SPECIFIC CONTRACT No 7
“Field Work Mission to France”
Implementing framework contract
MARE/2009/2008 “Assistance for the monitoring
of the implementation of national programmes for
the collection, management and use of data in the
fisheries sector”.
COUNTRY REPORT
December 2012
Framework Contract No.
MARE/2009/08
Specific Contract No. 7
Field work specific contract for France,
Bulgaria, Italy and Portugal. (SI2.623038)
Third field work mission to France
Activity
Activity 3. Reporting
Date of submission:
16 January 2013
Author(s):
Jose CERVERA, Pavel SALZ, Christine ALBERTISCHMITT, Iosu PARADINAS
Version:
V.1.0
1
TABLE OF CONTENTS
1.
2.
3.
4.
5.
6.
7.
EXECUTIVE SUMMARY
INTRODUCTION
GENERAL OVERVIEW
3.1.
ORGANIZATION AND MANAGEMENT
3.2.
IT INFRASTRUCTURE AND FLOW OF INFORMATION
3.3.
USER REQUESTS MANAGEMENT
BIOLOGICAL DATA- MÉTIER-RELATED VARIABLES
4.1.
PROGRAMME MONITORING
4.1.
DATA STORAGE AND ACCESS
4.2.
DATA PROCESSING
4.3.
STATISTICAL QUALITY
BIOLOGICAL DATA- STOCK
5.1.
PROGRAMME MONITORING
5.2.
DATA STORAGE AND DATA PROCESSING
5.3.
STATISTICAL QUALITY
RECREATIONAL FISHERIES
6.1.
PROGRAMME MONITORING
6.2.
DATA STORAGE
6.3.
DATA PROCESSING
6.4.
STATISTICAL QUALITY
TRANSVERSAL VARIABLES
7.1.
PROGRAMME MONITORING
Transversal variables on capacity
Transversal variables on effort
Transversal variables on landings
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7.2.
DATA STORAGE AND ACCESS
7.3.
DATA PROCESSING
7.4.
STATISTICAL QUALITY
8.
RESEARCH SURVEYS AT SEA
8.1.
PROGRAMME MONITORING
8.2.
DATA STORAGE AND ACCESS
9.
ECONOMIC DATA - CATCHING SECTOR
9.1.
PROGRAMME MONITORING
9.2.
DATA STORAGE AND ACCESS
9.3.
DATA PROCESSING
9.4.
STATISTICAL QUALITY
9.5.
Data collection as of 2013
10. ECONOMIC DATA – AQUACULTURE
10.1.
PROGRAMME MONITORING
10.2.
DATA STORAGE AND ACCESS
10.3.
DATA PROCESSING
10.4.
STATISTICAL QUALITY
11.
ECONOMIC DATA - PROCESSING INDUSTRY
11.1.
PROGRAMME MONITORING
11.2.
DATA STORAGE AND ACCESS
11.3.
DATA PROCESSING
11.4.
STATISTICAL QUALITY
12. VARIABLES ON THE EFFECTS OF FISHERIES ON THE MARINE ECOSYSTEM
13. CONCLUSIONS BY CHAPTER
13.1.
Biological data
13.2.
Research surveys at sea
13.3.
Evaluation of the effects of the fishing sector in the maritime ecosystem
13.4.
Economic data
14. RECOMMENDATIONS BY CHAPTER
14.1.
Biological data
14.2.
Recreational fisheries
14.3.
Research surveys at sea
14.4.
Economic data
14.5.
Transversal variables
14.6.
Evaluation of the effects of the fishing sector in the marine ecosystem
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ACRONYMS
AR
BTS
CGA
CGPA
DCF / DCR
DG MARE
DPMA
EC
EFF
EWG
FAM
FIS/SIH
FNPF
FTE
FWC
Ifremer
INSEE
JRC
LEMNA
NC
NP
PWC
ONEMA
SBS
SIPA
STECF
ToR
TR
VMS
Annual Report
Bottom trawl survey
Approved management centre - Centre de gestion agréé
Centre de gestion de pêche artisanale
Data Collection Framework / Regulation
Directorate General for Maritime Affaires and Fisheries
Directorate of Maritime Affairs and Aquaculture
European Commission
European Fisheries Fund
Expert Working Group
FranceAgriMer
Fisheries Information System / Système d'Informations
Halieutiques
National Federation for Fishing in France and the protection of
aquatic habitats
Full-time equivalent
Framework Contract
Institut Français de Recherche pour l'Exploitation de la Mer
French Research Institute for Exploitation of the Sea
Institut national de la statistique et des études économiques
Joint Research Centre
Laboratoire d’Economie et de Management de Nantes
National Correspondent
National Programme
Price, Waterhouse, Coopers
French National Agency for water and aquatic environments
Structural Business Survey
Information system related to fisheries and aquaculture
(Système d’Information de la Pêche et de l’Aquaculture)
managed by DPMA
Scientific, Technical and Economic Committee for Fisheries
Terms of Reference
Technical Report
Vessels Monitoring System
3
1. EXECUTIVE SUMMARY
This report presents the results of the third field work mission within the Second Horizontal Contract
for 2012 of the Framework contract “Assistance for the monitoring of the implementation of national
programmes for the collection, management and use of data in the fisheries sector”, which took
place in France.
The main organizations intervening in the implementation of the DCF in France are the Directorate of
Maritime Affairs and Aquaculture (DPMA), the French National Agency for water and aquatic
environments (ONEMA), the National Federation for Fishing in France and the protection of aquatic
habitats (FNPF), the French Research Institute for Exploitation of the Sea (Ifremer), the Institute for
Research and Development (IRD) , the National Institute for Agriculture and Sea Products
(FranceAgriMer) and the Laboratory of Economics and Management of Nantes-Atlantic (LEMNA).
France has developed a strong national network to collect information, animated by the main
institutions leading the network: depending on the situation, either experienced own staff
implements the survey data collection or there is strong collaboration with external specific
structures like the Centres de Gestion Agréé (CGA) to automate the collection of information. The
collection of information is very efficient and can be considered as a good practice even so it cannot
be reproduced easily in other countries as it needs many resources.
The checking of the information is made quite deeply by the responsible institution and the process
is generally well documented. But the tools set in place for the compilation and checking tasks are in
some cases, very basic and many of the check are made manually and depend on the expert
knowledge.
At the opposite, some very advanced systems were implemented, like the SIPA system at the DPMA
the SIH/FIS system at the Ifremer, and an ORACLE database at ONEMA for inland water. Of course
the coverage of these systems encompasses the DCF requirement but is going far beyond. (ex. the
database Harmonie behind the SIH system is a common database used for all Ifremer projects on
fisheries). These systems point out the need for standardisation and implementation of common
referential tables and consequently the interoperability between the systems. Nevertheless there is
still room for improvements. Many requests, although quite simple, would require the intervention
of IT experts to be performed and much processing for the preparation has to be done outside the
system, as we could see in Ifremer.
The same remark is valid for the security aspects for primary data, where it is noted that DPMA set
up a very secured platform for the transmission of the data to the DPMA (DCF ftps site) but in the
data collection phase, the data are often transmitted by emails between the contractors and the
main institute in charge of the data compilation (ex: Ifremer, FAM).
In relation to biological variables, data collection is executed mainly by Ifremer(IRD for tropical
tunas). Different institutes take part in data collection of stock-related variables. Sampling for stockrelated variables is done during scientific surveys and on-shore sampling of commercial landings by
observers. As for métier- related variables, the sampling is designed in a more detailed strata level
than the métier level 6. Both sea sampling and on-shore sampling is carried out by observers in this
case. Age reading is centralised in a specialised centre in Boulogne. Stockrelated data is directly
introduced in the database. Precision measures (coefficients of variation) are calculated, and the
corresponding DCF objectives achieved in most cases.
4
Different working groups collect recreational fishery data. Inland waters are the responsibility of
ONEMA, which in coordination with FNPF collects data for salmon and eel. Complementarily, marine
recreational fisheries data collection is run by Ifremer, which in some cases subcontracts other
partners for some purposes. Recreational fishery for cod is surveyed every three years using the
same methodology to the sea bass. Concerning the bluefin tuna, catches are reported to
FranceAgriMer, who will pass the data to the DPMA. It was not possible to assess the quality of
recreational fishery data collected by FranceAgriMer.
Economic data on catching sector is collected by two organizations – Lemna (University of Nantes)
and Ifremer. Lemna collects exclusively data from fishing firms which maintain formal accounting
and it relies on data obtained from the ‘centres de gestion agréés’. As for vessels over 40m, Lemna
receives the data from a private consultancy (PWC). Ifremer collects mostly from small scale fleet
(below 12m), focussing on vessels which do not maintain formal accounting. The individual data is
submitted by Lemna and Ifremer to DPMA which merges the two sets of data, eliminates any
duplication, and calculates the aggregates required by DCF.
Data on aquaculture sector is collected by Lemna, in a similar way as the catching sector, i.e. through
the ‘centres de gestion’. The primary data is submitted to DPMA, which produces the aggregates
required by the data calls.
Economic data on fish processing is collected by AND International, a sub-contractor to
FranceAgriMer. The data is collected through an annual census of all firms for which fish processing
generates more than 50% of their turnover but also of firms, for which fish product generate less
than 50% of the turn-over, but the absolute value is significant. FranceAgriMer processes the primary
data and submits it to DPMA. In 2010 the level of coverage reached 94% of total turnover.
The combination of administrative (accountant) and survey data is a good practice for all economic
statistics, as it reduces the response burden and allows checking the coherence of values.
The transversal variables are collected by regional services of DPMA. Raw data, submitted either
electronically or on paper, is processed (data entry) by FranceAgriMer. In addition to fleet register,
VMS, logbooks and sales notes, the transversal variables are complemented by ‘fiches de pêche’, a
simplified logbook used by vessels below 10m. In addition, Ifremer carries out an on-going survey of
fishing activities called ‘calendrier des activités de pêche’, which is also an important source of
information for the determination of the métiers of the vessels and a basis to estimate the
completeness of the fishery statistics data available. A powerful system called SACROIS is used for
the reconciliation of information between logbooks, sales notes, VMS and activity calendar
information.
5
2. INTRODUCTION
This report is the result of the third field work visit planned for 2012 within the 7 th Specific Contract
signed between DevStat and DG MARE on 23rd May 2012 whose objective includes the monitoring of
the implementation of the data collection framework in France.
The main objective of this third field work contract is to verify whether and to which extent the
programme implementation is being followed up and if all the biological, technical, environmental
and socio-economic data specified in the programme are being collected according to the specified
methods, procedures and quality.
For this specific fieldwork mission, the team members were:




Mr José Cervera. Project Manager of the Framework Contract and statistical expert;
Mr Pavel Salz. Leading Technical Expert for the Horizontal Contract and fisheries socioeconomics expert;
Mrs Christine Alberti-Schmitt. Information System expert;
Mr Iosu Paradinas. Fisheries biology and environmental expert and Project Assistant.
To achieve the mission objectives, the team of experts conducted a preparatory work for the field
work mission to France consisting mainly in the revision of the basic documentation and specific
technical documentation in order to obtain a first evaluation of the French situation.
After this first revision and diagnosis, the team visited from 17 th to 21st of December 2012 the French
scientific organisations dealing with the National Programme. The findings of the mission are
detailed in this report.
Acknowledgements
The team wants to acknowledge the fruitful collaboration and openness of all involved institutions
and their staff for their personal contribution to the success of the fieldwork mission.
Implementation of the mission (counterparts, calendar)
The agenda of the mission, shared with Mrs Valérie Dehaudt (National Correspondent for the DCF)
prior to the mission, was implemented as planned and all the topics were revised according to the
agenda.The team worked in parallel during the mission, sharing afterwards the findings of the
different meetings.
Participants met from the French Institutions involved in DCF were:
1.
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o
o
o
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Directorate of Maritime Affairs and Aquaculture (DPMA, Paris)
Mrs Valérie DEHAUDT, National Correspondent for the DCF;
Mrs Marie-Bénédicte PEYRAT, Office Manager Scientific Affairs;
Mrs Ingrid BERGERET, Office Manager Information Systems;
Mr Pierre VERDIER, Office Manager Statistics;
Mr Jacques TRANGUANY, dealing with economical data;
Mr Marc CHAUVIÈRE, dealing with aquaculture data;
Mr Alexandre TISSERANT, IT expert.
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2.
French Research Institute for Exploitation of the Sea (IFREMER)
o
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Mr Christian DINTHEER, FIS -Ressources project manager, Ifremer’s coordinator for DCF
activities;
Mr Sébastien DEMANÈCHE, statistician, transversal data;
Mr Kelig MAHE, IT biologist, leader of the sclerochronoly center team in Boulogne sur Mer;
Mr Vincent BADTS, quality;
Mr Joel VIGNEAU, statistician, metier variables;
M. Gilbert MANDIRE, Computer-science and Network;
Mrs Emilie LEBLOND, coordinator IT, fishery expert;
Mr Harold LEVREL, recreational fisheries;
Mr Fabienne DAURES, FIS-Usages project manager, economical and transversal data;
Mrs Sophie LEONARDI, Economical data;
Mrs Valerie HARSCOAT, IT project manager;
Mr Patrick BERTHOU,IT, FIS project manager;
Mrs Christelle LEGRAND, Statistician, economical data (external staff).
3.
French National Agency for Water and Aquatic Environments (ONEMA, Paris)
o
o
Mrs Caroline PENIL;
Mr Alexandre LICCARDI.
4.
National Federation for Fishing in France and the Protection of Aquatic Habitats (FNPF,
Paris)
o
Mr Jérôme GUILLOUET.
5.
National Institute for Agriculture and Sea Products (FranceAgriMer, Nantes)
o
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Monique MEIZELS, Chief of service « information databases »
Eva LACARCE, Responsible for fish processing data;
Bruno BORDEAU, AND International, responsible for census of fish processing.
6.
Laboratory of Economics and Management of Nantes-Atlantic (LEMNA, Nantes)
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Mr Arnaud SOUFFEZ, Responsible for catching sector data;
Mrs Laurent BARANGER, Programme coordinator;
Ms Veronique LEVIHAN, Aquaculture data;
Ms Marie BENCENY, Aquaculture data (was not available for the meeting).
Structure of the report
The Country Report is organised according to the requirements of the Terms of Reference (ToR) and
includes the following sections:
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


Section 3: General Overview.
Section 4: Biological data – Métier-related variables.
Section 5: Biological data – Stock-related variables.
Section 6: Recreational Fisheries.
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Section 7: Transversal data.
Section 8: Research Survey at Sea.
Section 9: Economic data – Catching sector.
Section 10: Economic data – Aquaculture.
Section 11: Economic data - Processing Industry.
Section 12: Ecosystem data.
Section 13: Conclusions.
Section 14: Recommendations
The Country Report is accompanied by 4 Annexes.
3. GENERAL OVERVIEW
This chapter contains a presentation of the main French institutions involved in DCF as well as their
organization, management, IT infrastructure and inter-institutional coordination established
between them.
3.1.
ORGANIZATION AND MANAGEMENT
The Directorate of Maritime Fisheries and Aquaculture (DPMA) is the national counterpart for the
exchange of information between the European Commission and France regarding the DCF, with
Mrs Valérie Dehaudt being the National Correspondent.
The main institutions involved in DCF in France are described below.
The Directorate of Maritime Fisheries and Aquaculture (DPMA) depended from the Ministry of
Agriculture (Ministère de l’agriculture, de l’alimentation de la pêche, de la ruralité de l’aménagement
du territoire) but was moving to the Ministry of Environment (Ministère de l’écologie, du
développement durable et de l’énergie) at the end of 2012.
DPMA is in charge of the overall coordination of the implementation of the National Data Collection
Programme. Six people are working part time in the program.
It main tasks are:

To maintain communications and data collation gathered from different sources for
transmission to the Commission and other parties;

To prepare the economical and aquaculture data based on the data collected by the partner.

To ensure that activities are effectively carried out by the different French organisations;
DPMA delegates data collection to the following partner institutes in the framework of a
“convention cadre” for the national program 2011-2013.

Ifremer,

LEMNA,
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FranceAgriMer: more specifically The DCF 2011 -2013 national program activities is managed
by Surveys and food-processing data unit belonging to Economic Database Service in the
“Market, Studies and Prospects Direction”,

ONEMA,
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IRD,

and the national Museum of Natural History.
DPMA, Ifremer, Lemna, FranceAgriMer and ONEMA were visited during the mission.
DPMA is part of the French Statistical System, and its statistical unit has staff with experience in
INSEE (and Eurostat).
INSEE is not directly involved but participated in the definition of the methodology for the economic
surveys.
Figure 1. Sharing of tasks for the DCF data collection.
Source: own production
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3.2.
IT INFRASTRUCTURE AND FLOW OF INFORMATION
The standard definition for IT infrastructure consists of the equipment, systems, software, and
services used in common across an organization, regardless of mission/program/project.
The objective of the mission regarding the IT components was to check how the DCF requirements
are implemented and not to make an audit of the information system which would have required
more time. This section aims at giving a general overview of the IT organisation and databases in
place, and of the flow of information between the main institutions involved in the DCF in France.
Figure 2. Flow of information between the involved institutions visited
Source: own production
Security in the data transmission: All DCF partners transmit the data to DPMA using the secured
channel of the DCF web interface. Nevertheless, it is to be noted that the transmission between the
contractors and the institute in charge of the DCF data collection regarding the primary data is not
always secured.
Security in data storage: The storage of the primary data is secured on a server in each institution
visited, but sometimes the access to the data is not limited to the person responsible only, but to the
service.
Backups: in all institutions visited, backups of servers are regularly done.
Documentation: As a general remark, it is to be noted that the methodological documentation is in
general very complete, but in some cases, the technical documentations regarding the database or
processing of data is lacking (ex: fish industry processing).
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As general observation, it is to be noted that no common database for the DCF data, accessible to all
partners is available. Feedback is provided to the partners when DPMA detects errors on the data.
3.2.1.
DPMA databases
A sophisticated system called SIPA (Système d’Information de la Pêche et de l’Aquaculture) is
working since a few years to manage the fisheries control data (administrative data). It is managed
by the CERIT in Toulouse and it allows the data collection, storage, exchange of information,
processing and dissemination of fisheries data. It is composed of different layers:
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SECOIA: platform allowing data exchange between the information of the partners
involved in the Control Regulation (FranceAgrimer, directorate of maritime affairs,
Ifremer...).
The control databases: logbook (SACAPT (including electronic logbook collected in
ERS)/SATORO), sales notes (ERS), VMS database.
The referential databases including the fleet register.
The modules for the control procedure and validation rules: for testing the quality of the
data at all stages of the processing.
The modules for adjustment of data at dissemination stage when producing the report in
Business Object (SAS).
Figure 3. Overview of the SIPA System
Source: slide 9, presentation by DPMA to the mission: SIPA
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The SIPA system is not used directly by the actors of the DCF in France. Some data are extracted
from SIPA as basic input or referential data for the information system of the partners. Thus:
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The fleet register is used as a basis for the definition of the sample and for the transversal
variable. It is provided to LEMNA and Ifremer. Nevertheless for biological data sample, the
Ifremer has developed its own fleet register with updated gear information as that
information of the fleet register was not updated regularly. The fleet register in that case, is
only used to append new boats, change vessel owner…
Data from VMS, logbooks and monthly declarative forms (SACAPT/SATORO) and sales notes
(ERS) databases are extracted from the SIPA system and daily transmitted to Ifremer for
being used in SACROIS software to produce adjusted data (see 7.3). The adjusted data are
transmitted back to DPMA and uploaded in SIPA. If DPMA identifies any inconsistency in
SACROIS data, a corrected file is transmitted to Ifremer.
For the DCF, DPMA received excel files from the partners and prepared the relevant answers
to EU data calls based on the excel files.
Aquaculture data are not stored in the system.
Data from ONEMA on water control, in particular the SANDRE (Système d’Administration
des Données Référentielles sur l’Eau) is not integrated with SIPA (but some metadata are
provided by ONEMA to Ifremer).
3.2.2.
IFREMER
Since 2002 Ifremer has developed an information system called “Fish Information System/Système
d’Information Halieutique” (FIS/ SIH in French) for the compilation of all fisheries data in order to
standardise the tools used in the institute for the different projects, as well as the referential (i.e.
metadata on gears, species, vessels, métiers and other concepts) used (definition of referential,
setting up of transcoding if needed…). This is also designed in the perspective of increasing
interoperability with the other partners. The main concept was to develop a generic model to
manage very different kinds of data as shown in the figure bellow.
Figure 4: Data managed within FIS.
Source: Ifremer
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In relation to the figure above, it is to be noted that for the “Surveys aboard scientific vessels” and
the “Landings and Effort Data collected at land”, work is under progress to integrate the data into
the Harmonie database underlying the FIS system.
The economical data, the biological parameters are planned to be integrated in 2013, while the
recreational fisheries integration is planned for a later period.
The database behind the FIS/SIH information system is an Oracle database called “Harmonie”
accessed from different applications (often developed in Java).
The access to the applications and to the stored data is regulated, and depends on the type of users
(login and password).
The FIS/SIH can be divided in four main areas:

