Crop production (apro_cp)

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

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

Time Dimension: 2016-A0

Data Provider: HR1

Data Flow: CROPROD_ESQRSCP_A


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



For any question on data and metadata, please contact: EUROPEAN STATISTICAL DATA SUPPORT

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1. Contact Top
1.1. Contact organisation

REPUBLIC OF CROATIA - CROATIAN BUREAU OF STATISTICS

Address:

Ilica 3
10000 Zagreb
Republic of Croatia

1.2. Contact organisation unit

AGRICULTURE, FORESTRY, FISHERIES AND ENVIRONMENT DIRECTORATE/Agriculture, Forestry and Fisheries Production Statistics Department/Crop Production 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

See points 3.1, 3.2, 3.3 and 3.4

3.1. Source data

  Source 1 Source 2 Source 3 Source 4
Have new data sources been introduced since the previous Quality Report (2014)?  NO      
If yes, which new data sources have been introduced since the previous quality report (2014)?
Type of source?
To which Table (Reg 543/2009) do they contribute?
Have some data sources been dropped since the previous Quality Report (2014)? NO      
Which data sources have been dropped since the previous quality report (2014)?
Type of source?
Why have they been dropped?
Additional comments


Data sources: Please indicate the data sources which were used for the reference year 2016

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

Survey on autumn sowing, 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

Survey on actual yields of early crops and fruits, Survey on actual yields of late crops, fruits and grapes

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

Survey on actual yields of early crops and fruits, Survey on actual yields of late crops, fruits and grapes

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

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)
 
Table 2: Vegetables, melons and strawberries       
Early estimates for harvested areas Survey
Administrative data

Survey on spring sowing and permanent crops

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

Survey on actual yields of early crops and fruits, Survey on actual yields of late crops, fruits and grapes

Production Survey

Survey on actual yields of early crops and fruits, Survey on actual yields of late crops, fruits and grapes

Non-existing and non-significant crops Survey
Administrative data

Survey on spring sowing and permanent crops

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

Survey on actual yields of early crops and fruits, Survey on actual yields of late crops, fruits and grapes, Survey on yields of subtropical fruits and olives 

Production Survey

Survey on actual yields of early crops and fruits, Survey on actual yields of late crops, fruits and grapes, Survey on yields of subtropical fruits and olives

Non-existing and non-significant crops Survey
Administrative data

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

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
Non-existing and non-significant crops Survey
Administrative data

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)
 
Total number of different data sources

12

   
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 autumn sowing

Survey on spring sowing and permanent crops

Survey on actual yields of early crops and fruits

Survey on actual yields of late crops, fruits and grapes

Survey on yields of subtropical fruits and olives

Planning (month-month/year)

9-10/2015

3-5/2016

6-7/2016

7-8/2016

10-11/2016

Preparation (month-month/year)

9-10/year 2015

4-5/2016

7-8/2016

8-10/2016

10-11/2016

Data collection (month-month/year)

11-12/2015

6-7/2016

9/2016

11-12/2016

1/2017

Quality control (month-month/year)

12/2015-1/2016

7-12/2016

9-10/2016

11-12/2016

1-2/2017

Data analysis (month-month/year)

1-2/2016

1-5/2017

9-10/2016

1-4/2017

1-3/2017

Dissemination (month-month/year)

2/2016

5/2017

10/2016

1/2017

3/2017

If there were delays, what were the reasons?

not were delays

not were delays

not were delays

not were delays

not were 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, red cabbage and brussels sprouts.

V2300 Lettuce include endives and corn salad.

V2900 Other leafy or stalked vegetables include spinach, celery, chicory, articokes, asparagus, mangold, rucola and rhubarb.

V3200 Cucumbers includes gherkins.

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

V4210 Onions inludes shallots and chive.

V4900 Other root, tuber and bulb vegetables n.e.c. include radishes, celeriac, 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.
V1900 - Other brassicas 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.

C1900 include buckwheat, millet and sorghum.

P9000 include broad beans, lentils and chickpeas.

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

G2900 include clovers and mixtures, fodder peas, mixture of leguminous plants and cereals, vetches, sainfoin and lupins.

