Crop production (apro_cp)

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

Compiling agency: Statistics Lithuania


Eurostat metadata
Reference metadata
1. Contact
2. Statistical presentation
3. Statistical processing
4. Quality management
5. Relevance
6. Accuracy and reliability
7. Timeliness and punctuality
8. Coherence and comparability
9. Accessibility and clarity
10. Cost and Burden
11. Confidentiality
12. Comment
Related Metadata
Annexes (including footnotes)



For any question on data and metadata, please contact: Eurostat user support

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1. Contact Top
1.1. Contact organisation

Statistics Lithuania
1.2. Contact organisation unit

Agricultural 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?  NO      
If yes, which new data sources have been introduced since the previous quality report?
Type of source?
To which Table (Reg 543/2009) do they contribute?
Have some data sources been dropped since the previous quality report? NO      
Which data sources have been dropped since the previous quality report?
Type of source?
Why have they been dropped?
Additional comments


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

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

IACS

Early estimates for crop products (EECP)

  
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

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

IACS

Data on area and production of hemp
Production Census
Survey
Administrative data
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)

Data on area and production of hemp

 
Yield Census
Survey
Administrative data
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)

Data on area and production of hemp

 
Non-existing and non-significant crops 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

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

IACS

Early estimates for crop products (EECP)
Final harvested area 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

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

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) (October year n)
Non-existing and non-significant crops 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

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

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

IACS

Early estimates for crop products (EECP)
Final production area 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

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

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) (October year n)
Non-existing and non-significant crops 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

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

IACS
Table 4: Agricultural land use      
Main area 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

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

IACS
Non-existing and non-significant crops 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

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

IACS

 
Total number of different data sources

7

  
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

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

Modelling of data of survey on crops area from arable land, vegetables, permanent crops, land use for small units

IACS

Data on area and production of hemp

Modelling of data of 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)

10/2021-3/2022

10/2021-3/2022

 
Preparation (month-month/year)

4-9/2022

4-9/2022

 
Data collection (month-month/year)

10-12/2022

10-12/2022

 

4-7/2022

7/2023
Quality control (month-month/year)

10/2022-3/2023

10/2022-3/2023

 
Data analysis (month-month/year)

12/2022-3/2023

12/2022-3/2023

9/2022-3/2023

1-3/2020
Dissemination (month-month/year)

12/2022-9/2023

12/2022-9/2023

12/2022-3/2023

6-8/2022

9/2023

2-3/2020

12/2021-11/2022
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)? YES  
In case yes, how do they differ? ( list all items and explanations)

P1300 Sweet lupins: all  varieties of lupins.

V2300S Lettuces under glass or high accessible cover – lettuces and other vegetables under glass or high accessible cover, excluding tomatoes, cucumbers.
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)

08/2022
Was a threshold applied? NO
If yes, size of the excluded area  Area excluded on the basis of the threshold in % of the total area for that crop
Cereals for the production of grain (in %)
Dried pulses and protein crops (in %)
Root crops (in %)
Oilseeds (in %)
Other industrial crops (included all industrial crops besides oilseeds)  (in %)
Plants harvested green from arable land (in %)
Total vegetables, melons and strawberries (in %)
Cultivated mushrooms (in %)
Total permanent crops (in %)
Fruit trees (in %)
Berries (in %)
Nut trees (in %)
Citrus fruit trees (in %)
Vineyards (in %)
Olive trees (in %)


Survey method (only for census and surveys)

  Survey 1  Survey 2 Survey 3  Survey 4 Survey 5  Survey 6 Survey 7
Name of the survey

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 
Which survey method was used? On-line electronic questionnaire filled in by respondent On-line electronic questionnaire filled in by respondent
Telephone interview, electronic questionnaire		 Face-to-face interview
If 'other', please specify
Please provide a link to the questionnaire
 ŽŪ-29
 
 
 
 

 

https://apklausos.stat.gov.lt/statistines-anketos

 

 

ŽŪŪ-29

 

 

Annex - Questionnaire – small units

 

 
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)

2022

2022
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 ?
Were the data used for sample frame? 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 Centre for Social Sciences Institute of Economics and Rural Development
Update frequency (e.g. 1 year or 6 months) 6 times per year
Reference date (Month/Year  e.g. 1/22 - 8/22)

1-11/2022
Legal basis

the annual Official Statistical 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)?

