Crop production (apro_cp)

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

Compiling agency: REPUBLIC OF CROATIA - CROATIAN BUREAU OF STATISTICS Address: Ilica 310000 ZagrebRepublic of Croatia


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

REPUBLIC OF CROATIA - CROATIAN BUREAU OF STATISTICS

Address:

Ilica 3
10000 Zagreb
Republic of Croatia
1.2. Contact organisation unit

Spatial Statistics Directorate/Agricultural, Production and Structural Statistics Department/Crop production statistics, agricultural structural statistics and Register of agricultural holdings Unit
1.5. Contact mail address

Branimirova 19, 10 000 Zagreb, Republic of Croatia


2. Statistical presentation Top
2.1. Data description

Annual crop statistics provide statistics on the area under main arable crops, vegetables and permanent crops and production and yield levels.  The statistics are collected from  a wide variety of sources: surveys, administrative sources, experts and other data providers. The data collection covers early estimates (before the harvest) and the final data. Data are collected mostly at national level but for some crops also regional data exist (NUTS1/2).
2.2. Classification system

Hierarchical crop classification system.
2.3. Coverage - sector

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

See: Annual crop statistics Handbook
2.5. Statistical unit

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

All agricultural holdings growing crops.
2.7. Reference area

The entire territory of the country.
2.8. Coverage - Time

Crop year.
2.9. Base period

Not applicable.


3. Statistical processing Top
3.1. Source data

  Source 1 Source 2 Source 3 Source 4
Have new data sources been introduced since the previous quality report?  YES      
If yes, which new data sources have been introduced since the previous quality report?

The four surveys on crop and animal production have been merged into a single annual survey titled "Annual Survey on Crop and Animal Production".
Type of source? Survey
To which Table (Reg 543/2009) do they contribute? Table1
Have some data sources been dropped since the previous quality report? YES      
Which data sources have been dropped since the previous quality report?

Surveys on autumn sowing and actual yields of early and late crops/fruits/grapes dropped from previous report.
Type of source? Survey
Why have they been dropped?

The three surveys on autumn sowing, actual yields of early crops and fruits, and actual yields of late crops, fruits, and grapes have been integrated into one survey called "Annual Survey on Crop and Animal Production" to decrease respondent burden.
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
Administrative data
Expert estimate

Survey on spring sowing and permanent crops.

The Farm Register of the Paying Agency for Agriculture, Fisheries and Rural Development (IACS-Orchard Register,Vineyard Register and Olive groves Register)

Producers Association of oilseeds, Producers Association of sugar beets and Institute for seed and seedlings.

  
Final area under cultivation Survey
Administrative data
Expert estimate

Annual Survey on Crop and Animal Production, Survey on spring sowing and permanent crops 

The Farm Register of the Paying Agency for Agriculture, Fisheries and Rural Development (IACS-Orchard Register,Vineyard Register and Olive groves Register)

Producers Association of oilseeds, Producers Association of sugar beets and Institute for seed and seedlings
Production Survey
Expert estimate

Annual Survey on Crop and Animal Production

Producers Association of oilseeds, Producers Association of sugar beets and Institute for seed and seedlings
 
Yield Survey
Expert estimate

Annual Survey on Crop and Animal Production

Producers Association of oilseeds, Producers Association of sugar beets and Institute for seed and seedlings
Non-existing and non-significant crops Survey
Administrative data

Annual Survey on Crop and Animal Production

The Farm Register of the Paying Agency for Agriculture, Fisheries and Rural Development (IACS-Orchard Register,Vineyard Register and Olive groves Register)
 
Table 2: Vegetables, melons and strawberries       
Early estimates for harvested areas Administrative data

The Farm Register of the Paying Agency for Agriculture, Fisheries and Rural Development (IACS)
Final harvested area Survey

Annual Survey on Crop and Animal Production
Production Survey

Annual Survey on Crop and Animal Production
Non-existing and non-significant crops Survey
Administrative data

