Crop production (apro_cp)

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

Compiling agency: ISTAT - Istituto Nazionale di Statistica


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

ISTAT - Istituto Nazionale di Statistica
1.2. Contact organisation unit

Crop statistics
1.5. Contact mail address

Piazza Marconi 26/C - Rome - Italy


2. Statistical presentation Top
2.1. Data description

Annual crop statistics provide statistics on the area under main arable crops, vegetables and permanent crops and production and yield levels.  The statistics are collected from  a wide variety of sources: surveys, administrative sources, experts and other data providers. The data collection covers early estimates (before the harvest) and the final data. Data are collected mostly at national level but for some crops also regional data exist (NUTS1/2).
2.2. Classification system

Hierarchical crop classification system
2.3. Coverage - sector

Growing of non-perennial crops, perennial crops and plant propagation (NACE A01.1-01.3)
2.4. Statistical concepts and definitions

See: Annual crop statistics Handbook
2.5. Statistical unit

Utilised agricultural area cultivated by an agricultural holding.
2.6. Statistical population

All agricultural holdings growing crops.
2.7. Reference area

The entire territory of the country.
2.8. Coverage - Time

Crop year.
2.9. Base period

Not applicable.


3. Statistical processing Top
3.1. Source data

  Source 1 Source 2 Source 3 Source 4
Have new data sources been introduced since the previous quality report?       
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?      
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 Survey

Early Estimates Survey

  
Final area under cultivation Administrative data
Expert estimate

EnteRisi

Absi

Agea

Expert Estimation
Production Administrative data
Expert estimate

EnteRisi

Absi

Agea

Expert Estimation
Yield Expert estimate

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

Early Estimates Survey
Final harvested area Expert estimate

Expert Estimation
Production Expert estimate

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

Early Estimates Survey
Final production area Expert estimate

Expert Estimation
Production Expert estimate

Expert Estimation
Non-existing and non-significant crops
Table 4: Agricultural land use      
Main area
Non-existing and non-significant crops
Total number of different data sources

5

  
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

X

Other type

X

If other type, please explain

EU Handbook

Additional information

 
   


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

Yield is calculated by the ratio between the harvested production and the total area




 
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

Expert Estimation

AGEA

(The Agency for disbursement in agriculture) 

(Administrative data)

EnteRisi

(Administrative data)

Absi

(Administrative data)

Early estimates survey
Planning (month-month/year)

2022

2022

2022

2022

2021
Preparation (month-month/year)

2021

2022

2022

2022

2021
Data collection (month-month/year)

12 month/2022

2022

2022

2022

2021
Quality control (month-month/year)

12 month/2022

2022

2022

2022

2021- 2022
Data analysis (month-month/year)

12 month/2022

2022

2022

2022

2022
Dissemination (month-month/year)

12 month/2022/2023

2022

2022

2022

2022
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)? 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).

Provincial/Regional estimates cover the whole reference population
Is the data collection based on holdings? NO
If yes, how the holdings were identified?
If not, on which unit the data collection is based on?

Estimation by provincial/Regional experts. Data are provided by local authorities that collect experts evaluations on area and yield of different crops.
When was last update of the holding register? (month/year)

No farm/agricultural holding register is available at the moment
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

Early estimates survey
Which survey method was used? Telephone interview, electronic questionnaire
If 'other', please specify
Please provide a link to the questionnaire
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

AGEA

Enterisi

Absi
Description

Declarations from producers

Declarations from producers

Industrial treatments
Data owner (organisation)

AGEA

The Agency for disbursement in agriculture 

Enterisi

The Ente Nazionale Risi (rice sector)

Absi (Association sugar beet  Italian) 
Update frequency Once per year or more often Once per year or more often Once per year or more often
Reference date (month/year)

2022

2022

2022
Legal basis

Bilateral agreement

Bilateral agreement

Bilateral agreement
Reporting unit

Producer

Category associations

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

Aggregated data (no microdata)

Aggregated data (no microdata)

Aggregated data (no microdata)
Percentage of mismatches (%)

Not applicable

Not applicable

Not applicable
How were the mismatches handled?
Degree of coverage (holdings, e.g. 80%)
Degree of completeness (variables, e.g. 60%)
If not complete, which other sources were used ?
Were the data used for sample frame? Validation
Directly for estimates		 Validation
Directly for estimates		 Validation
Directly for estimates
Data used for other purposes, which?
Which variables were taken from administrative sources?

