Crop production (apro_cp)

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

Compiling agency: Statistics Portugal


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

Statistics Portugal

1.2. Contact organisation unit

Economic Statistics Department / Agriculture and Environment Statistics Unit
1.5. Contact mail address

Av. António José de Almeida, 5
1000-043 LISBOA


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 data 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.
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?  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? YES      
Which data sources have been dropped since the previous quality report?

Agriculture census
Type of source? Census
Why have they been dropped?

Past reference period
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
Expert estimate

IACS;

Data from producer organizations (in the scope of Regs. (CE) No 2200/96 and No 1234/07);

Focal points of the main agriculture associations and cooperatives;

Correspondents from Agriculture ministry.

  
Final area under cultivation Administrative data
Expert estimate

IACS;

Data from producer organizations (in the scope of Regs. (CE) No 2200/96 and No 1234/07);

Focal points of the main agriculture associations and cooperatives;

Correspondents from Agriculture ministry.
Production Administrative data
Expert estimate

Data from producer organizations (in the scope of Regs. (CE) No 2200/96 and No 1234/07);

Focal points of the main agriculture associations and cooperatives;

Correspondents from Agriculture ministry.

 
Yield Administrative data
Expert estimate

Data from producer organizations (in the scope of Regs. (CE) No 2200/96 and No 1234/07);

Focal points of the main agriculture associations and cooperatives;

Correspondents from Agriculture ministry.
Non-existing and non-significant crops Administrative data

IACS;

Data from producer organizations (in the scope of Regs. (CE) No 2200/96 and No 1234/07);

Correspondents from Agriculture ministry.
Table 2: Vegetables, melons and strawberries       
Early estimates for harvested areas Survey
Administrative data
Expert estimate

Vegetable survey;

IACS;

Data from producer organizations (in the scope of Regs. (CE) No 2200/96 and No 1234/07);

Focal points of the main agriculture associations and cooperatives;

Correspondents from Agriculture ministry.
Final harvested area Survey
Administrative data
Expert estimate

Vegetable survey;

IACS;

Data from producer organizations (in the scope of Regs. (CE) No 2200/96 and No 1234/07);

Focal points of the main agriculture associations and cooperatives;

Correspondents from Agriculture ministry.
Production Census
Survey
Administrative data
Expert estimate

Processed tomato census;

Vegetable survey;

Data from producer organizations (in the scope of Regs. (CE) No 2200/96 and No 1234/07);

Focal points of the main agriculture associations and cooperatives;

Correspondents from Agriculture ministry.
Non-existing and non-significant crops Survey
Administrative data

Vegetable survey;

IACS;

Data from producer organizations (in the scope of Regs. (CE) No 2200/96 and No 1234/07);

Correspondents from Agriculture ministry.
Table 3: Permanent crops      
Early estimates for production area Administrative data
Expert estimate

IACS;

Data from producer organizations (in the scope of Regs. (CE) No 2200/96 and No 1234/07);

Wine statistics (includes vineyard register);

Focal points of the main agriculture associations and cooperatives;

Correspondents from Agriculture ministry;

Rural development programme indicators.
Final production area Census
Survey
Administrative data
Expert estimate

Nurseries annual census;

IACS;

Data from producer organizations (in the scope of Regs. (CE) No 2200/96 and No 1234/07);

Wine statistics (includes vineyard register);

Focal points of the main agriculture associations and cooperatives;

Correspondents from Agriculture ministry;

Farm structural surveys;

Rural development programme indicators.
Production Census
Administrative data
Expert estimate

Olive oil press census;

Data from producer organizations (in the scope of Regs. (CE) No 2200/96 and No 1234/07);

Wine statistics (includes vineyard register);

Focal points of the main agriculture associations and cooperatives;

Correspondents from Agriculture ministry.
Non-existing and non-significant crops Administrative data

IACS;

Data from producer organizations (in the scope of Regs. (CE) No 2200/96 and No 1234/07);

Correspondents from Agriculture ministry.
Table 4: Agricultural land use      
Main area Census
Survey
Administrative data
Expert estimate

Vegetable survey;

Farm Structural Surveys;

IACS;

Data from producer organizations (in the scope of Regs. (CE) No 2200/96 and No 1234/07);

Land Parcel Identification System;

Focal points of the main agriculture associations and cooperatives;

Correspondents from Agriculture ministry.
Non-existing and non-significant crops Survey
Administrative data

IACS;

Data from producer organizations (in the scope of Regs. (CE) No 2200/96 and No 1234/07);

Correspondents from Agriculture ministry;

Farm Structural Surveys.
Total number of different data sources

10

  
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

 

If other type, please explain

 

Additional information

 
   


Which method is used for calculating the yield for main arable crops? Production divided by sown area
If another method, describe it.




 
3.2. Frequency of data collection

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

Olive oil press survey

Processed tomato survey

Vegetable survey

Nurseries annual survey

Correspondets from Agriculture ministry

IACS

Wine statistics (includes vineyard register)

Land Parcel Identification System

Data from producer organizations (in the scope of Regs. (CE) n.º 2200/96 e n.º 1234/07)
Planning (month-month/year)

10-11/2022

08-09/2022

10-11/2022

06-07/2022

N.A.

