Crop production (apro_cp)

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

Compiling agency: MINISTRY OF AGRICULTURE AND FOOD AGROSTATISTICS DEPARTMENT


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)



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1. Contact Top
1.1. Contact organisation

MINISTRY OF AGRICULTURE AND FOOD

AGROSTATISTICS DEPARTMENT
1.2. Contact organisation unit

AGROSTATISTICS DEPARTMENT
1.5. Contact mail address

Sofia 1606,
55, Hristo Botev Blvd.


2. Statistical presentation Top
2.1. Data description

Annual crop statistics provide statistics on the area under main arable crops, vegetables and permanent crops for 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 is collected mostly at national level but for some crops also regional data exists (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

An agricultural holding that processes the land. For the territorial samples - Point of the land cover - more than 111 thousand points located in 3 123 square segments with a country size of 1 410 m and containing 36 points each, and the distance between these points is 234 m.

 
2.6. Statistical population

All agricultural holdings growing crops. For the territorial samples - the entire territory of Bulgaria is divided into segments.
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?  YES      
If yes, which new data sources have been introduced since the previous quality report?

Operational information
Type of source? Other
To which Table (Reg 543/2009) do they contribute? Table1 Table2 Table3
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 Survey
Expert estimate
Administrative data

Early estimation-wheat, barley. The evaluation is performed by specialists-agronomists who visit sampled segments of arable land where wheat and barley points are observed. The aim is to determine the potential yield of wheat and barley before harvest. Survey on land cover and land use of the territory of Bulgaria. At the municipality level, the operational information is collected by farmers on the area and production of main crops for the current year (during the harvest) is collected by employees of the Ministry of Agriculture and Food, and Table 1 is filled with the data until January of the following year (n+1). Preliminary data from the statistical sample survey of field crop yields shall be used to complete Table 1 no earlier than March n+1.

  
Final area under cultivation Survey

Survey on yiеlds from main crops, the survey of vegetable production in BG, (which collects data on potatoes). Dried leguminous and other dry pulses for human consumption were added to the main crop production from 2019. Previously this data was collected with vegetable production.
Production Survey
Expert estimate

Survey on yiеlds from main crops, the survey of vegetable production in BG, (which collects data on potatoes). Dried leguminous and other dry pulses for human consumption were added to the main crop production from 2019. Previously this data was collected with vegetable production. Operational information and expert estimation are used for the preliminary data.
Yield Survey
Other

Survey on yiеlds from main crops, the survey of vegetable production in BG, (which collects data on potatoes). Dried leguminous and other dry pulses for human consumption were added to the main crop production from 2019. Previously this data was collected with vegetable production. Operational information and expert estimation are used for the preliminary data.
Non-existing and non-significant crops Survey
Expert estimate
Administrative data

Survey on yields from main crops, survey on vegetable production in BG, (which collects data on potatoes) IFS 2020 and administrative data.
Table 2: Vegetables, melons and strawberries       
Early estimates for harvested areas Expert estimate
Administrative data
Other

 Operational information and expert astimate are used for preliminary data.
Final harvested area Survey

Survey on vegetable production in BG
Production Survey

Survey on vegetable production in BG. Operational information and expert astimate are used for preliminary data.
Non-existing and non-significant crops Survey

Survey on vegetable production in BG - expert estimate and administrative data.
Table 3: Permanent crops      
Early estimates for production area Expert estimate
Administrative data
Other
Final production area Survey

Survey on fruit production in BG, Survey on vine and wine production in BG. 
Production Survey

Survey on fruit production in BG, Survey on vine and wine production in BG. Operational information and expert astimate are used for preliminary data.
Non-existing and non-significant crops Survey
Expert estimate

Survey on fruit production in BG, Survey on vine and wine production in BG, and administrative data.
Table 4: Agricultural land use      
Main area Survey

Survey on land cover and land use of the territory of Bulgaria
Non-existing and non-significant crops

none
Total number of different data sources

7

  
Additional comments
  Put x, if used
Surveyed: farmers report the humidy  X
Surveyed: farmers convert the production/yield into standard humidity       X
Surveyed: whole sale purchasers report the humidy  
Surveyed: whole sale purchasers convert the production/yield into standard humidity       
Surveyed by experts (e.g. test areas harvested and measured)  
Estimated by experts  X
Other type  
If other type, please explain  
Additional information The farmers provide moisture content data on cereals, dried legumes and oilseeds. For the plants harvested green, the collected data shows the way of harvest (hay or fresh ) and the experts estimate the required humidity.
   


