Crop production (apro_cp)

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

Compiling agency: Statistical Office of the Republic of Serbia


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

Statistical Office of the Republic of Serbia
1.2. Contact organisation unit

Department of Agriculture and Forestry Statistics  - Unit for Crop and Forestry Statistics
1.5. Contact mail address

Milana Rakica 5, Belgrade, Republic of Serbia


2. Statistical presentation Top
2.1. Data description

Annual statistics of crop production collects the data on areas sown in spring and autumn, harvested areas and yields for about 60 crops, as well as areas and yields of vineyards and orchards. The statistics are collected from  a wide variety of sources: sample surveys, administrative sources, experts estimates and other data providers. The data collection covers early estimates (before the harvest) and the final data. Data are collected mostly at national level but for some crops also regional data exist (NUTS1/2).
2.2. Classification system

Hierarchical crop classification system
2.3. Coverage - sector

Growing of non-perennial crops, perennial crops and plant propagation (NACE A01.1-01.3)
2.4. Statistical concepts and definitions

See: Annual crop statistics Handbook

Since 2014, the Republic of Serbia has officially carried out statistical surveys on crop production in accordance with the Regulation (EC) No 543/2009 and Eurostat Annual crop statistics Handbook.

2.5. Statistical unit

Utilised agricultural area cultivated by an agricultural holding
2.6. Statistical population

All agricultural holdings growing crops
2.7. Reference area

The entire territory of the country
2.8. Coverage - Time

Crop year
2.9. Base period

Not applicable.


3. Statistical processing Top
3.1. Source data

  Source 1 Source 2 Source 3 Source 4
Have new data sources been introduced since the previous quality report?  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 Survey
Expert estimate

Sample survey on sown areas at the end of spring sowing season, May/Jun 2022;

Sample survey on expected yield of late crops, fruit and grapes and realized production of early crops and fruits, September 2022;

Sample survey on autumn sowing; November 2022.

Weekly reporting on the seasonal agricultural works, area estimates and condition of major agricultural crops, permanent crops and the yield forecast, by Agricultural advisory services.

  
Final area under cultivation Survey
Administrative data

Sample survey on sown areas at the end of spring sowing season, May/Jun 2022

Register of agricultural holdings by Ministry of agriculture
Production Survey
Expert estimate

Sample survey on expected yield of late crops, fruit and grapes and realized production of early crops and fruits, September 2022;

The sample survey on crop production, November 2022

Weekly reporting on the seasonal agricultural works, area estimates and condition of major agricultural crops, permanent crops and the yield forecast, by Agricultural advisory services.
Yield Survey
Expert estimate

Sample survey on expected yield of late crops, fruit and grapes and realized production of early crops and fruits, September 2022

The sample survey on crop production, November 2022

Weekly reporting on the seasonal agricultural works, area estimates and condition of major agricultural crops, permanent crops and the yield forecast, by Agricultural advisory services.
Non-existing and non-significant crops Survey

The sample survey on crop production, November 2022

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

Weekly reporting on the seasonal agricultural works, area estimates and condition of major agricultural crops, permanent crops and the yield forecast, by Agricultural advisory services.
Final harvested area Survey

Sample survey on horticultural production, November 2022
Production Survey

Sample survey on horticultural production, November 2022
Non-existing and non-significant crops Survey

The sample survey on crop production , November 2022

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

 

Sample survey on expected yield of late crops, fruit and grapes and realized production of early crops and fruits, September 2022

Weekly reporting on the seasonal agricultural works, area estimates and condition of major agricultural crops, permanent crops and the yield forecast, by Agricultural advisory services.
Final production area Survey

The sample survey on crop production , November 2022
Production Survey

The sample survey on crop production , November 2022
Non-existing and non-significant crops Census
Survey

 

The sample survey on crop production , November 2022

Farm structure survey, October 2018.
Table 4: Agricultural land use      
Main area Survey

Sample survey on sown areas at the end of spring sowing season, May/Jun 2022
Non-existing and non-significant crops Survey

Sample survey on sown areas at the end of spring sowing season, May/Jun 2022

Farm structure survey, October 2018.
Total number of different data sources

5                                                                                                                                                                                            

 

