Crop production (apro_cp)

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

Compiling agency: Statistics Lithuania

Time Dimension: 2016-A0

Data Provider: LT1

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

Statistics Lithuania

1.2. Contact organisation unit

Agricultural and environmental statistics division

1.5. Contact mail address

29 Gedimino Ave., LT-01500, Vilnius, Lithuania


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 and experts. The data collection covers early estimates (before the harvest) and the final data. Early estimates are collected at national  level (NUTS 1), the final data – at national  and regional level (NUTS 1/2/3/4).

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

Agricultural companies, enterprises of all legal and ownership forms, farmers’ and family farms which have agricultural crop area, pastures, meadows, permanent crops, nurseries, fallows, or grow mushrooms.

2.7. Reference area

The entire territory of the country.

2.8. Coverage - Time

Since 1990

2.9. Base period

-


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)?  YES      
If yes, which new data sources have been introduced since the previous quality report (2014)?

Data on area and production of hemp

Type of source? Administrative data
To which Table (Reg 543/2009) do they contribute? Table1
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 Census
Survey
Administrative data

Total survey (census) on crops production from arable land, vegetables, permanent crops and winter crops sowing area for agricultural companies and enterprises

Sample survey on crops production from arable land, vegetables, permanent crops and winter crops sowing area for farmers’ and family farms

IACS

Final area under cultivation Census
Survey
Administrative data

Total survey (census) on crops production from arable land, vegetables, permanent crops and winter crops sowing area for agricultural companies and enterprises

Sample survey on crops production from arable land, vegetables, permanent crops and winter crops sowing area for farmers’ and family farms

IACS

Production Census
Survey
Expert estimate

Total survey (census) on crops production from arable land, vegetables, permanent crops and winter crops sowing area for agricultural companies and enterprises

Sample survey on crops production from arable land, vegetables, permanent crops and winter crops sowing area for farmers' and family farms

Early estimates for crop products (EECP) (August-November year n)

 

Yield Census
Survey
Expert estimate

Total survey (census) on crops production from arable land, vegetables, permanent crops and winter crops sowing area for agricultural companies and enterprises

Sample survey on crops production from arable land, vegetables, permanent crops and winter crops sowing area for farmers’ and family farms

Early estimates for crop products (EECP) (August year n)

 

Non-existing and non-significant crops Census
Survey
Administrative data

IACS

FSS 2016

Total survey on crops area from arable land, vegetables, permanent crops, land use for small units

Table 2: Vegetables, melons and strawberries       
Early estimates for harvested areas Administrative data
Expert estimate

Early estimates for crop products (EECP)

IACS

Final harvested area Census
Survey
Administrative data

IACS, FSS 2016

Total survey (census) on crops production from arable land, vegetables, permanent crops and winter crops sowing area for agricultural companies and enterprises

Sample survey on crops production from arable land, vegetables, permanent crops and winter crops sowing area for farmers’ and family farms

Total survey on crops area from arable land, vegetables, permanent crops, land use for small units

Production Census
Survey
Expert estimate

Total survey (census) on crops production from arable land, vegetables, permanent crops and winter crops sowing area for agricultural companies and enterprises

Sample survey on crops production from arable land, vegetables, permanent crops and winter crops sowing area for farmers’ and family farms

Early estimates for crop products (EECP) (October year n)

Non-existing and non-significant crops Census
Survey
Administrative data

IACS, FSS 2016

Total survey on crops area from arable land, vegetables, permanent crops, land use for small units

Table 3: Permanent crops      
Early estimates for production area Administrative data
Expert estimate

Early estimates for crop products (EECP)

IACS

Final production area Census
Survey
Administrative data

IACS, FSS 2016

Total survey (census) on crops production from arable land, vegetables, permanent crops and winter crops sowing area for agricultural companies and enterprises

Sample survey on crops production from arable land, vegetables, permanent crops and winter crops sowing area for farmers’ and family farms

Total survey on crops area from arable land, vegetables, permanent crops, land use for small units

Production Census
Survey
Expert estimate

Total survey (census) on crops production from arable land, vegetables, permanent crops and winter crops sowing area for agricultural companies and enterprises

Sample survey on crops production from arable land, vegetables, permanent crops and winter crops sowing area for farmers’ and family farms

Early estimates for crop products (EECP) (October year n)

Non-existing and non-significant crops Census
Survey
Administrative data

IACS, FSS 2016

Total survey on crops area from arable land, vegetables, permanent crops, land use for small units

Table 4: Agricultural land use      
Main area Census
Survey
Administrative data

IACS

FSS 2016

Total survey on crops area from arable land, vegetables, permanent crops, land use for small units