G9900 include annual 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, red cabbage and brussels sprouts. 

V2900 Other leafy or stalked vegetables include spinach, celery, chicory, articokes, asparagus, mangold, rucola and rhubarb.

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

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

F1190 Other pome fruits n.e.c. include quinces and common medlar.

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/2016

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 %)

0.01%

Dried pulses and protein crops (in %)

0,09%

Root crops (in %)

0.08%

Oilseeds (in %)

0%

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

0%

Plants harvested green from arable land (in %)

0.06%

Total vegetables, melons and strawberries (in %)

0%

Cultivated mushrooms (in %)

Not aplicable.

Total permanent crops (in %)

0.1%

Fruit trees (in %)

0,1%

Berries (in %)

0.1%

Nut trees (in %)

0%

Citrus fruit trees (in %)

0%

Vineyards (in %)

0.1%

Olive trees (in %)

0.06%


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 autumn sowing

Survey on spring sowing and permanent crops

Survey on actual yields of early crops and fruits

Survey on actual yields of late crops, fruits and grapes

Survey on yields of subtropical fruits and olives

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

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

 

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

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

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

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

Data entry method, if paper questionnaires? 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

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)

6/2016

12/2016

12/2016

6/2016

Legal basis

OG no:76/11

OJ of the Republic of Croatia No.149/11

Commission Regulation (EC) No 436/2009.

OG no:140/05 amended by 35/08,25/09,124/10,55/11 and 14/14.

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 ?
How were the data used?
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. 1/16 - 8/16)

1/16-9/16

6/16-9/16

10/16-12/16

Legal basis

National Statistics Act (OJ HR No. 103/03, 75/09. and 59/12.)

National Statistics Act (OJ HR No. 103/03, 75/09. and 59/12.)

National Statistics Act (OJ HR No. 103/03, 75/09. and 59/12.)

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
Farm Structure Survey
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 2014? Stable Stable Stable Stable Stable
Is there a quality management process in place for crop statistics? YES        
If, yes, what are the components?

The 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://www.dzs.hr/Eng/international/Quality_Report/Quality_Report_Pages/Quality_Report_Bullet_3.htm

       
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

See points 5.1 and 5.2

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


Annexes:
User satisfaction survey 2015
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

The results of sample surveys are extrapolated with one (sometimes two) factor for a wide range of characteristics.Since post-stratification is not done for surveys in crop statistics,misclassification was not assessed. Statistics corrects possible errors of measurement using the logic-numeric control. We are trying to avoid the measurement error by training of interviewers and supervisor, control data and process validation. After data entry, extreme values of variables are checked and corrected if necessary. The probability of undercoverage in the crop statistics very low since there are not many new agricultural holdings. The unit non-response was 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

Survey on actual yields of early crops and fruits

Survey on actual yields of late crops, fruits and grapes

Survey on autumn sowing

Survey on yields of subtropical fruits and olives 

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

126

15

15

13

8

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

157.684

157.684

157.684

78.261

24.706

Size of sample

25.100 (including buisness entities).

8500  (including buisness entities).

10550  (including buisness entities).

9000  (including buisness entities).

5500  (including buisness entities).

Which methods were used to assess the sampling 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
Non-response
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 series linearization

Taylor series linearization

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.

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?

too small sample size

too small sample size

too small sample size

too small sample size

too small sample size


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

Survey on actual yields of early crops and fruits

Survey on actual yields of late crops, fruits and grapes

Survey on autumn sowing

Survey on yields of subtropical fruits and olives 

Cereals for the production of grain (in %)

0,91%

Soft winter wheat 1,9%.

Maize for dry grain 1.57%

Soft winter wheat 2%.

Not relevant.

Dried pulses and protein crops (in %)

5,72%

Not relevant.

Not relevant.

Not relevant.

Not relevant.

Root crops (in %)

3,64 %

Not relevant.

Administrative source for sugar beet.

Not relevant.

Not relevant.

Oilseeds (in %)

1,1%

Rapeseed 2,9%.

Soya beans 2,28%.

Not relevant.

