Crop production (apro

Crop production (apro_cp)

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

Compiling agency: Ministère de l'Agriculture et de la Souveraineté Alimentaire

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

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1.1. Contact organisation

Ministère de l'Agriculture et de la Souveraineté Alimentaire

1.2. Contact organisation unit

Service de la Statistique et de la Prospective

1.5. Contact mail address

Service de la Statistique et de la Prospective (SSP)

3, rue Barbet de Jouy

75 349 Paris CEDEX 07

2. Statistical presentation

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2.1. Data description

Annual crop statistics provide statistics on the area under main arable crops, vegetables and permanent crops, as well as on production levels and yields. The statistics are collected from a wide variety of sources, such as surveys, administrative data and experts views. The data collection covers early estimates (before the harvest) and final results. Data are collected mostly at national level but for some crops also at regional level (NUTS1/2).

2.2. Classification system

Hierarchical crop classification system.

2.3. Coverage - sector

crops from arable land, permanent crops and vegetables

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 or vegetable.

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

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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

Census
Survey
Administrative data
Expert estimate

Arable lands survey

IACS

Agricultural census

Final area under cultivation

Survey
Administrative data
Census

Arable lands survey

IACS

Agricultural census

Production

Survey
Administrative data

Arable lands survey

Selling and marketing statistics from FranceAgriMer

Yield

Survey
Expert estimate

Arable lands survey

Non-existing and non-significant crops

Table 2: Vegetables, melons and strawberries

Early estimates for harvested areas

Expert estimate

Final harvested area

Census
Administrative data
Expert estimate

agricultural census

Production

Survey
Administrative data
Expert estimate

Non-existing and non-significant crops

Table 3: Permanent crops

Early estimates for production area

Expert estimate

Final production area

Census

Agricultural census

Production

Survey
Administrative data

For wine, vineyard register

Non-existing and non-significant crops

Table 4: Agricultural land use

Main area

Census
Survey
Administrative data

agricultural census

Teruti-Lucas

IACS

Non-existing and non-significant crops

Total number of different data sources

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
If other type, please explain
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 organisations
Planning (month-month/year)	december to december	june to december	november Y to december Y+1
Preparation (month-month/year)	november	may	november Y
Data collection (month-month/year)	december, april, july	may to november	january to december
Quality control (month-month/year)	december, april, july	may to november	january to december
Data analysis (month-month/year)	january, may, august	june to december (projected results)	january to december
Dissemination (month-month/year)	february, june, september	june to december (projected results)	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?	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)	R1000 - Potatoes (including seed potatoes) F1110 - Apples F1120 - Pears	Potatoes : before March Y+1, our estimate does not include new potatoes and seeds (starch and ware potatoes only). Apples : before March Y+1, apples for fresh consumption only. Pears : before March Y+1, pears for fresh consumption only.
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 (each year)
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	Agricultural census	Teruti Lucas
Which survey method was used?	Telephone interview, electronic questionnaire Face-to-face interview	On-line electronic questionnaire filled in by respondent Face-to-face interview Telephone interview, electronic questionnaire	Other
If 'other', please specify			Points observation
Please provide a link to the questionnaire	https://agreste.agriculture.gouv.fr/agreste-web/download/methode/S-TerLab%202022/questionnaire_Terlab2022.pdf	https://agreste.agriculture.gouv.fr/agreste-web/download/methode/S-RA%202020/RA2020_Questionnaire%20DOM_specimenV2.pdf	https://agreste.agriculture.gouv.fr/agreste-web/download/publication/publie/Chd2211/cd2022-11_teruti_2018.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	vegetable and fruit 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 datas
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 to 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
Were the data used for sample frame?	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 the last agricultural census	Study on historic relation between selling and production for each cereal	Comparison with aperiodic surveys 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	fruit	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. 1/22 - 8/22)	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 production	estimates of marketing production
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

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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

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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?	NO
If not, which additional data are collected?	Some data 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	Users are satisfied on the basis of meetings and contacts we have 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

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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	Agricultural census	Teruti-Lucas
Sampling basis?	Area		Area
If 'other', please specify
Sampling method?	Stratified		Stratified
If stratified, number of strata?	913		909
If stratified, stratification basis?	Location Size Specialisation
If 'other', please specify
Size of total population	176 000	450 000	1 600 000
Size of sample	17 000		85 000
Which methods were used to assess the sampling error?	Relative standard error
If other, which?
Which methods were used to derive the extrapolation factor?	Basic weight Non-response		Basic weight Non-response
If other, which?
If CV (co-efficient of variation) was calculated, please describe the calculation methods and formulas	The estimated variance of the estimator is calculated using the stratified sampling formula. For the yield (which is a ratio), we need to linearize the variables.		The variance is estimated with the usual formula for a simple random draw with stratification.
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		Teruti-Lucas
Cereals for the production of grain (in %)	betwteen 0.2 and 7 %
Dried pulses and protein crops (in %)	Between 2 and 6 %
Root crops (in %)	between 0.6 and 5 %
Oilseeds (in %)	between 0.4 and 3 %
Other industrial crops (included all industrial crops besides oilseeds) (in %)
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 %)
Olive trees (in %)
Additional comments

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	Agricultural census
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	The coverage rate for a crop is defined simply as the ratio of the database to the real universe of all the holdings having the crop considered. Farms of less than 5 hectares are excluded from the selection universe; the coverage rate is greater than 95%.