Data entry - Input :
o “Allegro” is anintegrated software for the data entry (input) to feed
“Harmonie” working on a common referential for metadata.

It is operational for capturing Activity surveys, catches (incl. discards)
collected on-board commercial vessels [Obsmer], Biological Data
collected in auctions [Obsventes]. VMS, logbooks and sales notes are
transmitted and stored every day in the database

Surveys aboard scientific vessels is currently under construction

The other data are entered with specific software but are planned to be
integrated in “Allegro” in the future. The next interfaces foreseen are
economic data, individual biological parameters, landings and effort
data collected at land [Obsdeb].
Figure 5. Example of “Allegro” screen shot.
Source: Ifremer
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o
o
Import of data coming from other information system like SIPA.
For the moment, the data are not directly interoperable with other information
systems, and data cannot be read directly accessed from a database outside
”Harmonie”.

Management of raw data is done through the “Adagio” application and corresponding
tables for the storage and validation of raw data in “Harmonie” (265 Gb of data, 1,8
billions of rows).

Aggregation of data and management of aggregated data: is done through the
“Presto” application and corresponding aggregated data tables (41 Gb of data, 140
billions of rows). Operation like SACROIS, preparation of data according to COST
format are performed in “Presto”.

Dissemination: through a web portal, or extraction and download of data stored in
“Harmonie” using an extraction interface. Data can also be presented in a GIS format
Figure 6. Overview of the FIS
Source: Ifremer
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Documentation: Technical documentation is available.
There is some overlapping concerning the storage of information, since DPMA sends quarterly the
information on the Fleet Register, and daily that from logbooks, sales notes and VMS. The use of
SACROIS for data reconciliation is a powerful instrument but requires an impressive computational
capacity at Ifremer.
3.3.
USER REQUESTS MANAGEMENT
Management of user requests
The official data calls from JRC or ICES WGs are received by the NC and passed on to the specific staff
members responsible for the compilation of the data.

A specific DCF web interface is used for collection of the data files for answering the EU
calls. The partners have access to this web platform via a logging and password and can
securely deposit the required data files. The DPMA is able to follow up the answers of the
partners.
Figure 7. DCF Web interface
Source: DPMA

Final economic data on the catching sector and aquaculture are prepared by staff of DPMA
based on raw information provided by Ifremer and Lemna, fish processing prepared by –
FranceAgrimer, which are reviewed by DPMA. All final biological data are prepared by
specialists of Ifremer.
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Transmission of data
The transmission of data is usually done through the DCF web interface for the collection of data for
the call.
Primary Input/output data needed to be shared with other institutions are provided through a
secured ftp (ex: transmission of logbooks information extracted from SIPA to Ifremer).
Copies of answers to EU calls (aggregated data) are sent to the partners
The NC has not direct access to any of the data, but only passes on the information obtained from
the DCF-network.
DCF web site
No DCF dedicated web pages exist for the promotion and explanations on the DCF. Nevertheless it
must be pointed out that a part of the Ifremer web site is dedicated to the dissemination of some
documentation related to the DCF. (http://sih.Ifremer.fr/Acquisition-des-donnees/Cadresreglementaires/Data-Collection-Framework-DCF).
Figure 8. Ifremer web page dedicated to information on DCF
User satisfaction
There is no formal follow-up of user satisfaction, apart from the procedure regarding the correctness
of responses to formal data calls.
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4.
4.1.
BIOLOGICAL DATA- MÉTIER-RELATED VARIABLES
PROGRAMME MONITORING
Organisation for the production of métier-related data
Métier -related data production is mainly done by Ifremer.
Ifremer works with a more detailed classification of métiers than level 6, as more detailed
information is needed for the Ifremer studies. This more detailed classification (about 350 métiers) is
clustered to métier level 6 for DCF requirements.
The sampling range is correctly defined by analyzing the transversal variables (value, effort and
landings) at métier level 6 through the 90-percent ranking system methodology (Table III.C.1).
Sample design and allocation is calculated in a higher strata level than métier level 6. This is due to
the fact that vessels can switch gears at any time and it is impossible to foresee this in advance.
Consequently, different métiers are merged by coastal zone (ports) as one single sample target. (See
compliance with the methods and procedures).
The sampling plans are uploaded to the so-called WAO server which will be used for administrative
and logistic purposes. WAO is progressively updated, so achievement rates can be checked at any
time of the year.
As for the data collection, a total of about 25 observers are spread along the French coast.
Achievement of objectives with respect to sampling plans
The production of tables III.C.3, III.C.4, III.C.5 and III.C.6 with the results of the sampling in 2011 in
relation to what was planned in the NP has been monitored:
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

Table III.C.3 presents the expected number of trips by métier level 6 as required by the DCF
but columns Q and S, “Achieved number of trips” and “Achieved number of trips on-shore”
respectively have not been filled except for ICCAT, IOTC and WECAF areas.
Table III.C.4 presents the sampling strategy in a way that it is not compatible with table
III.C.3. The sampling strategy is planned in a different strata level and therefore comparing
both tables is very complicated. (See next section, deviations in sampling from the plans).
Tables III.C.5 and III.C.6 have been correctly filled in apart from the CV in table C.5 , which is
empty for ICCAT, IOTC and some cases of the Mediterranean.
There have been many difficulties in embarking observers for sea sampling. For this reason and for
the sample design characteristics (see “compliance with methods”), some remarkable low
achievement rates have occurred, mostly in trawlers in the North Atlantic and Mediterranean. A
human resources problem in Port-en-Bessin caused a very low achievement rate of some métiers in
the North Sea & Eastern Arctic.
The team asked for the report that commented a poor data quality of the French on-board observers
program reported in 2009 due to inexperienced observers that couldn’t sample exhaustively all
species caught. The response received from Ifremer was that from 2012 on, observers were going to
sample exhaustively all the species caught regardless the time needed.
17
Deviations in sampling achievements from the plans
Most commonly, two type of deviations occurred, deviations in achievement rates and
incompatibilities between tables III.C.3 and III.C.4.
North Sea and Eastern Arctic:

The team asked for the deviations seen in métiers OTB_DEF and OTM_SPF. When analyzing
table III.C.4, the response received was that as mentioned before, the strata level for the
sampling strategy is wider than métier level 6 and consequently this kind of deviations can
occur, as the sampling size for the métier level 6 cannot be known in advance. However, if
we analyze table III.C.3, numbers are completely different:
Table 1. Deviations in métiers OTB_DEF and OTM_SPF
Source: TR excel file, tables III.C.3 and III.C.4.

All the expected trips in “Port-en Bessin” were not achieved due to a human resources
problem. This problem will be overcome for the following years.
North Atlantic:
Table 2. Example of incompatibilities between tables C.3 and C.4
Source: TR excel file, tables III.C.3 and III.C.4.

Low achievement rates are also present. For example, “OTB_DWS + OTB_DEF” had planned
a total of 52 trips to sample both in auction and at sea. 0 were achieved according to table
C.4. In contrast, if we have a look at table C.3, we can find 25 trips sampled at sea in the
sampling frame code “Obs-AT1”. The team could not verify the real figures.
Mediterranean:
Table 3. Example of incompatibilities between tables C.3 and C.4
Source: TR excel file, tables III.C.3 and III.C.4.
18