  
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? yield is surveyed in the field and production volume is assessed on the basis of the yield
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

Sample survey on sown areas at the end of spring sowing season, May/Jun 2022

Sample survey on expected yield of late crops, fruit and grapes and realized production of early crops and fruits, September 2022

Sample survey on crop production,  November 2022

Sample survey on autumn sowing, November 2022

Sample survey on horticultural production, November 2022

Weekly reporting on the seasonal agricultural works, area estimates and condition of major agricultural crops, permanent crops and the yield forecast
Planning (month-month/year)

January-February/2022

June-August/2022

July-Septembar/2022

July-Septembar/2022

July-Septembar/2022

January/2022
Preparation (month-month/year)

April -May/2022

August/2022

October-November/2022

October-November/2022

October-November/2022

March-November 2022
Data collection (month-month/year)

23 May-06 Jun/2022

06-13 September/2022

24 November - 16 December 2022

24 November - 16 December 2022

24 November - 16 December 2022

March-November 2022
Quality control (month-month/year)

Jun/2022

September/2022

November - December/2022

November - December/2022

November - December/2022

March-November 2022
Data analysis (month-month/year)

Jun-July/2022

September/2022

December-January /2022

December-January /2022

December-January /2022

March-November 2022
Dissemination (month-month/year)

July/2022

September/2022

February/2022

February/2022

February/2022

no dissemination
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:


Population

Which measures were taken in order to make sure that the requirement stipulated in Art. 3.2 are met?
(Statistics shall be representative of at least 95 % of the areas of each table in the Regulation).

In order to provide data in accordance with the requirements of Regulation, SORS didn’t make cut off while creating a sample frame for the Sample survey on sown areas at the end of spring sowing season, May/Jun 2022. Also, there was no cut off for a sample frame  for Sample survey on expected yield of late crops, fruit and grapes and realized production of early crops and fruits, September 2022.

Sample for survey on crop production, November 2022 is created as a subsample of the agricultural holdings selected for the Survey on sown areas at the end of spring sowing season, May/Jun 2022 and all sample selection criteria are valid as for this survey.

Within the Sample survey on crop production, it was created and a separate samples for survey on areas sown in autumn sowing (for 6 winter crops) and separate sample for survey on horticultural production. Sampling frame was created from the SFR base. There  were allocated only those family farms that had Cereals for the production of grain or horticultural production. In this way, statistics is representative of over 97% for total area under cultivation of crops from arable land.

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)

February 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

Sample survey on sown areas at the end of spring sowing season May/Jun 2022

Sample survey on expected yield of late crops, fruit and grapes and realized production of early crops and fruits, September 2022

Survey on crop production, November 2022

Survey on autumn sowing, November 2022

Survey on horticultural production, November 2022

 
Which survey method was used? On-line electronic questionnaire filled in by respondent
Telephone interview, electronic questionnaire		 Other On-line electronic questionnaire filled in by respondent
Telephone interview, electronic questionnaire		 On-line electronic questionnaire filled in by respondent
Telephone interview, electronic questionnaire		 On-line electronic questionnaire filled in by respondent
Telephone interview, electronic questionnaire
If 'other', please specify

Experts estimation by Agricultural exstension services at the Ministry of Agriculture.
Please provide a link to the questionnaire

https://www.stat.gov.rs/en-US/istrazivanja/methodology-and-documents/?a=13&s=1301

 

https://publikacije.stat.gov.rs/G2021/PdfE/G202124081.pdf

 

 

https://publikacije.stat.gov.rs/G2021/PdfE/G202124081.pdf

 

 

https://publikacije.stat.gov.rs/G2021/PdfE/G202124081.pdf

 
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

Register of agricultural holdings by Ministry of Agriculture, Forestry and Water Management
Description

Register record a members of the agricultural budget, record the number of agricultural holdings, production structure and capacity, and give the opportunity to define a strategy of agricultural development and lead a successful agricultural policy by Ministry of agriculture. In 2022  the number of registered agricultural holdings was 450.000
Data owner (organisation)

 Ministry of Agriculture, Forestry and Water Management
Update frequency Once per year or more often
Reference date (month/year)

May/2022
Legal basis

Law on agriculture and rural development ("Off.Gazette ofRS", no.41/09)
Reporting unit

Farmers, entrepreneurs and legal entities
Identification variable (e.g. address, unique code, etc.)

