Crop production (apro_cp)

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

Compiling agency: Central Statistical Bureau of Latvia

Time Dimension: 2016-A0

Data Provider: LV1

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

Central Statistical Bureau of Latvia

1.2. Contact organisation unit

Agricultural Statistics Section, Agricultural and Environment Statistics Department of the CSB of Latvia

1.5. Contact mail address

Central Statistical Bureau of Latvia, Lacplesa street 1, Riga, LV-1301


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

The entire territory of the country.

2.8. Coverage - Time

Crop year.

2.9. Base period

Not applicable.


3. Statistical processing Top
3.1. Source data

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


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

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

Integrated Administration and Control Systems (IACS)

Final area under cultivation Survey

Sample survey on areas, harvested production and average yield of agricultural crops

 

 

Production Survey

Sample survey on areas, harvested production and average yield of agricultural crops

Yield Survey

Sample survey on areas, harvested production and average yield of agricultural crops

Non-existing and non-significant crops Survey

Sample survey on areas, harvested production and average yield of agricultural crops

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

Sample survey on areas, harvested production and average yield of agricultural crops

Final harvested area Survey

Sample survey on areas, harvested production and average yield of agricultural crops

Production Survey

Sample survey on areas, harvested production and average yield of agricultural crops

Non-existing and non-significant crops Survey

Sample survey on areas, harvested production and average yield of agricultural crops

Table 3: Permanent crops      
Early estimates for production area Survey

Sample survey on areas, harvested production and average yield of agricultural crops

Final production area Survey

Sample survey on areas, harvested production and average yield of agricultural crops

Production Survey

Sample survey on areas, harvested production and average yield of agricultural crops

Non-existing and non-significant crops Survey

Sample survey on areas, harvested production and average yield of agricultural crops

Table 4: Agricultural land use      
Main area Survey

Sample survey on areas, harvested production and average yield of agricultural crops

Non-existing and non-significant crops Survey

Sample survey on areas, harvested production and average yield of agricultural crops

Total number of different data sources

2

 

   
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     

 X

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

 

   


Which method is used for calculating the yield for main arable crops? production divided by harvested area
If another method, describe it.

 For national needs - reference is the sown area.

3.2. Frequency of data collection

  Source 1 Source 2 Source 3 Source 4 Source 5 Source 6 Source 7 Source 8 Source 9
Name of data source

Sample survey on areas, harvested production and average yield of agricultural crops

Planning (month-month/year)

10-12/2015

Preparation (month-month/year)

08-09/2016

Data collection (month-month/year)

10-11/2016

Quality control (month-month/year)

11-12/2016

Data analysis (month-month/year)

01-04/2017

Dissemination (month-month/year)

01-09/217

If there were delays, what were the reasons?

No delays

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)? YES  
In case yes, how do they differ? ( list all items and explanations)

Onions (V4210) include shallots (V4220)

Cherries (F1240) include sour (F1241) and sweet (F1242) cherries

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).

Before selection of holdings, when creating the population frame, holdings with crop standard output (SO) >= EUR 1500 are included. In addition, it is planned to add to the results estimated areas of agricultural crops in holdings with crop SO < EUR 1500. As the result, requirements stipulated in Art.3.2. of Regulation (EC) No 543/2009 are taken into account

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)

07/2016

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

Sample survey on areas, harvested production and average yield of agricultural crops

Which survey method was used? On-line electronic questionnaire filled in by respondent
Postal questionnaire filled in by respondent
Telephone interview, paper questionnaire
Telephone interview, electronic questionnaire
Face-to-face interview
If 'other', please specify
Please provide a link to the questionnaire

http://www.csb.gov.lv/sites/default/files/veidlapas/2-ls_v10117040.pdf

Data entry method, if paper questionnaires? 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

(Integrated Administration and control System (IACS))

Description

(Data on the declared sown area of agricultural crops)

Data owner (organisation)

(Rural Support Service)

Update frequency Once per year or more often
Reference date (month/year)

(June/2016)

Legal basis

(Agreement between CSB and Rural Support Service on exchange of information)

Reporting unit

(area of crop, ha)

Identification variable (e.g. address, unique code, etc.)

(Unique code of the crop)

Percentage of mismatches (%)

(not applicable)

How were the mismatches handled?