Not relevant.

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

4,6%

Not relevant.

Not relevant.

Not relevant.

Not relevant.

Plants harvested green from arable land (in %)

1,5 %

Not relevant.

Not relevant.

Not relevant.

Not relevant.

Total vegetables, melons and strawberries (in %)

5,39 %

Not relevant.

Not relevant.

Not relevant.

Not relevant.

Cultivated mushrooms (in %)

not collected

Not relevant.

Not relevant.

Not relevant.

Not relevant.

Total permanent crops (in %)

1,28 %

Not relevant.

Not relevant.

Not relevant.

Not relevant.

Fruit trees (in %)

3,48 %

Not relevant.

Not relevant.

Not relevant.

Not relevant.

Berries (in %)

9,02 %

Not relevant.

Not relevant.

Not relevant.

Not relevant.

Nut trees (in %)

4,39%

Not relevant.

Not relevant.

Not relevant.

Not relevant.

Citrus fruit trees (in %)

administrative data

Not relevant.

Not relevant.

Not relevant.

Not relevant.

Vineyards (in %)

1,96 %

Not relevant.

Not relevant.

Not relevant.

Not relevant.

Olive trees (in %)

3,56 %

Not relevant.

Not relevant.

Not relevant.

OLives for oil 1,34%.

Additional comments

We counted Coefficient of variation (CV) only for Survey on spring sowing and permanent crops, but sample for other survey is the same and CV can be applied to all survey.

           
6.3. Non-sampling error

See hereunder.

6.3.1. Coverage error

Over-coverage - rate

1,76%


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

Error type Over-coverage
Degree of bias caused by coverage errors None
What were the reasons for coverage errors?

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).

Additional comments

Error type - and multiple listing




 

6.3.1.1. Over-coverage - rate

1,76%

6.3.1.2. Common units - proportion

not applicable

6.3.2. Measurement error

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

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)?

10

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

Training of interviewers, Logic-numeric controls and process validation

6.3.3. Non response error

Unit non-response - rate

4,9%


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

Survey on actual yields of early crops and fruits

Survey on actual yields of late crops, fruits and grapes

Survey on autumn sowing

Survey on yields of subtropical fruits and olives.

Unit level non-response rate (in %)

4,9%

10,66%

3,74%

8,34%

15,77%

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.

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.

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 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)
Follow-up interviews
Reminders
Imputations
Weighting
Legal actions
Reminders
Legal actions
Imputations
Weighting
Follow-up interviews
Legal actions
Imputations
Weighting
Reminders
Reminders
Legal actions
Imputations
Weighting
Reminders
Legal actions
Imputations
Weighting
In case of imputation which was the basis? Imputation based on the same unit in previous data Imputation based on similar units Imputation based on similar units Imputation based on similar units Imputation based on similar units
In case of imputation, which was the imputation rate (%)?

10%

2%.

2%

2%

2%

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

Survey processor

Survey processor

Which organisation did the corrections?

CBS

CBS

CBS

CBS

CBS

Additional comments
6.3.3.1. Unit non-response - rate

4,9%

6.3.3.2. Item non-response - rate

Not computed.

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

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

3,5%.

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?

7

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

0

0

0

Not relevant.

0

0

0

0

0

0

When was  the first  forecasting published for crop year 2016? (day/month/year) 25/02/2016 30/01/2017 01/07/2016 25/02/2016 30/01/2017 30/01/2017 30/03/2017 30/03/2017 30/03/2017 30/03/2017 30/03/2017 30/03/2017 30/03/2017 30/03/2017
When were the final results published for crop year 2016? (day/month/year) 12/05/2017 12/05/2017 12/05/2017 12/05/2017 12/05/2017 12/05/2017 12/05/2017 12/05/2017 12/05/2017 12/05/2017 12/05/2017 12/05/2017 12/05/2017 12/05/2017
Additional comments
7.1.1. Time lag - first result

1 month

7.1.2. Time lag - final result

11 months

7.2. Punctuality

0

7.2.1. Punctuality - delivery and publication

0


8. Coherence and comparability Top

See points 8.2 and 8.3

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 since 2013? 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

  Data source
With which other data sources the crop statistics data have been compared?  Farm structure survey 2016
IACS
Other
Vineyard survey 2015
If others, which?

