Crop production (apro_cp)

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

Compiling agency: Service d'Economie Rurale


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

Service d'Economie Rurale
1.2. Contact organisation unit

Division des statistiques agricoles
1.5. Contact mail address

Service d'Economie Rurale

Division des statistiques agricoles

B.P. 2102

L-1021 Luxembourg


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. All data are collected at national level.
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.

Crop production statistics are aligned to IFS, so all the areas managed by luxembourgish holdings are covered. Thus some areas outside the territory of Luxembourg are also covered by the production statistics.
2.8. Coverage - Time

Crop year.
2.9. Base period

Not applicable.


3. Statistical processing Top
3.1. Source data

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


Data sources: Please indicate the data sources which were used for the reference year on which is reported

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

IACS

  
Final area under cultivation Administrative data

IACS
Production Other

Calculation
Yield Survey
Expert estimate
Other

Harvest Inventory combined with expert estimation

Test fields

Potato producer organisation
Non-existing and non-significant crops Administrative data

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

Fruit and vegetable expert
Production Expert estimate

Fruit and vegetable expert
Non-existing and non-significant crops Expert estimate

Fruit and vegetable expert
Table 3: Permanent crops      
Early estimates for production area
Final production area Administrative data
Expert estimate

Vineyard register

Fruit and vegetable expert
Production Administrative data
Expert estimate

Vineyard register

Fruit and vegetable expert
Non-existing and non-significant crops Expert estimate

Fruit and vegetable expert
Table 4: Agricultural land use      
Main area Administrative data

IACS
Non-existing and non-significant crops Administrative data

IACS
Total number of different data sources

8

  
Additional comments    


Which method is used for calculating the yield for main arable crops? yield is surveyed in the field and production volume is assessed on the basis of the yield
If another method, describe it.




 
3.2. Frequency of data collection

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

IACS

Harvest inventory

Test fields

Potato producer organisation

Fruit and vegetable expert

Vineyard register

Production estimates

Humidity
Planning (month-month/year)

10/N-1

07/N

07/N

07/N

07/N

09/N

08/N

11/N
Preparation (month-month/year)

03/N

07-08/N

07/N

07/N

07/N

09-10/N

09/N

11/N
Data collection (month-month/year)

02/N

07-11/N

08-11/N

08-11/N

08-12/N

11-12/N

09/N

11-12/N
Quality control (month-month/year)

07/N

01/N+1

01/N+1

01/N+1

01/N+1

12/N

09/N

 

12/N
Data analysis (month-month/year)

08/N

01/N+1

01/N+1

01/N+1

01/N+1

12/N

09/N

12/N
Dissemination (month-month/year)


09/N

02/N+1

01/N+1

02/N+1

03/N+1

12/N

09/N

01/N+1
If there were delays, what were the reasons?

expert not available






 
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) P9000 - Other dry pulses and protein crops n.e.c.
I1190 - Other oilseed crops n.e.c.
I9000 - Other industrial crops n.e.c.

P1300 Sweet lupins is included in P9000 Other dry pulses and protein crops n.e.c.
I1140 Lineseed is included in I1190 Other oilseed crops n.e.c.
I2900 other textile plants is included under I9000
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
I9000 - Other industrial crops n.e.c.
G9100- Other cereals harvested green (excluding green maize)
ARA99 - Other arable land crops n.e.c.

C1900: mix of cereals and leguminous plants wih < 60% leguminous plants + any other cereals not included elsewhere

P9000: Other dry pulses and protein crops n.e.c. is including sweet lupins and any other dry pulses and protein crops n.e.c.

R9000: fodder beets

I1190: including lineseed and hemp for oil production

I9000: turf + any other industrial plants not included elsewhere

G9100: Whole crop silage (for fodder and energy)

ARA99: arable land without cultures (wild fields)

 

 


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).

area is based on IACS (100% coverage)
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)

04/2022
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 %)

<1%
Dried pulses and protein crops (in %)

<1%
Root crops (in %)

<1%
Oilseeds (in %)

<1%
Other industrial crops (included all industrial crops besides oilseeds)  (in %)

<1%
Plants harvested green from arable land (in %)

<1%
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 %)

<1%
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

harvest inventory

Humidity
Which survey method was used? Postal questionnaire filled in by respondent Other
If 'other', please specify

E-Mail list sent by respondent
Please provide a link to the questionnaire

see file attached

no link available
Data entry method, if paper questionnaires? 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

IACS

Vineyard register
Description

agricultural area (crops + wine)

data on production of wine
Data owner (organisation)

Ministry of agriculture

Institut Viti-Vinicole (Ministry of agriculture)
Update frequency Once per year or more often Once per year or more often
Reference date (month/year)

04/2022

10/2022
Legal basis

reg CE 73/2009

reg (CE) 1308/2013
Reporting unit

agricultural holding

wine producing unit
Identification variable (e.g. address, unique code, etc.)

holding id

holding id
Percentage of mismatches (%)

0

0
How were the mismatches handled?
Degree of coverage (holdings, e.g. 80%)

100%

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

100%

100%
If not complete, which other sources were used ?
Were the data used for sample frame? Validation
Directly for estimates		 Validation
Directly for estimates
Data used for other purposes, which?
Which variables were taken from administrative sources?

area

quantities of wine produced
Were there any differences in the definition of the variables between the administrative source and those described in the Regulation? 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?