Only 49 out of 150 trawl trips have been sampled at sea. Administrative reasons have been
reported. Improvement in this aspect is expected for the following years.
The main explanation received for the discrepancies between tables III.C.3 and III.C.4 was that the
tables were filled by hand and it had been very challenging to do so. The fact that the sampling
design is done in a wider strata level than the required by the DCF complicates very much the
extraction of data at métier level 6.
In order to overcome this important problem, outputs from “Allegro” will be used. The observer,
when uploading the sample data, has to introduce the WAO (i.e. stratum) code of what has been
sampled and the system finds, using SACROIS, the métier and area at which the sample has been
taken. The implementation of this methodology will be tested soon and prove its operability to fill in
table III.C.3.
Deviations from the sampling plans are mostly due to the sampling scheme, planned on a higher
stratification level. Consequently, it is impossible to know a priori what sample sizes are going to be
achieved for the different métiers. It is even more difficult to achieve the expected samples if the
number of vessels that which are accessible is very restricted. The fact that embarking observers has
been very problematic is another important reason in some cases of low achievement rates.
Compliance with methods, procedures and derogations
In principle the selection of métier s to sample is correctly done according to the ranking system at
the corresponding métier level 6. The mission team confirmed the quality of the selection using the
reported figures in table III.C.1 except for the value in the Mediterranean, this column was left empty
in the technical report excel file. In any case, more métier s than needed had been selected for
sampling.
On the contrary, the sampling strategy is planned in less specific strata than the métier level 6.
Ifremer calculates the sampling scheme merging different métier s by geographic coastal area in the
way we can see in Figure 9. This approach is used because of the fact that an observer cannot know
a priori what métier is going to be used by the vessel and therefore a better coverage of the
sampling scheme is expected using this methodology.
Figure 9. WAO screenshot of the sampling plan using the French strata merging different métier s by
geographic areas.
Source: WAO
19
This methodology combined with the restrictive number of vessels accessible to embark observers
may result in important deviations a posteriori of the sampling plan (as seen in deviations in sampling
achievements from the plans).
The sampling intensity is not planned proportionally to the effort generated by the métier and the
variability of the catches. See Table 4 for an example.
Table 4. Example of the low correlation in proportion of the sample sizes according to the effort and
variability of the métier.
Source: Own production based on table III.C.4 of the Techinacl Report 2011.
Last but not least, métier FPO_MOL VIIe in the North Atlantic was not sampled in 2011 even if 4 trips
were planned. In 2009 a derogation to sample this métier was rejected.
4.1.
DATA STORAGE AND ACCESS
The data related to activity are registered in Harmonie and the relevant extractions can be
performed using the extraction tool (see chapter 3.2.1).
4.2.
DATA PROCESSING
Vessel Activity calendar /métier definition
A specific report by vessel called the “activity calendar” is registered in the French system, as
logbooks are not compulsory for all vessels. It is a minimal but exhaustive set of information on
vessel activity collected by survey. The vessel activity calendar is used among others for the yearly
update of the definition of the métier as explained below.
Observers (from Ifremer and other organizations) along the sea front have to follow/enquire a
subset of identified vessels: some vessels are registered as high priority (new boats, not stable
activities over the time) as good information is not available, other are only there to check pre-filled
information (as they are considered to declare properly their logbook and sales).
The information is split by months in the “activity calendar”. It records if the vessel was active, the
number of men on board, the number of days at sea, the number of fishing days, the gear used and
fishing area over the month.
The observer fills the activity calendar in may June, transmits the information to Ifremer and after
validation, activity calendar are made available by October.
The métier for a given trip is assigned by an observer (not by the fisherman) from monthly
information about effort which comes from the “activity calendar”. It is based on the combination of
fishing strategies rather than on the dominant strategy. When collecting the information from a
20
particular vessel, the observer receives a “proposed métier” based on historical information, but this
has to be confirmed on the spot. See section 7 on the “activity calendar”.
The métier selected can then be related to a DCF-compatible métier.
Figure 10: Sheet for the registration of the activity calendar.
Source: Ifremer
Data processing
The sampling plans for catches collected onboard commercial vessel (Obsmer), landing and effort
data collected at land (Obsdeb) and biological data from auctions (Obsvente) are uploaded in WAO,
the application to follow up the fishing vessel activity in the framework of scientific observation. (see
documentation web site http://suiviobsmer.labs.libre-entreprise.org/wao/common/utilisateurs.html).
The information registered here will be used for administrative and logistic purposes for ensuring the
follow up of the data collection process.
Information on WAO is progressively updated, so achievement rates can be checked at any time of
the year. A web interface is available for that purpose.
21
As for the data collection itself, a total of about 25 observers are spread along the French coast.
Sample data entry is done using the “Allegro” interface into the “Harmonie” database. (See
chapter3.2).
4.3.
STATISTICAL QUALITY
Coverage: identification of métiers, fleet segmentation
The identification of métiers to sample is correctly run through the ranking system of the 90 % for
effort, landings and value. However, it has been noticed that the value in not available for some
métiers in the Mediterranean, but these métiers have been selected for cumulating the 90% value.
Ifremer explained that this was based on expert opinions.
Sample design: selection of métiers by ranking, merging métiers, sample sizes per métier
As mentioned before in “compliance with methods and procedures”, the sample design is done in a
wider level than the métier level 6.
The sample sizes planned in some métiers demonstrate that the sampling allocation is not calculated
taking into account the effort and the variability of catches of the métier. Midwater trawls have had
especially high planned numbers compared to bottom trawls, which have a higher number of fishing
trips per year and in principle more heterogeneous catches (see table 1 in compliance with methods).
Ifremer explained that sample size is calculated to have god precision in discards instead.
The sampling design for on shore sampling is a probability based sampling. The methodology
implemented through a systematic sampling for timing and choose of the auction to sample assures
the quality of the sample.
On-board sampling, in exchange, is a non-probability based sample due to the difficulties that
observers have to embark certain vessels. This fact may also influence the achievement rates of the a
priori planned samples sizes.
In the reporting of sample sizes, as the stratification if not based on the “DCF métiers” but on a
higher level, it may happen that there are no observations for a given métier level 6. Therefore, it
should be differently reported when the “0 samples” are due to “no planned sampling for this
métier” (or “structural zeros”) and when the “0 samples” are due to “no trip observed in the
sampling of the higher-level métier” (observed zeros).
Precision: calculation methods and achievements in precision
Precision levels are calculated using the COST functions, originally designed by Ifremer staff. One
single person is responsible for the calculation of the CVs and reporting the figures in the Technical
Precision measures are reported manually in the Excel tables of the TR and therefore subject to error
(some errors were detected).
Achieved precision levels do not reach the minimum required level most of the time for métier related data.
22
5. BIOLOGICAL DATA- STOCK
5.1.
PROGRAMME MONITORING
Organisation for the production of stock- related data
Ifremer is responsible for stock-related data production except for tropical tunas which are
responsibility of IRD and eel and salmon managed by ONEMA. Biological data collection is run by
fisheries observers spread along the French coast or during research surveys.
Collected otoliths are sent to a specialised centre in Boulogne where age reading is done for all the
specimens. This centre in Boulogne deals with all the individual stock parameters, which are entered
using dedicated software called “BARGEO” (to be integrated with “Harmonie” in 2013-14).
One person produces table III.E.3, all manually.
Achievement of objectives
The mission team checked the production of table III.E.3 with the results of the sampling in 2011 in
relation to what was planned in the NP.
Sampling achievement rates were most of the time satisfactory. Nevertheless, some stocks had very
low or null achievement rates reported in table III.E.3 such as: Hommarus gammarus, Nephrops
norvegicus, Molva molva and different Raja species. The team asked for two of these deviations:


In the case of Nephrops norvegicus, after a query, the team checked that individuals had
been collected only for area VIII and none in the area VII. The reason mentioned was that in
2011 there were very low catches of this species in area VII and that vessels targeted other
species.
As for Hommarus gammarus, the reason explained was that some species were held in
another database rather than “Harnomie”. The team didn’t have time to check the
existence of this other database.
Precision levels were not reported in table III.C.3. The team confirmed that these CV had been
calculated using COST tool but not reported in the Excel file. The output when using COST is very
extensive and reporting precision levels for each species and parameter can be very time-consuming.
Ifremer will do their best to overcome this problem in the following years by formatting the output
of R procedures so as to import them directly into Excel.
The mission team confirmed that precision levels for length at age were most of the time
satisfactory, apart from the bigger species such as Lophius. Achievement rates for other parameter
coefficients of variation were more heterogeneous.
23
Figure 11. COST output. First value corresponds to the CV for sole length; second value represents CV for age.
Source: Ifremer
Planning problems for maturity data have been noticed. It is known that maturity data cannot be
collected at any season for certain species and therefore, planning maturity data collection during a
scientific survey which is not done in the appropriate season doesn’t make sense.
Compliance with methods and procedures and derogations
The good compliance with Commission Decision 2008/949/EC, Chapter III B. Collection of biological
variables, B2, Stock-related variables, has been checked. Almost all required rules have been
followed except:


Sample sizes for each species have not been modified by taking into account achieved CV
levels;
Planning for maturity data collection has not been correctly planned to assure best
achievement rates.
In the other hand, as a good practice, Ifremer tries to distribute the sampling intensity
proportionately with the fishing effort in each season. (See figure 12). In some cases, little variations
in these intensities have shown better precision levels.
24
Figure 12. Red mullet. Correlation between the fishing effort and the sampling effort (size) is intended
although not always achieved. (Sampling intensity in red dots, Fishing effort in light blue).
Source: Ifremer
5.2.
DATA STORAGE AND DATA PROCESSING
For the moment the individual biological data are stored in a biological database in Access called
BARGEO for the individual data (age, size and weight).
The BARGEO is using the same referential as the one used in the “Harmonie” database.
It is foreseen to store the individual biological data in Harmonie in 2013-2014 and to enter them using
Allegro.
The “Arpège” database (Sybase) was used until 2012 to store biological sampling data collected from
on-shore auctions. The “Arpège” database is not used anymore since 2012 as its content was totally
migrated into “Harmonie” database which contains all biological sampling information (obsmer ,
obsvente).
As individual biological data are not stored in “Harmonie”, they are imported in a specific COST
environment to be analysed to assess the accuracy of parameters estimates collected for stock
assessment purposes.
25
At the moment, one person does all these calculations at the central level in Ifremer because the
specific software (R) skills are very heterogeneous. To overcome this problem, a new user friendly
interface has been developed in “R Commander” application. The team had the opportunity to see
this new version and prove its operability.
The person in charge is responsible for the calculation of the CVs and reporting (manually) the
figures in the Technical Report.
5.3.
STATISTICAL QUALITY
The selection of sample sizes has not changed during the past years. Sample sizes are not calculated
to achieve the precision levels of each species but are based on tradition. Nonetheless, planning
sample sizes using the CV as a goal can be too challenging since some precision levels have not been
achieved even with a huge number of samples measured. On the other hand, the sample allocation is
intended to be proportionately to the effort realised in the different seasons of the year see Figure
12.
The calculation of precision by means of the COST procedure is a major contribution of the French
system for DCF that can be used by other Member States. The mathematical procedures for
precision calculation include those based on analytical formulas (for probabilistic samples) and
replication methods (bootstrap, for non-probabilistic samples).
It has to be noted that reported CVs are an average of CVs by age classes. This average measure of
precision, while useful to grasp an idea of precision, does not have any statistical meaning
(differently of the CV for each age class, which is a relative measure of precision). This is not specific
of the French reporting system, though.
26
6. RECREATIONAL FISHERIES
6.1.
PROGRAMME MONITORING
Organisation for the production of recreational fisheries variables
The production of recreational fisheries variables is split into two components. On one hand inland
waters for salmon and eel, on the other hand sea recreational fisheries for Bluefin tuna, cod, and sea
bass. Recreational fisheries data collection in coastal areas is carried out by Ifremer, which in some
cases subcontracts other partners for certain activities. The inland waters recreational fishery is
competence of ONEMA with a direct assistance of FNPF for the salmon.
Data are transmitted to DPMA who is in charge of the management of the Quotas
Achievement of objectives
As for the achievement of objectives in inland waters, total catches of both eel and salmon have
been calculated. The team confirmed that biological parameters were stored for both eel and
salmon.
Figure 13. Raw data Stored in SNPE for salmon.
Source: ONEMA
Sea bass and Bluefin tuna total catch and the corresponding precision levels have been calculated. A
new cod recreational fishery total catch estimation as well as sharks and rays first total catch
estimation will be available in 2013.
27
Figure 14. Raw database on Bluefin tuna.
Source: Ifremer
Compliance with methods and procedures
Quarterly weight of capture of eels and salmon are collected, exhaustively in the case of the salmon.
An important effort has been carried out to monitor sea bass captures through a telephone survey,
where 10.067 out of 15.000 surveys were achieved. Data for Bluefin tuna is also collected
exhaustively by FranceAgriMer.
Derogation for recreational fisheries in marine waters for salmon and eel had been conceded as well
as derogation for cod to collect data every three years. In 2012, cod recreational fishery data
collection in the North Sea has been conducted using a similar approach to the sea bass survey.
A pilot study concerning sharks and rays is being conducted and results will be obtained for 2013.
Table 5. Surveyed species for the recreational fishery.
DCF species
2009
2010
2011
2012
2013
Seabass
No
Yes
Yes
Yes
Yes
Cod
Yes
No
No
Yes*
No
Eel marine waters
No
No
No
No
No
Other fishes
No
No
No
Yes*
¿**
Bluefin tuna
Yes
Yes
Yes
Yes
Yes
Shark & rays
No
No
No
Yes*
?**
* Results will be obtained in the following months since last log-books will be collected on January 2013.
** Depending on the results obtained in the survey, derogation for sharks and rays will be requested and other
fishes might be included in the sampling plans.
6.2.
DATA STORAGE
The system in place for entering the information related to salmon and eel is a web-based application
(https://snpe.onema.fr/snpe/). The data are stored in an Oracle database and queries/extraction can
be made using oracle SQLPlus tool. The access to the database for dissemination purpose is
restricted to one person in an intranet. Metadata for the identification of waters and taxonomies are
common to other institutions.
28
Figure 15. SNPE website for data management.
Source: ONEMA
Coastal recreational fisheries data is not yet integrated into “Harmonie”.
Data on bluefin tuna stored in excel files in DPMA. Data for the other sea species surveys is entered
by the subcontracted partner BVA and it is stored in both access and excel format in Ifremer. In both
cases, there is a visual data validation process done by Ifremer plus a first validation by BVA for
survey data.
There are plan to integrate recreational fisheries data directly in the “Harmonie” database at Ifremer
in the future.
6.3.
DATA PROCESSING
Inland fisheries:
When talking about inland recreational fisheries, two categories have to be taken into account:
fishermen using rod and line and fishermen using nets and fishing gears. Fishermen using nets and
other fishing gears are monitored by compulsory monthly declaration of catches while the rod and
line fishery is monitored through collaboration between ONEMA and FNPF.
SALMON:
The effort of about 8.000 recreational fishermen is estimated by using a fishing license subscription
managed by FNPF and biological parameters as well as the total catch is estimated in ONEMA using
29
compulsory catch declarations from the anglers that receive a declaration kit every year through the
collaboration between ONEMA, FNPF and regional federations and associations 1.
The salmon declaration kit sent to about 5.000 fishermen by FNPF includes:



Plastic seal for catch identification.
Individual fish declaration forms (See Annex 1)
An annual catch declaration form.
Every fisherman receives a recapitulative feedback of the national data on recreational fishery results
in order to encourage them to collaborate in the data collection programme.
EELS:
Glass eel is a special case because catches have to be reported within 48 hours to ONEMA. ONEMA
will daily transmit data to DPMA, which will cross-validate these reports with sales notes and take
decisions in the quota management of glass eel.
Anglers fishing yellow eel are more difficult to approach. ONEMA is attempting a similar approach to
the salmon for biological variables. A new web application for declaration has been created (SNPL)
where anglers can report their catches online.
Eel biological parameters are estimated in ONEMA using an annual purchase of 500 eels. Ageing is
done in the specialised centre of Ifremer in Boulogne.
Coastal fisheries:
All Bluefin tuna targeting fishermen have to be registered and have to report officially their catches
within 48 hours through the fishing associations. Ifremer is responsible for the data collection from
these associations but there is since 2009 a new legal system which obliges each recreational
fisherman targeting blue fin tuna to get a licence, to tag the tunas fished and to declare in a standard
format every catch to FranceAgriMer. Data is transferred to DPMA, which is responsible to meet
national obligations. Once quota is reached, the recreational fishery of bluefin tuna is closed. This
data is yearly transmitted to ICCAT.
Sea bass recreational fishery is managed by Ifremer, which subcontracted BVA carry out a telephone
survey. Results are estimated combining logbooks completed by voluntary fishermen recruited for
the telephone survey and the distribution of fishermen along the French coast (also estimated after
the identification of fishermen through the telephone survey).
A new large-scale survey based on an extensive telephone survey and logbook (filled by volunteers
during one year) is being carried out as a pilot study to estimate all the coastal recreational fisheries
in France. The last logbooks will be collected by January 2013 and after this a new estimation of cod
catches will be calculated and a first estimation of shark and ray catches will be provided.
1
FNPF is a national federation of the 93 department fishermen federations representing about 3.900 associations. A
total of about 1.300.000 fishermen belong to these. About 5.000 catch salmons (about 1.000 specimens caught per
year).
30
6.4.
STATISTICAL QUALITY
Bluefin tuna, glass eel and salmon data is collected exhaustively so no sampling statistical treatment
is applied. There is no estimation of the undeclared catches, what might result in an underestimation
of the total catches. In the case of the salmon there is a recapitulative, annual questionnaire that
volunteers fill in that enables ONEMA to confirm the data acquired during the year.
Yellow eel has more difficulties in the data collection as explained before (see chapter 6.1.)
Therefore, it is very complicated to estimate any bias in the results.
Sea bass total catch is estimated by extrapolation from the logbook fulfilled by volunteers. In
principle the extrapolation is correctly done since weights for the different strata are assigned by the
results obtained through a random dialling telephone survey.
No calculation of precision measures is reported. However, the mission was told that for some
species, a Directorate for Research inside ONEMA calculates precision values.
31
7. TRANSVERSAL VARIABLES
7.1.
PROGRAMME MONITORING
General
Transversal variables developed for the purpose of DCF are based on the following sources:






Fleet register
VMS
Logbooks
Sales notes
Monthly fishing declarations (Fiches de pêche: simplified logbook declaration for
vessels below 10m, which are not subject to the logbook obligation)
Activity calendar (Calendrier d’activité).
The first five sources are compiled by DPMA, sales notes being processed by FAM. The ‘calendrier
d’activité’ is an on-going survey of the French fishing fleet by Ifremer (see 4.2). The original data of
the first 5 sources is stored in the SIPA database (see chapter 3.3.1). However, on the basis of past
experience it was concluded that the data is neither always consistent nor complete. For this reason,
Ifremer has developed the SACROIS software which compares the information available on the level
of fishing trip and contains algorithms to resolve the inconsistencies and to combine the various
sources into one final set.
This final data set provides the basis to respond to data calls requiring transversal variables.
However, this is not necessarily the final response. The data generated in this way is going through
one final check by DPMA before it is submitted. This is considered necessary as SACROIS estimates
could contain errors as it sometimes applies rules which do not reflect the reality of the French
fishery, e.g. some catches are allocated to beam trawlers. Therefore, SACROIS data has to be
checked and corrected prior to the transmission. It must be stressed, however, that the corrections
imputed by DPMA at this stage regard exclusively very minor values of effort or catch.
Transversal variables on capacity
The fleet register is maintained by DPMA. In addition to a general capacity license (PME – Permis de
mise en exploitation), vessels may have a variety of other permits in relation to allowed gears or area
of activity.
Ifremer survey demonstrated that the dominant gears (used to define the fishing fleet of the vessel)
used by the vessels do not correspond to the main gears stated in the fleet register in approximately
30% of the cases. Therefore, the definition of the fishing fleet of the vessel is based on the
“calendrier d’activité”.
Ifremer pointed out that the allocation of a vessels to a segment should be based on combination of
main fishing gears. Ifremer concluded that for certain vessels, e.g. trawlers / dredgers, the dominant
gear may be trawl in one year and dredge in another year. Strict application of one dominant gear
implies that the vessels move regularly from one segment to another. Allowing for a combination of
gears as determinant of the fleet segment would lead to a more constant / stable composition of the
fleet.
32
Transversal variables on effort
Data on effort are based on the six above mentioned sources and on the Sacrois estimates.
Transversal variables on landings
Landings are determined on the basis of logbooks, sales notes and fiches de pêche which are
compiled to one single consistent source, the SACROIS data.
7.2.
DATA STORAGE AND ACCESS
Information on capacity is stored in the fleet register maintained by DPMA in SIPA system (see
chapter 3.2.1.)
The SACROIS software works with the basic sources of information stored in Harmonie at Ifremer
but the SACROIS data are transmitted on a monthly basis to DPMA.