Annual Survey on Crop and Animal Production

The Farm Register of the Paying Agency for Agriculture, Fisheries and Rural Development (IACS)
Table 3: Permanent crops      
Early estimates for production area Survey
Administrative data
Expert estimate

Survey on spring sowing and permanent crops

The Farm Register of the Paying Agency for Agriculture, Fisheries and Rural Development(IACS-Orchard Register,Vineyard Register and Olive groves Register)
 
Producers Association of permanent crops
 
Final production area Survey

Annual Survey on Crop and Animal Production

Survey on yields of subtropical fruits and olives 
Production Survey

Annual Survey on Crop and Animal Production

Survey on yields of subtropical fruits and olives
Non-existing and non-significant crops Survey
Administrative data

Annual Survey on Crop and Animal Production

The Farm Register of the Paying Agency for Agriculture, Fisheries and Rural Development (IACS-Orchard Register,Vineyard Register and Olive groves Register)
 
Table 4: Agricultural land use      
Main area Survey
Administrative data

Annual Survey on Crop and Animal Production

The Farm Register of the Paying Agency for Agriculture, Fisheries and Rural Development(IACS);Orchard Register;Vineyard Register;The register of olive groves
Non-existing and non-significant crops Survey
Administrative data

Annual Survey on Crop and Animal Production

The Farm Register of the Paying Agency for Agriculture, Fisheries and Rural Development ((IACS-Orchard Register,Vineyard Register and Olive groves Register)
 
Total number of different data sources

9

  
Additional comments

Early estimates of areas of some important crops is part of the Survey on spring sowing and permanent crops and it is not separately survey. 

 

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

 x

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

Survey on spring sowing and permanent crops

Annual Survey on Crop and Animal Production

Survey on yields of subtropical fruits and olives
Planning (month-month/year)

3-5/2022

7-8/2022

10-11/2022
Preparation (month-month/year)

4-5/2022

8-10/2022

10-11/2022
Data collection (month-month/year)

6-7/2022

11-12/2022

1/2023
Quality control (month-month/year)

7-12/2022

11-12/2022

1-2/2023
Data analysis (month-month/year)

1-5/2023

1-4/2023

1-3/2023
Dissemination (month-month/year)

5/2023

1-5/2023

3-5/2023
If there were delays, what were the reasons?

There were no delays.

There were no delays.

There were no delays.






 
3.3. Data collection

Definitions Question In case yes, how do they differ?
Do national definitions differ from the definitions in Article 2 of Regulation (EC) No 543/2009? NO
Are there differences between the national methodology and the methodology described in the Handbook concerning e.g. the item and aggregate calculations? NO
Are special estimation/calculation methods used for main crops from arable land? NO
Are special estimation/calculation methods used for vegetables or strawberries? NO
Are special estimation/calculation methods used for permanent crops for human consumption? NO
Are special estimation/calculation methods used for main land use? NO
Do national crop item definitions differ from the definitions in the Handbook  (D-flagged data)? YES  
In case yes, how do they differ? ( list all items and explanations) V1900 - Other brassicas n.e.c
V2300 - Lettuces
V2900 - Other leafy or stalked vegetables n.e.c.
V3200 - Cucumbers
V3900 - Other vegetables cultivated for fruit n.e.c.
V4210 - Onions
V4900 - Other root, tuber and bulb vegetables n.e.c.

V1900 Other brassicas include Savoy cabbage.

V2300 Lettuce include endives, salad chicory and corn salad.

V2900 Other leafy or stalked vegetables include celery, parsley leaves, artichokes, mangold, rucola and rhubarb.

V3200 Cucumbers includes gherkins.

V3900 Other vegetables cuktivated for fruit n.e.c. include  sweet maize and kiwano.

V4210 Onions inludes shallots and chive.

V4900 Other root, tuber and bulb vegetables n.e.c. include radishes, celeriac, sweet potatoes, turnips and kohlrabi.

 
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.)
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.
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.
F3900 - Other berries n.e.c.
T2900 - Other small citrus fruits (including hybrids) n.e.c.
V1900 - Other brassicas n.e.c.

C1900 include buckwheat and  millet.