N.A.

N.A.

N.A.

N.A.
Preparation (month-month/year)

01/2023

09-10/2022

12/2022

08/2022

N.A.

N.A.

N.A.

N.A.

N.A.
Data collection (month-month/year)

02-05/2023

11-01/2022-2023

01-02/2023

09-11/2022

01-12/2022

01-05/2022

04-05/2023

06/2022

01-03/2023
Quality control (month-month/year)

02-06/2023

11-01/2022-2023

01-03/2023

09-11/2022

01-03/2023

06/2022

05-06/2023

06-09/2022

01-03/2023
Data analysis (month-month/year)

02-06/2023

11-02/2022-2023

01-03/2023

09-11/2022

01-03/2023

06/2022

05-06/2023

06-09/2022

01-03/2023
Dissemination (month-month/year)

07/2023

07/2023

07/2023

07/2023

07/2023

07/2023

07/2023

07/2023

07/2023
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? YES

- Area under cultivation (tab 1) - corresponds to the sown area. During the crop year the area can change in case of winter/spring crops.  Data on harvested area are not collected, but only data on sown area. In the event of a decrease in the area under cultivation due to natural disasters (floods, drought, hail, etc.), this is taken into account as a smaller estimated average yield per hectare of area under cultivation, so that any decrease in harvested area is taken into account in calculating total crop production.

- Harvested area for vegetable (tab 2) - is considered only as a sown.  Areas harvested more than once during a year are also recorded, which means it is possible to make available besides total area, the basic area.

- Permanent crops (table 3) - data on production area includes new plantations, isolated/sparsed trees, linear-planted trees and trees not belonging to agricultural holdings.
Are there differences between the national methodology and the methodology described in the Handbook concerning e.g. the item and aggregate calculations?
Are special estimation/calculation methods used for main crops from arable land?
Are special estimation/calculation methods used for vegetables or strawberries?
Are special estimation/calculation methods used for permanent crops for human consumption?
Are special estimation/calculation methods used for main land use? YES

The PT data on area is based on agricultural statistical system forseen for crop statistics complemented by data on the last FSS surveyed related to the following crops:  Plants harvested green, Other arable-land crops, Fallow land, Permanent grassland, Nurseries,
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) C1900 - Other cereals n.e.c. (buckwheat, millet, canary seed, etc.)
P1200 - Broad and field beans
P9000 - Other dry pulses and protein crops n.e.c.
F4000 - Nuts
P0000 - Dry pulses and protein crops for the production of grain (including seed and mixtures of cereals and pulses)
P1300 - Sweet lupins

C1900 - Other cereals n.e.c includes the area and production of sorghum, which are not possible to individualize in C1211 - sorghum;

F4000 - Nuts does not include the area of pine trees located outside agricultural holdings. It also does not include any production (in or outside agricultural holdings);

P9000 - Other dry pulses and protein crops n.e.c. includes the area and production of field peas, broad beans and sweet lupins, which are not possible to individualize in P1100 - Field peas, in P1200 - Broad and field beans and in P1300 - Sweet lupins;

P1200 - Broad and field beans only includes the area and production of field beans.

P0000 - Production of P1100 and P1300 are not available

P1300 - correspond to P1000 data
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.
G2900 - Other leguminous plants harvested green n.e.c.
G9100- Other cereals harvested green (excluding green maize)
G9900 - Other plants harvested green from arable land n.e.c.
V1900 - Other brassicas n.e.c.
V2900 - Other leafy or stalked vegetables n.e.c.
V3900 - Other vegetables cultivated for fruit n.e.c.
V4900 - Other root, tuber and bulb vegetables n.e.c.
V5900 - Other fresh pulses n.e.c.
F1190 - Other pome fruits n.e.c.
F1290 - Other stone fruits n.e.c.
F2900 - Other fruits from subtropical and tropical climate zones n.e.c.
F3900 - Other berries n.e.c.
F4900 - Other nuts n.e.c.
T2900 - Other small citrus fruits (including hybrids) n.e.c.
R9000 - Other root crops n.e.c.

C1900 - includes: sorghum and other cereals;

P9000 - includes:  other dry pulses

R9000 - includes: fodder roots and brassicas

G2900 -  leguminous plants harvested green like sweet lupins (Lupinus luteus, L. albus), vetches (Vicia sativa), crimson (Trifolium incarnatum L.) and others.

G9100 - rye, oat and sorghum harvested green

G9900 - ryegrass and consociations.  

V1900 - Brassica napus L., Kohlrabi (stem turnip), Chinese cabbage, Cow cabbage, Savoy cabbage and Tronchuda cabbage

V2900 - Spinach beet, Watercress, Common purslane, Chives, Turnip leaves, Rucola, Corn-salad and other leafy or stalked vegetables; Other leafy or stalked vegetables

V3900 - Sweet corn; Piri piri; Other cultivated for fruit

V4900 - Turnip; Fodder parsnips; Rutabaga; Other root, tuber and bulb vegetables

V5900 - Green broad been; Other fresh pulses

F1190 - quince

F1290 - loquats/Japanese medlar

F2900 - pineapple; pomegranate; persimmon ; Other fruits from subtropical and tropical climate zones

F3900 - blackberry

F4900 - pine nut

T2900 - Other small citrus fruits (including hybrids) like tangerina, tangelo, and others.