Fleet register updated on a quarterly basis.
VMS provided by the DPMA /DAM information system raw data updated on a daily basis.
SACAPT provided by the DPMA /SIPA information system: logbook data updated on a daily
basis.
ERS sales provided by the DPMA /SIPA information system: updated on a daily basis.
Calendar activity data: monthly data updated on a yearly basis. (See chapter 4.1 on métier).
The results of SACROIS (data reconciliation system) are stored in specific tables in “Harmonie”.
SACROIS data is considered as a supplementary data to refine the landing and effort based on the
analysis and synthesis of various statistical sources.
For Mediterranean and overseas departments (DOM), the landings and effort data collected at land
are stored in a specific “Obsded” database. The integration of “Obsdeb” in “Harmonie” is in
progress, so it will not be developed in this report.
An evolution of SACROISv4, scheduled in 2013, will automate several exchanges and manual actions.
7.3.
DATA PROCESSING
The transversal data are compiled through an impressive application called SACROIS making
reconciliation and applying consistency checks between various data sources to produce a final set
of validated data on capture and effort. SACROIS does not correct the data.
The application is run every month. This is used as a support for the estimation of DCF transversal
variables but also in some cases for the estimations/checks of some economical or biological data.
The processing is divided into the following main steps:

Compilation of information requested for the processing of SACROIS in “Harmonie”
o Fleet register updated on a quarterly basis.
o VMS raw data updated on a daily basis.
o SACAPT: logbooks and monthly declarative forms data updated on a daily basis.
o ERS sales: updated on a daily basis.
o Calendar activity data: monthly data updated on a yearly basis. (See chapter 4.1 on
métier.
33


The fishing trips are calculated based on the raw VMS data for the vessel equipped with VMS
system: a fishing trip start when the boat is leaving the harbour and stop when it comes
back or if no VMS data is registered for more than 10 hours. Fishing time is registered by day
and fishing area and is counted depending on the speed of the boat and its direction.
Reconciliation of the data: The SACROIS program will automatically generate the data
synthesis data file. The programme needs a lot of resources and is time consuming but the
analysis performed is impressive.
o The SACAPT data is based on logbooks and fiches de pêche allows building also
fishing trip based on the logbook information.
o The VMS and SACAPT fishing trips are compared to build the more accurate fishing
trips information.
o The information available is then compared with the sales notes and the activity
calendar to complete the picture
The synthesis table can lead to the different cases, and some rules are applied in the system to make
choices and to reconciliate the data as far as possible.
Figure 16. Synthesis table for the reconciliation in SACROIS
Source: Ifremer
For Mediterranean and overseas departments, the “Obsded” data source (fishing trip information
collected at landing) is used to refine/complete the reconciliated information on effort and landing
for the small fishery units. “Obsdeb” is storing the data of a specific survey of units whose fishing
activity is not well-known due to a lack of declarative data.
As a summary for the processing of transversal data, see the figure below.
34
Figure 17. Summary of processing of transversal variables.
Source: Ifremer
7.4.
STATISTICAL QUALITY
The statistical quality of the transversal data is assured by the procedure of data compilation,
processing and in particular the cross-checking of various sources, as described in sections 7.2 and
7.3.
35
8. RESEARCH SURVEYS AT SEA
8.1.
PROGRAMME MONITORING
Organization for the production of research surveys data.
France is involved in 5 international scientific surveys.


Two acoustic surveys; PELGAS in the Bay of Biscay and MEDIAS (PELMED) in the Gulf of
Lion.
Three bottom trawl surveys; IBTS-Q1 in the North sea and and eastern Channel, and
Western-IBTS-Q4 in Bay of Biscay + Celtic sea) and MEDITS in the Mediterranean (Gulf of
Lion and Corsica)
All these surveys are coordinated in international working groups in annual meetings and conducted
by Ifremer in those areas in which France is involved.
All survey data will be stored in the long term in the “Harmonie” database. A R-based application
has been developed to analyse survey data, it is called “R sufi”. The Coser tool is also used to check
the quality of the data.
Moreover, a web-based application (http://www.ifremer.fr/SIH-indices-campagnes/) enables any user
to have a look on some indices estimated from these scientific surveys.
Achievement of objectives: results and deviations from National Programme
The team confirmed after a “R sufi” software demonstration, that the corresponding records of one
of the surveys were in the database. The team could not check more records due to time problems.
In all of them the percentage between planned and achieved data is above 100%. The expected
results from such research surveys have been achieved.
8.2.
DATA STORAGE AND ACCESS
The integration of the data in “Harmonie” is in progress. Part of the data is currently in “Harmonie”
and part is stored in Access databases store on Ifremer servers.
All data will be migrated in “Harmonie” in 2013.
36
9. ECONOMIC DATA - CATCHING SECTOR
This section presents the situation (mode of data collection) until 2012. As of 2013 a new approach to
data collection will be introduced. This new system has been developed in cooperation with INSEE
and certified according to their quality standards. Main changes to be introduced in 2013 are also
presented below.
9.1.
PROGRAMME MONITORING
Organisation for the production of economic data of the catching sector
Organization of the production of economic fleet data is presented in figure 18. While DPMA bears
the final responsibility for the implementation of DCF, the actual collection has been delegated to
and divided between Lemna (Univ. of Nantes) and Ifremer. These two organizations rely further on
sub-contractors (private firms) to carry out the actual field work.
Lemna collects data from firms associated to an approved management centre (‘centre de gestion
agréé (CGA))2’. The obligation for detailed accounting applies to firms with an annual turn-over in
access of 80,500 euro. These are almost all firms operating vessels between 12 and 40m. In addition
Lemna receives data on about 80 large vessels (so called ‘pêche hauturière’, vessels over 24m, tuna
purse seiners, pelagic freezers) from PriceWaterhouseCoopers (PWC) dealing with analytical
accounting. Data from the overseas departments are provided by RECEPE. Data from Lemna reflect
largely the fiscal reality. The CGAs submit anonymised data to Lemna. The vessels can be retraced on
the basis of a reference number and the name of the administrator (accounting firm or CGA).
Ifremer is responsible for collection of data from vessels whose accounting is not systematically
done by an accounting office (mostly vessels under 12 m). It relies on a survey, which is executed by
Ifremer staff and a number of private consulting firms, with offices in the various regions.
Figure 18. Organization of the production of economic data
Source: own production
2
CGA: Structures aiming at assisting the members in their accounting and ensure fiscal security. Since 2006, the
center also has in its mission to warn very small firms from economic difficulties.
37
Achievement of objectives
The mission was given access to the primary data, which is stored in Excel at DPMA. The database
contained 1405 records (one record representing one vessel) for 2010, which is consistent with the
statement of the technical report (table III-B-1), which states that data from 1394 vessels was
collected.
For 2010 Ifremer provided data on 251 vessels in the Mediterranean and 449 vessels in Atlantic and
the DOM. Lemna provided data on 821 vessels in the Mediterranean, Atlantic and DOM (shrimpers in
French Guyana). There were 116 vessels in both data sets, which were eliminated by DPMA, mostly
retaining the information provided by Lemna, as it is considered more accurate.
Compliance with methods and procedures
The methods and procedures in relation to data collection and processing have been described in
detail in cooperation with INSEE during the preparation of quality certification, under which the
whole system will operate as of 2013.
Derogations
No derogations have been requested.
9.2.
DATA STORAGE AND ACCESS
AT LEMNA
CGA uses common analytical database/software called ANACENTRE. A module was added to
complete available fiscal information with the additional information requested and to make an
extraction in an excel format for LEMNA.
For each new exercise, a new data file is prepared.
The raw data provided in Excel by the different CGAs (153 columns per file) are merged in a unique
Excel file. The Excel file is not very readable as the different fields are coded like U001, U002… but
their meaning is well known by the staff.
Additional columns are manually added in the compiled file by LEMNA to do specific calculation and
to prepare the tests.