Such kind of estimates are appropriate.
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
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

Technological solutions for data entry meet the following criteria: technology (information system) integrated into the existing statistical information system, statistical data are linked to data from administrative and statistical registers, data security and confidentiality are ensured, technical and communication equipment meets the minimum requirements of system users.

Statistical indicators on agricultural crops area, harvest and yield (specified in the Regulation (specified in the Regulation (EC) No 543/2009) for agricultural companies and enterprises are obtained by summarizing the statistical data collected through exhaustivehhttps://osp.stat.gov.lt/documents/10180/550594/ZU_augalu_paseliu_metodika_2012.pdfttps://osp.stat.gov.lt/documents/10180/550594/ZU_augalu_paseliu_metodika_2012.pdf (census) survey, for farmers and family farms – assessed using probability sampling methods, applying the plan weights of a stratified simple random sampling.

When preparing statistical information, data are checked, edited, weights are adjusted for non-response.

Before submitting the results of the survey to users, the quality of the statistical data is checked. The values of statistical indicators are compared with the results of previous surveys and data from administrative sources. If the results obtained differ, the reasons for these differences shall be explained or exclusions affecting the results shall be sought. The discrepancies found are clarified and the microdata are corrected or, if the data are correct, the reasons for the deviations are identified.

The process of compiling statistical indicators of  the agricultural crop area, harvest and yield statistical survey is described in more detail in the Statistical Survey of Agricultural Crop Area, Harvest and Yield methodology (only in Lithuanian)
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 the previous quality report? 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 and ESS Quality Assurance Framework. 

In 2007, a quality management system, conforming to the requirements of the international quality management system standard ISO 9001, was introduced at Statistics Lithuania. The main trends in activity of Statistics Lithuania aimed at quality management and continuous development in the institution are established in the Quality Policy. Monitoring of the quality indicators of statistical processes and their results and self-evaluation of statistical survey managers is regularly carried out in order to identify the areas which need improvement and to promptly eliminate the shortcomings.

        
Is there a quality report available? YES        
If yes, please provide a link(s)

https://osp.stat.gov.lt/documents/10180/5118910/Augalininkyst%C4%97+%5BEN%5D+667.html

        
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 agricultural companies and enterprises, 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. According to the annually updated plan of measurement of performance indicators of Statistics Lithuania, the results of annual survey quality indicators are presented in fields 14, 15, 16 and 17 of this metadata description. 

In 2020, a self-assessment of the head of these statistical surveys was carried out. It showed that the results of the survey meet the requirements for the quality of statistical information.

In 2020, a review of the agricultural crops area, production and yield statistical survey questionnaires was carried out, the received recommendations have been implemented.

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? 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

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.

More information on user surveys and their results is available in section User surveys on the Statistics Lithuania website.






 
5.3. Completeness

See the European level Quality Report
5.3.1. Data completeness - rate

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


6. Accuracy and reliability Top
6.1. Accuracy - overall

The population of farmers and family farms includes farmers and family farms producing agricultural products with the utilised agricultural area of one or more hectares or those with the utilised agriultural area of less than one hectare and annual agricultural income of no less than 40 minimum basic social benefits. Statistical survey on such farms is carried out through sampling methods.

Farms with the utilised agriultural area of less than one hectare, area data modeling was performed based on the 2020 Agricultural Census and 2010 data of farms with the utilised agriultural area of less than one hectare.

Statistical survey on agricultural companies and enterprises is carried out through an exhaustive (census) survey.

When calculating estimates, non-response is evaluated, i. e. reweighting estimates are used – when sampling plan weights are recalculated. Statistical data are analysed by estimating outliers and edited.

 
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 crops production from arable land, vegetables, permanent crops for farmers’ and family farms
Sampling basis? List
If 'other', please specify
Sampling method? Random
Stratified
If stratified, number of strata?

259 

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

 
Size of total population

125587

 

 

 

 
Size of sample

7008

 

 

 

 
Which methods were used to assess the sampling error?  Relative standard error
Comparisons with other sources
If other, which?
Which methods were used to derive the extrapolation factor?  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 

 

 

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

Other data sources confirm crop statistics data. 

 

 

 
Which were the main sources of errors?