P9000 include lentils and chickpeas.

R9000 include sweet potatoes, fodder beet, fodder kale and turnips for fodder.

G2900 include clovers and mixtures, multi-annual mixtures of clover and grasses, fodder peas, mixture of leguminous plants and cereals, vetches, sainfoin and lupins.

G9900 include annual and multi annaul grasses, sorghum for fodder, sudan grass and lacy phacelia.

I1190 include pumkins for oil, hemp for oil, poppy seed and linseed.

V1900 Other brassicas include Savoy cabbage. 

V2900 Other leafy or stalked vegetables include celery, parsley leaves, artichokes, mangold, rucola and rhubarb.

V3900 OOther vegetables cuktivated for fruit n.e.c. include  sweet maize and kiwano. 

V4900 OOther root, tuber and bulb vegetables n.e.c. include radishes, celeriac, sweet potatoes, turnips and kohlrabi.

F1190 Other pome fruits n.e.c. include quinces.

F3900 Other berries include chokeberries, blackberries, cranberries, currants, gooseberries, goji berries and elderberries.

T2900 Other small citrus fruits (including hybrids) includes mandarins.

 

 


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).

All registered holdings are surveyed.
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)

June/2022
Was a threshold applied? YES
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 %)

0.02%
Dried pulses and protein crops (in %)

0,01%
Root crops (in %)

0.36%
Oilseeds (in %)

0%
Other industrial crops (included all industrial crops besides oilseeds)  (in %)

0%
Plants harvested green from arable land (in %)

0.01%
Total vegetables, melons and strawberries (in %)

0%
Cultivated mushrooms (in %)

Not aplicable.
Total permanent crops (in %)

0.2%
Fruit trees (in %)

0,11%
Berries (in %)

0%
Nut trees (in %)

0%
Citrus fruit trees (in %)

0%
Vineyards (in %)

0.26%
Olive trees (in %)

0.01%


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

Survey on spring sowing and permanent crops

Annual Survey on Crop and Animal Production

Survey on yields of subtropical fruits and olives
Which survey method was used? On-line electronic questionnaire filled in by respondent
Postal questionnaire filled in by respondent
Face-to-face interview		 On-line electronic questionnaire filled in by respondent
Postal questionnaire filled in by respondent
Telephone interview, electronic questionnaire		 On-line electronic questionnaire filled in by respondent
Postal questionnaire filled in by respondent
Telephone interview, electronic questionnaire
If 'other', please specify
Please provide a link to the questionnaire

https://www.dzs.hr/Hrv/important/Obrasci/01-Poljoprivreda/Lista.htm

https://podaci.dzs.hr/media/bxepkqtb/po-71.pdf

 

https://www.dzs.hr/Hrv/important/Obrasci/01-Poljoprivreda/Lista.htm
Data entry method, if paper questionnaires? 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

Farm Register of the Paying Agency for Agriculture, Fisheries and Rural Development (including Unique reguest database

Orchard and olive groves Register 

Vineyard Register

Institute for seed and seedlings.
Description

Contains basic information and data on cadastral parcels owned by holdings as well as information on the areas under crops on LPIS parcels for which subsidy is requested

It contains information on the characteristics of olive groves and orchards recorded in LPIS system

Contains information about vineyards, compulsory declarations and supporting documents.

The Institute for Seed and Seedlings is an institution established by the Government of the Republic of Croatia in 1998 as a nationally accredited body for carrying out activities in the field of seed crops, nurseries and the recognition of varieties of agricultural plants under the supervision of the Ministry of Agriculture.
Data owner (organisation)

Paying Agency for Agriculture, Fisheries and Rural Development

Paying Agency for Agriculture, Fisheries and Rural Development

Paying Agency for Agriculture, Fisheries and Rural Development.

Institute for seed and seedlings.
Update frequency Continuous Continuous Continuous Continuous
Reference date (month/year)

6/2022

12/2022

12/2022

6/2022
Legal basis

OJ of the Republic of Croatia No. 80/23

OJ of the Republic of Croatia No. 1/23

Commission delegated regulation (EU) 2018/273

OJ of the Republic of Croatia No. 110/21
Reporting unit

Business entities and parts thereof, private family farms.