7 columns were added to complete the raw data like sea font, region, SACROIS data,
average price of oil by QAM (maritime administrative region)
18 columns are copies of existing columns to have the data tested close to tests results
18 information are calculated by formula
37 columns were added to calculate filters and perform tests (see detail in the data
processing part –validation)
o For the filter, if the condition applied is valid, the filter cell is initialised to 1 else to 0.
o Some tests or filter are made by the application of formula (filter or tests calculated
using a formula), other are initialised manually by users (filter edited).
Recommendation: it would be recommended to implement a system in a database, allowing to
automatically generating the tests as the preparation of the tests has to be redone every year which is
time consuming and can lead to errors. In addition, the tests could be implemented more easily in a
database.
38
Even though they are methodological documents available, there exists no comprehensive technical
documentation on the data processing.
Recommendation: build a comprehensive documentation
Security: Files are stored on the server at LEMNA. Raw data files transmitted by CGA to Lemna were
password-protected but sent by email. LEMNA takes care of the confidentiality aspects, as the
relation with the provider is based on confidence and the data are only used based on the purpose of
data collection. Any other use of the data is subject to specific requests.
At Ifremer
For the moment, the data are keyed in by the interviewer using the FESTIF software developed in
Windev. The interface is looking like this with different tabs allowing the capture of the
questionnaire information.
Figure 19: FESTIF Software
Source: Ifremer
When finalised, the files are transmitted to a generic email address at Ifremer and the attached data
were stored in a secured drive on Ifremer server.
Note: due to a recent change in the personal in charge of the management of the FESTIF data until now,
the rules are a bit more flexible regarding the security of data as the data need to be copied from the
secured place to a less secured place. In addition part of the processing (conversion from Windev format
to text and Excel format) is subcontracted to an external company.
39
The text and Excel format are loaded in an access database.
Figure 20. Example of database
Source Ifremer
The checking is done in SAS. For each variable, when errors are encountered suspicious data are
marked as well as missing values (see data processing –data validation). When errors are
encountered the interviewer is contacted but in some specific cases, some errors may be corrected.
Nevertheless the original value is always saved.
Figure 21. Validation of data
Source : Ifremer – Validation and use of the data - Méthodologie de qualification, validation et redressement des
données d’enquêtes économiques Ifremer – SIH Validation à l’aide de l’outil SAS
40
Documentation : The current processing method and database system are comprehensively
described in detailed working documents “Méthodologie de qualification, validation et
redressement des données d’enquêtes économiques Ifremer – SIH Validation à l’aide de l’outil SAS ».
In 2013, it is planned to integrate the economical data directly in “Harmonie”. The FESTIV data
capture tool will be replaced by an “Allegro” interface and the economical data will be managed and
their access secured directly in “Harmonie” database.
In addition to the data which are sent usually aggregated, the interviewer also uses a follow up
software allowing Ifremer to follow the progress in the survey. In this software, he/she indicates
whether he/she was able to interview a given vessel owner, if he/she encountered problem or
refusal, then it gives the possibility to Ifremer to find a substitute to interview.
Figure 22. Sampling follow up tool.
Source: Ifremer
The follow up software is de-connected from the data capture software.
9.3.
DATA PROCESSING
Every year Ifremer elaborates the fleet segmentation on the basis of the data of the preceding year
and determines the required size of the sample in each stratum. The strata defined by Ifremer are
based on ‘quartiers maritimes’ which is a more detailed regional sub-division than what is required by
DCF. For the purposes of DCF, the strata are than aggregated to DCF segments. Ifremer draws a new
sample every year.
The sample is divided between Lemna and Ifremer on the basis of previous information regarding
the availability of formal accounting and adherence to a CGA.
41
Lemna transfers the list of required vessels to the various CGA. However, Lemna cannot assure
access to the accounting of the vessels which were allocated to it for two reasons:
1.
2.
Data may not be available, as the vessel may belong to a firm operating more than one vessel
and separate accounts per vessel may not exist.
Data may not be accessible, as the accounting is done by a non-fishery accountant, not
pertaining to any CGA within Lemna’s network.
85% of the requested units are returned by CGAs. In order to meet the required number of
observations, The CGAs are requested to replace the missing firms by comparable ones. However,
how this replacement is done is beyond the influence of Lemna. This means that the Lemna
approach is ‘quota sampling’, each data provider having been obliged to provide a given number of
observations.
In order to assure the required contents and quality of the data submission, Lemna regularly
organizes meetings with the representatives of the CGAs. The meetings are dedicated especially to
definitions of the various indicators and questions which the CGAs have encountered in the
preceding period. The representatives are expected to pass on the conclusions to their members, i.e.
individual accountants or firms providing the data.
As indicated above, the CGA information system was updated to handle the information requested
by Lemna. CGA complete the information not registered in the balance sheet by surveying the
accountant responsible for the accounting of CGA ‘s member firms or directly the CGA’s member
firms in the case they do not have accountant.
Ifremer collects the data using an extensive 22-page questionnaire (see Annex 2). The field staffs
personally visits the vessel owners and fills in the questionnaire on paper. The questionnaire contains
a large number of questions, well beyond the strict requirements of DCF. The approach allows
adding in the margin any comment which is considered necessary. The comments provide additional
information relevant either for proper interpretation of the data or for general analysis of the
fisheries situation. The information which is already available in the Ifremer databases (e.g. technical
details of the vessel) is prefilled in the questionnaire so that the responsible interviewer only needs
to check the correctness (if required) and can otherwise focus on information which is really missing.
Data Validation
Lemna
The CGAs submit the data to Lemna in a prescribed format in Excel. Lemna staff checks the data
from various perspectives as:
o
o
o
o
Completeness – are any indicators missing
Continuity – comparison to preceding year
Consistency – are the indicators mutually consistent
Homogeneity - the spread of the results within one stratum
The following checks were implemented in the Excel worksheet used to manage the data to validate
the data:

For each filter, if the condition applied is valid, the filter cell is initialised to 1 else to 0.
42

Some tests or filters are made by the application of formula (filter or tests calculated using a
formula), other are initialised manually by users (filter edited).
Table 6. Filter data editing.
Column
Type of filter/tests
Filter: valid if only one vessel for the ship owner
filter calculated
Filter: identify active vessel
valid if fishing technique is "non active".
Fishing technique extracted from FIS database (Ifremer)
filter calculated
Filter on closing accounting period:
valid if the accounting was closed between September and March
filter calculated
Filter: valid if codestruct <7
filter calculated
Filter: valid if duree =12 month
filter calculated
Filter U000 :only one vessel in CGPA
filter calculated
Combined filter: valid if only one vessel in CGPA or by ownership
filter edited
E303/jdm
test calculated
% evolution of CA compared to SACROIS
test calculated
Test E303/LOA
test calculated
Filter E303 >0
filter calculated
Atypic filter (E303/LOA, etc.) :edited manually as it summarises the results of different tests which
are not clearly indicated.
filter edited
Filter E309 >0 : fiche compl ok
filter calculated
Test E309/CA
test calculated
Atypic Filter E309 : valid if ratio E309/CA is in the range ]0,0,65]: the ratio s defined by expert based
on experiences.
filter calculated
test E310/CA
test calculated
test E311/CA
test calculated
Atypic Filter E311 : valid if ratio E311/CA is in the range ]0,0,55]: ]: the ratio s defined by expert based
on experiences.
filter calculated
test E312/CA
test calculated
Atypic Filter E312 : : valid if ratio E312/CA is in the range ratio accepted [0,0,8]
filter calculated
test E314/CA
test calculated
test E315/CA
test calculated
Filter E316 > 0
filter calculated
test E316/CA
test calculated
test E317/CA
test calculated
test E317/E316
test calculated
Test ratio (SAL + CS) / E303 non retraité
test calculated
test E318/CA
test calculated
test E320/CA
test calculated
test E321/CA
test calculated
Test Income_Other/CA
test calculated
test E322/CA
test calculated
43
filter : valid if volume fuel is provided
filter calculated
TEST PRIX GO E309/VOLCARB
test calculated
Volume GO déduit de l'application des ratios par segment sur E309 + prix moyen par QM
test calculated
Volume GO ARRONDI déduit de l'application des ratios par segment sur E309 + prix moyen par QM
test calculated
Filer E309 >0 : fiche compl ok après évaluation selon ratios : edited manually as it summarises the
results of different tests which are not clearly indicated.

filter edited
Data validation includes the comparison of ratios for individual vessels with average ratios
to identify outliers.
Table 7. Comparison of ratios.
Ratio moyen E309/E312 - par métier et façade
Evaluation E309 avec ratio moyen
Ratio moyen E310/E312 - par métier et façade
Evaluation E310 avec ratio moyen
Ratio moyen E311/E312 - par métier et façade
Evaluation E311 avec ratio moyen ou déduction selon cas
Ratio moyen E312/E312 - par métier et façade
Evaluation E312 avec ratio moyen
Variable contrôle Eval E309,310, 311, 312

By the end, the column “Navire atypique - Motif suppression” (atypical vessel –reason of
deletion) is initialised to 1 or 0 manually by the expert depending if he considered that based
on the tests, the line is to be taken into account or not.
These checks are carried out on the basis of expert judgement. There is no written procedure, there
are no explicit thresholds and there is no software which would check the data automatically and
produce a list of items which need further attention. The data validation procedure is therefore
rather time consuming.
Lemna does not correct any data on its own initiative. All indicators which are considered unlikely or
wrong are communicated back to the CGA with a request to double-check and/or correct the value.
Transmission of Lemna data to DPMA: The data file transmitted to DPMA contains raw data validated
completed by the “navire atytipique” indicator.
44
Ifremer
The data provided by interviewer are uploaded in an Access database and validated/corrected using
SAS. The procedure checks the data from various perspectives as:
o
o
o
o
Completeness – are any indicators missing
Continuity – comparison to preceding year
Consistency – are the indicators mutually consistent
Homogeneity - the spread of the results within one stratum
Apart from the checks implemented in FESTIV software at data capture stage, the data validation is
done in two steps in SAS:


First: identification of empty values or inconsistency. Corrections are immediately applied if
the other information available is reliable.
Second: spreading statistics allowing detecting extreme values. The data are then corrected
or the interviewer is contacted for further details.
Corrections can be done by deduction from other survey or other external source like SACROIS or
by imputation of the average value of the fleet
A quality indicator is defined for each variable in order to evaluate the quality of the data and keep a
trace of the correction applied as the original data is saved. The indicators can be