Unique code of agricultural holding, name, surname and personal identification number of residence ofthe holding, residential address and farm location address, numberof family membersandtheir personal data and other banking and accounting data holdings.
Percentage of mismatches (%)
How were the mismatches handled?
Degree of coverage (holdings, e.g. 80%)

70%
Degree of completeness (variables, e.g. 60%)
If not complete, which other sources were used ?

AC 2012, FSS 2018 and regular annual statistical surveys.
Were the data used for sample frame? Sample frame
Validation
Directly for estimates
Data used for other purposes, which?
Which variables were taken from administrative sources?

Identification variables and area of main crops registered (cereals, root crops, oil seed crops, other industrial crops, plants harvested green)
Were there any differences in the definition of the variables between the administrative source and those described in the Regulation? NO
Please describe the differences
What measures were taken to eliminate the differences?
How was the reliability, accuracy and coherence (comparison to other available data) of the data originated from administrative data source (ante- and/or ex-post) checked?

Data comperison is done on the level of holdings and there were negligible differences.
What were the possible limitations, drawbacks of using the data from administrative source(s)?

Registration renewal and updating is voluntary and based on the request of holder. Also, the register doesn’t contain data on orchards and vineyards by holdings.


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 production
Data owner (organisation)

Ministry of Agriculture, Forestry and Water Management
Update frequency (e.g. 1 year or 6 months) Weekly
Reference date (Month/Year  e.g. 1/22 - 8/22)

Week
Legal basis

Memorandum of anderstending between Ministry of Agriculture, Forestry and Water Management (MoA) and Statistical Office of Republic of Serbia (SORS)
Use purpose of the estimates?

Expert estimates are made in order to monitor the seasonal agricultural work, area planting and condition of major agricultural crops, permanent crops and yield forecast by Ministry of Agriculture, Forestry and Water Management.
What kind of expertise the experts have?

They are agricultural engineers, employed in agricultural advisory services at the Ministry of Agriculture.
What kind of estimation methods were used?

The methodology defines regular reporting on planted and harvested areas of crops, fruit trees and grape vines , using estimates from agricultural advisory services, as the only reliable way to determine the yield forecasts every week.

For this purpose, SORS has created a web questionnaire for data entry by the Agriculture extension services as well as final reports from the same aplication. This ensures a unique collection of high quality data, necessary for the MoA to conduct agricultural policy, and information which SORS use for Eurostat early estimates.
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?
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

In 2014, SORS has replaced the previously used method of providing EECP and establish a new system, which means engagement of agriculture extension services. For now, there are no plans to replace the expert’s estimates for EECP by other data sources. 


 
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

Available administrative sources. 

Records of associations and communities for cereals, fruits and vegetables, records of subsidies applications by Ministry of Agriculture, Data of sugar production industry, export/import data, survey on purchase of agricultural products, data of oil producers, calculation of supply balance sheets.

Which kind of data validation : Logical and computational control at data entry. 






 
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?

In conducting surveys on crop production statistics, SORS respected quality elements: relevance, accuracy, timeliness, punctuality, accessibility, comparability and coherence. It also aim to meet the standards prescribed by Regulation 543/2009 relating to the coverage, frequency and surveys reference period as well as precision requirements (coefficient of variation of the data to be provided by 30 September n+1, at national level).   

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

https://data.stat.gov.rs/Metadata/13_Poljoprivreda/Html/1301_ESMS_G3_2019_1.html

 

 

 

 

        
To which data source(s) is it linked?

Annual crop production statistics

        
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? Increase of resources
Systematic validation improvements
Quality report
Other		        
If, other, please specify

Census of Agriculture 2023

        
Additional comments        






 
4.2. Quality management - assessment

See the European level Quality Report.