IACS is submitted to internal and EU controls assuring reliability, accuracy and coherence

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

none

none


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

Fruit and vegetable expert

Production estimates
Data owner (organisation)

Ministry of agriculture

Ministry of agriculture
Update frequency (e.g. 1 year or 6 months) Yearly Yearly
Reference date (Month/Year  e.g. 1/22 - 8/22)

07/22 - 12/22

07/22 - 12/22
Legal basis
Use purpose of the estimates?

area and production of fruit and vegetables

first estimate of crop production when survey result is not yet available

non significant and non existent crops
What kind of expertise the experts have?

LU ministry of agriculture has experts in fruits and vegetables who have good knowledge of the horticultural sector of Luxembourg

agricultural expert
What kind of estimation methods were used?

estimation based on producer survey (telephone interview with the main producers), consideration of administrative data (a part of the data on area is available in IACS) and contact with the producer organisations

estimation based on administrative data (area available in IACS)
Were there any differences in the definition of the variables between the experts' estimates and those described in the Regulation? 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?

no assessement possible

no assessement possible
What were the possible limitations, drawbacks of using the data from expert estimate(s)?

no limitations

no limitations
Additional comments


 
3.4. Data validation

Which kind of data validation measures are in place? Manual
What do they target? Completeness
Outliers
Aggregate calculations
Is the data cross-validated against an other dataset? YES
If yes, which kind of dataset? Previous results
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 the previous quality report? Stable Stable Stable Stable Stable
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?        
If, other, please specify        
Additional comments        






 
4.2. Quality management - assessment

See the European level Quality Report.


5. Relevance Top
5.1. Relevance - User Needs

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






 
5.2. Relevance - User Satisfaction

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






 
5.3. Completeness

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

See the European level Quality Report


6. Accuracy and reliability Top

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

See the European level Quality Report.
6.2. Sampling error

Sampling method and sampling error

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

Harvest inventory
Sampling basis? Other
If 'other', please specify

agricultural holdings doing a farm accountancy
Sampling method? Other
If stratified, number of strata?
If stratified, stratification basis?
If 'other', please specify

all agricultural holdings withe crop production participating in farm accountancy
Size of total population

1320 (2022)
Size of sample

+- 600 crop farms
Which methods were used to assess the sampling error?  Other
If other, which?

None
Which methods were used to derive the extrapolation factor?  Other
If other, which?

None
If CV (co-efficient of variation) was calculated, please describe the calculation methods and formulas

 
If the results were compared with other sources, please describe the results
Which were the main sources of errors?


Sampling error - indicators

Not applicable


Coefficient of variation (CV) for the area (on the MS level)

  Survey 1  Survey 2 Survey 3  Survey 4 Survey 5  Survey 6 Survey 7
Name of the survey

harvest inventory
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 %)
Additional comments

not applicable, the intention of the survey is to collect data on crop yields, not area

            




 
6.3. Non-sampling error
6.3.1. Coverage error

Over-coverage - rate

Not applicable


Common units - proportion

Not applicable


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

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

coverage depending on the respondant rate
Which actions were taken for reducing the error or to correct the statistics?

statistics corrected by taking into consideration expert estimates
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

harvest inventory
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)?

+-50
Preparatory (field) testing of the questionnaire? NO
Number of units participating in the tests? 

0
Explanatory notes/handbook for surveyors/respondents?  NO
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

0
Were some actions taken for reducing the measurement error or to correct the statistics? YES
If yes, describe the actions and their impact

systematical interview with the respondents, with possibility for the bookkeeper to adress possible errors or questions concerning the harvest inventory






 
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

harvest inventory
Unit level non-response rate (in %)

+- 33%

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

unknown
               - Max% / item

unknown
               - Overall%

unknown
Was the non-response been treated ? NO
Which actions were taken to reduce the impact of non-response?

the survey results were combined with an expert estimation
Which items had a high item-level non-response rate? 

no particular items were outstanding
Which methods were used for handling missing data?
(several answers allowed)		 Follow-up interviews
Reminders
In case of imputation which was the basis?
In case of imputation, which was the imputation rate (%)?
Estimated degree of bias caused by non-response? None
Which tools were used for correcting the data?

none
Which organisation did the corrections?

none
Additional comments

 




 
6.3.4. Processing error

Not applicable.
6.3.4.1. Imputation - rate

Not applicable.
6.3.5. Model assumption error

Not applicable.
6.4. Seasonal adjustment

Not applicable.
6.5. Data revision - policy

Not applicable.
6.6. Data revision - practice

Not applicable.
6.6.1. Data revision - average size

Not applicable.