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

Early estimation-wheat, barley.

Survey on land cover and land use of the territory of Bulgaria

Survey of yield from main crops

Survey on vegetable production in BG

Survey on fruit production in BG

Survey on vine and wine production in BG

Operational information.
Planning (month-month/year)

June/2022

May/2022

July-August/2022

July-August/2022

 

July-August/2022

 

July-August/2022

Jan. 2023

 
Preparation (month-month/year)

June-July/2022

May/2022

Aug-Oct/2022

Aug-Oct 2022

Aug-oct/2022

Aug-Oct/2023

jan-March2023
Data collection (month-month/year)

July/2022

June-Aug/2022

Nov2022 - Jan2023

Nov2022 Jan2023

nov 2022/ Jan 2023

Nov 2022/ Jan 2023

March-Dec2023
Quality control (month-month/year)

July/2022

Sept-Oct/2022

Jan-May/2023

Jan-March/2023

Jan-March/2023

Jan-March/2023

weekly quality control
Data analysis (month-month/year)

July/2022

Oct/2022

March-May/2023

April/2023

March-Apr/2023

March-April/2023

weekly
Dissemination (month-month/year)

Aug/2022

Dec/2022

March 2023 - preliminary results, June 2023 - final results.

April 2023 - final results.

April 2023 - final results.

April 2023 - final results.

MAF inner purposes only.
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? YES

Table 3 shows the production area only, with the exception of young trees, witch are not included. And area from which production was not harvested due to climatic and other reasons (non-practical agroecology, economic reasons, temporarily abandoned plantations).
Are there differences between the national methodology and the methodology described in the Handbook concerning e.g. the item and aggregate calculations? NO
Are special estimation/calculation methods used for main crops from arable land? NO
Are special estimation/calculation methods used for vegetables or strawberries? NO
Are special estimation/calculation methods used for permanent crops for human consumption? NO
Are special estimation/calculation methods used for main land use? NO
Do national crop item definitions differ from the definitions in the Handbook  (D-flagged data)? NO  
In case yes, how do they differ? ( list all items and explanations)
In case data are delivered for one of the items below, describe the crop species included in the item: P9000 - Other dry pulses and protein crops n.e.c.
R9000 - Other root crops n.e.c.
I1190 -Other oilseed crops n.e.c
G2900 - Other leguminous plants harvested green n.e.c.
G9100- Other cereals harvested green (excluding green maize)
G9900 - Other plants harvested green from arable land n.e.c.
V2900 - Other leafy or stalked vegetables n.e.c.
V3900 - Other vegetables cultivated for fruit n.e.c.
V4900 - Other root, tuber and bulb vegetables n.e.c.
F1190 - Other pome fruits n.e.c.
F1290 - Other stone fruits n.e.c.
F3900 - Other berries n.e.c.

P9000 - Other dry pulses and protein crops n.e.c. includes vetches, lentils and chick-peas.

R9000 - Other root crops n.e.c. includes fodder beetroots, carrots and cabbages.

I1190 -Other oilseed crops n.e.c includes peanuts, pumpkin seeds and sesame seeds

G2900 includes temporary leguminous used for hay, exclude lucerne (clover, sainfoin and others) and annual leguminous crops (vetch, pea, sweet lupine and other), whole plant for hay

G9100 includes cereals for production of  silage, green fodder and for energy purposes (a whole plant), excluding grain-maize and cereals for production of hay, whole plant (oats, rye and others)

G9900 includes mixtures of cereals and leguminous crops (oats, vetch and others), a whole plant used for hay and other annual fodder crops (sunflower, repko and others excluding cereals and legumes), whole plant

V2900 includes fresh onion, fresh garlic and other green leafy or stalked vegetables n.e.c.

