components?

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 12 of the Statistics Law No. 15(I) of 2000 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. 

       
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

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? 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

Since 2008 (with the exception of 2010 and 2013) CYSTAT carries out an annual online “Users Satisfaction Survey”. The results of the surveys are available on CYSTAT’s website at the link attached below.

Overall, the users of statistical data published by CYSTAT are satisfied.



Annexes:
Results of CYSTAT’s User Satisfaction Surveys
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

Survey on Cereals

Survey on Vines

survey on the main crop products

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

4 strata

4 strata

3 strata

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

7166

9840

31709

Size of sample

3879

4614

11338

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 Basic weight Basic weight
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

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

Survey on Cereals

Survey on Vines

Survey on main crop products

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 we found that some holdings 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: First reason is that they changed the use of the holding in the sense that the land was no longer agricultural but became land plots for building and second 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 we found that some holdings 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: First reason is that they changed the use of the holding in the sense that the land was no longer agricultural but became land plots for building and second 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 we found that some holdings 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: First reason is that they changed the use of the holding in the sense that the land was no longer agricultural but became land plots for building and second 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




 

6.3.1.1. Over-coverage - rate

not applicable

6.3.1.2. Common units - proportion

not applicable

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 Vines

Survey on the main crop products

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)?

4

4

4

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 at the very moment they were found, therefore by the end of the survey the measurement errors in the data were minimised. 

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. This was done by phone and the need for such corrections was minimal. Followup interviews were carried out during the data collection process in those cases where the checking process suggested that these should be done. These checks were based on relevant information about each holding which was already available from previous surveys of the Statistical Service. After the completion of data collection, however, neither followup interviews took place nor imputations were made.

The analytical checking process in conjunction with the intensive callback strategy minimised 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 of statistics was necessary.

When cases of measurement errors were found, they were corrected at the very moment they were found, therefore by the end of the survey the measurement errors in the data were minimised. 

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. This was done by phone and the need for such corrections was minimal. Followup interviews were carried out during the data collection process in those cases where the checking process suggested that these should be done. These checks were based on relevant information about each holding which was already available from previous surveys of the Statistical Service. After the completion of data collection, however, neither followup interviews took place nor imputations were made.

The analytical checking process in conjunction with the intensive callback strategy minimised 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 of statistics was necessary.

When cases of measurement errors were found, they were corrected at the very moment they were found, therefore by the end of the survey the measurement errors in the data were minimised. 

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. This was done by phone and the need for such corrections was minimal. Followup interviews were carried out during the data collection process in those cases where the checking process suggested that these should be done. These checks were based on relevant information about each holding which was already available from previous surveys of the Statistical Service. After the completion of data collection, however, neither followup interviews took place nor imputations were made.

The analytical checking process in conjunction with the intensive callback strategy minimised 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 of statistics was necessary.

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

Survey on cereals (census)

Survey on vines (Census)

Survey on the main crop products (Census)

Unit level non-response rate (in %)

(1.2 %)

(0.6 %)

(1.9%)

Item level non-response rate (in %)              
               - Min% / item
               - Max% / item
               - Overall%
Was the non-response been treated ? NO NO NO
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? Insignificant Insignificant Insignificant
Which tools were used for correcting the data?
Which organisation did the corrections?
Additional comments
6.3.3.1. Unit non-response - rate

not applicable

6.3.3.2. Item non-response - rate

not applicable

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

A data revision policy is in place at CYSTAT. It is published on CYSTAT’s website, at the following link:

http://www.mof.gov.cy/mof/cystat/statistics.nsf/dissemination_en/dissemination_en?OpenDocument

 

CYSTAT also publishes a list of scheduled revisions (regular or major revisions), also published on its website, at the following link:

http://www.mof.gov.cy/mof/cystat/statistics.nsf/releasecalendar_en/releasecalendar_en?OpenDocument

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 crop year 2016? (day/month/year)
When were the final results published for crop year 2016? (day/month/year) 29/06/2018 29/06/2018 29/06/2018 29/06/2018 29/06/2018 29/06/2018 29/06/2018 29/06/2018 29/06/2018 29/06/2018 29/06/2018 29/06/2018 29/06/2018 29/06/2018 29/06/2018
Additional comments
7.1.1. Time lag - first result
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?  Farm structure survey 2016
If others, which?
If no comparisons have been made, why not?


Results of comparisons FSS 2016 (if available) Vineyard survey 2015 IACS Other source(s)  In case of other sources, which?
Cereals

+5.5%

 

 

Dried pulses and protein crops

+6.4%

 

 

Root crops

-45.4%

 

 

Oilseeds

-107.8%

 

 

Other industrial crops (than oilseeds)

Not applicable

 
Plants harvested green

+0.2%

 
Total vegetables, melons and strawberries

-4.6%

 

 

Vegetables and melons

Not applicable

 

 

Strawberries

Not applicable

 

 

Cultivated mushrooms

Not applicable

 
Total permanent crops

-0.5%

 

 

Fruit trees

-3.9%

 

 

Berries

Not applicable

 
Nut trees

-19.1%

 

 

Citrus fruit trees

-6.8%

 

 

Vineyards

-1%

+8.8%

 

Olive trees

0%

 

 

If there were considerable differences, which factors explain them?

Comparing the results between FSS and ACS there are some differences that are mostly atriibuted to the fact that these are the results from two different surveys with some differences in the methodology and definitions (for example main areas vs harvested areas). For crops with very small areas all differences are considered insignificant. 

 

 
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 Electronic

www.cystat.gov.cy

Publications in English Electronic

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 National language
English

www.cystat.gov.cy

9.3.1. Data tables - consultations

not applicable

9.4. Dissemination format - microdata access

Availability Links
YES

Statistical micro-data from CYSTAT’s surveys are accessible for research purposes only and under strict provisions as described below: 

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 attached below.

  • Link to the application for access to microdata on CYSTAT's website:

http://www.cystat.gov.cy/mof/cystat/statistics.nsf/dissemination_en/dissemination_en?OpenDocument

9.5. Dissemination format - other

Free : www.cystat.gov.cy

 

9.6. Documentation on methodology

  Availability Links
Methodological report English
National language

www.cystat.gov.cy

Quality Report English

www.cystat.gov.cy

Metadata National language
English

www.cystat.gov.cy

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 reference year (2013) Further automation
If other, which?
Burden reduction measures since the previous reference year  Less respondents
If other, which?


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


12. Comment Top

Available information for cereals, vines and the most significant crops for Cyprus is a result from specific annual surveys. For the rest crops the available information, as far as areas are concenred, is the results of FSS. 


Related metadata Top


Annexes Top
Code of Practice for the Collection, Publication and Storage of Statistical Data
Statistics Law No. 15(I) of 2000
Regulation (EC) No 223/2009 on European statistics (consolidated text)
European Statistics Code of Practice
Code of Practice for the Collection, Publication and Storage of Statistical Data
Results of CYSTAT’s User Satisfaction Surveys