Crop production (apro_cp)

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

Compiling agency: Central Statistical Office of Poland

Time Dimension: 2016-A0

Data Provider: PL1

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

Central Statistical Office of Poland

1.2. Contact organisation unit

Agricultural Division

1.5. Contact mail address

00-925 Warszawa, Al. Niepodległości 208, Poland


2. Statistical presentation Top
2.1. Data description
2.2. Classification system
2.3. Coverage - sector
2.4. Statistical concepts and definitions
2.5. Statistical unit
2.6. Statistical population
2.7. Reference area
2.8. Coverage - Time
2.9. Base period


3. Statistical processing Top
3.1. Source data

  Source 1 Source 2 Source 3 Source 4
Have new data sources been introduced since the previous Quality Report (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)?      
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 Expert estimate

Estimation of area

Final area under cultivation Survey
Administrative data
Expert estimate

FSS

GIJHARS (Agricultural and Food Quality Inspection - Main Inspectorate)

ARR (Agricultural Market Agency)

Estimation of area on individual species of crops

Production Expert estimate

Estimation of production

Yield Survey
Expert estimate

Surveys of yields

Estimation of yields

Non-existing and non-significant crops Expert estimate

Estimation of  NSC

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

Estimation of area

Final harvested area Administrative data
Expert estimate

GIJHARS

Estimation of area on individual species of crops

Production Expert estimate

Estimation of production

Non-existing and non-significant crops Expert estimate

Estimation of  NSC

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

Estimation of  NSC

Final production area Administrative data
Expert estimate

GiJHARS

Estimation of area on individual species of crops

Production Expert estimate

Estimation of production

Non-existing and non-significant crops Administrative data
Expert estimate

ARR

Estimation of  NSC

Table 4: Agricultural land use      
Main area Survey
Administrative data

FSS

GIJHARS

ARR

Non-existing and non-significant crops Administrative data
Expert estimate

Estimation of  NSC

Total number of different data sources

3

   
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     

 

Surveyed: whole sale purchasers report the humidity

 

Surveyed: whole sale purchasers convert the production/yield into standard humidity     

 

Surveyed by experts (e.g. test areas harvested and measured)

 

Estimated by experts

 

Other type

 

If other type, please explain

 

Additional information

 

   


Which method is used for calculating the yield for main arable crops? Production divided by sown 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

FSS

Survey of yields of  cereals and rape (R-r-oz)

Survey of yields of  cereals and rape (R-r-zb)

Survey of yields of some crops (R-r-pw)

Expert Estimation

Administrative Data:

GIJHARS (Agricultural and Food Quality Inspection - Main Inspectorate)

Administrative Data:

ARR (Agricultural Market Agency)

Planning (month-month/year)

11.2014-05.2015

deleted

03-05.2015

03-05.2015

03.2015-02.2016

not relevant

not relevant

Preparation (month-month/year)

05.2015-06.2016

deleted

02-08.2016

02-08.2016

02-05.2016

not relevant

not relevant

Data collection (month-month/year)

06-07.2016

deleted

08.2016

10.2016

06-11.2016

not relevant

not relevant

Quality control (month-month/year)

06.2015

-05.2016

 

deleted

08.2016

10.2016

06-11.2016

04-06.2017

not relevant

Data analysis (month-month/year)

09.2016-06.2017

deleted

08-09.2016

10-11.2016

07-12.2016

04-06.2017

04.2017

Dissemination (month-month/year)

07-09.2017

deleted

not relevant (auxiliary data)

not relevant (auxiliary data)

07.2017

07-09.2017

07.2017

If there were delays, what were the reasons?

data on organic farming from administrative source were delayed

deleted

not relevant

not relevant

not relevant

data on organic farming were delayed

not relevant

3.3. Data collection

Definitions

  Question In case yes, how do they differ?
Do national definitions differ from the definitions in Article 2 of Regulation (EC) No 543/2009? YES

Poland uses the concept “sown area” (and only in case of huge damages or disasters it could be recalculated). Usually Poland doesn't survey the area after the harvest and the yields are calculated on the basis of the sown area and not the harvested area.

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).

Stratification and sample allocation in such way that the requirement stipulated in Art. 3.2 were met.

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?