V3900 includes sweet corn and other vegetables cultivated for fruit n.e.c.

V4900 includes turnips, parsnips and other tuber and bulb vegetables n.e.c.

F1190 includes quince, medlar and other pome fruits

F1290 includes: dogwood, sloe and others stone fruits

F3900 includes aronia, mulberry, blackberry, goji-berry and other berries and small fruits

 


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).

The Survey on land cover and land use of the territory of Bulgaria (Table 4) covers the entire territory of the country without applying a threshold. For crop Tabl.1, 2, 3 there is a population, including all units, growing the relevant crops for which data are available from the census of agricultural holdings and/or from administrative sources. While drawing the sample the units are chosen from the relevant population. In the selected samples representative farms of different sizes and specialization are chosen.
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?

The observed units are land cover points from surveys Early estimation-wheat, barley and land cover and land use of the territory of Bulgaria.
When was last update of the holding register? (month/year)

Bg has no full administrative register.
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

Early estimation-wheat, barley

Survey on land cover and land use of the territory of Bulgaria

Survey of yield from main crops

Survey on vegetable production in BG

Survey on fruit production in BG

Survey on vine and wine production in BG
Which survey method was used? Other Other Telephone interview, paper questionnaire
On-line electronic questionnaire filled in by respondent		 Telephone interview, paper questionnaire
On-line electronic questionnaire filled in by respondent		 Telephone interview, paper questionnaire
On-line electronic questionnaire filled in by respondent		 Telephone interview, paper questionnaire
Postal questionnaire filled in by respondent
If 'other', please specify

The field is defined at the points land use, size class, density and other factors determining yields.

The field is defined and the land use at the point is determined.
Please provide a link to the questionnaire

The questionnaires are not published on the MAF website.

https://www.agrostat.bg/ISASPublic/LandUse

https://www.agrostat.bg/ISASPublic/Crops

https://www.agrostat.bg/ISASPublic/Crops

https://www.agrostat.bg/ISASPublic/Crops

https://www.agrostat.bg/ISASPublic/Crops
Data entry method, if paper questionnaires? Manual Manual Manual Manual Manual Manual


Administrative data (This question block is only for administrative data)

  Admin source 1 Admin source 2 Admin source 3 Admin source 4 Admin source 5 Admin source 6
Name of the register
Description
Data owner (organisation)
Update frequency
Reference date (month/year)
Legal basis
Reporting unit
Identification variable (e.g. address, unique code, etc.)
Percentage of mismatches (%)
How were the mismatches handled?
Degree of coverage (holdings, e.g. 80%)
Degree of completeness (variables, e.g. 60%)
If not complete, which other sources were used ?
Were the data used for sample frame?
Data used for other purposes, which?
Which variables were taken from administrative sources?
Were there any differences in the definition of the variables between the administrative source and those described in the Regulation?
Please describe the differences
What measures were taken to eliminate the differences?
How was the reliability, accuracy and coherence (comparison to other available data) of the data originated from administrative data source (ante- and/or ex-post) checked?
What were the possible limitations, drawbacks of using the data from administrative source(s)?


Expert estimations (This question block is only for expert estimates)

  Expert estimate 1 Expert estimate 2 Expert estimate 3 Expert estimate 4 Expert estimate 5 Expert estimate 6
Name of the estimation
Data owner (organisation)
Update frequency (e.g. 1 year or 6 months)
Reference date (Month/Year  e.g. 1/22 - 8/22)
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
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

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

1. Control of interviewers;
2. Inspections on-site and by a phone;

3. Checks for logical connections;

4. Comparison of the data with previous surveys;
5. Comparison of the data with administrative sources.  

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

The quality report from 2022 is not published.

        
To which data source(s) is it linked?
 