5. Relevance Top
5.1. Relevance - User Needs

Are there known unmet user needs? YES
Describe the unmet needs

SORS is not in a position, from the reason of budget limitation, to carry out sample surveys big enough to provide easily all the data on the level of districts and municipalitys, which is in most cases the needs of our data users. Some data on the level of districts and municipalitys are not reliable in the final assessment, so these data can not be published.
Does the Regulation 543/2009 meet the national data needs? YES
Does the ESS agreement meet the national needs? YES
If not, which additional data are collected?
Additional comments






 
5.2. Relevance - User Satisfaction

Have any user satisfaction surveys been done? YES
If yes, how satisfied the users were? Satisfied
Additional comments






 
5.3. Completeness

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

See the European level Quality Report


6. Accuracy and reliability Top
6.1. Accuracy - overall

See the European level Quality Report.
6.2. Sampling error

Sampling method and sampling error

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

Sample survey on sown areas at the end of spring sowing season, May/Jun 2022

 Sample survey on crop production , November 2022

 Sample survey on autumn sowing, November 2022

Sample survey on horticultural production, November 2022
Sampling basis? Other Other Other Other
If 'other', please specify

Statistical Farm Register

Statistical Farm Register

Statistical Farm Register

Statistical Farm Register

 
Sampling method? Random
Stratified		 Random
Stratified		 Random
Stratified		 Random
Stratified
If stratified, number of strata?

121

 121

35

32

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

Take-all strata determined using Hidirouglou algorithm for main groups of crops

Take-all strata determined using Hidirouglou algorithm for main groups of crops

Take-all strata determined using Hidirouglou algorithm for main groups of crops

Take-all strata determined using Hidirouglou algorithm for main groups of crops
Size of total population

601199

601199

445578

113073

 
Size of sample

7074

5235

3626

2508

 
Which methods were used to assess the sampling 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
Non-response		 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

Taylor linearization was used for variance estimation by applying Swedish ETOS based on software SAS. Sample design and weighting has been taken into account because it is required by ETOS.

Taylor linearization was used for variance estimation by applying Swedish ETOS based on software SAS. Sample design and weighting has been taken into account because it is required by ETOS.

Taylor linearization was used for variance estimation by applying Swedish ETOS based on software SAS. Sample design and weighting has been taken into account because it is required by ETOS.

Taylor linearization was used for variance estimation by applying Swedish ETOS based on software SAS. Sample design and weighting has been taken into account because it is required by ETOS.

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

Comparison were done with Register of agricultural holdings by MoA and there were expected differences. 

The difference occurs because of a different number of farms covered by statistics and the number of farms covered by this administrative source.

The comparison was made also with the estimates of Agricultural extension services and there were no significant differences.  

Comparison were done withestimates of Agricultural extension services:

The data on harvested area and yield for cereals obtained in this sample survey on crop production were almost the same as the estimates of Agricultural extension services (especially for wheat and maize) as well as for industrial crops.

Comparison were done with estimates of Agricultural extension services:

The data on sown area for cereals obtained in this sample survey  were almost the same as the estimates of Agricultural extension services.

Comparison were done with estimates of Agricultural extension services:

The data on horticultural production obtained in this sample survey  were almost the same as the estimates of Agricultural extension services.

 

 

 
Which were the main sources of errors?


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 sown areas at the

end of spring sowing season, May/Jun 2022

 The sample survey on crop production , November 2022

Sample survey on autumn sowing; November 2022

Sample survey on horticultural production, Novmber 2022

 
Cereals for the production of grain (in %)

3.5

4.74

5.03

 

 
Dried pulses and protein crops (in %)

6.9

12.94

 
Root crops (in %)

6.76

7.08

 
Oilseeds (in %)

2.8

7.37

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

12.9

12.65

 
Plants harvested green from arable land (in %)

6.08

8.05

 
Total vegetables, melons and strawberries (in %)

9.64

 9.0

5.01

 
Cultivated mushrooms (in %)

-

-
Total permanent crops (in %)

5.8

4.46

 

 
Fruit trees (in %)

6.7

6.72

 
Berries (in %)

8.2

7.9

 
Nut trees (in %)

15.5

24.4

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

12.87

9.27

 
Olive trees (in %)
Additional comments

The reason of increased the coefficient of variation for the group of other industrial crops is very low persentage of respondent units for this crop group (only 3%). The same reason is for increased the coefficient of variation of nut trees in sample survey of crop production, where the respondent units are only 2% for nut trees. 