(not applicable)

Degree of coverage (holdings, e.g. 80%)

(78%)

Degree of completeness (variables, e.g. 60%)

(100%)

If not complete, which other sources were used ?

(not applicable)

How were the data used?
Sample frame
Validation
Directly for estimates
Other
Data used for other purposes, which?

(For recalculation of economic size of agricultural holdings, as a pre-print for Crop Production Survey)

Which variables were taken from administrative sources?

(All variables in ha for making early estimates)

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

(not applicable)

What measures were taken to eliminate the differences?

(not applicable)

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?

(no any other data source available for making early estimates)

What were the possible limitations, drawbacks of using the data from administrative source(s)?

(no any other data source available for making early estimates)


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
Other
Is the data cross-validated against an other dataset? YES
If yes, which kind of dataset? Previous results
Other dataset
If other, please describe

IACS

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
Is there a quality management process in place for crop statistics? YES        
If, yes, what are the components?

Activities of the Total Quality Management Systen: to identify statistical and organizational processes and develop their descriptions in compliance with requirements of the quality management system. Components are fundamental processes such as project preparation, data collection, data processing, data analysis, data dissemination and support processes as metadata and documentation of processes. Quality Management System is maintained and updated electronically in QPR portal

       
Is there a Quality Report available? YES        
If yes, please provide a link(s)

http://ads.csb.gov.lv/Apsekojums/Sakums.rails?id=ccb293be-05da-4248-88b7-a7450097b612&apskatesRezims=True&fromPublic=True&listPage=1

       
To which data source(s) is it linked?

Survey on sown areas, harvested crop production, average yield of agricultural crops

       
Has a peer-review been carried out for crop statistics? NO        
If, yes, which were the main conclusions?        
What quality improvement measures are planned for the next 3 years? Systematic validation improvements
Further automation
Quality report
       
If, other, please specify        
Additional comments

In order to facilitate correct interpretation of data and to foster dialogue with data users, the CSB develops a unified quality report standard where different data preparation aspects are described in detail: legal basis, methodology, sample design and sample size, data collection and processing methods, definitions etc. (available in Latvian, a link is provided). Data entry system for respondents with improved validation rules has been worked out.

       
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
5.3. Completeness

See the European level Quality Report

5.3.1. Data completeness - rate

See the European level Quality Report


6. Accuracy and reliability Top
6.1. Accuracy - overall

See the European level Quality Report.

6.2. Sampling error

Sampling method and sampling error

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

Sample survey on areas, harvested production and average yield of agricultural crops

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

201

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

22652

Size of sample

3763

Which methods were used to assess the sampling error?  Relative standard error
If other, which?
Which methods were used to derive the extrapolation factor?  Basic weight
Non-response
Wrong classification
Other
If other, which?

Calibration

If CV (co-efficient of variation) was calculated, please describe the calculation methods and formulas

The relative standard error (percentage) is defined as the standard error divided by the estimated value.

The total estimated value is the sum of two estimates, where the first comes from sample (holdings with crop standard output (SO) >= EUR 1500) and the second estimated correspond to unobserved population (holdings with crop SO < EUR 1500).

RSE=sqrt(estimate of sampling variance)/the total estimated value

The estimate of sampling variance is done by the ultimate cluster method (Hansen, Hurwitz and Madow, 1953) and residual estimation from the regression model to take weight calibration into account.

The R procedure "vardom" from the package "vardpoor" (Breidaks and Liberts, 2014) is used for the variance estimation.

If the results were compared with other sources, please describe the results
Which were the main sources of errors?

Non-response and over coverage


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

Sample survey on areas, harvested production and average yield of agricultural crops

Cereals for the production of grain (in %)

0.6

Dried pulses and protein crops (in %)

1.4

Root crops (in %)

1.2

Oilseeds (in %)

1.3

Other industrial crops (included all industrial crops besides oilseeds)  (in %)

18.8

Plants harvested green from arable land (in %)

6.7

Total vegetables, melons and strawberries (in %)

1.8

Cultivated mushrooms (in %)
Total permanent crops (in %)

6.3

Fruit trees (in %)

10.3

Berries (in %)

10.8

Nut trees (in %)
Citrus fruit trees (in %)
Vineyards (in %)
Olive trees (in %)
Additional comments

 

           
6.3. Non-sampling error

Not applicable

6.3.1. Coverage error

Over-coverage - rate

3.6 %


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

Sample survey on areas, harvested production and average yield of agricultural crops.