Survey on crops area from arable land, vegetables, permanent crops, land use for kitchen gardens

Non-existing and non-significant crops Census
Survey
Administrative data

IACS, FSS 2016

Total survey on crops area from arable land, vegetables, permanent crops, land use for small units

 

Total number of different data sources

8

   
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.
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

FSS 2016

Total survey (census) on crops production from arable land, vegetables, permanent crops and winter crops sowing area for agricultural companies and enterprises

Sample survey on crops production from arable land, vegetables, permanent crops and winter crops sowing area for farmers’ and family farms

Total survey on crops area from arable land, vegetables, permanent crops, land use for small units

IACS

Data on area and production of hemp

Survey on crops area from arable land, vegetables, permanent crops, land use for kitchen gardens

Experts of the Ministry of Agriculture

Planning (month-month/year)

02-05/2015

10/2015-03/2016

10/2015-03/2016

02-05/2009

4-12/2005

Preparation (month-month/year)

6/2015-6/2016

4-9/2016

4-9/2016

06/2009-06/2010

1-5/2006

Data collection (month-month/year)

6-10/2016

10-12/2016

10-12/2016

6-8/2010

4-7/2016

7-8/2017

6-9/2006

Quality control (month-month/year)

6/2016-12/2016

10/2016-9/2017

10/2016-9/2017

6-12/2010

6-12/2006

Data analysis (month-month/year)

1/2017-3/2018

10/2016-9/2017

10/2016-9/20174

1-10/2011

12/2006-5/2007

Dissemination (month-month/year)

10/2017-4/2018

1-9/2017

1-9/2017

12/2011

6-8/2016

9/2017

6/2007

1/2016-11/2016

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: C1900 - Other cereals n.e.c. (buckwheat, millet, etc.)
R9000 - Other root crops n.e.c.
I1190 -Other oilseed crops n.e.c
V1900 - Other brassicas n.e.c.
F3900 - Other berries n.e.c.

C1900 - Other cereals n.e.c. (buckwheat, millet, etc.) include buckwheat, millet, canary seed, sorghum.

R9000 - Other root crops n.e.c. include Jerusalem artichoke, fodder beet, fodder carrot, rutabaga.

I1190 - Other oilseed crops n.e.c. include mustard, oilseed radish, sunflower seed, hemp, camelina.

V1900 - Other brassicas n.e.c. include chinese cabbage, Brussels sprouts, kohl-rabi.

F3900 - Other berries n.e.c. include sea buckthorn, chokeberries, blackberries, cranberries, gooseberries, bilberries,  rose, guelder rose, hawthorn, rowan, blue honeysuckle, boxthorn.


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).

Statistical processes are in accordance with the Methodology of statistical survey of agricultural crop area, harvest and yield approved by Order of the Director General of Statistics Lithuania

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)

08/2016

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

FSS 2016

Total survey (census) on crops production from arable land, vegetables, permanent crops and winter crops sowing area for agricultural companies and enterprises

Sample survey on crops production from arable land, vegetables, permanent crops and winter crops sowing area for farmers’ and family farms

Total survey on crops area from arable land, vegetables, permanent crops, land use for small units 

Survey on crops area from arable land, vegetables, permanent crops, land use for kitchen gardens

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

http://estatistika.stat.gov.lt/statistiniu-ataskaitu-formos.html

http://estatistika.stat.gov.lt/statistiniu-ataskaitu-formos.html

http://estatistika.stat.gov.lt/statistiniu-ataskaitu-formos.html

Annex - Questionnaire – small units

Annex - Questionnaire – kitchen gardens

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

IACS

Hemp farms register

Description

The Integrated Administration and Control System

Data on area and production of hemp

Data owner (organisation)

National Paying Agency under the Ministry of Agriculture

The State Plant Service under the Ministry of Agriculture

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

2016

2016

Legal basis

Law on Agricultural and Rural Development 25 June 2002 No. IX-987

Law of the Republic of Lithuania on hemp 23 May 2013 No. XII-336

Reporting unit

Agricultural entity - a person registered in accordance with the procedure established by laws or other legal acts, and engaged in agricultural activities

Hemp grower – an agricultural holding registered by the Republic of Lithuania Agricultural Holdings and Rural Business Register who grows hemp and provides data on their cultivation in accordance with the procedure established by legal acts
Identification variable (e.g. address, unique code, etc.)