            




 
6.3. Non-sampling error

Non sampling errors include: people who refused to answer the questionnaires, size of questionnaires (both surveys: Sample survey on sown areas at the end of spring sowing season, May/Jun 2019  and sample survey on crop production , November 2019 includes for about 100 indicators), questions interpretation by CATI interviewers  and data entry errors by CATI and regional offices interviewers.  
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

Sample survey on sown areas at the end of spring sowing season, May/Jun 2022

 The sample survey on crop production , November 2022

Sample survey on autumn sowing; November 2022

Sample survey on horticultural production, November 2022

 
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 Unknown Unknown Unknown Unknown
What were the reasons for coverage errors?

Sample frame is constructed using data from Statistical farm register, which has been updated only for the units which were sampled in previous agricultural surveys or linked with the Register of the Ministry of Agriculture. Some set of units was interviewed in previous year by phone so it was hard conclude either it was non response or dead household. 

Sample frame is constructed using data from Statistical farm register, which has been updated only for the units which were sampled in previous agricultural surveys or linked with the Register of the Ministry of Agriculture. Some set of units was interviewed in previous year by phone so it was hard conclude either it was non response or dead household. 

Sample frame is constructed using data from Statistical farm register, which has been updated only for the units which were sampled in previous agricultural surveys or linked with the Register of the Ministry of Agriculture. Some set of units was interviewed in previous year by phone so it was hard conclude either it was non response or dead household. 

Sample frame is constructed using data from Statistical farm register, which has been updated only for the units which were sampled in previous agricultural surveys or linked with the Register of the Ministry of Agriculture. Some set of units was interviewed in previous year by phone so it was hard conclude either it was non response or dead household. 

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

None

None

None

None

 
Additional comments

Statistical Farm Register is created 2014. From 2015 it is regularly updated with available statistical and administrative sources and used as sample frame for crop statistics surveys.

Statistical Farm Register is created 2014. From 2015 it is regularly updated with available statistical and administrative sources and used as sample frame for crop statistics surveys.

Statistical Farm Register is created 2014. From 2015 it is regularly updated with available statistical and administrative sources and used as sample frame for crop statistics surveys.

Statistical Farm Register is created 2014. From 2015 it is regularly updated with available statistical and administrative sources and used as sample frame for crop statistics surveys.

 

 

 

 

 




 
6.3.2. Measurement error

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

Sample survey on sown areas at the end of spring sowing season, May/Jun 2022

 The sample survey on crop production , November 2022

Sample survey on autumn sowing; November 2022

Sample survey on horticultural production, November 2022

 
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)?

9

9

9

9

 
Preparatory (field) testing of the questionnaire? YES NO YES YES
Number of units participating in the tests? 

500

500

500

500

 
Explanatory notes/handbook for surveyors/respondents?  YES YES YES YES
On-line FAQ or Hot-line support for surveyors/respondents? YES YES YES YES
Were pre-filled questionnaires used? NO NO NO NO
Percentage of pre-filled questions out of total number of questions
Were some actions taken for reducing the measurement error or to correct the statistics? YES YES YES YES
If yes, describe the actions and their impact

All fields in the questionnaire were coded, summary fields have codes listed for collecting, categories Others include a list of cultures that belong to them. Definitions of all indicators are given in the Handbook with the codes from the questionnaire.

All fields in the questionnaire were coded, summary fields have codes listed for collecting, categories Others include a list of cultures that belong to them. Definitions of all indicators are given in the Handbook with the codes from the questionnaire.

All fields in the questionnaire were coded, summary fields have codes listed for collecting, categories Others include a list of cultures that belong to them. Definitions of all indicators are given in the Handbook with the codes from the questionnaire..