R9000 - fodder roots and brassicas; Other root crops

 


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).

Use, as much as possible, exaustive surveys. In the case of sample surveys, the samples are design according this threshold and in the case of administrative sources the data are calibrated with farm register.
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)

December/2021
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 %)
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 %)

4,5%
Cultivated mushrooms (in %)

0%
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

Vegetable survey

Processed tomato census

Olive oil press census

Nurseries annual census
Which survey method was used? Face-to-face interview On-line electronic questionnaire filled in by respondent On-line electronic questionnaire filled in by respondent On-line electronic questionnaire filled in by respondent
If 'other', please specify
Please provide a link to the questionnaire

http://smi.ine.pt/SuporteRecolha/Detalhes/10176

Questionnaire processed tomato survey - industries;

Questionnaire processed totato survey cooperatives

 

http://smi.ine.pt/SuporteRecolha/Detalhes/10051

 http://smi.ine.pt/SuporteRecolha/Detalhes/10075
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 - Direct payments

LPIS - Land Parcel Identification System

Wine statistics

Data from producers organisations

Rural development programme indicators
Description

Area declared for each crop, by agricultural holding, for subsidy purposes (direct payments)

Data on land use

Vineyard register

Annual wine declaration

Area and production declared by producers organisations in the scope of Regs. (CE) n.º 2200/96 and n.º 1234/07.

Area of permanent crops instaled under the Rural development programme
Data owner (organisation)

IFAP (IACS)

IFAP (IACS)

IVV (Vineyard and Wine Institute)

(Number / Text)

PDR 2020 (Rural Development Programme) Management Authority
Update frequency Once per year or more often Once per year or more often Once per year or more often Once per year or more often Less frequently than once per year
Reference date (month/year)

May/year n

June/year n

November/year n

December/year n-1

N.A.
Legal basis

Reg. (CE) N.º 1307/2013

Reg. (CE) N.º 1307/2013

Reg. (CE) N.º 1493/99 and Reg. (CE) N.º1282/2001

Regs. (CE) n.º 2200/96 and n.º 1234/07

Reg. (CE) N.º 1306/2013
Reporting unit

Beneficiary

Parcel

Vineyard register: parcel

Annual wine declaration: wine producer

Producer

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

Name, tax number, adress, zip code, phone

Tax number

N.A.

N.A.

N.A.
Percentage of mismatches (%)
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 Validation
Directly for estimates		 Validation Validation
Data used for other purposes, which?

Update farm register

Calibration

No

No

No
Which variables were taken from administrative sources?

Surfaces per crop and identification variables

Main crop groups

Wine production

Surface and production per crop

Surface per crop
Were there any differences in the definition of the variables between the administrative source and those described in the Regulation? YES YES NO NO NO
Please describe the differences

Diferent scopes and concepts

Diferent scopes and concepts

N.A.

N.A.

N.A.
What measures were taken to eliminate the differences?

It depends from european legislation

It depends from european legislation

N.A.

N.A.

N.A.
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?

Analyses of trends; level of adherence to crop year; time series coherence

Spatial analyses and trends

Analyses of trends; level of adherence to wine year; time series coherence

Analyses of trends; level of adherence to crop year; time series coherence

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

Not exhaustive data; it depends from politics; the observation unit is not the holding but rather the beneficiary; diferent concepts and purposes.

Not exhaustive data; it depends from politics; the observation unit is not the holding but rather the parcel; diferent concepts and purposes

Timeliness

Timeliness

Not exhaustive data; it depends from politics. 


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

Rice estimations

Winter cereals and grain maize estimations

Pear estimations

Processed tomato estimations

Olive oil estimations

Area, production and non-existing and non-significant crops of arable land and permanent crops. Agricultural land use.
Data owner (organisation)

AOP

ANPOC 

ANP

AIT

Casa do Azeite

Ministry of Agriculture, Forestry and Rural Development
Update frequency (e.g. 1 year or 6 months) 4 times per year 4 times per year Yearly Yearly Yearly Monthly
Reference date (Month/Year  e.g. 1/22 - 8/22)

April/Year n - July/Year n

January/Year n - April/Year n - July/Year n

June/Year n

August/Year n

May/Year n

January to December/Year n
Legal basis

N.A.

N.A.

N.A.

N.A.

N.A.

N.A.
Use purpose of the estimates?

Validation

Validation

Validation

Validation

Validation

Directly for estimates;

Validation 
What kind of expertise the experts have?

Sectorial knowledge (CEO of national association)

Sectorial knowledge (member of the Executive Board of national association)

Sectorial knowledge (senior technician of national association)

Sectorial knowledge (senior technician of national association)

Sectorial knowledge (CEO of national association)

Sectorial knowledge (ministry experts)
What kind of estimation methods were used?

AOP is the most representative association of rice producers. The information on area and production of their associates, combined with the information on the agrometeorological conditions, the prevalence of pests and diseases affecting this crop and the market conditions, provide a fairly trustable estimation to validate/cross-check the data from other sources.