Error type Over-coverage
Degree of bias caused by coverage errors Unknown
What were the reasons for coverage errors?

Divergence between the frame population and the target population occurs becausethere are holdings, which do not grow defined crop, during the survey it is found that agricultural land of the holding is sold or rented out.

Which actions were taken for reducing the error or to correct the statistics?
Additional comments




 

6.3.1.1. Over-coverage - rate

3.6 %

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

Sample survey on areas, harvested production and average yield of agricultural crops

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

11

Preparatory (field) testing of the questionnaire? NO
Number of units participating in the tests? 
Explanatory notes/handbook for surveyors/respondents?  YES
On-line FAQ or Hot-line support for surveyors/respondents? YES
Were pre-filled questionnaires used? NO
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? YES
If yes, describe the actions and their impact

Method of data collection is improved giving possibilities for  respondents to apply method of filling- in questionnaire electronically via application ISDAVS CASIS, where automatic data control takes place and it facilitates receiving of correct data from respondents. The errors attributable to the interviewer are reduced by adequate training.

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

Sample survey on areas, harvested production and average yield of agricultural crops 

Unit level non-response rate (in %)

3.1

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

Design weights were corrected by non-response effects and calibration

Which items had a high item-level non-response rate? 

Not applicable

Which methods were used for handling missing data?
(several answers allowed)
Follow-up interviews
Reminders
Imputations
In case of imputation which was the basis? Imputation based on similar units
Imputation based on other sources
In case of imputation, which was the imputation rate (%)?

1.1

Estimated degree of bias caused by non-response? Unknown
Which tools were used for correcting the data?

application ISDAVS

Which organisation did the corrections?

CSB

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

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?

1

1

1

1

1

1

1

1

1

1

How many of them are forecasts (releases before the harvest)?
When was  the first  forecasting published for crop year 2016? (day/month/year) 17/02/2017 30/04/2017 30/04/2017 17/02/2017 30/04/2017 30/04/2017 17/02/2017 30/04/2017 30/04/2017 30/04/2017
When were the final results published for crop year 2016? (day/month/year) 30/04/2017 30/04/2017 30/04/2017 30/04/2017 30/04/2017 30/04/2017 30/04/2017 30/04/2017 30/04/2017 30/04/2017
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
IACS
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

0.2%

 

0.3%

Dried pulses and protein crops

0.2%

 

3.5%

Root crops

0.6%

 

11.0%

Oilseeds

0.2%

 

0.3%

Other industrial crops (than oilseeds)

1.2%

 
Plants harvested green

0.1%

 

19.2%

Total vegetables, melons and strawberries

0.9%

 
Vegetables and melons

0.9%

 

6.6%

Strawberries

0.4%

 

8.2%

Cultivated mushrooms  
Total permanent crops

2.7%

 
Fruit trees

2.8%

 
Berries

2.2%

 
Nut trees  
Citrus fruit trees  
Vineyards
Olive trees  
If there were considerable differences, which factors explain them?

 

Data in ACS includes data on areas of kitchen gardens.Not all holdings apply for subsidies.

 
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

9.2. Dissemination format - Publications

9.3. Dissemination format - online database

Data tables - consultations


  Availability Links
On-line database accessible to users YES

http://data.csb.gov.lv/pxweb/en/lauks/lauks__ikgad__03Augk/?tablelist=true&rxid=a79839fe-11ba-4ecd-8cc3-4035692c5fc8

Website English

http://www.csb.gov.lv

9.3.1. Data tables - consultations
9.4. Dissemination format - microdata access

Availability Links
9.5. Dissemination format - other
9.6. Documentation on methodology

9.7. Quality management - documentation

Not applicable.

9.7.1. Metadata completeness - rate

Not applicable.

9.7.2. Metadata - consultations

Not applicable.


10. Cost and Burden Top

Efficiency gains if compared to the previous reference year (2013) Further automation
Increased use of administrative data
Staff further training
If other, which?

There is no efficiency gains into monetary terms due to increasing costs of services (rent costs,
electricity, heating, computer rent etc.)

Burden reduction measures since the previous reference year  Less respondents
Easier data transmission
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

Descriptive text was added to the separate concept name


Related metadata Top


Annexes Top
ANNEX I