Business entities and parts thereof, private family farms.

Business entities and parts thereof, private family farms.

Producers of seed and seedlings.
Identification variable (e.g. address, unique code, etc.)

All Identificatons 
 
All Identificatons

All Identificatons.

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

All business entities and parts thereof, and only those private farms which are in subsidies

100%

100%

100%
Degree of completeness (variables, e.g. 60%)

100%

100%

100%

100%
If not complete, which other sources were used ?
Were the data used for sample frame? Sample frame
Validation
Directly for estimates		 Sample frame Sample frame 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? YES NO NO NO
Please describe the differences

dried pulses, some variables are aggregate in "other"
What measures were taken to eliminate the differences?

List od crop products in administrative source is complemented by missing or disaggregated variables
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 are compared with data coming from sample at county and the farm level

Data are compared with data coming from sample at county and the farm level.

Data are compared with data coming from sample at county and the farm level.

Data are compared with data coming from sample at county and the farm level.
What were the possible limitations, drawbacks of using the data from administrative source(s)?

There are some differences between the population covered in the ACS and the population covered by Vineyard survey. The difference is in number of winegrowers who produce for their own use and they are covered in ACS, but not in Vineyard survey.


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

Producers Association of oilseeds, tobbaco and vegetables

Producers Association of sugar beets 

Producers Association citrus fruit and olives
Data owner (organisation)

CBS

CBS

CBS
Update frequency (e.g. 1 year or 6 months) 4 times per year 4 times per year 2 times per year
Reference date (Month/Year  e.g. 1/22 - 8/22)

1/22-9/22

6/22-9/22

10/22-12/22
Legal basis

National Statistics Act (OJ HR No. 25/20.)

National Statistics Act (OJ HR No. 25/20.)

National Statistics Act (OJ HR No. 25/20.)
Use purpose of the estimates?

For CBS and users needs.

For CBS and users needs.

For CBS and users needs.
What kind of expertise the experts have?

Scientific and practical expertise in the field.

Practical expertise in the field.

Practical expertise in the field.
What kind of estimation methods were used?

Using of data on contracted production of oilseeds, tobacco and vegetables.

Using of data on contracted production of sugar beet.

Using of many different sources of information (for NUTS 2).
Were there any differences in the definition of the variables between the experts' estimates and those described in the Regulation? NO NO 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?

Data on area, yields and production originated from experts' estimates were compared with data from the survey. Data on area, yields and production from experts' estimates were more representative.  

Data on area, yields and production originated from experts' estimates were compared with data from the survey. Data on area, yields and production from experts' estimates were more representative.  

Data on area, yields and production originated from experts' estimates were compared with data from the survey. Data on area, yields and production from experts' estimates were more representative.  
What were the possible limitations, drawbacks of using the data from expert estimate(s)?

There are no drawbacks, expert estimates improve our surveys.

There are no drawbacks, expert estimates improve our surveys.

There are no drawbacks, expert estimates improve our surveys.
Additional comments


 
3.4. Data validation

Which kind of data validation measures are in place? Automatic and Manual
What do they target? Completeness
Outliers
Aggregate calculations
Other
Is the data cross-validated against an other dataset? YES
If yes, which kind of dataset? Previous results
Other dataset
If other, please describe

Dataset from administrative source.






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

The componenets are:

  • High quality statistical processes and products
  • Users satisfaction
  • Professional orientation of the employees
  • Efficiency of the processes
  • Reduction of the response burden
        
Is there a quality report available? YES        
If yes, please provide a link(s)

https://dzs.gov.hr/UserDocsImages/dokumenti/Quality%20report/Quality%20Report_%20crop%20production_%202020%20-%20ENG.pdf

        
To which data source(s) is it linked?

It is linked on all data sources indicated under 3.1.

        
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
Further automation
Quality report
Peer review
Other		        
If, other, please specify

Plan to use data  from Paying Agency for population who are in system of subsidies.

