Pesticide use in agriculture (aei_pestuse)

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

Compiling agency: ISTAT -  Italian National Institute of Statistics


Eurostat metadata
Reference metadata
1. Contact
2. Statistical presentation
3. Statistical processing
4. Quality management
5. Relevance
6. Accuracy and reliability
7. Timeliness and punctuality
8. Coherence and comparability
9. Accessibility and clarity
10. Cost and Burden
11. Confidentiality
12. Comment
Related Metadata
Annexes (including footnotes)
 



For any question on data and metadata, please contact: EUROPEAN STATISTICAL DATA SUPPORT

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1. Contact Top
1.1. Contact organisation

ISTAT -  Italian National Institute of Statistics

1.2. Contact organisation unit

  

DIPS- Department for statistical production

DCAT – Directorate for environmental and territorial statistics

ATC – Division for agricultural statistics

1.5. Contact mail address

Via della Civiltà del Lavoro 52, 00144 Roma, Italy

Room 310 (2nd floor)


2. Statistical presentation Top
2.1. Data description

See sub-categories below.

2.1.1. Main characteristics of statistics

Object : Statistics on the use of  pesticides, which are plant protection products, data-ref years 2015-2019.

Italy collects data on the use of pesticides by farmers and growers, by means of sample surveys.

Surveys produce estimates at national level.

Data collection is by means of Computer Assisted Telephone Interviews (CATI). In the last years a preliminary phase of Computer Assisted Web Interview (CAWI) was introduced.

Estimates are about quantities and areas treated (the basic area treated, defined as the physical area of the crop treated at least once with a given active substance, independently of the number of applications).

2.1.2. Reference period of data collection

2015-2019

2.1.3. National legislation
No
2.1.3.1. Name of national legislation
2.1.3.2. Link to national legislation
2.1.3.3. Responsible organisation for national legislation
2.1.3.4. Year of entry into force of national legislation
2.1.3.5. Coverage of variables required under EU regulation
2.1.3.6. Divergence national definitions from EU regulation
2.1.3.7. Legal obligation for respondents
2.1.4. Additional comments data description
2.2. Classification system

The classification used for pesticides corresponds to Annex III of Regulation (EC) No 1185/2009 (http://data.europa.eu/eli/reg/2009/1185/2017-03-09) of the European Parliament and of the Council.
The classification system for crops derives from the Annual crop statistics Handbook 2019 (https://ec.europa.eu/eurostat/cache/metadata/Annexes/apro_cp_esms_an1.pdf).

2.3. Coverage - sector

See sub-categories below.

2.3.1. Crops covered by the statistics

See the attached Excel file in the Annexes.

2.3.2. Commercial non-agricultural uses of pesticides

Not applicable

2.4. Statistical concepts and definitions

The data reported are the quantity of each active substances listed in Annex III of Regulation 1185/2009 contained in plant protection products used on a selected crop, expressed in kg. The area treated with each substance are expressed in hectares.

2.5. Statistical unit

Farms and growers

2.6. Statistical population

Agricultural holdings with relevant crops included in the list of the Minister for Agriculture

2.7. Reference area

See sub-categories below.

2.7.1. Geographical area covered

The entire territory of the country.

Vatican and San Marino are excluded.

2.7.2. Inclusion of special territories
2.8. Coverage - Time

One crop per year from 2003 to 2014, two crops per year since 2015.

2.9. Base period

Not applicable for Pesticide Use Statistics, because it is not based on an index number of time series.


3. Statistical processing Top
3.1. Source data

See the attached Excel file in the Annexes.

3.2. Frequency of data collection

Two surveys per year

3.3. Data collection

See the attached Excel file in the Annexes.

3.4. Data validation

Validation activities include: comparing the statistics with previous cycles; confronting the statistics against other relevant data; investigating inconsistencies in the statistics; performing micro and macro data editing; outlier detection.