Mostly on parishes (i.e. the smallest administrative units)

When was last update of the holding register? (month/year)

03-08.2016

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

FSS

Survey of yields of winter cereals (R-r-oz)

Survey of yields of  cereals and rape (R-r-zb)

Survey of yields of some crops (R-r-pw)

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

http://form.stat.gov.pl/formularze/2013/passive/R-SGR.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 organic farms

Register of farms cultivating hops (area and production)

Register of farms cultivating sugar beet (area and production)

Description

individual data

aggregated data

aggregated data

Data owner (organisation)

GIJHARS (Agricultural and Food Quality Inspection - Main Inspectorate)

GIJHARS (Agricultural and Food Quality Inspection - Main Inspectorate)

ARR (Agricultural Market Agency)

Update frequency Once per year or more often Once per year or more often Once per year or more often
Reference date (month/year)

31.12.2016

31.12.2016

31.12.2016

Legal basis
Reporting unit

Farm

NUTS2

NUTS2

Identification variable (e.g. address, unique code, etc.)
Percentage of mismatches (%)

0%

0%

0%

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

100%

100%

100%

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

100%

100%

100%

If not complete, which other sources were used ?
How were the data used?
Sample frame
Validation
Directly for estimates
Directly for estimates Directly for estimates
Data used for other purposes, which?
Which variables were taken from administrative sources?

The area of land use and individual crops

The area and production of hops

The area and production of sugar beet

Were there any differences in the definition of the variables between the administrative source and those described in the Regulation? NO NO NO
Please describe the differences
What measures were taken to eliminate the differences?
How was the reliability, accuracy and coherence (comparison to other available data) of the data originated from administrative data source (ante- and/or ex-post) checked?

other data were not available

data were similar

data were similar

What were the possible limitations, drawbacks of using the data from administrative source(s)?

data from administrative source were delayed in comparison to the deadline

not relevant

not relevant


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

Autumn assessment on crops condition

Spring assessment on crops condition

First expert estimation - provisional

Second expert estimation - provisional

Third expert estimation - final

Estimation of NSC

Data owner (organisation)

CSO

CSO

CSO

CSO

CSO

CSO

Update frequency (e.g. 1 year or 6 months) Yearly Yearly Yearly Yearly Yearly Yearly
Reference date (Month/Year  e.g. 1/16 - 8/16)

11/2015

05/2016

06/2016-07/2016

08/2016-09/2016

10/2016-11/2016

 11/2015 - 12/2015

Legal basis

Law issued on 29 June 1995 
on official statistics

and

Regulation of the Council of Ministers of 21 July 2015. on the program of official statistics for 2016

Law issued on 29 June 1995 
on official statistics

and

Regulation of the Council of Ministers of 21 July 2015. on the program of official statistics for 2016

Law issued on 29 June 1995 
on official statistics

Regulation of the Council of Ministers of 21 July 2015. on the program of official statistics for 2016

Law issued on 29 June 1995 
on official statistics

and

Regulation of the Council of Ministers of 21 July 2015. on the program of official statistics for 2016

Law issued on 29 June 1995 
on official statistics

and

Regulation of the Council of Ministers of 21 July 2015. on the program of official statistics for 2016

Law issued on 29 June 1995 
on official statistics

and

Regulation of the Council of Ministers of 21 July 2015. on the program of official statistics for 2016

Use purpose of the estimates?

for CSO and users needs 

for CSO and users needs 

for CSO and users needs 

for CSO and users needs 

for CSO and users needs 

for Eurostat needs

What kind of expertise the experts have?

scientific and practical expertise in the field

scientific and practical expertise in the field

practical expertise in the field

practical expertise in the field

practical expertise in the field

scientific and practical expertise in the field

What kind of estimation methods were used?

NUTS2 experts' estimates

NUTS5 experts' estimates

(ca. 2,5 thousand experts in the field)

NUTS5 experts' estimates (ca. 2,5 thousand experts in the field)

NUTS5 experts' estimates (ca. 2,5 thousand experts in the field)

NUTS5 experts' estimates (ca. 2,5 thousand experts in the field)

using of many different sources of information (for NUTS0)

Were there any differences in the definition of the variables between the experts' estimates and those described in the Regulation? NO NO YES YES YES
If yes, please describe the differences

e.g. rape and turnip rape together

e.g. rape and turnip rape together

e.g. rape and turnip rape together

What measures were taken to eliminate the differences?

the differences were not significant and they were described in the handbook (in chapter "Country notes")

the differences were not significant and they were described in the handbook (in chapter "Country notes")

the differences were not significant and they were described in the handbook (in chapter "Country notes")

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?

other data were not available

other data were not available

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

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

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

What were the possible limitations, drawbacks of using the data from expert estimate(s)?

Such kind of estimates is appropriate.

Such kind of estimates is appropriate.

The data on area were not accepted from expert estimates because experts had no possibility to assess precisely such kind of data.

The data on area were not accepted from expert estimates because experts had no possibility to assess precisely such kind of data.

The data on area were not accepted from expert estimates because experts had no possibility to assess precisely such kind of data.

Additional comments
3.4. Data validation

Which kind of data validation measures are in place? Automatic
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
If other, please describe
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?
Is there a quality management process in place for crop statistics? NO        
If, yes, what are the components?        
Is there a Quality Report available? NO        
If yes, please provide a link(s)        
To which data source(s) is it linked?
       