        
Additional comments

After the data entry, we used special logic-numeric control for micro data. These controls were calculation controls and logical controls. Before corrections were accepted and entered, the field supervisors or farmers had been contacted by telephone if necessary.The following controls were done at micro level:

  • by coded answers (e.g. 1, 2) we checked if the foreseen codes were used,
  • completeness of data
  • for every characteristic the maximum value was determined on the basis of previous surveys. If the entered value exceeded the maximum value, the controller had to contact the farmer.
  • Relations among certain characteristics were checked, e.g.:
    • density of trees in orchards and vines in vineyards,
    • in the part of the questionnaire on the relation between tables several cross checking
    • etc

      By SQL, errors were divided into:

  • fatal errors (e.g. wrong sums, wrong codes used, illogical data, etc.) – The error should be solved before the data entry continues. In most cases data with hard errors were checked by farmers by telephone.
  • active signals (e.g. maximum values of certain characteristics were exceeded) – CBS provided instructions how to proceed in this kind of errors.  

    After checking with SQL, we undertook analysis at macro level with SAS software:

  • we checked the maximum values for each characteristic,
  • we compared our data with applications for subsidies at macro level.
  • comparison with other sources (Paying Agency Register, Vineyard register etc.) 

In this way we have tried to avoid errors at data entry.

        






 
4.2. Quality management - assessment

Data is collected from reliable sources applying high standards with regard to the methodology and ensuring a high degree of comparability.


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? NO
Does the ESS agreement meet the national needs? YES
If not, which additional data are collected?

1. Data on vegetables breakdown on: vegetable on open filed, vegatables in market gardening, vegatables under protection and vegetables in kitchen gardens 

2. Detailed breakdown on fodder crop harvested green.
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

The last one satisfaction survey was conducted in 2022.






 
5.3. Completeness

The crop statistics is fully aligned with the EU regulations and the Eurostat methodology for crop statistics. The crop statistics cover all the variables that are defined by the methodology.
5.3.1. Data completeness - rate

100%.


6. Accuracy and reliability Top

See points 6.2, 6.3.1, 6.3.2 and 6.3.3
6.1. Accuracy - overall

Sample surveys are often used to obtain data on a wide range of characteristics. However, in crop statistics, post-stratification is not performed and misclassification is not assessed. To correct any possible errors of measurement, statistics uses the logic-numeric control. To prevent measurement errors, interviewers and supervisors are trained, data and process validation are controlled, and extreme variable values are checked and corrected as necessary after data entry. The likelihood of undercoverage in crop statistics is very low because there are not many new agricultural holdings. However, unit non-response remains one of the main sources of error.
6.2. Sampling error

Sampling method and sampling error

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

Survey on spring sowing and permanent crops

Annual Survey on Crop and Animal Production

Survey on yields of subtropical fruits and olives 
Sampling basis? Other List List
If 'other', please specify

The survey is conducted only for business entities and parts thereof, and it is done on a full coverage basis.
Sampling method? Other Stratified Stratified
If stratified, number of strata?

20

6
If stratified, stratification basis? Location
Size		 Location
Size
If 'other', please specify
Size of total population

4300

124.434

25.818
Size of sample

4300

26.327 (including buisness entities).

5571  (including buisness entities).
Which methods were used to assess the sampling 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
If other, which?
If CV (co-efficient of variation) was calculated, please describe the calculation methods and formulas

Taylor series linearization

Taylor series linearization

Taylor series linearization
If the results were compared with other sources, please describe the results

With ADMIN. source (IACS), the data are compared at county level and for each crop.

With ADMIN. source (IACS), the data are compared at county level and for each crop.

With ADMIN. source (IACS), the data are compared at county level and for each crop.
Which were the main sources of errors?

Measurement errors.

Unit non-response.

Unit 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

Survey on spring sowing and permanent crops

Annual Survey on Crop and Animal Production

Survey on yields of subtropical fruits and olives 
Cereals for the production of grain (in %)

Not applicable.

0,52%

Not relevant.
Dried pulses and protein crops (in %)

Not applicable.