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	Agricultural census
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)?	27	1
Preparatory (field) testing of the questionnaire?	YES
Number of units participating in the tests?	20
Explanatory notes/handbook for surveyors/respondents?	YES	YES
On-line FAQ or Hot-line support for surveyors/respondents?	NO	YES
Were pre-filled questionnaires used?	YES	YES
Percentage of pre-filled questions out of total number of questions	30% (we prefilled the areas with the ones from CAP database)	areas pre-filled with the ones from CAP database
Were some actions taken for reducing the measurement error or to correct the statistics?	YES
If yes, describe the actions and their impact	Thresholds for returning error message to indicate to surveyor that the yield is unusual

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
Unit level non-response rate (in %)	2 %
Item level non-response rate (in %)
- Min% / item
- Max% / item
- Overall%
Was the non-response been treated ?	YES
Which actions were taken to reduce the impact of non-response?	The construction of the estimators and the calculation of the associated details is carried out on the basis of the responding farms (the extrapolation coefficient of a farm is corrected taking into account only the farms responding to the denominator)
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
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?
Which organisation did the corrections?
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

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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 10 (maize, sorghum, rice) and 12 (winter cereals)

between 10 (soya, sunflower) and 13 (rapeseed)

10 for green maize, 2 for others

between 4 (melons, courgette) and 5 (others)

between 5 (cherries) and 7 (apples, peaches)

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

between 3 and 5

between 3 and 4

3 for green maize, 0 for others

From 7 to 8 according the vegetable

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

07/12/2021

12/04/2022

07/12/2021

28/06/2023

16/05/2023

31/05/2022

31/05/2023

28/06/2023

10/05/2022

28/06/2023

31/05/2023

When were the final results published for the crop year on which is reported? (day/month/year)

28/09/2023

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

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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	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

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?

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				0.1 wheat to -2.7 sorgho 9231	Administrative source(s) Annual crop statistics (ha)
Dried pulses and protein crops				-0.1 397000	Administrative source(s) Annual crop statistics (ha)
Root crops				157000	Annual crop statistics (ha)
Oilseeds				-0.4	Administrative source(s)
Other industrial crops (than oilseeds)				2233000	Annual crop statistics (ha)
Plants harvested green
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 %	Not applicable.		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		Not applicable.
Vineyards	0.9
Olive trees		Not applicable.		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

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9.1. Dissemination format - News release

Availability	Links
YES	https://agreste.agriculture.gouv.fr/agreste-web/disaron/!searchurl/93378747-cae6-48dc-9ad6-534cdcd4347a!4545f1a9-afe6-4c86-a141-693f2c72d550!1b69a349-ca8f-4353-82bb-4c00c502412c!729f399f-53c3-4952-9971-4753794a7c1b!c6be0c43-70a0-4666-853f-80de38a08ec7!0c593aed-b1d0-476e-9359-12d6347d8243!b125c6dc-13b7-4260-9abd-6e9321b2b963!fec0e278-6655-4c48-ac47-aab6d8847e15/search/

9.2. Dissemination format - Publications

	Availability	Links
Publications	Electronic	https://agreste.agriculture.gouv.fr/agreste-web/download/publication/publie/IraGcu23107/2023_107inforapgdescultures.pdf https://agreste.agriculture.gouv.fr/agreste-web/disaron/Chd2310/detail/
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	National language	https://agreste.agriculture.gouv.fr/agreste-web/disaron/BulConj/detail/

9.4. Dissemination format - microdata access

Availability	Links

9.5. Dissemination format - other

9.6. Documentation on methodology

	Availability	Links
Methodological report	National language	https://agreste.agriculture.gouv.fr/agreste-web/download/publication/publie/IraGcu23107/2023_107inforapgdescultures.pdf https://agreste.agriculture.gouv.fr/agreste-web/disaron/Chd2310/detail/ https://agreste.agriculture.gouv.fr/agreste-web/methodon/S-SAA/methodon/
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

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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

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11.1. Confidentiality - policy

Restricted from publication

11.2. Confidentiality - data treatment

Restricted from publication

12. Comment

Top

Related metadata

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Annexes

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