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
       
If, other, please specify        
Additional comments        
4.2. Quality management - assessment


5. Relevance Top
5.1. Relevance - User Needs

Are there known unmet user needs? NO
Describe the unmet needs
Does the Regulation 543/2009 meet the national data needs? YES
Does the ESS agreement meet the national needs?
If not, which additional data are collected?

Assessments on crops condition (autumn and spring)

Additional comments
5.2. Relevance - User Satisfaction

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

We try to satisfy users’ needs as they come

5.3. Completeness
5.3.1. Data completeness - rate


6. Accuracy and reliability Top
6.1. Accuracy - overall
6.2. Sampling error

Sampling method and sampling error

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

FSS

Survey of yields of winter cereals (R-r-oz)

Survey of yields of  cereals and rape (R-r-zb)

Survey of yields of some crops (R-r-pw)

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

24

40

44

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

1411 thousand

1411 thousand

1411 thousand

Size of sample

about 180 thousand

about 18 thousand

about 18 thousand

Which methods were used to assess the sampling 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
Wrong classification
Basic weight
Non-response
Wrong classification
Basic weight
Non-response
Wrong classification
If other, which?
If CV (co-efficient of variation) was calculated, please describe the calculation methods and formulas

see attached file

see attached file

see attached file

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

results were not compared

compared with experts' assessments (there were noticed some differences as a result of methodological aberrations)

compared with experts' assessments (there were noticed some differences as a result of methodological aberrations)

Which were the main sources of errors?

too small sample size for rarely occurring variables

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

FSS

Survey of yields of winter cereals (R-r-oz)

Survey of yields of  cereals and rape (R-r-zb)

Survey of yields of some crops (R-r-pw)

Cereals for the production of grain (in %)

0.20

No relevant

0.6

Grain maize: 0,8

Dried pulses and protein crops (in %)

1.47

No relevant

No relevant

1,3 (for edible only)

Root crops (in %)

0.64

No relevant

No relevant

Potatoes 1,2

Sugar beet 0,3

Oilseeds (in %)

0.58

No relevant

Rape and turnip rape 0,7

No relevant

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

5.50

No relevant

No relevant

Plants harvested green from arable land (in %)

0.52

No relevant

Green maize: 1,1

Total vegetables, melons and strawberries (in %)

1.64

No relevant

No relevant

Cultivated mushrooms (in %)

2.03

No relevant

No relevant

No relevant

Total permanent crops (in %)

1.20

No relevant

No relevant

No relevant

Fruit trees (in %)

1.54

No relevant

No relevant

No relevant

Berries (in %)

2.5

No relevant

No relevant

No relevant

Nut trees (in %)

6.22

No relevant

No relevant

Citrus fruit trees (in %)

No relevant

No relevant

No relevant

No relevant

Vineyards (in %)

No relevant

No relevant

No relevant

No relevant

Olive trees (in %)

No relevant

No relevant

No relevant

No relevant

Additional comments            
6.3. Non-sampling error
6.3.1. Coverage error

Over-coverage - rate

6,2%


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

FSS

Survey of yields of winter cereals (R-r-oz)

Survey of yields of  cereals and rape (R-r-zb)

Survey of yields of some crops (R-r-pw)

Error type Under-coverage
Over-coverage
Misclassification
Contact errors
Multiple listing errors
Over-coverage Over-coverage
Degree of bias caused by coverage errors Low Unknown Low Low
What were the reasons for coverage errors?

Some part of frame was out-of-date

Some holdings were eliminated and some holdings changed kind of production

Some holdings were eliminated and some holdings changed kind of production

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

Over-coverage units were eliminated and weights were corrected.

Misclassification errors were captured after the survey and were classified as outliers.

Multiple listings were eliminated during the data collection.

Over-coverage units were eliminated and weights were corrected.

Over-coverage units were eliminated and weights were corrected.

Additional comments

Main reasons for coverage errors and actions which were taken for reducing them, were described in detail in FSS QR.

Main reasons for coverage errors and actions which were taken for reducing them, were described in detail in FSS QR.

Main reasons for coverage errors and actions which were taken for reducing them, were described in detail in FSS QR.

Main reasons for coverage errors and actions which were taken for reducing them, were described in detail in FSS QR.

Main reasons for coverage errors and actions which were taken for reducing them, were described in detail in FSS QR.

Main reasons for coverage errors and actions which were taken for reducing them, were described in detail in FSS QR.

Main reasons for coverage errors and actions which were taken for reducing them, were described in detail in FSS QR.

Main reasons for coverage errors and actions which were taken for reducing them, were described in detail in FSS QR.