3.4.1. Data validation measures
Manual
Automatic
3.4.2. Target of data validation measures
Outliers
Aggregates
Consistency
3.4.3. Specification target of data validation
3.5. Data compilation

Computation of aggregates: the quantity of pesticides products used is collected and the relative quantities of active substances are derived, then aggregates are obtained by summing up according to the hierarchical classifications (major groups, categories of products, chemical classes).
Conversion of unit: quantities are reported in kg and collected in grams, litres (1l=1kg) and kilograms
Imputation for replacing missing data is not needed.
Weighting: in 2015 basic weights were used, 2016-2019 calibration was used to adjust weights
Outlier detection: clerical detection of most influential units, for surfaces. Quantities are checked on the basis of the ratio quantity by hectare and it is compared with amount to be used as specified in the label

3.6. Adjustment

Not applicable


4. Quality management Top
4.1. Quality assurance

See sub-categories below.

4.1.1. Quality management system in organisation
Yes
4.1.2. Specification of implementation

Since the 90s Istat adopted a systematic approach to ensure quality in both statistical information and service to the community. For this purpose, the Italian National Institute of Statistics has defined a quality policy providing itself with appropriate tools as well as management changes to carry it out.

Istat quality policy is aimed at the improvement of statistical outputs and processes through the development of appropriate methodologies and tools as well as an appropriate scientific and technical support, provided to the personnel directly involved in the production and dissemination of statistical information. Moreover, a set of standard quality indicators are regularly collected. From 2010 to 2016 a cycle of quality audits/self assessment procedures was applied to the main statistical processes.

Istat quality policy is consistent with the European quality framework developed by Eurostat, and transposes its main principles and definitions. The endorsement in 2005 of the European Statistics Code of Practice (last revised in 2017) established the principles to be applied in order to ensure and strengthen both the trust and the quality of the European Statistical System.

Essential points of Istat quality policy are:

Process quality: consisting in the production of accurate statistical information efficiently and effectively;
Product quality: consisting in the dissemination of high-quality timely statistical data which are relevant for the users, also the potential ones;
Documentation: consisting in the storage and availability of information necessary not only for a proper use of data but also to ensure transparency in all the production activities of statistical data;
Respect for respondents: consisting in the reduction of response burden and in the respect of respondent’s privacy;
Strengthening of statistical literacy: consisting in promoting a proper use of statistical information in policy-making to better support decisions and policies;
Users’ orientation: consisting in making statistical information easily accessible and understandable and in satisfying user needs as much as possible.

A centralised team is in charge of performing the following tasks:

to promote the development and use of methodologies and techniques to improve the statistical quality within Istat and the National Statistical System as well, in accordance with the European and international principles and standards.
to encourage the systematic improvement of the quality of statistical products and processes through the definition and dissemination of Guidelines and the process documentation and analysis of standard quality indicators.

This unit is supported by a network of more than 100 quality pilots, trained on quality issues and documentation, in charge of monitoring the quality of production processes they are involved in, calculating quality indicators as well as keeping updated the documentation for their own production process.

For further information: https://www.istat.it/en/organisation-and-activity/institutional-activities/quality-commitment 

In 2011 a self-assessment procedure was applied to this survey.

4.1.3. Peer review
No
4.1.4. Main conclusions peer review
4.1.5. Future quality improvements
4.1.6. Specification of quality improvements
4.1.7. Additional comments quality assurance
4.2. Quality management - assessment

Output accuracy is strongly affected by the weakness of the frame. In particular overcoverage and undercoverage of the initial list can cause bias that can not be evaluated. Further bias is caused by unavailability of effective telephone numbers in the list (CAWI could help in overcoming this problem but a test showed that it works only partially). 

4.2.1. Overall quality
Stable
4.2.2. Relevance
Stable
4.2.3. Accuracy and reliability
Stable
4.2.4. Timeliness and punctuality
Stable
4.2.5. Comparability
Stable
4.2.6. Coherence
Stable
4.2.7. Additional comments quality assessment


5. Relevance Top
5.1. Relevance - User Needs

Main data users: 

- Institute for Environmental Protection and Research, ISPRA (Istituto Superiore per la Protezione e la Ricerca Ambientale)

- Ministry of agriculture.

They use these data, for example, ISPRA draws up a Report on the presence of pesticides in water in order to regularly provide information on the water quality in relation to the risks of these substances.