ANPOC is the most representative association of cereals, oilseeds and protein crops producers. The information on area and production of their associates, combined with the information on the agrometeorological conditions, the prevalence of pests and diseases affecting these crops and the market conditions, provide a fairly trustable estimation to validate/cross-check the data from other sources.

ANP is the most representative association of pear producers (the production of its associates represents around 85% of the total production). The information on area and production of their associates, combined with the information on the agrometeorological conditions, the prevalence of pests and diseases affecting this crop and the market conditions, provide a quite trustable estimation to validate/cross-check the data from other sources.

AIT is an association of tomato industries. The information on area and production of their associates, combined with the information on the agrometeorological conditions, the prevalence of pests and diseases affecting this crop and the market conditions, provide a quite trustable estimation to validate/cross-check the data from other sources.

Casa do Azeite is an association of manufacters and packers of olive oil. The information on production/comercialisation of their associates, combined with the information on the agrometeorological conditions, the prevalence of pests and diseases affecting this crop and the market conditions, provide a quite trustable estimation to validate/cross-check the data from other sources.

Field observation and field knowledge (field experience) acquired by the technicians of the Regional Directorates of Agriculture of the Ministry of Agriculture, Forestry and Rural Development usually designated by statistical coordinators located in the various agrarian zones.
Were there any differences in the definition of the variables between the experts' estimates and those described in the Regulation? NO NO NO NO NO NO
If yes, please describe the differences

N.A.

N.A.

N.A.

N.A.

N.A.

N.A.
What measures were taken to eliminate the differences?

N.A.

N.A.

N.A.

N.A.

N.A.

N.A.
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?

Analyses of trends; level of adherence to crop year; time series coherence

Analyses of trends; level of adherence to crop year; time series coherence

Analyses of trends; level of adherence to crop year; time series coherence

Analyses of trends; level of adherence to crop year; time series coherence

Analyses of trends; level of adherence to crop year; time series coherence

Analyses of trends; level of adherence to crop year; time series coherence
What were the possible limitations, drawbacks of using the data from expert estimate(s)?

Continuity over time, as it is a relatively personal information;

Possible politicization.

Continuity over time, as it is a relatively personal information;

Possible politicization.

Continuity over time, as it is a relatively personal information;

Possible politicization.

Continuity over time, as it is a relatively personal information;

Possible politicization.

Continuity over time, as it is a relatively personal information;

Possible politicization.
Additional comments

The expert estimates are used to validate/cross-check data from other sources. As PT has a large number of agricultural holdings, a wide variety of crops and a wide range of physical dimension of agricultural holdings, it plans to continue with this source of validation.

The expert estimates are used to validate/cross-check data from other sources. As PT has a large number of agricultural holdings, a wide variety of crops and a wide range of physical dimension of agricultural holdings, it plans to continue with this source of validation.

The expert estimates are used to validate/cross-check data from other sources. As PT has a large number of agricultural holdings, a wide variety of crops and a wide range of physical dimension of agricultural holdings, it plans to continue with this source of validation.

The expert estimates are used to validate/cross-check data from other sources (namely processed tomato survey and information from Agriculture ministry). PT plans to continue with this source of validation.

The expert estimates are used to validate/cross-check data from other sources. As PT has a large number of agricultural holdings, a wide variety of crops and a wide range of physical dimension of agricultural holdings, it plans to continue with this source of validation.

The data collected by these correspondents is essential to the direct determination of the estimations. Although there is a reduction on the human resources allocated to this project, PT plans to continue with this source of information.


 



Annexes:
Nurseries Annual Census
Olive Oil Press Census
Vegetable survey
Processed Tomato Survey Industries
Processed Tomato Survey Cooperatives
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? Previous results
Farm Structure Survey
If other, please describe






 
3.5. Data compilation

Not Applicable.
3.6. Adjustment

Not applicable.


4. Quality management Top
4.1. Quality assurance

  Completeness Punctuality Accuracy Reliability Overall quality
How would you describe the overall quality development since 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

Agricultural statistics in PT already achieved, comparing with other statistical domains at Statistics Portugal, a good performance. Since 2017 that Statistics Portugal has been envolved in Agricultural Census, a task which absorved most of the human resources of Agricultural Unit. In the comming years we didn't expect investments and improvments in this statistical domain, not only because of the data quality already achieved but also because of the huge requirements expected from ESS (SAIO implementig acts, FSS 2023, organic farming, etc).

        






 
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?
If not, which additional data are collected?
Additional comments

Statistics Portugal decided to answer with “No” to the question: Are there known unmet user needs?. However this is a tricky question because there are always new data needs, particularly cyclical ones. For example at some point the national pharmaceutical industry had contracts with farmers for the production of poppy. At that time several users asked if Statistics Portugal collected that data. COVID 19, Ukrain war, and new CAP are other examples that create emergent needs. 

With theses examples we just want to mention that the question for the future in the quality report should be:
Are there unmet user needs of a structural and relevant nature that are not being considered in the field of crop statistics?

 

 






 
5.2. Relevance - User Satisfaction

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

Despite Statistics Portugal doesn’t  have a specific satisfaction survey for this domain, we are aware of the quality level of data produced, either because the statistical press releases disseminated under this domain are often presented in specialized magazines and newspapers, or through opinion articles written by experts praising the quality of these statistics, yet either because of the statistical information produced in this area to be often part of the speech of renowned national experts. Another aspect that allows us to measure customer satisfaction is the feedback from experts and the invitations received to participate in seminars and studies.