Name of the data source
Early estimation-wheat and barley
Survey for land use and land cover (Bancik)
Survey of yield from main crops
Survey on vegetable production in BG
Survey on fruit production in BG
Survey on vine and wine production in BG
Operational information
 
       
Has a peer-review been carried out for crop statistics? NO        
If, yes, which were the main conclusions?        
What quality improvement measures are planned for the next 3 years? Further automation
Other		        
If, other, please specify

In 2017, an ISAS program was created for data entry and data processing for various surveys. This program is under development.

Use of administrative data

        
Additional comments

The purpose of the additional automation in the next three years is to improve data quality and compliance with the specified timing.

        






 
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? YES
Describe the unmet needs

data at NUTS3 level.
Does the Regulation 543/2009 meet the national data needs? NO
Does the ESS agreement meet the national needs?
If not, which additional data are collected?

To collect the following crops additionally: "Aromatic and medicinal plants": rose oil, lavender, coriander, valerian, mint, lemon balm, fennel, silibum; "Other leguminous": lentils, chickpeas, "Other oil seed": peanut and pumpkins seeds; "Vegetables": okra, salad beetroot, parsnip, sweet corn, "Permanent crops": quince, aronia, blackberries and cultivated rose hips.
Additional comments

The Regulation № 543/2009 doesn't require a collection of additional data listed above, but aggregated values require additional information.






 
5.2. Relevance - User Satisfaction

Have any user satisfaction surveys been done? NO
If yes, how satisfied the users were?
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

See points 6.2, 6.3.1, 6.3.2 and 6.3.3
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

Early estimation-wheat, barley

Survey on land cover and land use of the territory of Bulgaria BANCIK

Survey of yield from main crop.

Survey on vegetable production in BG.

Survey on fruit production in BG.

Survey on vine and wine production in BG.
Sampling basis? Area Area List List List List
If 'other', please specify
Sampling method? Other Other Stratified Stratified Stratified Stratified
If stratified, number of strata?

1008

458

649

391
If stratified, stratification basis? Location
Size
Specialisation		 Location
Size
Specialisation		 Location
Size
Specialisation		 Location
Size
Specialisation
If 'other', please specify

territorial

The land is observed and for each point of the sample the land use is determined. The points occupied with wheat and barley, determine the size of the class, density and other factors determining yields.

territorial

BANCIK is a “territorial sample”, based on the implementation of a network of North-South, East-West oriented straight lines with 6 km distance between them. The first north-western point is randomly selected. Each intersection of the network determines the center of the segment, where 36 locations in a network 6 x 6 meters in 234 meters are monitored.

 
Size of total population

32 707 points

111 000 km2

50988 farmers

33850 farmers

27734 farmers

15393 farmers
Size of sample

17 244 points

112 428 points

8911 farmers

3917 farmers

5147 farmers

2254 farmers
Which methods were used to assess the sampling error?  Relative standard error Relative standard error Relative standard error Relative standard error Relative standard error Relative standard error
If other, which?
Which methods were used to derive the extrapolation factor?  Basic weight Basic weight Basic weight Basic weight Basic weight Basic weight
If other, which?
If CV (co-efficient of variation) was calculated, please describe the calculation methods and formulas

Not applicable

1. Sd= √((∑〖(X-X ̂)〗^2)/(n-1));
2. var=N^2(1-n/N)(Sd/n);

3. CV(X ̂ )=√(var(X ̂ ) )/X ̂ ×100%.

The accuracy is obtained by calculating the random sampling error. It takes into account the general allowance in the form of basic dispersion between segments and internal dispersion (between points). The accuracy in determining a type of land use in a territory increases with increase of the size and distribution of the surveyed type of land use.

1. Sd= √((∑〖(X-X ̂)〗^2)/(n-1));
2. var=N^2(1-n/N)(Sd/n);

3. CV(X ̂ )=√(var(X ̂ ) )/X ̂ ×100%.

The accuracy is obtained by calculating the random sampling error. It takes into account the general allowance in the form of basic dispersion between segments and internal dispersion (between points). The accuracy in determining a type of land use in a territory increases with increase of the size and distribution of the surveyed type of land use.