 

6.3.1.1. Over-coverage - rate

6,2%

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

FSS

Survey of yields of winter cereals (R-r-oz)

Survey of yields of  cereals and rape (R-r-zb)

Survey of yields of some crops (R-r-pw)

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

4

more than 20

more than 20

Preparatory (field) testing of the questionnaire? NO NO NO
Number of units participating in the tests? 
Explanatory notes/handbook for surveyors/respondents?  YES YES YES
On-line FAQ or Hot-line support for surveyors/respondents? YES NO NO
Were pre-filled questionnaires used? 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 NO NO
If yes, describe the actions and their impact

Usage of administrative register of organic farms improved survey quality  

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

FSS

Survey of yields of winter cereals (R-r-oz)

Survey of yields of  cereals and rape (R-r-zb)

Survey of yields of some crops (R-r-pw)

Unit level non-response rate (in %)

14%

3.99%

3,31%

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

               - Overall%

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

Was the non-response been treated ? YES
Which actions were taken to reduce the impact of non-response?

Incomplete questionnaires were forwarded for completion

Which items had a high item-level non-response rate? 

None

Which methods were used for handling missing data?
(several answers allowed)
Follow-up interviews
Weighting
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? Insignificant None None
Which tools were used for correcting the data?

Weights were corrected

Which organisation did the corrections?

Central Statistical Office (experts and IT specialists)

Additional comments
6.3.3.1. Unit non-response - rate

Not applicable

6.3.3.2. Item non-response - rate

Not applicable

6.3.4. Processing error
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?
How many of them are forecasts (releases before the harvest)?
When was  the first  forecasting published for crop year 2016? (day/month/year)
When were the final results published for crop year 2016? (day/month/year)
Additional comments

No

No

No

No

No

No

No

No

No

No

No

No

No

No

7.1.1. Time lag - first result
7.1.2. Time lag - final result
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
8.1.1. Asymmetry for mirror flow statistics - coefficient
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?
Which items were affected?
Year of break (number)
Impact on comparability
Additional comments
8.2.1. Length of comparable time series

Not applicable

8.3. Coherence - cross domain

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

Orchards survey

If no comparisons have been made, why not?

Some data and data sources are not comparable or not available  


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

0%

 

7400258

Annual crop statistics (ha)

Dried pulses and protein crops

1.3%

 

260418

Annual crop statistics (ha)

Root crops

0%

 

511334

Annual crop statistics (ha)

Oilseeds

0%

 

862198

Annual crop statistics (ha)

Other industrial crops (than oilseeds)

0%

 

118908

Annual crop statistics (ha)

Plants harvested green

0%

 

1086995

Annual crop statistics (ha)

Total vegetables, melons and strawberries

8.5%

 

244923

Annual crop statistics (ha)

Vegetables and melons

10.0%

 

190410

Annual crop statistics (ha)

Strawberries

5.2%

 

54513

Annual crop statistics (ha)

Cultivated mushrooms

0%

 

240

Annual crop statistics (ha)

Total permanent crops

0%

 

393457

Annual crop statistics (ha)

Fruit trees  

251701

Annual crop statistics (ha)

Berries  

111939

Annual crop statistics (ha)

Nut trees  

4033

Annual crop statistics (ha)

Citrus fruit trees  
Vineyards
Olive trees  
If there were considerable differences, which factors explain them?

In FSS fruit trees and bushes from plantations were surveyed separately

 

Between orchard survey and ACS there were some differences in methodology and terms, in orchard survey the population was dedicated to holdings with special fruit production (e.g. apple, peach and apricot production in orchard)  

 
8.4. Coherence - sub annual and annual statistics

Not applicable.

8.5. Coherence - National Accounts

Not applicable.

8.6. Coherence - internal

Not applicable.


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

Availability Links
NO
9.2. Dissemination format - Publications
9.3. Dissemination format - online database

Data tables - consultations

Not applicable


  Availability Links
On-line database accessible to users YES

https://bdl.stat.gov.pl/BDL/dane/podgrup/temat

Website None
National language

www.stat.gov.pl

9.3.1. Data tables - consultations
9.4. Dissemination format - microdata access

Availability Links
9.5. Dissemination format - other
9.6. Documentation on methodology

  Availability Links
Methodological report None
National language

http://stat.gov.pl/obszary-tematyczne/rolnictwo-lesnictwo/uprawy-rolne-i-ogrodnicze/wyniki-produkcji-roslinnej-w-2016-roku,6,13.html

Quality Report
Metadata None
National language

http://stat.gov.pl/en/metainformations/glossary/

 

Additional comments  
9.7. Quality management - documentation
9.7.1. Metadata completeness - rate

Not applicable.

9.7.2. Metadata - consultations

Not applicable.


10. Cost and Burden Top


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


12. Comment Top

GIJHARS and ARR are maintained by ministry.

The Polish IACS is the register conducted by ARiMR (The Agency for Restructuring and Modernisation of Agriculture - ARMA), which is also maintained by ministry.

However the scope of information from the Polish IACS is not suitable for our statistical needs and requirements.


Related metadata Top


Annexes Top