Orchard survey

 

If no comparisons have been made, why not?


Results of comparisons FSS 2016 (if available) Vineyard survey 2015 IACS Other source(s)  In case of other sources, which?
Cereals

The final data on FSS 2016 are not available.

 

-6,1%/reference year 2016

Dried pulses and protein crops

The final data on FSS 2016 are not available.

 

not applicable

Root crops

The final data on FSS 2016 are not available.

 

-16,7%/reference year 2016

15493 hectares of sugar beet

The producers associations of sugar beet.

Oilseeds

The final data on FSS 2016 are not available.

 

-5,4%/reference year 2016

 

 

The producers associations of oilseeds.

Other industrial crops (than oilseeds)

The final data on FSS 2016 are not available.

 

0,0%/reference year 2016

Plants harvested green

The final data on FSS 2016 are not available.

 

-0,6%/reference year 2016

Total vegetables, melons and strawberries

The final data on FSS 2016 are not available.

 

not applicable

Vegetables and melons

The final data on FSS 2016 are not available.

 

not applicable

Strawberries

The final data on FSS 2016 are not available.

 

not applicable

Cultivated mushrooms

not applicable (NS crop).

 

not applicable

Total permanent crops

not applicable

 

-10,9%/reference year 2016

Fruit trees

The final data on FSS 2016 are not available.

 

-6,3%/reference year 2016

Berries

The final data on FSS 2016 are not available.

 

not applicable

Nut trees

The final data on FSS 2016 are not available.

 

-13,9%/reference year 2016

Citrus fruit trees

The final data on FSS 2016 are not available.

 

-20,2%/reference year 2016

Vineyards

The final data on FSS 2016 are not available.

Not comparable

-10,9%/reference year 2016

Olive trees

The final data on FSS 2016 are not available.

 

-6,00%/reference year 2016

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

See points 9.1, 9.2, 9.3, 9.5 and 9.6

9.1. Dissemination format - News release

9.2. Dissemination format - Publications

  Availability Links
Publications Electronic

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

Publications in English Electronic

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

9.3. Dissemination format - online database

Data tables - consultations

not applicable


9.3.1. Data tables - consultations

not applicable

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 reference year (2013) 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
If other, which?


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


12. Comment Top

In Croatia, CBS (Croatian Bureau of Statistics) is responsible for agriculture statistics. Since 2005 Croatia carries out regularly sample surveys using the interview methods. The legal units are surveyed separately by post (full scope surveys). In June the sample survey on areas sown, number of fruit trees and number of vines is carried out (as of 1st June).  In December the survey on autumn sowing is carried out (as of 15th December). Between June and January there is a series of surveys related to expected yield and actual area harvested, yield and production. The data on expected yields are collected on selected sample for private family farms and on fully scope for business entities. The data on actual yield and area harvested are collected by post from legal entities and by interview from a sample of agricultural holdings. The time table below is related to crop calendar.
Croatian crop production statistics comprises annual surveys, as follows:
1. Survey on autumn sowing PO-21 November 10 , the sample covers units in Continental part of the Croatia
2. Survey on spring sowing and permanent crops PO-22 June 1 (including questions on expected yields of important early crops)
3. Survey on actual yields of early crops and fruits PO-32, August 15 (including questions on expected yields of important late crops))
4. Survey on actual yields of late crops, fruits and grapes PO-33a, November 10
5. Survey on yields of subtropical fruits and olives PO-34 December 31, exclusively organized for Mediterranean region of Croatia

All surveys are designed in such a way that the results are available at the NUTS1 and NUTS2.
From 2009 supply balance sheet are calculating by Ministry og Agriculture. Further work on calculation of SBS is continued in cooperation with MA. Also, for the first  time in 2014, we introduced CATI mode of collecting data in some surveys crop statistics and from 2016 CAPI and CATI modes of data collection.


Related metadata Top


Annexes Top
User satisfaction survey 2015