1. Sd= √((∑〖(X-X ̂)〗^2)/(n-1));
2. var=N^2(1-n/N)(Sd/n);

3. CV(X ̂ )=√(var(X ̂ ) )/X ̂ ×100%.

The accuracy is obtained by calculating the random sampling error. It takes into account the general allowance in the form of basic dispersion between segments and internal dispersion (between points). The accuracy in determining a type of land use in a territory increases with increase of the size and distribution of the surveyed type of land use.

1. Sd= √((∑〖(X-X ̂)〗^2)/(n-1));
2. var=N^2(1-n/N)(Sd/n);

3. CV(X ̂ )=√(var(X ̂ ) )/X ̂ ×100%.

The accuracy is obtained by calculating the random sampling error. It takes into account the general allowance in the form of basic dispersion between segments and internal dispersion (between points). The accuracy in determining a type of land use in a territory increases with increase of the size and distribution of the surveyed type of land use.

1. Sd= √((∑〖(X-X ̂)〗^2)/(n-1));
2. var=N^2(1-n/N)(Sd/n);

3. CV(X ̂ )=√(var(X ̂ ) )/X ̂ ×100%.

The accuracy is obtained by calculating the random sampling error. It takes into account the general allowance in the form of basic dispersion between segments and internal dispersion (between points). The accuracy in determining a type of land use in a territory increases with increase of the size and distribution of the surveyed type of land use.
If the results were compared with other sources, please describe the results

Comparable to IACS data

Comparable to IACS data.

Comparable to IACS data.
Which were the main sources of errors?

The sample includes units from census 2020 that are no longer active.

The sample includes units from census 2020 that are no longer active.

The sample includes units from census 2020 that are no longer active.

The sample includes units from census 2020 that are no longer active.


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

Early estimation-wheat, barley

Survey on land cover and land use of the territory of Bulgaria BANCIK

Survey of yield from main crops

Survey on vegetable production in BG.

Survey on fruit production in BG.

Survey on vine and wine production in BG.
Cereals for the production of grain (in %)

1.78

0.59
Dried pulses and protein crops (in %)

11.73

2.46
Root crops (in %)

17.99

1.39
Oilseeds (in %)

2.42

0.68
Other industrial crops (included all industrial crops besides oilseeds)  (in %)

8.27

1.56
Plants harvested green from arable land (in %)

5.27

2
Total vegetables, melons and strawberries (in %)

8.96

1.01
Cultivated mushrooms (in %)
Total permanent crops (in %)

6.51
Fruit trees (in %)

8.06

0.79
Berries (in %)

1.63
Nut trees (in %)

1.17
Citrus fruit trees (in %)
Vineyards (in %)

12.21

0.42
Olive trees (in %)
Additional comments            




 
6.3. Non-sampling error

See points 6.3.1 too 6.6
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

Early estimation-wheat, barley

Survey on land cover and land use of the territory of Bulgaria

Survey of yield from main crops.

Survey on vegetable production in BG.

Survey on fruit production in BG.

Survey on vine and wine production in BG.
Error type Under-coverage
Over-coverage		 Under-coverage
Over-coverage		 Under-coverage
Over-coverage		 Under-coverage
Over-coverage
Degree of bias caused by coverage errors Low Low Low Low Low Low
What were the reasons for coverage errors?

IACS, farmers with specific crop during Census 2020, tobacco register and register of farmers maintained under Ordinance 3 of 1999 is used for sampling. The sample includes units managing natural meadows, some of which do not harvest. Part of the farms from the population are closed.

In addition to the holdings that have declared areas with vegetables in IACS, the sample includes small farms from Census 2020 which have not declared area in IACS, and some of them have already stop their activity.

The plantations change their owners frequently.

The plantations change their owners frequently.
Which actions were taken for reducing the error or to correct the statistics?