 
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

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

9
If stratified, stratification basis? Size
Specialisation
If 'other', please specify
Size of total population

Initial 7557 -> Final 7455

 
Size of sample

Inictal 1087 -> Answers 838
Which methods were used to assess the sampling error?  Relative standard error
Comparisons with other sources
If other, which?
Which methods were used to derive the extrapolation factor?  Basic weight
If other, which?
If CV (co-efficient of variation) was calculated, please describe the calculation methods and formulas

See RSE - Formulae applied for estimation methods.doc annex
If the results were compared with other sources, please describe the results

The results are compared with administrative data, namely the data colected from Farmers Organisations, sent to DGAgri. As the scope of the survey is wider, the results are higher than the sum of the different sources.
Which were the main sources of errors?

Sampling


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

Vegetable survey
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 %)

2.8%
Cultivated mushrooms (in %)

40.8%
Total permanent crops (in %)
Fruit trees (in %)
Berries (in %)
Nut trees (in %)
Citrus fruit trees (in %)
Vineyards (in %)
Olive trees (in %)
Additional comments            




 



Annexes:
Coefficient of variation
6.3. Non-sampling error
6.3.1. Coverage error

Over-coverage - rate

In 2022, the Vegetable survey had a over-coverage rate of:

 - Number of holdings with ceased activity: 38;

 - Number of holdings with activity that stoped producing vegetables: 113;

 - Number of holdings in the gross sample: 1078

 - Ratio: 151/1078=14,0%


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

Vegetable Survey

Processed tomato census

Olive oil press census

Nurseries annual census
Error type Under-coverage
Over-coverage
Misclassification
Contact errors		 Misclassification
Contact errors		 Misclassification
Contact errors		 Misclassification
Contact errors
Degree of bias caused by coverage errors Low None None None
What were the reasons for coverage errors?

Under-coverage errors: the possibility that the farm register (BAA) might not be exhaustive;

Over-coverage: the possibility that the farm might change its productive orientation, stop producing vegetables and, therefore, be under no conditions in the scope of the survey.

Under-coverage and over-coverage errors: the processed tomato survey is an exaustive survey, inquiring all active enterprises and cooperatives with activity related to producing/processing tomato and which are registered in IACS. Therefore coverage errors are not expected.

Under-coverage errors and over-coverage errors: the olive oil press survey is an exaustive survey, inquiring all active olive oil press which are registered in IACS. Therefore coverage errors are not expected.

Under-coverage errors and over-coverage errors: the nurseries annual survey is an exaustive survey, inquiring all nurseries (with activity in production and marketing of fruit plant propagating material) which are registered in DGADR - Directorate-General for Agriculture and Rural Development. Therefore coverage errors are not expected.
Which actions were taken for reducing the error or to correct the statistics?

Under-coverage errors: the necessary steps were taken to identify holdings not included therein – new agricultural holdings – thereby ensuring an exhaustive coverage of data collection. For this purpose, during an interview with a holder in their list of agricultural holdings, or through other contacts, interviewers would ask about land transactions. Subsequently, they would check whether such holders were included in BAA, and, if not, they would collect information enabling them to enter into contact with the holders in question.

Over-coverage: no method was applied to correct data for over-coverage errors.

Misclassification errors: no method was applied. We assumed that this kind of errors if exist, are residual.

Contact errors: Wrong details in contact data were investigated and correted during the operation.

Misclassification errors: no method was applied. We assumed that this kind of errors if exist, are residual.

Contact errors: Wrong details in contact data were investigated and correted during the operation.

Misclassification errors: no method was applied. We assumed that this kind of errors if exist, are residual.

Contact errors: Wrong details in contact data were investigated and correted during the operation.

Misclassification errors: no method was applied. We assumed that this kind of errors if exist, are residual.

Contact errors: Wrong details in contact data were investigated and correted during the operation.
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

Vegetable Survey

Processed tomato survey

Olive oil press survey

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

3

16

18

17
Preparatory (field) testing of the questionnaire? NO NO NO NO
Number of units participating in the tests? 

N.A.

N.A.

N.A.

N.A.
Explanatory notes/handbook for surveyors/respondents?  YES YES YES YES
On-line FAQ or Hot-line support for surveyors/respondents? YES YES YES YES
Were pre-filled questionnaires used? NO NO NO NO
Percentage of pre-filled questions out of total number of questions

N.A.

N.A.

N.A.

N.A.
Were some actions taken for reducing the measurement error or to correct the statistics? YES NO NO NO
If yes, describe the actions and their impact

The methodology used to avoid/minimise incorrect and/or incomplete data included:
• Interview techniques (interpretation of the questions) – questions would be posed to the interviewee in a way to avoid personal interpretations;
• Outline of the agricultural holding – on the occasion of the interview, the interviewer would always prepare an outline of the agricultural holding characterising it correctly, to be used as an auxiliary tool in subsequent analyses. The outline would be duly identified and attached to the questionnaire;
• Entry of “Observations” – the “Observations” field of the questionnaire should include all information deemed relevant by the interviewer, which would help to validate and analyse collected data after the interview. This prevented questionnaires from being returned and/or avoided subsequent contacts with the interviewee to confirm/justify the information.