4,33%

Not relevant.
Root crops (in %)

Not applicable.

2,37%

Not relevant.
Oilseeds (in %)

Not applicable.

0,77%.

Not relevant.
Other industrial crops (included all industrial crops besides oilseeds)  (in %)

Not applicable.

4,69%

Not relevant.
Plants harvested green from arable land (in %)

Not applicable.

1,05%

Not relevant.
Total vegetables, melons and strawberries (in %)

Not applicable.

5,03%

Not relevant.
Cultivated mushrooms (in %)

Not applicable.

Not relevant.

Not relevant.
Total permanent crops (in %)

Not applicable.

Not relevant.

Not relevant.
Fruit trees (in %)

Not applicable.

1,56%

Not relevant.
Berries (in %)

Not applicable.

7,72%

Not relevant.
Nut trees (in %)

Not applicable.

2,53%

Not relevant.
Citrus fruit trees (in %)

Not applicable.

Not relevant.

5,64%
Vineyards (in %)

Not applicable.

1,70%

Not relevant.
Olive trees (in %)

Not applicable.

Not relevant.

1,84%.
Additional comments            




 
6.3. Non-sampling error

See hereunder.
6.3.1. Coverage error

Over-coverage - rate

0%;0,91%;0,80%


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

Survey on spring sowing and permanent crops

Annual Survey on Crop and Animal Production

Survey on yields of subtropical fruits and olives 
Error type Misclassification Over-coverage Over-coverage
Degree of bias caused by coverage errors None None None
What were the reasons for coverage errors?

Agricultural holding is no longer active, cessation of work, liquidation etc

Agricultural holding is no longer active, cessation of work, liquidation etc

 

 

Agricultural holding is no longer active, cessation of work, liquidation etc
Which actions were taken for reducing the error or to correct the statistics?

With aid of questions on questionnare  we also recorded the reasons for non-eligibility. This helps us for updating Statistical farm register (exclusion negligible farms from the frame).

With aid of questions on questionnare  we also recorded the reasons for non-eligibility. This helps us for updating Statistical farm register (exclusion negligible farms from the frame).
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

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

13
Preparatory (field) testing of the questionnaire? NO
Number of units participating in the tests? 
Explanatory notes/handbook for surveyors/respondents?  YES
On-line FAQ or Hot-line support for surveyors/respondents? YES
Were pre-filled questionnaires used? NO
Percentage of pre-filled questions out of total number of questions
Were some actions taken for reducing the measurement error or to correct the statistics? YES
If yes, describe the actions and their impact

Logic-numeric controls and process validation






 
6.3.3. Non response error

Unit non-response - rate


Item non-response - rate

Not computed.

There were no specific units discovered which had not responded to a particular item.


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

Survey on spring sowing and permanent crops

Annual Survey on Crop and Animal Production

Survey on yields of subtropical fruits and olives.
Unit level non-response rate (in %)

Not relevant.

7,58%

24,09%
Item level non-response rate (in %)              
               - Min% / item

There were no significant non-responses for any of the surveyed characteristic.

There were no significant non-responses for any of the surveyed characteristic.

There were no significant non-responses for any of the surveyed characteristic.
               - Max% / item

There were no significant non-responses for any of the surveyed characteristic.

There were no significant non-responses for any of the surveyed characteristic.

There were no significant non-responses for any of the surveyed characteristic.
               - Overall%
Was the non-response been treated ? YES YES YES
Which actions were taken to reduce the impact of non-response?
Which items had a high item-level non-response rate? 
Which methods were used for handling missing data?
(several answers allowed)		 Reminders
Legal actions
Imputations		 Reminders
Legal actions
Imputations
Weighting		 Reminders
Legal actions
Weighting
In case of imputation which was the basis? Imputation based on other sources Imputation based on similar units
In case of imputation, which was the imputation rate (%)?

5%

30%.
Estimated degree of bias caused by non-response? None
Which tools were used for correcting the data?

Application in SQL, SAS and etc.

Survey processor

Survey processor
Which organisation did the corrections?