Administrative data for identification of the holdings from IACS. Over-coverage has an impact on statistical data costs, the impact on data quality is low.

Administrative data for identification of the holdings from IACS.  Over-coverage has an impact on statistical data costs, the impact on data quality is low.

Administrative data for identification of the holdings from IACS. Over-coverage has an impact on statistical data costs, the impact on data quality is low.

Administrative data for identification of the holdings from IACS or Wine register
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

Early estimation-wheat, barley

Survey on land cover and land use of the territory of Bulgaria

Survey of yield from main crops.

Survey on vegetable production in BG.

Survey on fruit production in BG.

Survey on vine and wine production in BG.
Was the questionnaire based on usual concepts for respondents? YES YES YES YES YES YES
Number of surveys already performed with the current questionnaire (or a slightly amended version of it)?

19

21

23

20

17

20
Preparatory (field) testing of the questionnaire? YES YES YES YES YES YES
Number of units participating in the tests? 

60

14

60
Explanatory notes/handbook for surveyors/respondents?  YES YES YES YES YES YES
On-line FAQ or Hot-line support for surveyors/respondents? YES YES YES YES YES YES
Were pre-filled questionnaires used? NO NO NO NO NO NO
Percentage of pre-filled questions out of total number of questions

Not applicable

Not applicable

Not applicable

Not applicable

Not applicable

Not applicable
Were some actions taken for reducing the measurement error or to correct the statistics? NO YES YES YES YES YES
If yes, describe the actions and their impact

The employment is compared with IACS and ortho-photo maps.

Comparison of the information with the admininstrative sources and previous surveys and limits on average yields.

Comparison of the information with the admininstrative sources and previous surveys and limits on average yields.

Comparison of the information with the admininstrative sources and previous surveys and limits on average yields.

Comparison of the information with the admininstrative sources and previous surveys and limits on average yields.






 
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

Early estimation-wheat, barley

Survey on land cover and land use of the territory of Bulgaria

Survey of yield from main crops

Survey on vegetable production in BG

Survey on fruit production in BG

Survey on vine and wine production in BG
Unit level non-response rate (in %)

Not applicable

Not applicable

<1%

<1%

<1%

<1%
Item level non-response rate (in %)              
               - Min% / item

Not applicable

Not applicable
               - Max% / item

Not applicable

Not applicable
               - Overall%

Not applicable

Not applicable
Was the non-response been treated ? YES YES
Which actions were taken to reduce the impact of non-response?

Not applicable

Not applicable

Second interview and a letter to the respondents or replacement of the farm with another farm.

Second interview and a letter to the respondents or replacement of the farm with another farm.

Second interview and a letter to the respondents or replacement of the farm with another farm.

Second interview and a letter to the respondents or replacement of the farm with another farm.
Which items had a high item-level non-response rate? 

Not applicable

Not applicable

Moisture content or refusal of the owner to deliver data for all variables.

Moisture content or refusal of the owner to deliver data for all variables.

Moisture content or refusal of the owner to deliver data for all variables.

Moisture content or refusal of the owner to deliver data for all variables.
Which methods were used for handling missing data?
(several answers allowed)		 Follow-up interviews
Reminders
Imputations		 Follow-up interviews
Reminders
Imputations		 Follow-up interviews
Reminders
Imputations		 Follow-up interviews
Reminders
Imputations
In case of imputation which was the basis? Imputation based on similar units Imputation based on similar units Imputation based on similar units Imputation based on similar units
In case of imputation, which was the imputation rate (%)?

Not applicable

Not applicable

<1%

<1%

<1%

<1%
Estimated degree of bias caused by non-response? None None Insignificant Insignificant Insignificant Insignificant
Which tools were used for correcting the data?

Comparison with administrative sources.

Comparison with former surveys and administrative sources.

Missing data on area is taken from IACS data. The humidity is calculated. Missing yields are replaced by NUTS3 averages.  When the extrapolation coefficients differ from 1, farms were replaced.