All fields in the questionnaire were coded, summary fields have codes listed for collecting, categories Others include a list of cultures that belong to them. Definitions of all indicators are given in the Handbook with the codes from the questionnaire.

 






 
6.3.3. Non response error

Unit non-response - rate

Not applicable


Item non-response - rate

Not applicable


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

Sample survey on sown areas at the end of spring sowing season, May/Jun 2022

The sample survey on crop production, November 2022

Sample survey on autumn sowing, November 2022

Sample survey on horticultural production, November 2022
Unit level non-response rate (in %)

12%

17%

14.64%

21.1%

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

Basic weights are adjusted. Responding units: units for which data have been collected, units not engaged in crop production, and farms where all the members are absent.

Basic weights are adjusted. Responding units: units for which data have been collected, units not engaged in crop production, and farms where all the members are absent.

Basic weights are adjusted. Responding units: units for which data have been collected, units not engaged in crop production, and farms where all the members are absent.

Basic weights are adjusted. Responding units: units for which data have been collected, units not engaged in crop production, and farms where all the members are absent.

 
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		 Follow-up interviews
Reminders		 Follow-up interviews
Reminders		 Follow-up interviews
Reminders
In case of imputation which was the basis?
In case of imputation, which was the imputation rate (%)?
Estimated degree of bias caused by non-response? Unknown Unknown Unknown Unknown Unknown
Which tools were used for correcting the data?
Which organisation did the corrections?
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?

5

2

3

3

-

1

-

1

-

3

3

1

-

2

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

3

-

 

2

2

 

-

-

1

-

-

2

2

-

-

1

-
When was the first  forecasting published for the crop year on which is reported? (day/month/year) 01/02/2022 30/06/2022 30/06/2022 30/06/2022 30/06/2022 23/09/2022
When were the final results published for the crop year on which is reported? (day/month/year) 01/03/2022 28/02/2022 28/02/2022 28/02/2022 28/02/2022 28/02/2022 28/02/2022 28/02/2022 28/02/2022 28/02/2022 28/02/2022 28/02/2022
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 YES YES
If yes, to which were they related? Methods Other
If other, which?

 

 

 

Revision of data on utilized agricultural area as a result of revision of data on area of permanent crops
Which items were affected? F1110 - Apples
F1120 - Pears
F1210 - Peaches
F1220 - Nectarines
F1230 - Apricots
F1240 - Cherries
F1241 - Sour cherries
F1250 - Plums
F3110 - Blackcurrants
F3200 - Raspberries
F3300 - Blueberries
F4100 - Walnuts
F4200 - Hazelnuts		 PECR - Permanent crops
F0000 - Fruits, berries and nuts (excluding citrus fruits, grapes and strawberries)
Year of break (number)

 

2017

2017
Impact on comparability Low Low
Additional comments

Serbia has carried out the Orchard survey in 2017, for the first time. The survey collected data on areas, planting density and age for apples, pears, apricots and peaches, by varieties, regardless of whether they are dessert or varieties for industrial processing. Also, data on areas for fruit species in total (not by varieties) were collected, for: plums, cherries, cherries, raspberries, quinces, walnuts, hazelnuts, almonds, blackberries and blueberries.

Due to the low differences beatween data collected before and after Orchard survey, data for years 2013-2016 were recalculated and new time series were published.

Serbia has carried out the Orchard survey in 2017, for the first time. The survey collected data on areas, planting density and age for apples, pears, apricots and peaches, by varieties, regardless of whether they are dessert or varieties for industrial processing. Also, data on areas for fruit species in total (not by varieties) were collected, for: plums, cherries, cherries, raspberries, quinces, walnuts, hazelnuts, almonds, blackberries and blueberries.

Due to the low differences beatween data collected before and after Orchard survey, data for years 2013-2016 were recalculated and new time series were published.





 
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?