 
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

Vegetable survey

Processed tomato survey

Olive oil press survey

Nurseries annual survey
Unit level non-response rate (in %)

0.7%

0%

1%

1%
Item level non-response rate (in %)              
               - Min% / item

Not available. All known cases of non-response items were solved by re-interviewing the unit.

Not available. All known cases of non-response items were solved by re-interviewing the unit.

Not available. All known cases of non-response items were solved by re-interviewing the unit.

Not available. All known cases of non-response items were solved by re-interviewing the unit.
               - Max% / item

Not available. All known cases of non-response items were solved by re-interviewing the unit.

Not available. All known cases of non-response items were solved by re-interviewing the unit.

Not available. All known cases of non-response items were solved by re-interviewing the unit.

Not available. All known cases of non-response items were solved by re-interviewing the unit.
               - Overall%

Not available. All known cases of non-response items were solved by re-interviewing the unit.

Not available. All known cases of non-response items were solved by re-interviewing the unit.

Not available. All known cases of non-response items were solved by re-interviewing the unit.

Not available. All known cases of non-response items were solved by re-interviewing the unit.
Was the non-response been treated ? NO NO NO NO
Which actions were taken to reduce the impact of non-response?

Not available. All known cases of non-response items were solved by re-interviewing the unit.

Not available. All known cases of non-response items were solved by re-interviewing the unit.

Not available. All known cases of non-response items were solved by re-interviewing the unit.

Not available. All known cases of non-response items were solved by re-interviewing the unit.
Which items had a high item-level non-response rate? 

Not available. All known cases of non-response items were solved by re-interviewing the unit.

Not available. All known cases of non-response items were solved by re-interviewing the unit.

Not available. All known cases of non-response items were solved by re-interviewing the unit.

Not available. All known cases of non-response items were solved by re-interviewing the unit.
Which methods were used for handling missing data?
(several answers allowed)		 Follow-up interviews Follow-up interviews Follow-up interviews Follow-up interviews
In case of imputation which was the basis?
In case of imputation, which was the imputation rate (%)?
Estimated degree of bias caused by non-response? Insignificant None Insignificant Insignificant
Which tools were used for correcting the data?

The following procedures where taken during the data collection period. No errors where detected/corrected after that period.

Questionnaire returned by the chain of collection
In addition to the interviewer, the questionnaire was analysed by different profiles in the chain of collection, implying its return in case of error and/or information misaligned with local circumstances. This led to a more thorough analysis/validation. Returning the questionnaire to the interviewer and identifying the reasons avoided the perpetuation of possible errors and erroneous interpretations of the concepts. 

Regular meetings involving the collection structure
Meetings carried out at the different levels of the chain of collection facilitated information flows among them. Discussing the main issues/problems arising from the work developed made it easier to standardise the criteria to solve similar situations.

Procedures to confirm/correct microdata
When incorrections are detected or when questions raise doubts, the solution may involve confirming the situation after analysis of the “Observations” field, in a simpler case, or a new contact with the holder, in the most complex case.

N.A.

N.A.

N.A.
Which organisation did the corrections?

The corrections were made by the interviewers and the diferent profiles in the chain (local and regional staff and national staff) on the custom-made software application that supports the agricultural survey system of Statistics Portugal (SAGR).
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

As a rule, data in surveys are not subject to revisions.

Not relevant
6.6. Data revision - practice

As a rule, data in surveys are not subject to revisions.

 
6.6.1. Data revision - average size

As a rule, data in surveys are not subject to revisions.


7. Timeliness and punctuality Top
7.1. Timeliness

Time lag - first result

Depends from the crop. The first result concerns the cereals area which is published 2 months after the sowing.


Time lag - final result

Depends on the crop. Usually, the final result is published 6 months after the last harvest. In this particularly crop year, as Agricultural Census occurred simultaneously, final data was released with a larger delay (18 mounths).


  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?

9

7

7

7

1

6

0

1

0

9

1

9

3

6

6
How many of them are forecasts (releases before the harvest)?

7

5

5

5

0

4

1

0

0

7

0

7

1

5

5
When was the first  forecasting published for the crop year on which is reported? (day/month/year) 20/12/2021 19/04/2022 21/03/2022 19/04/2022 19/04/2022 20/05/2022 19/07/2022 19/08/2022 19/07/2022 20/09/2022
When were the final results published for the crop year on which is reported? (day/month/year) 21/07/2023 21/07/2023 21/07/2023 21/07/2023 21/07/2023 21/07/2023 21/07/2023 21/07/2023 21/07/2023 21/07/2023 21/07/2023 21/07/2023 21/07/2023 21/07/2023 21/07/2023
Additional comments

Statistics Portugal plan for the dissemination includes an annual publication that is published on the 3rd week of july of year n+1, 4 agricultural forecasts and 12 monthly newsletters that includes monthly forecasts and further information has price, milk and fisheries statistics.