Missing data on area is taken from IACS data. The humidity is calculated. Missing yields are replaced by NUTS3 averages.  When the extrapolation coefficients differ from 1, farms were replaced.

Missing data on area is taken from IACS data. The humidity is calculated. Missing yields are replaced by NUTS3 averages.  When the extrapolation coefficients differ from 1, farms were replaced.

Missing data on area is taken from IACS data. The humidity is calculated. Missing yields are replaced by NUTS3 averages.  When the extrapolation coefficients differ from 1, farms were replaced.
Which organisation did the corrections?

Experts of Agrostatistic

Experts of Agrostatistic

Experts of Agrostatistic

Experts of Agrostatistic

Experts of Agrostatistic

Experts of Agrostatistic
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?

2

2

2

2

2

2

1

1

1

1 

1

1

1
How many of them are forecasts (releases before the harvest)?

1

1

1

1

1

1

 

 
When was the first  forecasting published for the crop year on which is reported? (day/month/year) 31/03/2023 31/03/2023 31/03/2023 31/03/2023 31/03/2023 31/03/2023
When were the final results published for the crop year on which is reported? (day/month/year) 29/06/2023 29/06/2023 29/06/2023 29/06/2023 29/06/2023 29/06/2023 11/04/2023 11/04/2023 11/04/2023 11/04/2023 11/04/2023 11/04/2023 11/04/2023
Additional comments




 
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 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

Not applicable
8.3. Coherence - cross domain

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

IFS 2020
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    

100.2%

95.7%
Dried pulses and protein crops    

95%

67.58%
Root crops    

117.6%

77.6%
Oilseeds    

101.2%

111.13%
Other industrial crops (than oilseeds)    

181%

66.83%
Plants harvested green    

95.6%

86.06%
Total vegetables, melons and strawberries    
Vegetables and melons    

105%
Strawberries    

103%
Cultivated mushrooms  

153%
Total permanent crops  
Fruit trees

Not applicable.

96%

82%
Berries  

97%

69%
Nut trees  

65%

49%
Citrus fruit trees

Not applicable.
Vineyards

107%

83%
Olive trees

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

Table 1, 2 and 3 / IFS2020
The difference in reported area of mushrooms in census - basic, in Table 2 of the ACS - reported number of loadings. The vegetables and strawberries are counted together in census and to compare the data it is not correct . If vegetables and strawberries are taken into account the ratio is Ve2022/Census2020 = 80%, given that the ACS data is for 2022 and census for 2020, and the area in ACS are requoted expanded and in IFS2020 basic.

Table 1, 2 and 3 / IFS2020
It should be noted that the data from Table 2 are for harvested area, for mushrooms the number of loadings is also reported, and in ISAK for sown area and for mushrooms no number of loadings is reported.

  





 
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

https://www.mzh.government.bg/bg/statistika-i-analizi/izsledvane-rastenievadstvo/danni/
https://www.mzh.government.bg/bg/statistika-i-analizi/statistichesko-nablyudenie-zaetost-i-izpolzvane-zemya/tseli/
Publications in English None






 
9.3. Dissemination format - online database

Data tables - consultations

Not applicable


  Availability Links
On-line database accessible to users YES

https://www.agrostat.bg/ISASPublic/Crops
Website None





 
9.4. Dissemination format - microdata access

Availability Links
NO






 
9.5. Dissemination format - other

Not applicable.
9.6. Documentation on methodology

  Availability Links
Methodological report None
Quality Report None
Metadata None
Additional comments  






 
9.7. Quality management - documentation

Not applicable.
9.7.1. Metadata completeness - rate

Not applicable.
9.7.2. Metadata - consultations

Not applicable.


10. Cost and Burden Top

Efficiency gains if compared to the previous quality report? Other
If other, which?

no change
Burden reduction measures since the previous reference year  Other
If other, which?

no change, additional steps are being taken for a greater number of self-filling of questionnaires by respondents in on-line survey information system and wider use of administrative data and expert assessments are foreseen for the forthcoming period






 


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