CBS

CBS

CBS
Additional comments




 
6.3.4. Processing error

The main sources of processing errors were mistakes in  survey proccesor aplication made by the CBS experts. 

Data on the number of corrections were not collected during data processing.
6.3.4.1. Imputation - rate

30%
6.3.5. Model assumption error

Not applicable.
6.4. Seasonal adjustment

Not applicable.
6.5. Data revision - policy

Revision Policy of the Croatian Bureau of Statistics is based on the principles of the European Statistics Code of Practice. Revision policy of the Croatian Bureau of Statistics distinguishes three types of revisions: regular revisions, major revisions and unscheduled revisions. Unplanned revision of the crop statistics may be carried out. In any case it is necessary to clarify the reasons for a revision (mistake in data sources or calculations or due to the unexpected changes in the methodology or data sources).
6.6. Data revision - practice

Data revision is not planned so far.
6.6.1. Data revision - average size

Not applicable.


7. Timeliness and punctuality Top
7.1. Timeliness

Time lag - first result

1 month


Time lag - final result

11 months


  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?

6

1

4

6

1

2

2

2

Not relevant.

2

2

2

2

2

2
How many of them are forecasts (releases before the harvest)?

3

0

2

3

0

1

0

0

Not relevant.

0

0

0

0

0

0
When was the first  forecasting published for the crop year on which is reported? (day/month/year) 18/02/2022 31/01/2023 30/06/2022 30/06/2022 31/01/2023 31/01/2023 31/03/2023 31/03/2023 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) 16/05/2023 16/05/2023 16/05/2023 16/05/2023 16/05/2023 16/05/2022 16/05/2023 16/05/2023 16/05/2023 16/05/2023 16/05/2023 16/05/2023 16/05/2023 16/05/2023
Additional comments




 
7.2. Punctuality

0
7.2.1. Punctuality - delivery and publication

0


8. Coherence and comparability Top
8.1. Comparability - geographical

Data are comparable and harmonized among Member States anf therefore the geographically comparable.
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?

no other breaks in the time series since 2011

no other breaks in the time series since 2011

no other breaks in the time series since 2011

no other breaks in the time series since 2011
Which items were affected?
Year of break (number)
Impact on comparability Low Low Low Low
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?

The producers associations of sugar beet. 

 
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    

- 1,94% lower in IACS for reference year 2022
Dried pulses and protein crops    

not applicable
Root crops    

-12.2 % lower in IACS for reference year 2022

8889 hectares of sugar beet

The producers associations of sugar beet.
Oilseeds    

+1,47% bigger in IACS for reference year 2022

 

 
Other industrial crops (than oilseeds)    

+ 4,7% bigger in IACS for reference year 2022
Plants harvested green    

2,4 % bigger in IACS for the reference year 2022
Total vegetables, melons and strawberries    

-7,6% lower in IACS for reference year 2022
Vegetables and melons    

-7,3% lower in IACS for reference year 2019
Strawberries    

-14,3% lower in IACS for reference year 2020.
Cultivated mushrooms

Not applicable

  

not applicable
Total permanent crops

-3,3% lower in IFS2020

  

-13,6% lower in IACS for reference year 2022
Fruit trees

-2,1% lower in IFS 2020

Not applicable.

+2,0% bigger in IACS for reference year 2019
Berries

+12,9% bigger in IFS2020

  

-10,5% lower in IAcs for reference year 2019
Nut trees

-12,6% lower in IFS2020

  

+1,5% bigger in IACS for reference year 2019
Citrus fruit trees

+0,2% in IFS2020

Not applicable.

-10,6% lower in IACS for reference year 2019
Vineyards

+3,0% bigger in IFS2020

-13,4% lower in Vineyard survey

-5,0% lower in IACS for reference year 2019
Olive trees

+0,3% in IFS2020

Not applicable.