Address, unique code, name, surname

Address, unique code, name, surname

Percentage of mismatches (%)

-

-

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

97% of agricultural entities 1 ha and over

100

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

100

100

If not complete, which other sources were used ?
How were the data used?
Sample frame
Validation
Directly for estimates
Validation
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? NO 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?

ante-and ex-post

ex-post

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

Early estimates for crop products (EECP)

Data owner (organisation)

Lithuanian Institute of Agrarian Economics under the Ministry of Agriculture

Update frequency (e.g. 1 year or 6 months) 6 times per year
Reference date (Month/Year  e.g. 1/16 - 8/16)

1-11/2016

Legal basis

the annual Official Statistical Work Programme

Use purpose of the estimates?

The statistical survey on crops production, area under cultivation/harvested/production area is carried out in November. Therefore the only data source till November is expert estimates

What kind of expertise the experts have?

The agricultural crop area, production, yield

What kind of estimation methods were used?

Area are estimated based on Data on Integrated Administration and Control System (IACS) of National Paying Agency

Yield are estimated based on Early estimates of agricultural crops' state and yield prepared by the Institute of Agriculture, Lithuanian Research Centre for Agriculture and Forestry

Production data are calculated multiplied area by yield

Were there any differences in the definition of the variables between the experts' estimates and those described in the Regulation? NO
If yes, please describe the differences
What measures were taken to eliminate the differences?

Early estimates for crop products are given according  Regulation (EC) No 543/2009 and Handbook for Annual Crop Statistics

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?

ante- and ex-post

What were the possible limitations, drawbacks of using the data from expert estimate(s)?

Sources of final data are statistical surveys and administrative data. Only sources of early estimates are expert estimates

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
Farm Structure Survey
Other dataset
If other, please describe

Administrative sources (Integrated Administration and Control System (IACS) of National Paying Agency and of Agricultural Information and Rural Business Center)

3.5. Data compilation
3.6. Adjustment


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? YES        
If, yes, what are the components?

The quality of statistical information and its production process is ensured by the provisions of the European Statistics Code of Practice. In 2007, a quality management system, conforming with the requirements of the international quality management system standard ISO 9001, was introduced at Statistics Lithuania. 

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

http://osp.stat.gov.lt/documents/10180/0/ZU+augalai_metainfo-EN/bcd65378-d397-4b15-8c1f-1e8a162c8320/

       
To which data source(s) is it linked?

Metadata

       
Has a peer-review been carried out for crop statistics? NO        
If, yes, which were the main conclusions?        
What quality improvement measures are planned for the next 3 years? Other        
If, other, please specify

to improve e-statistics system for farmers and family farms

       
Additional comments        
4.2. Quality management - assessment

Data quality is in accordance with principles of accuracy and reliability, timeliness and punctuality, coherence and compatibility. The quality of the information obtained is analysed. Outliers are identified and analysed. In case of significant discrepancies, data providers are contacted, and the reasons for discrepancies are clarified.  Additional statistical data control is exercised at the macrodata level. Statistical indicator estimates are compared with the previous period’s estimates and other relevant indicators obtained from statistical surveys or administrative sources.


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? NO
If not, which additional data are collected?

According ESS agreement for early estimates are additional collected:

1.area of crops from arable land in May;

2. area, production of fresh vegetables and permanent crops for human consumption in May, October.

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

From 2005, user opinion surveys have been conducted on a regular basis. Official Statistics Portal traffic is monitored, website visitor opinion polls, general opinion poll on the products and services of Statistics Lithuania, target user group opinion polls and other surveys are conducted. In 2007, the compilation of a user satisfaction index was launched. The said surveys are aimed at the assessment of the overall demand for and necessity of statistical information in general and specific statistical indicators in particular.

5.3. Completeness

See the European level Quality Report

5.3.1. Data completeness - rate

All indicators and their components established by legislation are published.

100 per cent of information produced in accordance with the Official Statistics Work Programme is published.


6. Accuracy and reliability Top
6.1. Accuracy - overall

When calculating indicator estimates, statistical data are analysed by estimating outliers and edited. In 2016, the proportion of edited values stood at 1.26 per cent. When calculating estimates, non-response is evaluated, i. e. reweighting estimates are used – when sampling plan weights are recalculated.

6.2. Sampling error

Sampling method and sampling error

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

Total survey (census) on crops production from arable land, vegetables, permanent crops for agricultural companies and enterprises

Sample survey on crops production from arable land, vegetables, permanent crops for farmers’ and family farms

Total survey on crops area from arable land, vegetables, permanent crops, land use for small units

Survey on crops area from arable land, vegetables, permanent crops, land use for kitchen garden.