Price statistics

National accounts
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    

29.82%

 

 
Dried pulses and protein crops    

23.65%

 

 
Root crops    

43.20%

 

 
Oilseeds    

39.10%

 

 
Other industrial crops (than oilseeds)    

13.05%

 

 
Plants harvested green    

37.43%

 

 
Total vegetables, melons and strawberries    

11.44%

 

 
Vegetables and melons    

7.17%

 

 
Strawberries    

60.67%

 

 
Cultivated mushrooms  
Total permanent crops

 0.37%

  

10.25%

 

 
Fruit trees

 -0.64%

0.5%

3.02%

 

 
Berries

 -4.17

  

-8.40%

 

 
Nut trees

 -1.47%

  

-25.45%

 

 
Citrus fruit trees
Vineyards

 0.17%

44.28%

 

 
Olive trees

 

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

 

Register of agricultural holdings on behalf of Ministry of Agriculture, Forestry and Water Managment (MoA) is register for subsidies applying. The result of differences between Register of MoA and regular annual crop statistics in 2022 are results of different total number of agricultural holdings as well as different number of agricultural holdings specialized for crop production. Register of MoA includes approximately 400 thousand of family agricultural holdings what is a half of the total number of family agricutural holdings in Statistical Farm Register (621.445).   

Also, the Ministry of Agriculture doesn’t perform subsidies for all grown crops, so the crop registration by holdings is restricted.

  





 
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
YES

http://www.stat.gov.rs/en-US/oblasti/poljoprivreda-sumarstvo-i-ribarstvo/biljna-proizvodnja






 
9.2. Dissemination format - Publications

  Availability Links
Publications Electronic

http://www.stat.gov.rs/sr-Cyrl/oblasti/poljoprivreda-sumarstvo-i-ribarstvo/biljna-proizvodnja
Publications in English Electronic

http://www.stat.gov.rs/en-US/oblasti/poljoprivreda-sumarstvo-i-ribarstvo/biljna-proizvodnja






 
9.3. Dissemination format - online database

Data tables - consultations

Not applicable


  Availability Links
On-line database accessible to users YES

http://data.stat.gov.rs/Home/Result/130102?languageCode=en-US

Website National language
English

http://www.stat.gov.rs/en-US/





 
9.4. Dissemination format - microdata access

Availability Links
NO






 
9.5. Dissemination format - other

http://data.stat.gov.rs/?caller=SDDB&languageCode=en-US

Data on crop production statistics can be found in the database of agricultural statistics, published on the SORS website. The database contains all  indicators collected in crop production statistics and displayed in the time series since the year 1947. Using of Database is for free.
9.6. Documentation on methodology

  Availability Links
Methodological report National language
English

http://data.stat.gov.rs/Home/Result/130102?languageCode=en-US#    

Quality Report National language
English

http://data.stat.gov.rs/Home/Result/130102?languageCode=en-US#    


  

Metadata National language
English

http://data.stat.gov.rs/Home/Result/130102?languageCode=en-US#
 

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
Increased use of administrative data
Staff further training
If other, which?

Implementation of new sample surveys insted of experts etimates, caused greater efficiency and speed
of data providing, as well as data reliability.
Burden reduction measures since the previous reference year  Less frequent surveys
Less variables surveyed
More user-friendly questionnaires
Multiple use of the collected data
If other, which?






 


11. Confidentiality Top
11.1. Confidentiality - policy
Restricted from publication
11.2. Confidentiality - data treatment
Restricted from publication


12. Comment Top

In the process of harmonization of agricultural statistical system with the statistical systems of the European Union (EU), SORS has adopted the standards and definitions of the EU, which completely changed the methodology of Annual Crop Statistics. Previously, till 2014th, the methodology of Annual Crop statistics were based on experts estimates. Several problems characterized this way of data collecting, but the most important were tardiness of cadastral data used as a base for experts estimation as well as the almost complete absence of quality checks of data on crop production statistics. Also, a report on the quality of the data was not possible.

Now, Annul Crop Statistics, is based on the principle of full harmonization with Regulation (EC) No 543/2009 as well as with the definitions and concepts presented in the Eurostat Handbook for Annual Crop Statistics. Data collection is conducted on the basis of several annual sample surveys, one three annul and one five annual sample survey.

This is the forth Quality report on Annual Crop Statistics, refers to 2022.


Related metadata Top


Annexes Top