Both the institutions cooperate with Istat for the  National Action Plan to achieve a sustainable use of pesticides (DIRECTIVE 2009/128/EC OF THE EUROPEAN PARLIAMENT AND OF THE COUNCIL).

5.1.1. Unmet user needs

Not available

5.1.2. Plans for satisfying unfilled user needs

Not applicable

5.1.3. Additional comments user needs
5.2. Relevance - User Satisfaction

Istat is constantly interested in understanding who the users of the statistics it produces are, what the information needs are, whether they match production and if the statistics produced satisfy users. To this aim, together with the analysis of user requests received through the Web Contact Center service, tools for direct consultation were developed, such as the annual online survey of customer satisfaction and indirect tools such as analysis of accesses and of users' browsing paths on the web site. 

5.2.1. User satisfaction survey
Yes
5.2.2. Year of user satisfaction survey

2019

5.2.3. Satisfaction level
Satisfied
5.2.4. Additional comments user satisfaction
5.3. Completeness

See sub-category below.

5.3.1. Data completeness - rate

Not applicable for Pesticide Use Statistics because in this data collection, there is no target on the number of data. Member States are asked to collect data on representative crops without stipulating the number of crops.


6. Accuracy and reliability Top
6.1. Accuracy - overall

See point 4.2

6.1.1. Grading of accuracy
Moderate
6.1.2. Factors lowering accuracy
Coverage error
Other
6.1.3. Specification of factors

The set of respondents can not be considered as a probability random sample.

6.1.4. Additional comments overall accuracy
6.2. Sampling error

See the attached Excel file in the Annexes.

6.3. Non-sampling error

See sub-categories below.

6.3.1. Coverage error

See the attached Excel file in the Annexes.

6.3.2. Measurement error

See the attached Excel file in the Annexes.

6.3.3. Non response error

See the attached Excel file in the Annexes.

6.3.4. Processing error

See the attached Excel file in the Annexes.

6.3.5. Model assumption error

Not applicable

6.4. Seasonal adjustment

Seasonal adjustment is not applicable to pesticide use statistics since all plant protection treatments associated directly or indirectly with the crop during the reference period are reported.

6.5. Data revision - policy

Not applicable

6.6. Data revision - practice

Only final data are released. 

6.6.1. Data revision - average size

Not applicable

6.6.2. Data revisions - conceptual changes
6.6.3. Reason for revisions
6.6.4. Impact of revisions
6.6.5. Additional comments data revisions


7. Timeliness and punctuality Top
7.1. Timeliness

See sub-categories below.

7.1.1. Time lag - first result

Not applicable 

7.1.2. Time lag - final result

180 days after reference year

7.1.3. Reasons for possible long production times?
7.2. Punctuality

See sub-categories below.

7.2.1. Punctuality - delivery and publication

0 days of delay, IT-problem on Eurostat’s side did not allow a transmission of the data in time.

7.2.2. Data release according to schedule
NO
7.2.3. Data release on target date
7.2.4. Reasons for delays

IT-problem on Eurostat’s side did not allow a transmission of the data in time.


8. Coherence and comparability Top
8.1. Comparability - geographical

Data are collected on a country level (NUTS 0). Therefore, the data are not comparable on a regional level. The geographical comparability between countries is evaluated by Eurostat.

8.1.1. Asymmetry for mirror flow statistics - coefficient

Not applicable, because there are no mirror flows in Pesticide Use Statistics.

8.2. Comparability - over time

Not applicable for Pesticide Use Statistics, because it is not based on time series.

8.2.1. Length of comparable time series

Not applicable for Pesticide Use Statistics, because it is not based on time series.

8.3. Coherence - cross domain

Not available.

8.4. Coherence - sub annual and annual statistics

Not applicable for Pesticide Use Statistics, because the data collection is based on a five-year period.

8.5. Coherence - National Accounts

Not applicable, because it has no relevance for national accounts.

8.6. Coherence - internal

A good internal coherence of the data was ensured due to the harmonised classification of pesticides and crops.