Tobacco : Surface  and production

Rice: Surface and production

Sugar beet: Surface 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?
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

expert  estimates
Data owner (organisation)

Regions and two Autononomous Province
Update frequency (e.g. 1 year or 6 months) Monthly
Reference date (Month/Year  e.g. 1/22 - 8/22)

Month/year
Legal basis

National Statistical Programme 
Use purpose of the estimates?

To provide update crops statistics for territorial bodies, policy decisions, category associations
What kind of expertise the experts have?

Statistical and agronomic skills
What kind of estimation methods were used?

Panel survey addressed to main crops producers and analysts, in order to estimate used surfaces and to update the average production by hectar.

Statistics are produced using expert information. Data are provided by local authorities that collect experts evaluations on area and yield of different crops. The auxiliary information could be included in expert's estimate, such as verifying the availability of external sources (eg professional bodies or associations of producers, administrative sources, auxiliary sources of data related to the cultivation being estimated). Crops under investigation are different for each month and take into account the phenological stage of cultivation. For this reason more than one estimate can be determined for each crop during the year (provisional, temporary or permanent).
Were there any differences in the definition of the variables between the experts' estimates and those described in the Regulation? 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?
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? Manual
What do they target? Completeness
Outliers
Aggregate calculations
Other
Is the data cross-validated against an other dataset? YES
If yes, which kind of dataset? Previous results
Other dataset
If other, please describe

agricultural census






 
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? Improvement Improvement Improvement Improvement Improvement
Is there a quality management process in place for crop statistics? YES        
If, yes, what are the components?

Specific working group (started in March 2014) involving  Istat, Regions, AGEA (Agency for disbursement in agriculture)  and Ministry of  Agriculture.  Questionnaire sent to Regions in July, main results elaborated in the following year.

        
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? Systematic validation improvements
Other		        
If, other, please specify

respect of deadline

        
Additional comments        






 
4.2. Quality management - assessment

Cfr. la relazione sulla qualità a livello europeo.


5. Relevance Top
5.1. Relevance - User Needs

Are there known unmet user needs? YES
Describe the unmet needs

Demand for crops reported in aggregate items or crops not detected (eg. truffles)
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?

On the contrary, the EU Regulation asks for too many data in comparison with the national needs
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

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

120
If stratified, stratification basis? Location
Size
If 'other', please specify
Size of total population

about 600.000
Size of sample

15.000
Which methods were used to assess the sampling error?  Relative standard error
If other, which?
Which methods were used to derive the extrapolation factor?  Other
If other, which?

calibration of basic weights using auxiliary variables and the corrisponding population totals derived from sampling frame 
If CV (co-efficient of variation) was calculated, please describe the calculation methods and formulas

Formulas related to the calibration estimators 
If the results were compared with other sources, please describe the results
Which were the main sources of errors?

Coverage errors; unit nonresponse error; sampling error


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

Early crop estimates
Cereals for the production of grain (in %)

6,5
Dried pulses and protein crops (in %)

8,2
Root crops (in %)

8,6
Oilseeds (in %)

9,7
Other industrial crops (included all industrial crops besides oilseeds)  (in %)

7,9
Plants harvested green from arable land (in %)

8,6
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

Early estimates survey
Error type Under-coverage
Over-coverage
Contact errors
Degree of bias caused by coverage errors Moderate
What were the reasons for coverage errors?

Incomplete, wrong or not updated contact information (mainly phone numbers)
Which actions were taken for reducing the error or to correct the statistics?

Compensation of the possible bias by means of reweighting procedures  (calibration procedures)
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

Early estimates survey
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)?