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 %)

0.79 %
Dried pulses and protein crops (in %)

2.17%
Root crops (in %)

8.27 %
Oilseeds (in %)

1.28 %
Other industrial crops (included all industrial crops besides oilseeds)  (in %)

15.02 %
Plants harvested green from arable land (in %)

3.17 %
Total vegetables, melons and strawberries (in %)

2.40 %
Cultivated mushrooms (in %)

16,65 %
Total permanent crops (in %)

6.75 %
Fruit trees (in %)

9.37 %
Berries (in %)

7.87 %
Nut trees (in %)

16.04 %
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

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		 Under-coverage
Misclassification
Contact errors		 Misclassification
Contact errors
Degree of bias caused by coverage errors 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
Which actions were taken for reducing the error or to correct the statistics?

Comparisons with IACS data

Comparisons with IACS data

Comparisons with data of National Land service under the Ministry of Environment
Additional comments




 
6.3.2. Measurement error

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

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
Number of surveys already performed with the current questionnaire (or a slightly amended version of it)?

20

20

2

 
Preparatory (field) testing of the questionnaire? NO NO NO
Number of units participating in the tests? 
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 YES NO
Percentage of pre-filled questions out of total number of questions

 

18

 
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

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

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 %)

1.8 %

9.4 %

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

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)		 Reminders
Legal actions		 Follow-up interviews
Reminders
Legal actions
Imputations
Weighting
In case of imputation which was the basis? 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? None Insignificant
Which tools were used for correcting the data?

R

R, manually correction

 
Which organisation did the corrections?

Statistics Lithuania

Statistics Lithuania
Additional comments




 
6.3.4. Processing error

Not applicable.
6.3.4.1. Imputation - rate

Not applicable.
6.3.5. Model assumption error

Not applicable.
6.4. Seasonal adjustment

Not applicable.
6.5. Data revision - policy

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

6

6

6

1

4

1

1

1 

2

1 

1 

–

–

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

6

5

5

5

-

3

-

-

–

1

–

–

–

–

–
When was the first  forecasting published for the crop year on which is reported? (day/month/year) 23/12/2021 01/07/2022 01/07/2022 01/07/2022 01/07/2022 31/10/2022
When were the final results published for the crop year on which is reported? (day/month/year) 03/02/2023 03/02/2023 02/03/2023 02/03/2023 02/03/2023 06/04/2023 02/03/2023 06/04/2023 02/03/2023 28/04/2023 28/04/2023 28/04/2023
Additional comments




 
7.2. Punctuality

See the European level Quality Report
7.2.1. Punctuality - delivery and publication

Statistical information is published in accordance with an Official Statistics Calendar. In case of delay, users are notified in advance by indicating the reason and a new date of publication. 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 in the previous 5 years? 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

With which other data sources the crop statistics data have been compared?  IACS
Other
If others, which?

Purchase Statistics
If no comparisons have been made, why not?


Differences between ACS and other data sources (%)

Results of comparisons IFS 2020 Vineyard survey 2020 IACS Other source(s)  In case of other sources, which?
Cereals    

0.4%
Dried pulses and protein crops    

2.7%
Root crops    

22.6%
Oilseeds    

0.04%
Other industrial crops (than oilseeds)    

0.3%
Plants harvested green    

4.3%
Total vegetables, melons and strawberries    

45%
Vegetables and melons    

46%
Strawberries    

29%
Cultivated mushrooms

 

  

5.5%
Total permanent crops

 

  

42%
Fruit trees

 

Not applicable.

55%
Berries

 

  

31%
Nut trees

 

  

0.0%
Citrus fruit trees

Not applicable.
Vineyards
Olive trees

Not applicable.
If there were considerable differences, which factors explain them?

FSS was not performed in 2022.

 

Not applicable.

Data of annual crop statistics include small units.

Not all holdings apply for subsidies.

IACS area corresponds to the sown area, ACS area corresponds to area under cultivation, harvested or production area.

 

  





 
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

Lithuania in figures
Publications in English Electronic

Lithuania in figures






 
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?theme=all#/

 
Website National language
English

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





 
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

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

https://osp.stat.gov.lt/documents/10180/550594/ZU_augalu_paseliu_metodika_2012.pdf

 
Quality Report None
Metadata National language
English

https://osp.stat.gov.lt/documents/10180/5118910/Augalininkyst%C4%97+%5BEN%5D+667.html

https://osp.stat.gov.lt/documents/10180/5118910/Augalininkyst%C4%97+%5BLT%5D+667.html
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 quality report? 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
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