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

309

259

If stratified, stratification basis? Location
Size
Location
Size
Other
If 'other', please specify

Sampling was made from data of National Land service under the Ministry of Agriculture

Size of total population

185967

664

130755

164502

177664

Size of sample

45928

664

7000

164502

87 (due to the fact that the llocated financial means were rather limited and with respect to the fact that land plots and agricultural crops of kitchen gardens are very similar. Average size of kitchen gardens is 0.06 ha. Its utilized agricultural area is 7.9 thous ha or 0 per cent of total utilized agricultural area in Lithuania.)

Which methods were used to assess the sampling error?  Relative standard error
Comparisons with other sources
Relative standard error
Comparisons with other sources
Comparisons with other sources
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

SAS surveymeans procedure

SAS surveymeans procedure

SAS surveymeans procedure

If the results were compared with other sources, please describe the results

IACS data confirm FSS data.

Other data sources confirm crop statistics data.

Other data sources confirm crop statistics data.

Which were the main sources of errors?

Non-response

Non-response


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 crops production from arable land, vegetables, permanent crops for farmers’ and family farms

Cereals for the production of grain (in %)

0.46 %

Dried pulses and protein crops (in %)

0.53 %

Root crops (in %)

1.10 %

Oilseeds (in %)

1.57 %

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

1.69 %

Plants harvested green from arable land (in %)

1.59 %

Total vegetables, melons and strawberries (in %)

1.34 %

Cultivated mushrooms (in %)

0%

Total permanent crops (in %)

2.69 %

Fruit trees (in %)

2.59 %

Berries (in %)

3.32 %

Nut trees (in %)

19.91 %

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

FSS 2016

Total survey (census) on crops production from arable land, vegetables, permanent crops and winter crops sowing area for agricultural companies and enterprises

Sample survey on crops production from arable land, vegetables, permanent crops and winter crops sowing area for farmers’ and family farms

Total survey on crops area from arable land, vegetables, permanent crops, land use for small units

Error type Under-coverage
Misclassification
Contact errors
Under-coverage
Misclassification
Under-coverage
Misclassification
Contact errors
Misclassification
Contact errors
Degree of bias caused by coverage errors Unknown Low Unknown Unknown
What were the reasons for coverage errors?

Not exactly known all population

Not exactly known all population

Not exactly known all population

Not exactly known all population

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

Comparisons with IACS data

Comparisons with IACS data

Comparisons with IACS data

Comparisons with data of National Land service under the Ministry of Agriculture

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

FSS 2016

Total survey (census) on crops production from arable land, vegetables, permanent crops and winter crops sowing area for agricultural companies and enterprises

Sample survey on crops production from arable land, vegetables, permanent crops and winter crops sowing area for farmers’ and family farms

Total survey on crops area from arable land, vegetables, permanent crops, land use for small units

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

5

14

14

2

Preparatory (field) testing of the questionnaire? NO NO NO NO
Number of units participating in the tests? 
Explanatory notes/handbook for surveyors/respondents?  YES YES YES YES
On-line FAQ or Hot-line support for surveyors/respondents? YES NO NO NO
Were pre-filled questionnaires used? YES YES YES NO
Percentage of pre-filled questions out of total number of questions

20

18

18

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

Primary data input control perform using ORACLE based program, which approach errors searching on data input step. Logical links and arithmetical inaccuracies is checked. The calculation results are compared with the results of previous year and administrative sources.

Primary data input control perform using ORACLE based program, which approach errors searching on data input step. Logical links and arithmetical inaccuracies is checked. The calculation results are compared with the results of previous year and administrative sources.

Primary data input control perform using ORACLE based program, which approach errors searching on data input step. Logical links and arithmetical inaccuracies is checked. The calculation results are compared with the results of previous year and administrative sources.

Primary data input control perform using ORACLE based program, which approach errors searching on data input step. Logical links and arithmetical inaccuracies is checked. The calculation results are compared with the results of previous year and administrative sources.

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

FSS 2016

Total survey (census) on crops production from arable land, vegetables, permanent crops and winter crops sowing area for agricultural companies and enterprises

Sample survey on crops production from arable land, vegetables, permanent crops and winter crops sowing area for farmers’ and family farms

Unit level non-response rate (in %)

7,8 %

1,5 %

13,5 %

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?

