Crop production (apro_cp)

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

Compiling agency: Ministère de l'agriculture et de l'Alimentation

Time Dimension: 2016-A0

Data Provider: FR6

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

Ministère de l'agriculture et de l'Alimentation

1.2. Contact organisation unit

Service de la statistique et de la prospective

1.5. Contact mail address

Service Statistique et Prospective

3, rue Barbet de Jouy

75 349 Paris CEDEX 07


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

Utilized agricultural area cultivated by an agricultural holding.

2.6. Statistical population

All agricultural holdings growing crops.

2.7. Reference area

The entire territory of the country.

2.8. Coverage - Time

Crop year.

2.9. Base period

Not applicable.


3. Statistical processing Top
3.1. Source data

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

Arable lands survey

IACS

earlier estimates on december 2015

Final area under cultivation Survey
Administrative data

Arable lands survey

IACS

Production Survey
Administrative data

Arable lands survey

Selling and marketing statistics from FranceAgriMer

Yield Survey
Expert estimate

Arable lands survey

Earlier estimates on june and july

Non-existing and non-significant crops
Table 2: Vegetables, melons and strawberries       
Early estimates for harvested areas Expert estimate

Producers organisations

Final harvested area Census
Survey
Administrative data
Expert estimate

FSS 2013

FSS

Producers organisations

experts panel

Production Survey
Administrative data
Expert estimate

Producers panel

Producers organisations

experts panel

Non-existing and non-significant crops
Table 3: Permanent crops      
Early estimates for production area Expert estimate

producers organisations

Final production area Census
Survey

FSS 2013

FSS

Production Survey
Administrative data

Producers panels

Producers organisations

Experts panel

For wine, vineyard register

Non-existing and non-significant crops
Table 4: Agricultural land use      
Main area Census
Survey

FSS 2013

Teruti-Lucas

IACS

Non-existing and non-significant crops
Total number of different data sources

6

   
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     

 x

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

 

Estimated by experts

 

Other type

 x

If other type, please explain

 orchard inventory

Additional information

 
   


Which method is used for calculating the yield for main arable crops?
If another method, describe it.

We mix two differents methods/sources : arable lands survey and experts panels

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

Arable land survey

IACS

producers panels

producers organisations

Planning (month-month/year)

december to december

june to december

November Y to december Y+1

November Y to december Y+1

Preparation (month-month/year)

november 

may

november Y

november Y

Data collection (month-month/year)

december, april, july

may to november

January to december

January to december

Quality control (month-month/year)

december, april, july

may to november

January to december

January to december

Data analysis (month-month/year)

january, may, august

june to december (projected results)

January to december

January to december

Dissemination (month-month/year)

february, june, september

june to december (projected results)

January to december

January to december

If there were delays, what were the reasons?
3.3. Data collection

Definitions

  Question In case yes, how do they differ?
Do national definitions differ from the definitions in Article 2 of Regulation (EC) No 543/2009? NO
Are there differences between the national methodology and the methodology described in the Handbook concerning e.g. the item and aggregate calculations? NO
Are special estimation/calculation methods used for main crops from arable land? NO
Are special estimation/calculation methods used for vegetables or strawberries? NO
Are special estimation/calculation methods used for permanent crops for human consumption?
Are special estimation/calculation methods used for main land use?
Do national crop item definitions differ from the definitions in the Handbook  (D-flagged data)?  
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).

There is a source for the main regions and departments covering 95 % of the areas 

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)

Permanent

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

Arable lands survey

FSS

Producers panels

 teruti-Lucas

Which survey method was used? Telephone interview, electronic questionnaire Face-to-face interview Telephone interview, paper questionnaire
Face-to-face interview
Other
If 'other', please specify

points observation

Please provide a link to the questionnaire

http://www.agreste.agriculture.gouv.fr/enquetes/structure-des-exploitations-964/recensement-agricole-2010/methodologie-718/

http://www.agreste.agriculture.gouv.fr/IMG/pdf/teruti2015questbsva.pdf

Data entry method, if paper questionnaires? 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

IACS

selling statistics

 producers organisations

Description

Integrated adinistration and control systeme (areas declarations for aids)