Statistics Portugal plan for the dissemination includes an annual publication that is published on the 3rd week of july of year n+1, 4 agricultural forecasts and 12 monthly newsletters that includes monthly forecasts and further information has price, milk and fisheries statistics.

Statistics Portugal plan for the dissemination includes an annual publication that is published on the 3rd week of july of year n+1, 4 agricultural forecasts and 12 monthly newsletters that includes monthly forecasts and further information has price, milk and fisheries statistics.

Statistics Portugal plan for the dissemination includes an annual publication that is published on the 3rd week of july of year n+1, 4 agricultural forecasts and 12 monthly newsletters that includes monthly forecasts and further information has price, milk and fisheries statistics.

Statistics Portugal plan for the dissemination includes an annual publication that is published on the 3rd week of july of year n+1, 4 agricultural forecasts and 12 monthly newsletters that includes monthly forecasts and further information has price, milk and fisheries statistics.

Statistics Portugal plan for the dissemination includes an annual publication that is published on the 3rd week of july of year n+1, 4 agricultural forecasts and 12 monthly newsletters that includes monthly forecasts and further information has price, milk and fisheries statistics.

Statistics Portugal plan for the dissemination includes an annual publication that is published on the 3rd week of july of year n+1, 4 agricultural forecasts and 12 monthly newsletters that includes monthly forecasts and further information has price, milk and fisheries statistics.

Statistics Portugal plan for the dissemination includes an annual publication that is published on the 3rd week of july of year n+1, 4 agricultural forecasts and 12 monthly newsletters that includes monthly forecasts and further information has price, milk and fisheries statistics.

Statistics Portugal plan for the dissemination includes an annual publication that is published on the 3rd week of july of year n+1, 4 agricultural forecasts and 12 monthly newsletters that includes monthly forecasts and further information has price, milk and fisheries statistics.

Statistics Portugal plan for the dissemination includes an annual publication that is published on the 3rd week of july of year n+1, 4 agricultural forecasts and 12 monthly newsletters that includes monthly forecasts and further information has price, milk and fisheries statistics.

Statistics Portugal plan for the dissemination includes an annual publication that is published on the 3rd week of july of year n+1, 4 agricultural forecasts and 12 monthly newsletters that includes monthly forecasts and further information has price, milk and fisheries statistics.

Statistics Portugal plan for the dissemination includes an annual publication that is published on the 3rd week of july of year n+1, 4 agricultural forecasts and 12 monthly newsletters that includes monthly forecasts and further information has price, milk and fisheries statistics.

Statistics Portugal plan for the dissemination includes an annual publication that is published on the 3rd week of july of year n+1, 4 agricultural forecasts and 12 monthly newsletters that includes monthly forecasts and further information has price, milk and fisheries statistics.

Statistics Portugal plan for the dissemination includes an annual publication that is published on the 3rd week of july of year n+1, 4 agricultural forecasts and 12 monthly newsletters that includes monthly forecasts and further information has price, milk and fisheries statistics.

Statistics Portugal plan for the dissemination includes an annual publication that is published on the 3rd week of july of year n+1, 4 agricultural forecasts and 12 monthly newsletters that includes monthly forecasts and further information has price, milk and fisheries statistics.




 
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? NO NO NO NO
If yes, to which were they related?
If other, which?
Which items were affected?
Year of break (number)
Impact on comparability
Additional comments





 
8.2.1. Length of comparable time series

Not applicable
8.3. Coherence - cross domain

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

Prices statistics
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    

Cereals are the crops where IACS presents a better coverage, since most of the cereals producers are in the IACS system. However different type of cereals presents different level of coverage, namely in what concerns maize either because in north of the country there is many small holdings outside of the system and because some times the producers inscribe the areas in green maize.

analysing the main species, we found low deviations comparing with IACS for rice (1%), burley (3%) and wheat (4%). Higher deviations could be found in maize (25%)

 

 
Dried pulses and protein crops    

Dried Pulses are not well covered by IACS. Traditionaly are cultivated in small areas / small holdings (many times in kitchen gardens). For those reasons many of these famers are not included in IACS, therefore the deviations are high.

Analyzing the main species, we found high deviations comparing with IACS for beans (37%) and chick pease (18%)

 

 
Root crops    

In PT, potatoes are produced as a single crop, as a vegetable, rotating with other vegetables and in kicthen gardens. Since vegetable producers are not in IACS and many of the small holdings too, the deviations are high.

For root crops, we only considered for comparisson the potatoes which deviation for 2016 was 212%.

 

 
Oilseeds    

The deviation is less than 10%

 

 
Other industrial crops (than oilseeds)    

 

 
Plants harvested green    

IACS is not a suitable data source for these kind of crops.

 

 
Total vegetables, melons and strawberries    

IACS is not a suitable data source for these kind of crops.

 

 
Vegetables and melons    

IACS is not a suitable data source for these kind of crops.

 

 
Strawberries    

IACS is not a suitable data source for these kind of crops.

 

 
Cultivated mushrooms  

IACS is not a suitable data source for these kind of crops.

 

 
Total permanent crops  

 

 
Fruit trees

Not applicable.

Many of the holdings with orchards are not in IACS system, therefore bigger deviations are expected.

 

 
Berries  

Many of the holdings prodicing berries are not in IACS system, therefore bigger deviations are expected.