Data inputation, re-weighting

Data inputation

Data inputation, re-weighting

Which items had a high item-level non-response rate? 
Which methods were used for handling missing data?
(several answers allowed)
Follow-up interviews
Reminders
Legal actions
Imputations
Weighting
Reminders
Legal actions
Follow-up interviews
Reminders
Legal actions
Imputations
Weighting
In case of imputation which was the basis? Imputation based on the same unit in previous data
Imputation based on similar units
Imputation based on other sources
Imputation based on similar units
Imputation based on other sources
Imputation based on similar units
Imputation based on other sources
In case of imputation, which was the imputation rate (%)?
Estimated degree of bias caused by non-response? Insignificant None Insignificant
Which tools were used for correcting the data?

Auto correction by formula, manually correction

Manually correction

Manually correction

Which organisation did the corrections?

Statistics Lithuania

Statistics Lithuania

Statistics Lithuania

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?

7

2

2

2

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

5

1

1

When was  the first  forecasting published for crop year 2016? (day/month/year) 01/02/2016 03/07/2016 03/07/2016 03/07/2016 01/09/2016 03/07/2016 01/09/2016 01/09/2016
When were the final results published for crop year 2016? (day/month/year) 01/09/2017 01/09/2017 02/10/2017 02/10/2017 02/10/2017 02/10/2017 02/10/2017 02/10/2017 02/10/2017 02/10/2017 02/10/2017 02/10/2017
Additional comments

The final results were published for crop year 2014, 2015 – 1 February

The final results were published for crop year 2014, 2015 – 1 February

The final results were published for crop year 2014, 2015 – 3 April

The final results were published for crop year 2014, 2015 – 3 April

The final results were published for crop year 2014, 2015 – 3 April

The final results were published for crop year 2014, 2015 – 3 April

The final results were published for crop year 2014, 2015 – 2 March

The final results were published for crop year 2014, 2015 – 3 April

The final results were published for crop year 2014, 2015 – 3 April

The final results were published for crop year 2014, 2015 – 28 April

The final results were published for crop year 2014, 2015 – 28 April

The final results were published for crop year 2014, 2015 – 28 April

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

Statistical information on agricultural crop area, harvest and yield is published:

– a news release on cereals and dried pulses is published on the 32, on rape, sugar beet, potatoes, field vegetables – on the 62th day after the end of the reference year;

– in statistical publications Agriculture in Lithuania, Statistical Yearbook of Lithuania;

– Database of Indicators (at  http://osp.stat.gov.lt).

Statistical information is published on the Official Statistics Portal, according to an approved statistical information release calendar pursuant to the Rules for the Preparation and Dissemination of Statistical Information, approved by Order No DĮ-212 of 26 September 2014 of the Director General of Statistics Lithuania.

 100 per cent of statistical information released on time.

 


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


  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
8.3. Coherence - cross domain

  Data source
With which other data sources the crop statistics data have been compared?  Farm structure survey 2016
IACS
Other
If others, which?

Purchase Statistics

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%

 

0.3%/2013

Dried pulses and protein crops

0%

 

1%/2013

Root crops

11%

 

8%/2013

Oilseeds

1%

 

1%/2013

Other industrial crops (than oilseeds)

-17%

 

0%/2013

Plants harvested green

0%

 

1%/2013

Total vegetables, melons and strawberries

47%

 

30%/2013

Vegetables and melons

FSS data available only on vegetables+strawberries

 

30%/2013

Strawberries

FSS data available only on vegetables+strawberries

 

29%/2013

Cultivated mushrooms

0%

 
Total permanent crops

17%

 

14%/2013

Fruit trees

25%

 

14%/2013

Berries

19%

 

14%/2013

Nut trees

0%

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

Data of annual crop statistics include small units.

The cause od differences on oilseeds and other industrial crops is hemp for oilseeds.

Table 1,2,3 of Regulation No 543/2009 -  area under cultivation/harvested/production area;

FSS data- main area.

 

Data of annual crop statistics include small units.;

 
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

Not applicable


  Availability Links
On-line database accessible to users YES

https://osp.stat.gov.lt/statistiniu-rodikliu-analize?indicator=S9R086#/

Website National language
English

https://osp.stat.gov.lt/zemes-ukis1

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 National language
English

http://osp.stat.gov.lt/documents/10180/550594/Statistical_survey_of_agricultural.pdf/91607f58-249e-4473-a1c6-668ab467cfee

Quality Report None
Metadata National language
English

http://osp.stat.gov.lt/documents/10180/0/ZU+augalai_metainfo-EN/bcd65378-d397-4b15-8c1f-1e8a162c8320/

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
Staff further training
If other, which?
Burden reduction measures since the previous reference year  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

-


Related metadata Top


Annexes Top
Questionnaire-Small-units
Questionnaire-Kitchen-gardens
List of sources