Cereals, Oilseeds, Protein Crops marketed are known monthly

vegetables and fruits productions

Data owner (organisation)

ASP-Ministry of agriculture

FAM-Ministry of agriculture

producers organisation at regional level

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

year

month

month or year

Legal basis

yes

yes

no

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

yes

 aggregated datas

 aggregated dats

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

99 for crops from arable lands

80 to 100 % production according the crop

20 à 100 % of production according the region and the crop

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

100

If not complete, which other sources were used ?

census for absolute level and administrative source for evolution

completed by historic relation between selling and production

completed by producers panels

How were the data used?
Other Other Other
Data used for other purposes, which?

evolutions used for estimates

 to control production estimates with area and yield

used to complete other sources

Which variables were taken from administrative sources?

areas

selling

areas and production

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?

Comparison with 2010 census

Study on historic relation between selling and production for each cereal

Comparison with aperiodic surveys on orchards and vegetables and FSS

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


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

Fields crops

fruits

vegetables

Data owner (organisation)

Ministry of agriculture

Ministry of agriculture

Ministry of agriculture

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

year

year or month

year or month

Legal basis

N0

N0

N0

Use purpose of the estimates?

earlier estimates of areas and yields

estimates of marketing

estimates of marketing

What kind of expertise the experts have?

marketing seeds, technical experts

marketing fruits and vegetables

marketing fruits and vegetables

What kind of estimation methods were used?

weekly surveys for pricing are carried on among packagers. Pollsters evaluate quantities selled by them

weekly surveys for pricing are carried on among packagers. Pollsters evaluate quantities selled by them

Were there any differences in the definition of the variables between the experts' estimates and those described in the Regulation?
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?

comparison with Mars program earlier estimates

comparison with producers organisations and producers panels

comparison with producers organisations and producers panels

What were the possible limitations, drawbacks of using the data from expert estimate(s)?
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
Is the data cross-validated against an other dataset? YES
If yes, which kind of dataset? Other dataset
If other, please describe

Aggregated datasets

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? 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

Sampling improvement of  Arable Land survey. Actual sampling is done with the Teruti-Lucas points. Since 2015 the sampling will be done with IACS

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

Datas at Department level

Additional comments
5.2. Relevance - User Satisfaction

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

We think that users are satisfied on the basis of meestings and contacts with the main users for each survey

5.3. Completeness

See the European level Quality Report

5.3.1. Data completeness - rate

See the European level Quality Report


6. Accuracy and reliability Top
6.1. Accuracy - overall

See the European level Quality Report.

6.2. Sampling error

Sampling method and sampling error

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

Arable Lands survey 

FSS survey on vegetables and fruits areas

Teruti-Lucas

Producers panels

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

6

3

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

370000

500000

Size of sample

15 143

74000

320000 observation points

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


Sampling error - indicators


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

Arable Lands survey 

FSS survey on vegetables and fruits areas

 Teruti-Lucas

Cereals for the production of grain (in %)

betwteen 0.4 and 10%

Dried pulses and protein crops (in %)

around 3 %

Root crops (in %)

around 10%

Oilseeds (in %)

around 1%

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

 

Plants harvested green from arable land (in %)

0.5%

Total vegetables, melons and strawberries (in %)

2,7 %

Cultivated mushrooms (in %)
Total permanent crops (in %)
Fruit trees (in %)

between 0.4 and 2 %

Berries (in %)
Nut trees (in %)

1 %

Citrus fruit trees (in %)

2 %

Vineyards (in %)
Olive trees (in %)
Additional comments

Teruti-Lucas is since 2012 used for land use and landcover. Relative standard deviation is 0.3 % for agriculture use, 0.7 for forest, 1.2 for courses and meadows

Producers panels for vegetables and fruits are are implemented at Nuts3 level by our regional agricultural statistics units for producers out of producers organisations.

           
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

Arable lands survey

FSS survey on vegetables and fruits

Error type Other No error
Degree of bias caused by coverage errors None None
What were the reasons for coverage errors?