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

Not applicable

9.1.1. Publication of news releases
No
9.1.2. Link to news releases
9.2. Dissemination format - Publications

Not applicable

9.2.1. Production of paper publication
No
9.2.2. English paper publication
No
9.2.3. Production of electronic publication
No
9.2.4. English electronic publication
No
9.2.5. Link to publications
9.3. Dissemination format - online database

Istat dissemination database I.Stat

9.3.1. Data tables - consultations

In 2019, more than 4000 consultations for PPP data including both sales and use of pesticides statistics

9.3.2. Accessibility of on-line database
Yes
9.3.3. Link to on-line database

http://dati.istat.it

9.4. Dissemination format - microdata access

Researchers and academics can access microdata through the Laboratory for Elementary Data Analysis (ADELE).

9.4.1. Accessibility of micro-data
Yes
9.4.2. Link to micro-data
9.5. Dissemination format - other
9.6. Documentation on methodology

http://siqual.istat.it/SIQual/visualizza.do?id=5000010&language=UK

9.6.1. Availability of national reference metadata
Yes
9.6.2. Link to national reference metadata

http://siqual.istat.it/SIQual/visualizza.do?id=5000010&language=UK

9.6.3. Availability of methodological papers
No
9.6.4. Link to methodological papers
9.6.5. Availability of handbook
No
9.6.6. Link to handbook
9.7. Quality management - documentation

Quality documentation is currently being updated

9.7.1. Metadata completeness - rate

Not yet available

9.7.2. Metadata - consultations

Not yet available

9.7.3. Availability of quality report
NO
9.7.4. Link to quality report


10. Cost and Burden Top

Not available

10.1. Efficiency gains
Other
10.2. Specification efficiency gains

CAWI technique as a possibility to answer to the survey has been introduced since 2018 

10.3. Measures to reduce burden
Other
10.4. Specification burden reduction

The sample is possibly disjoint from other samples in agriculture surveys


11. Confidentiality Top
11.1. Confidentiality - policy

See sub-categories below.

11.1.1. Transmission of confidential national data to Eurostat
Yes
11.1.2. Confidentiality according to Regulation
Yes
11.1.3. Data confidentiality policy

Several national legal acts guarantee the confidentiality of data requested for statistical purposes. In Italy, according to art. 9, paragraph 1 of the Legislative Decree n. 322 of 1989, statistical data cannot be disseminated but in aggregated form, in order to make it impossible to make any reference to identifiable individuals. They can only be used for statistical purposes.

Official statistics must also safeguard the rights, basic freedoms, and dignity of respondents, in particular with regard to the right to confidentiality and personal identity. Istat assures the protection of personal data according to the General Data Protection Regulation (Regulation (EU) 2016/679 of the European Parliament and of the Council of 27 April 2016 on the protection of natural persons with regard to the processing of personal data and on the free movement of such data, and repealing Directive 95/46/EC) and Italian Data Protection Code (Legislative Decree no. 196/2003) and Code of conduct and professional practice applying to the processing of personal data for statistical and scientific research purposes within the framework of the national statistical system.

In order to make statistical secrecy and protection of personal data effective, Istat is currently taking appropriate organisational, logistical, methodological and statistical measures in accordance with internationally established standards.

Moreover, Legislative Decree n. 322 of 1989, art. 6 and 6 bis provides that the exchange of personal data within the National Statistical System (Sistan) is possible if it is necessary to fulfil requirements provided by the National Statistical Programme or to allow the pursuit of institutional purposes.

Finally, in implementation of art. 5-ter of the legislative decree 14 March 2013, no. 33, the new "Guidelines for the access for scientific purposes to the elementary data of the National Statistical System" establish the conditions under which the bodies and offices of the National Statistical System can allow researchers to access their own elementary data for scientific purposes.

11.2. Confidentiality - data treatment

See sub-categories below.

11.2.1. Procedures for confidentiality

No confidentiality treatments are required for the current dissemination policy, since data are not disseminated by active substances.

11.2.2. Additional comments confidentiality - data treatment


12. Comment Top


Related metadata Top


Annexes Top
Annex ESQRS PESTUSE 2015-2019 IT
Questionnaire