9
Preparatory (field) testing of the questionnaire? NO
Number of units participating in the tests? 
Explanatory notes/handbook for surveyors/respondents?  YES
On-line FAQ or Hot-line support for surveyors/respondents? YES
Were pre-filled questionnaires used? NO
Percentage of pre-filled questions out of total number of questions
Were some actions taken for reducing the measurement error or to correct the statistics? YES
If yes, describe the actions and their impact

Same unit in previous data/similar units/other sources






 
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

Early estimates survey
Unit level non-response rate (in %)

20%
Item level non-response rate (in %)              
               - Min% / item

Not applicable
               - Max% / item

Not applicable
               - Overall%

Not applicable
Was the non-response been treated ? NO
Which actions were taken to reduce the impact of non-response?

informative letter to the holders in the list, telephone help desk  and e-mail address help desk
Which items had a high item-level non-response rate? 

not calculable
Which methods were used for handling missing data?
(several answers allowed)		 Imputations
Weighting
Other
In case of imputation which was the basis? Imputation based on the same unit in previous data
Imputation based on similar units
Imputation based on other sources
In case of imputation, which was the imputation rate (%)?

Not calculable
Estimated degree of bias caused by non-response? None
Which tools were used for correcting the data?

Same unit in previous data/similar units/other sources
Which organisation did the corrections?

Crop statistics Unit 
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

Not applicable


Time lag - final result

Not applicable


  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

2

2

2

1

2

1

3

1

not available

2

4

4
How many of them are forecasts (releases before the harvest)?

1

1

1

1

2

not available

not available

not available

not available

not available

not available

not available

not available
When was the first  forecasting published for the crop year on which is reported? (day/month/year) 29/03/2022 29/03/2022 29/03/2022 29/03/2022 21/04/2023 21/04/2023 21/04/2023 21/04/2023 21/04/2023 21/04/2023 21/04/2023
When were the final results published for the crop year on which is reported? (day/month/year) 17/09/2023 17/09/2023 17/09/2023 17/09/2023 17/09/2023 17/09/2023 30/05/2023 18/05/2023 18/05/2023 30/06/2023 19/05/2023 19/05/2023 19/05/2023 19/05/2023
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

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 in the previous 5 years? YES NO YES NO
If yes, to which were they related?
If other, which?
Which items were affected? C1111 - Common winter wheat and spelt
C1210 - Rye
C1310 - Winter barley		 F1111 - Apples for fresh consumption
F1121 - Pears for fresh consumption
T1100 - Navel oranges
Year of break (number)

2021

2020
Impact on comparability
Additional comments





 
8.2.1. Length of comparable time series

not applicable
8.3. Coherence - cross domain

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

Agricultural Census 2020
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    

Annual crop statistics (ha)

Agricultural Census 2020
Dried pulses and protein crops    

Annual crop statistics (ha)

Agricultural Census 2020
Root crops    

Annual crop statistics (ha)

Agricultural Census 2020
Oilseeds    

 

 

Annual crop statistics (ha)

Agricultural Census 2020
Other industrial crops (than oilseeds)    
Plants harvested green    

Annual crop statistics

(ha)

Agricultural Census

2020
Total vegetables, melons and strawberries    

Annual crop statistics (ha)

Agricultural Census 2020
Vegetables and melons    
Strawberries    

Annual crop statistics (ha)
Cultivated mushrooms  
Total permanent crops  

Annual crop statistics (ha)

Agricultural Census 2020
Fruit trees

Annual crop statistics (ha)

Agricultural Census 2020
Berries  
Nut trees  
Citrus fruit trees

Annual crop statistics (ha)

Agricultural Census 2020
Vineyards

Annual crop statistics (ha)

Agricultural Census 2020
Olive trees

Annual crop statistics (ha)

Agricultural Census 2020
If there were considerable differences, which factors explain them?

Different survey technique, very different years and different definitions

  





 
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

Annuario statistico italiano 2019 (istat.it)

 






 
9.2. Dissemination format - Publications

  Availability Links
Publications Electronic

https://www.istat.it/it/archivio/236772
Publications in English






 
9.3. Dissemination format - online database

Data tables - consultations

Not applicable


  Availability Links
On-line database accessible to users YES

http://dati.istat.it/

IstatData
Website National language

http://dati.istat.it/

IstatData





 
9.4. Dissemination format - microdata access

Availability Links
NO






 
9.5. Dissemination format - other

Not applicable.
9.6. Documentation on methodology

  Availability Links
Methodological report
Quality Report
Metadata None

http://dati.istat.it/
Additional comments

Definitions and classifications - http://dati.istat.it/

  






 
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? Further automation
Staff further training
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