Survey only in the main departments

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

Other departements estimates with other sources

Additional comments




 

6.3.1.1. Over-coverage - rate

Not applicable

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

Arable lands survey 

FSS survey on vegetables and fruits

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

24

4

Preparatory (field) testing of the questionnaire? YES YES
Number of units participating in the tests? 

50

50

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

Datas from last year survey

Datas from last year survey

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

arable lands survey

FSS survey on vegetables and fruits

Unit level non-response rate (in %)

close to 0 %

close to 0 %

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

reminder and then replacement

reminder and then replacement

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
Weighting
Follow-up interviews
Reminders
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? None None
Which tools were used for correcting the data?
Which organisation did the corrections?
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

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?

between 11 (maize, sorghum, rice) and 14 (wheat, barley and others cereals)

12

12

between 11 (soya, sunflower) and 14 (rapeseed)

From 7 to 8 according the vegetable

7

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

5

3

6

5

0

5

When was  the first  forecasting published for crop year 2016? (day/month/year) 04/12/2015 01/04/2016 01/04/2016 04/12/2015 15/03/2016 15/04/2016
When were the final results published for crop year 2016? (day/month/year) 01/12/2017 01/12/2017 01/12/2017 01/12/2017 01/12/2017 01/12/2017 01/12/2017 01/12/2017 01/12/2017 01/12/2017
Additional comments
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

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 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
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?

Orchard survey

Administrative data

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

0.1

 

0.1 wheat to -2.7 sorgho

9231

Administrative source(s)

Annual crop statistics (ha)

Dried pulses and protein crops

0.1

 

-0.1

397000

Administrative source(s)

Annual crop statistics (ha)

Root crops

1.5 (potatoes)

 

157000

Annual crop statistics (ha)

Oilseeds

0.0

 

-0.4

Administrative source(s)

Other industrial crops (than oilseeds)  

2233000

Annual crop statistics (ha)

Plants harvested green

-1.8

 
Total vegetables, melons and strawberries  

1428000

Annual crop statistics (ha)

Vegetables and melons  
Strawberries  

3100

Annual crop statistics (ha)

Cultivated mushrooms  
Total permanent crops

1 %

 

1%

1011000

Orchard survey

Annual crop statistics (ha)

Fruit trees

0 %

 

10 % apples and 11 % pears 

186000

Orchard survey

Annual crop statistics (ha)

Berries  
Nut trees  

3.5 %

19600

Orchard survey

Annual crop statistics (ha)

Citrus fruit trees  
Vineyards

0.9

Olive trees  

796000

Annual crop statistics (ha)

If there were considerable differences, which factors explain them?

on 2010 FSS is reference

comparison wasne with orchard 2013. FSS was too high for orchard (all orchards and not only productive orchards)

correct yearly (on 2013 in this case)for cereals, oilseeds and proteaginous except for maize (green maize not separate for administrative source)

 

DATS 2010

 

 

 
8.4. Coherence - sub annual and annual statistics

Not applicable.

8.5. Coherence - National Accounts

Not applicable.

8.6. Coherence - internal

Not applicable.


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

Availability Links
YES

http://agreste.agriculture.gouv.fr/IMG/pdf/2017_131inforapgrandescultures.pdf

9.2. Dissemination format - Publications

  Availability Links
Publications Electronic

http://agreste.agriculture.gouv.fr/IMG/pdf/2017_131inforapgrandescultures.pdf

Publications in English None
9.3. Dissemination format - online database

Data tables - consultations

Not applicable


  Availability Links
On-line database accessible to users NO
Website None

https://stats.agriculture.gouv.fr/disar/faces/report/tabDocBySource.jsp

9.3.1. Data tables - consultations

Not applicable

9.4. Dissemination format - microdata access

Availability Links
9.5. Dissemination format - other

Free : http://www.agreste.agriculture.gouv.fr/conjoncture/grandes-cultures-et-fourrages/

9.6. Documentation on methodology

  Availability Links
Methodological report None

https://stats.agriculture.gouv.fr/disar/faces/report/tabDocBySource.jsp

Quality Report
Metadata
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 reference year (2013) Further automation
Increased use of administrative data
If other, which?
Burden reduction measures since the previous reference year  Less variables surveyed
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


Related metadata Top


Annexes Top