7. Timeliness and punctuality Top
7.1. Timeliness

Time lag - first result


Time lag - final result


  Cereals Dried pulses and protein crops Root crops Oilseeds Other industrial crops Plants harvested green Vegetables and melons Strawberries Cultivated mushrooms Fruit trees Berries Nut trees Citrus fruit trees Vineyards Olive trees
How many main data releases there are yearly in the national crop statistics for the following types of crops?

 3

 3

 3

 3

 3

 2

1

 1

 na

 1

 1

 1

nd

 1

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

 2

 2

 2

 2

 2

 1

0

 0

 

 0

 0

 0
When was the first  forecasting published for the crop year on which is reported? (day/month/year) 30/09/2022 30/09/2022 30/09/2022 30/09/2022 30/09/2022 30/09/2022

 0
When were the final results published for the crop year on which is reported? (day/month/year) 31/03/2023 31/03/2023 31/03/2023 31/03/2023 31/03/2023 31/03/2023 31/03/2023 31/03/2023 31/03/2023 31/03/2023 31/03/2023 31/03/2023
Additional comments




 
7.2. Punctuality

See the European level Quality Report
7.2.1. Punctuality - delivery and publication

See the European level Quality Report


8. Coherence and comparability Top
8.1. Comparability - geographical

Not applicable.
8.1.1. Asymmetry for mirror flow statistics - coefficient

Not applicable.
8.2. Comparability - over time

Length of comparable time series


  Crops from arable land
(Table 1)		 Vegetables, melons and strawberries (Table 2) Permanent crops
(Table 3)		 Agricultural land use
(Table 4)
Have there been major breaks in the time series in the previous 5 years? NO NO YES NO
If yes, to which were they related? Other
If other, which?

area
Which items were affected? F1100 - Pome fruits
F1110 - Apples
F1112 - Apples for processing
F1120 - Pears
F1122 - Pears for processing
Year of break (number)

2020
Impact on comparability Moderate
Additional comments

area for apples and pears for processing has been aligned to IFS





 
8.2.1. Length of comparable time series
8.3. Coherence - cross domain

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

 national land use statistics (according to IFS 2020 rules)
If no comparisons have been made, why not?

comparison with IFS 2020 and vineyard survey 2020 was not made because of the different reference years. Instead we compare to results from national statistics for the same reference year that are calculated according to IFS 2020 rules


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    

 0%

national land use statistics 22
Dried pulses and protein crops    

 0%

national land use statistics 22
Root crops    

 0%

 national land use statistics 22
Oilseeds    

 0%

 national land use statistics 22
Other industrial crops (than oilseeds)    

-71%

 national land use statistics 22
Plants harvested green    

+6%

 national land use statistics 22
Total vegetables, melons and strawberries    

 0%

 national land use statistics 22
Vegetables and melons    

0%

 national land use statistics 22
Strawberries    

 na

 
Cultivated mushrooms

 

  

 0%

 national land use statistics 22
Total permanent crops

 

  

 -9%

 national land use statistics 22
Fruit trees

 

Not applicable.

 -2%

  national land use statistics 22
Berries  

 0%

  national land use statistics 22
Nut trees  

 na

 
Citrus fruit trees

Not applicable.

 na

 
Vineyards

 

 0%

  national land use statistics 22
Olive trees

Not applicable.

 na

 
If there were considerable differences, which factors explain them?

 

different concepts:

  • in national land use statistics all energy plants are included under "industrial crops", but in ACS a part of them is classified under "plants harvested green"
  • fruit trees in national statistics also include trees not in production while data on fruit trees in ACS only counts trees in production
  • permanent crops in national statistics include nurseries and christmas trees
  





 
8.4. Coherence - sub annual and annual statistics

Not applicable.
8.5. Coherence - National Accounts

Not applicable.
8.6. Coherence - internal

Not applicable.


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

Availability Links
NO






 
9.2. Dissemination format - Publications

  Availability Links
Publications Electronic

https://agriculture.public.lu/de/agrarstatistik/pflanzliche-produktion.html
Publications in English Electronic

https://lustat.statec.lu/?fs[0]=Topics%2C1%7CEnterprises%23D%23%7CAgriculture%20and%20forestry%23D2%23&pg=0&fc=Topics&lc=en






 
9.3. Dissemination format - online database

Data tables - consultations


https://agriculture.public.lu/de/agrarstatistik/pflanzliche-produktion.html

 


  Availability Links
On-line database accessible to users NO
Website





 
9.4. Dissemination format - microdata access

Availability Links
NO






 
9.5. Dissemination format - other

 
9.6. Documentation on methodology

  Availability Links
Methodological report None

 
Quality Report English

EUROSTAT
Metadata None

 
Additional comments  






 
9.7. Quality management - documentation

Not applicable.
9.7.1. Metadata completeness - rate

Not applicable.
9.7.2. Metadata - consultations

Not applicable.


10. Cost and Burden Top

Efficiency gains if compared to the previous quality report?
If other, which?
Burden reduction measures since the previous reference year 
If other, which?






 


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


12. Comment Top


Related metadata Top


Annexes Top
list of sources
questionnaire harvest inventory 2022