 

 
Nut trees  

Many of the holdings prodicing nuts are not in IACS system, therefore bigger deviations are expected.

 

 
Citrus fruit trees

Not applicable.

Many of the holdings prodicing citurs fruit are not in IACS system, therefore bigger deviations are expected.

 

 
Vineyards

Many of the holdings prodicing vineyards are not in IACS system, therefore bigger deviations are expected.

 

 
Olive trees

Not applicable.

Many of the holdings prodicing olive are not in IACS system, therefore bigger deviations are expected.

 

 
If there were considerable differences, which factors explain them?

Statistics Portugal questions this comparison, either because we are comparing datasets with different methodologies and with their own scopes, either because they occur in different reference periods.

  





 
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

Agricultural Statistics - 2021 (crop season 2020/21)

Statistics Portugal presents the 2021 compendium of “Agriculture Statistics” edition, reporting the information to the last available reference period and a wide scope of data concerning national agriculture activity. 

In crop year 2020/2021, winter cereal production was 189.2 thousand tonnes, one of the lowest in the last 35 years, reflecting an almost generalized reduction in all species. In summer crops, production increased by 10.3% in maize and 32.5% in rice.

Apple production reached 368.2 thousand tonnes, the second most productive harvest in the last 35 years, kiwi production exceeded 55 thousand tonnes for the first time and the cherry season was the most productive in the last 49 years. The entry into production of new intensive almonds groves contributed to a 31.1% increase in production, reaching 41.5 thousand tonnes of almonds.

Wine production increased by 14.7%, reaching 7.2 million hectolitres, a volume higher than the average of the last five years (6.4 million hectolitres) and olive oil production soared to an all-time high of 2.29 million hectolitres.

The number of rural fires in 2021 in Portugal was 8,230, 15.0% fewer occurrences compared to 2020 and the burnt area was 28.47 thousand hectares, the second lowest in the last decade.

The commercial deficit of agricultural and food products reached 3,845.9 million Euros in 2021, which represents an increase of 401.6 million Euros over the previous year, mostly due to Cereals (an increase of 154.6 million Euros in the deficit).

The decrease in production (-8.1%) and in exports (-4.5%), and the same level of imports, further worsen the self sufficiency of cereals (except rice), reaching 19.4% in 2021.

There were significant increases in the agricultural goods output price index (+5.6%), in the price index of goods and services currently consumed in agriculture (+14.2%) and in the price index of goods and services contributing to agricultural investment (+3.2%).

 See the Publication

 Agricultural Statistics - 2022 (crop season 2021/22)

Crop year 2021/2022 in mainland Portugal was meteorologically characterized as extremely hot (the hottest since 1931/32) and very dry. The meteorological drought of 2022 was one of the most severe since systematic records exist, with almost the entire territory in severe and extreme drought in the months of February, May, June, July and August.

The 2022 winter cereal campaign was the worst ever, having even been lower than in 2012, matching the worst cereal harvests with the most severe droughts.

The decrease in apple production was 20.9% compared to last year (which was the second most productive harvest in the last 35 years). The pear harvest ended with a 41.3% reduction in production compared to the previous campaign, due to adverse weather conditions and brown spot disease. 

In Cova da Beira, the cherry drop was lower than expected and the harvest took place in good conditions, which contributed to a production slightly higher than that achieved in the previous campaign (+3.1%), being the most productive ever. In orange, the increase in production, both in early and late varieties, contributed to the best campaign ever, 4.0% above the production recorded in 2021.

In Portugal, 10 439 rural fires broke out in 2022, 26.8% more occurrences than in 2021. The number of ignitions, although higher than the last two years and the average of the last five-year period, was about half the average number of fires recorded in the last 20 years

The commercial deficit of agricultural and food products (excluding beverages and fishery products) reached EUR 5,222.8 million in 2022, which represents an increase of EUR 1,374.5 million compared to the previous year.

in 2022, the deficit at the level of self sufficiency of meat worse, but Portugal maintained a self sufficiency surplus in milk for public consumption, wine, olive oil and rice.

Sharp increases in price indexes of agricultural goods output (+20.5%), goods and services currently consumed in agriculture (+30.0%) and of goods and services contributing to agricultural investment (+10.7%) were recorded.

See the Publication 

 

 

 






 
9.2. Dissemination format - Publications

  Availability Links
Publications Electronic
Paper

Agriculture Statistics - 2022 

Agriculure Statistics 2021      

 
Publications in English None






 
9.3. Dissemination format - online database

Data tables - consultations

Not applicable


  Availability Links
On-line database accessible to users YES

Base de dados
Website National language
English

www.ine.pt





 
9.4. Dissemination format - microdata access

Availability Links
NO






 
9.5. Dissemination format - other

Free : www.ine.pt
9.6. Documentation on methodology

  Availability Links
Methodological report National language
English

http://smi.ine.pt/DocumentacaoMetodologicaPorTema?clear=True
Quality Report
Metadata National language
English

http://smi.ine.pt/
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? Staff further training
If other, which?
Burden reduction measures since the previous reference year  Easier data transmission
If other, which?

 






 


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


12. Comment Top
Restricted from publication


Related metadata Top


Annexes Top