-7,1% lower in IACS for reference year 2019
If there were considerable differences, which factors explain them?

differences is because it is just part of population of private family farms

  





 
8.4. Coherence - sub annual and annual statistics

Procedures for checking the quality of administrative data depend on specific administrative source and statistics derived from it. Usually, agricultural surveys contain unique identifiers which enable linking of survey data with available administrative sources. Generally, quality of administrative data is being checked upon using analytical methods (scope, coding, double counting, consistency, year to year changes, etc.). CBS cooperates with owners of administrative data sources to define validation rules already within administrative source in order to raise quality of data. Results of quality analysis are communicated to the owner of the administrative data source for resolving the inconsistencies.
Concerning the other statistical domains the crop statistics is reconcilable with other statistical domains (economic, environment etc.).
8.5. Coherence - National Accounts

See under 8.4.
8.6. Coherence - internal

The same indicators are consistent among different surveys and they are regularly checked and compared.


9. Accessibility and clarity Top
9.1. Dissemination format - News release

Availability Links
YES

https://www.dzs.hr/app/kalendar/NavBySubjectEng.aspx






 
9.2. Dissemination format - Publications

  Availability Links
Publications Paper
Electronic

https://podaci.dzs.hr/media/t4jbehpz/stat-info-2023.pdf
Publications in English Paper
Electronic

https://podaci.dzs.hr/media/t4jbehpz/stat-info-2023.pdf






 
9.3. Dissemination format - online database

Data tables - consultations

not applicable


  Availability Links
On-line database accessible to users YES

https://www.dzs.hr/App/PXWeb/PXWebEng/Menu.aspx?px_language=en&px_type=PX&px_db=Agriculture%2c+Hunting%2c+Forestry+and+Fishing&rxid=04654694-2080-4769-8ff9-1140ef84ab42
Website National language
English

 

http://www.dzs.hr





 
9.4. Dissemination format - microdata access

Availability Links
YES

The conditions under which certain users can have access to microdata are regulated by The Ordinance on the conditions and manner of use of statistical data for scientific purposes.






 
9.5. Dissemination format - other

Not applicable.
9.6. Documentation on methodology

  Availability Links
Methodological report National language
English

https://www.dzs.hr/Eng/DBHomepages/Agriculture/Crop%20production/methodology.htm

 
Quality Report National language

https://www.dzs.hr/Hrv/international/Quality_Report/Quality_Report_Pages/Quality_Report_Bullet_3.htm
Metadata National language
English

http://www.dzs.hr
Additional comments

The data on quality are stored in database of quality information (DBQI), and the reports on quality are prepared on the bases of this information and will gradually be available on CBS Internet site.

Definitions and classifications are available.

  






 
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?
Burden reduction measures since the previous reference year  Less variables surveyed
Less respondents
More user-friendly questionnaires
Easier data transmission
Multiple use of the collected data
Less frequent surveys
If other, which?






 


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


12. Comment Top

The Croatian Bureau of Statistics (CBS) is responsible for collecting agriculture statistics in Croatia. Since 2005, Croatia has  been conducting regular sample surveys using interview methods. Legal units are surveyed separately by post, which are full scope surveys. The sample survey on areas sown, number of fruit trees and number of vines is carried out in June (as of 1st June), while the annual survey on crop and animal production is carried out in November (as of 1st November). The time table below is related to the crop calendar.

Croatian crop production statistics from 2022 comprises annual surveys, which include: 1. Survey on spring sowing and permanent crops PO-22 June 1 (only for business entities), including questions on expected yields of important early crops, conducted through CAWI and PDC.

2. Annual survey on crop and animal production PO-71 November 1 conducted through CATI, CAWI, PDC.

3. Survey on yields of subtropical fruits and olives PO-34 December 31, exclusively organized for the Mediterranean region of Croatia, conducted through CATI, CAWI, PDC.

 

All surveys are designed to provide results at the NUTS1 and NUTS2 levels. Since 2009, the Ministry of Agriculture has calculated supply balance sheets, and further work on the calculation of SBS is continued in cooperation with MA. In 2014, CBS introduced the CATI mode of data collection in some surveys crop statistics, and from 2016, they have been using CAPI, CAWI, PDC and CATI modes of data collection.


Related metadata Top


Annexes Top