Crop production (apro_cp)

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

Compiling agency: National Statistics Office (NSO)

Time Dimension: 2016-A0

Data Provider: MT1

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

National Statistics Office (NSO)

1.2. Contact organisation unit

Unit B3: Environment, Energy, Transport and Agriculture Statistics, Directorate B - Business Statistics

1.5. Contact mail address

National Statistics Office (NSO), Unit B3: Environment, Energy, Transport and Agriculture Statistics, Lascaris, Valletta VLT2000, Malta


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 and administrative sources.  The data collection covers early estimates (before the harvest) and the final data. Data are collected at national level.

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 (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
Final area under cultivation Survey

Farm Structure Survey

Production Administrative data

Data from the pitkali markets

Yield
Non-existing and non-significant crops Administrative data

Data from the pitkali markets

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

Farm Structure Survey

Production Administrative data

Data from the pitkali markets

Non-existing and non-significant crops Administrative data

Data from the pitkali markets

Table 3: Permanent crops      
Early estimates for production area
Final production area Survey

Farm Structure Survey

Production Administrative data

Data from the pitkali markets

Non-existing and non-significant crops Administrative data

Data from the pitkali markets

Table 4: Agricultural land use      
Main area Survey

Farm Structure Survey

Non-existing and non-significant crops Administrative data

Data from the pitkali markets

Total number of different data sources

1 - Administrative source

1 - Survey

   
Additional comments

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

 

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

Such information is difficult to collect.

   


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

Administrative data

Planning (month-month/year)
Preparation (month-month/year)
Data collection (month-month/year)

On a monthly basis

Quality control (month-month/year)

On a monthly basis

Data analysis (month-month/year)

On a monthly basis

Dissemination (month-month/year)

On a quarterly basis

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? YES

We know the amount that is sold through official markets.  We estimate the amount of direct sales and that amount that is cosumed directly by the farmers' family.  Such amounts are estimated by using coefficents that were a result of a survey carried out in the previous years.

Are special estimation/calculation methods used for permanent crops for human consumption? YES

We know the amount that is sold through official markets.  We estimate the amount of direct sales and that amount that is cosumed directly by the farmers' family.  Such amounts are estimated by using coefficents that were a result of a survey carried out in the previous years.

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:


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).

In Malta the majority of the farmers sell their produce through the pitkali markets of which such data is given to our section on a monthly basis.  Also in Malta we do not have very large areas since half of the agricultural land is with fodder.

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?

On the use of administrative source as descried above.

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

Pitkali Markets

Description

In Malta farmers sell their produce through the pitkali marktets and a record of their produce (quantity and value) of the respecitive crop is kept.

Data owner (organisation)

Pitkali Markets that fall under the responsibility of the Ministry for Sustainable Development, the Environment and Climate change.

Update frequency One per month or more often
Reference date (month/year)
Legal basis

Not available

Reporting unit
Identification variable (e.g. address, unique code, etc.)
Percentage of mismatches (%)

0

How were the mismatches handled?
Degree of coverage (holdings, e.g. 80%)

100

Degree of completeness (variables, e.g. 60%)
If not complete, which other sources were used ?
How were the data used?
Directly for estimates
Data used for other purposes, which?

Data is used for the input of the EAA

Which variables were taken from administrative sources?

Amount of kilograms sold through official markets together with the respective value

Were there any differences in the definition of the variables between the administrative source and those described in the Regulation? NO
Please describe the differences
What measures were taken to eliminate the differences?
How was the reliability, accuracy and coherence (comparison to other available data) of the data originated from administrative data source (ante- and/or ex-post) checked?

The data received from the admnistrative source is already validated directly by the source.  Once the data is received at our office, we compare the data with the previous months and whenever there are large discrepancies from one month to another we check again with the administrative source.

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? 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 2014? Stable Stable Stable Stable Stable
Is there a quality management process in place for crop statistics? NO        
If, yes, what are the components?        
Is there a Quality Report available? YES        
If yes, please provide a link(s)

https://nso.gov.mt/metadata/reports.aspx?id=15

       
To which data source(s) is it linked?

The quality report refers to both the animal and crop production.  For the crop production it refers to the administrative source of the pitkali markets

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

See the European level Quality Report.


5. Relevance Top
5.1. Relevance - User Needs

Are there known unmet user needs? NO
Describe the unmet needs
Does the Regulation 543/2009 meet the national data needs? 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


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


Common units - proportion


  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




 

6.3.1.1. Over-coverage - rate
6.3.1.2. Common units - proportion
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


Item non-response - rate


  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
6.3.3.1. Unit non-response - rate
6.3.3.2. Item non-response - rate
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

Not applicable.

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?

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

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)
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?  None
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  
Dried pulses and protein crops  
Root crops  
Oilseeds  
Other industrial crops (than oilseeds)  
Plants harvested green  
Total vegetables, melons and strawberries  
Vegetables and melons  
Strawberries  
Cultivated mushrooms  
Total permanent crops  
Fruit trees  
Berries  
Nut trees  
Citrus fruit trees  
Vineyards
Olive trees  
If there were considerable differences, which factors explain them?  
8.4. Coherence - sub annual and annual statistics

Not applicable.

8.5. Coherence - National Accounts

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
Publications in English None
9.3. Dissemination format - online database

Data tables - consultations

Not applicable


  Availability Links
On-line database accessible to users YES

https://nso.gov.mt/statdb/start

Website English

https://nso.gov.mt/statdb/start

9.3.1. Data tables - consultations

Not applicable

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

https://nso.gov.mt/metadata/reports.aspx?id=15

Metadata None
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)
If other, which?
Burden reduction measures since the previous reference year 
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


Related metadata Top


Annexes Top