0: original value correct
1: value which was supposed erroneous but is in fact correct after checking
2:incoherent value not to be taken into account in the calculation of the imputation value
3: suspicious value declared by the owner , but the value will be kept as such
4: interviewer error: entry error, interviewer calculation error, conversion error
8: panel value: when an indicator is not requested anymore in a panel survey, the previous
year value is taken and can be changed if needed.
9:missing value
10: incomplete: use when the indicator is used to describe the quality of a set of indicator.
Transmission of Ifremer data to DPMA: The data file transmitted to DPMA using the DCF site
contains all raw data financed by DCF validated/corrected including the quality indicators as well as
an indicator informing if the company was also surveyed by Lemna.
45
Data weighting/aggregation
Lemna and Ifremer submit the economic data independently of each other to DPMA in a predefined
format in Excel. DPMA integrates the two tables in one database in SAS 3. Lemna provides all data but
flags are indicated to point out if the quality of data is limited (flag “navire atypique”). Ifremer
indicates if some estimation is done but does not provide the original data and then DPMA cannot
distinguish original data from possible estimates made by its partners.
DPMA carries out estimates of historical capital values, based average insurance value per meter of
vessels of different segments.
DPMA calculates weighted averages of the economic indicators per DCF segment and extrapolates
them to the total population on the basis of the number of vessels in the fleet register and their
segmentation (métier) as determined by Ifremer. The calculation is carried out in SAS. The used
programme is not documented.
Data publication
Apart from the EC publication of the ‘Annual Economic Report’, some economic indicators are
published by Ifremer along with the transversal data in various publications such as “Synthèse des
Flottilles de pêche 2010 : Flotte Mer du Nord - Manche - Atlantique – Méditerranée”4.
Lemna uses its data for example as a background for its ‘Les rendez-vous de la Mer’ (latest edition
June 2012).
However, neither Ifremer nor Lemna has direct access to the complete data set of individual data
compiled by DPMA. Use and publication of Lemna data is constrained by the agreement between
Lemna and its partners, which requires that the partners must give their consent to every new use of
the data.
9.4.
STATISTICAL QUALITY
It is important to mention that DPMA has requested the report by the National Statistical
Information Council (CNIS) on the quality of economic data on the fleet, to be able to include this
statistical operation in the National Statistical Programme. This would certify the quality of statistical
outputs and can be considered a good practice to be recommended in other Member States.
Coverage
The economic survey achieves the level of coverage stated in the AR 2012, of approximately 25% of
the fleet. However, in view of the high coverage (49%) of the economically more important fleet over
12m, (sample of 489 vessels out of 1007), the estimates of the aggregated economic indicators can
be considered very reliable.
3 SAS
4
is used primarily because the responsible staff member knows it.
http://sih.Ifremer.fr/Publications/Syntheses/Synthese-des-flottilles-de-peche/2010
46
Sample design, calculation methods and achieved precision
Ifremer prepares annually a random sample, based on more detailed stratification than required by
DCF, as it also accounts for regional distribution of the fleet by ‘quartier maritime’. Lemna relies on
quota sampling as it depends on CGs which vessels they will provide. However, in view of the high
coverage, this does not necessarily present statistical problems.
Estimates are based on the Horwitz-Thomson formulas for stratified sampling. Coefficients of
variation are calculated by using step-by-step procedures (instead of SAS built-in formulas). It was
not clear to the mission team that the reported values were those required. In addition, when strata
are clustered the precision measures are not recalculated for the aggregated strata.
Achieved sample rate is presented as a proxy for achieved precision.
Recommendation: It is recommended that the procedures for economic data estimation (in particular
of the precision) are documented. The control of sampling at the level of replacement of vessels from
the original sample should be improved to avoid quota sampling and effectively work on the basis of a
probabilistic sample.
Coherence of different surveys and administrative registers
All administrative registers (VMS, logbooks, sales notes and fiches de pêche for the small vessels) are
integrated in one database which resolves any inconsistencies and generates one consistent set of
data. Economic data collected by Ifremer will be integrated in this database in 2013.
Surveys of Lemna and Ifremer are complementary. Doubles (vessels occurring in both surveys) are
eliminated by DPMA, usually maintaining the Lemna data. The mission did not identify any lack of
coherence between these two surveys.
Accessibility of data: possibilities to extract for user requests
Individual data is not accessible to outside users. Neither Lemna nor Ifremer dispose of the complete
data set. Only aggregated data, albeit at greater detail than DCF, is made available. Requests for data
have to be submitted to DPMA (NC).
9.5.
Data collection as of 2013
As mentioned above, the present approach to the collection of economic data has been reviewed in
detail by INSEE and a new approach has been designed, which will bear the INSEE quality label. The
label is issued under CNIS (Conseil national de l'information statistique) and law nr. 51, regarding the
data collection and confidentiality of micro data. As of 2013 the design of sample will be prepared by
DPMA. It is foreseen to draw a sample of about 1600 vessels, so that with an expected response of
about 60% the DCF commitment to collect data from some 990 vessels will be met.
The list of firms to be sampled will be provided to IFREMER and LEMNA. This will give a more
integrated approach but some pending issues regarding the dissemination of the primary data
needed for analysis at a more detailed level than DCF is still under discussion.
The new approach has been described in detail in the file elaborated for getting the « label d'intérêt
général et de qualité statistique » for the economical data survey in the sector of national maritime
fisheries (Combined data collection from Ifremer and LEMNA).
47
10.
ECONOMIC DATA – AQUACULTURE
10.1.
PROGRAMME MONITORING
Organization of data collection
The data on aquaculture is collected by Lemna in a similar way as the data on the fishing fleet.
However, there is slight difference between the collection of data on fish farming and shellfish
farming. As for shellfish farming, Lemna relies fully on two CGAs for this purpose: CGO – Centre de
Gestion Océan collects for 100 firms, and Nautil5, a part of CER-France, an organization encompassing
some 700 accounting offices6, collects 400 firms. As for fish farming, data on 35 firms was collected
in 2012 by Nautil while other 35 firms were visited by aquaculture extension workers of CIPA7 (Comité
interprofessionnel des produits de l'aquaculture).
CIPA is animating the new network under the supervision of Lemna. Lemna informs the CGAs about
the required data and the number of firms which they should provide, which means that the survey
is implemented through quota sampling. Which firms are included in the sample depends on the
CGAs. The CGAs provide anonymized data, each firm having a reference number.
The first actual collection of data took place in 2012, with data on 2011. Until 2011 only preparatory
work was taking place.
Achievement of objectives
For 2011 (AR 2012) the foreseen sample amounted to 157-457 firms8, while data from 295 firms was
received (table IV.A.2). This means an average coverage of about 10%. Neither CVs nor the coverage
rate have been calculated, so that it is not possible to assess whether this size of the sample is
sufficient to represent the whole population.
Compliance with methods and procedures
The stratification of aquaculture firms allows for aggregating to DCF requirements (see below). The
selection of the sample cannot be considered fully probabilistic, but rather based on quota
samplings; this is a methodological drawback, as it can produce biased samples. In addition,
response to the questionnaire is not compulsory, which is another source for non-sampling error.
10.2.
DATA STORAGE AND ACCESS
The data are collected using a template for data entry containing very detailed information (example
of information collected for shellfish farms in Annex 3).
The Excel file provided by the subcontractor are merged in an Excel data file and like fish processing,
additional tests are implemented directly in excel to evaluate the quality of the data like:


Realistic average price to detect data capture errors (if higher than twice the average price,
the data is considered doubtful)
Employment compared to employment costs
5
http://www.nautil-gestion.fr/
http://www.cerfrance.fr/qui-sommes-nous/
7 http://www.aquaculturedenosregions.com/#
8 The Annual Report specifies these two values as mínimum and maximum
6
48


Exhaustiveness of data
Sum of detailed information <= total
Same recommendations as for fish processing can be mentioned regarding the implementation of a
database and the provision of a technical documentation.
10.3.
DATA PROCESSING
Data collection
The economic indicators are collected from fiscal accounts by the CGAs and submitted to Lemna in
pre-defined Excel files.
The non-accounting data is collected from the production census, carried out by DPMA in
cooperation with CIPA for the purpose of Eurostat statistics.
Data Validation
Validation of the data is done at three levels
o
o
o
CGs controls the data prior to the submission to Lemna on coherence.
Lemna checks the economic indicators on coherence, consistency and homogeneity.
DPMA checks on homogeneity
Controls carried out by Lemna regard:
o
o
o
o
o
Check on average price of output. Data on producers receiving double of the average price
are considered questionable.
Completeness and missing indicators.
Calculation of FTE, which is based on the minimum salary (SMIG).
Structure of costs
Revenues of the firm in comparison to the segment average.
The validation is done ‘by hand’. Software has not been developed (yet) for this purpose.
Data weighting/aggregation
Aggregation of survey data to national totals is the responsibility of DPMA. Lemna submits the data
in Excel and DPMA processes it further in Access.
A complete list of aquaculture enterprises is based on the SIRENE/SIRET numbers (SIREN = Système
d’Identification du Répertoire des Entreprises and SIRET = Système d’Identification du Répertoire
des ETablissements), which are used by INSEE for classification of firms. The list serves on one hand
for the stratification of the sector and on the other hand for the calculation of the national totals.
The approach to data collection on aquaculture was designed during four meetings held in the
course of 2011 which were attended by DPMA, Lemna, CGO, Nautil, CIPA and CGPA. These meetings
have produced in particular a stratification of the aquaculture sector which is much more detailed
than the segmentation required by DCF. The agreed stratification accounts for the following criteria:
49
Shellfish
1.
Mussels
a. Rafts
b.
c.
2.
3.
9
i. Group 1 – More than 50% of sales for final consumption
ii. Group 2 - Less than 50% of sales for final consumption
Long line – no stratification, only Mediterranean firms will be included as it is not
possible to distinguish the longlines on the Atlantic coast
Bottom
i. Group 1 – More than 50% of sales for final consumption
1. <2 FTE
2. 2-5 FTE
3. >5 FTE
ii. Group 2 - Less than 50% of sales for final consumption (incl. those who sell
consumption mussels to traders). It is to be determined still if the further
subdivision is relevant.
1. Selling less than 50% of the production to a GIE (groupement
d’intérêt économique)
2. Selling more than 50% of the production to a GIE
Oysters
a. Raft, relevant for the Mediterranean, stratification to be established on the basis of
a study by Cepralmar9 et.al.
b. Long line – only very few firms use this technique
c. Bottom
i. Group 1 – Pure on-growing – more than 80% of the value sold to other
producers
1. Group 1.1 Production of spat and juveniles
a. Group 1.1.1 Collection in tidal areas
b. Group 1.1.2 Collection in enclosed spaces (hatcheries and
nurseries)
2. Group 1.2 On-growing
a. Group 1.2.1 <2 FTE
b. Group 1.2.2 >2 FTE
ii. Group 2 – Grower / traders
1. Group 2.1 More than 70% of sales directly for consumption
2. Group 2.2 Own production is more than 30% of total sales value,
but less than 70% is for direct consumption (i.e. sales go to other
traders)
a. Group 2.2.1 <2 FTE
b. Group 2.2.2 >2 FTE
3. Group 2.3 Traders – own production is less than 30% of total sales
Mixed
a. Mixed / raft – to be based on Cepralmar study
b. Mixed/ bottom
i. Group 1 Growers, selling >80% to other producers
ii. Group 2 Growers / traders
La conchyculture en Méditerranée
50
In addition to the above criteria, the sample is selected in relation to the regions where these
activities are located.
The detailed stratification allows aggregation to DCF segments. The totals are calculated first for the
detailed national strata, which are subsequently aggregated to the DCF segments.
Data publication
The data has not yet been published for other purposes than the EC data call of May 2012. It must be
pointed out that publication may be premature as only one year of data has been collected (on 2011)
and therefore some initial problems may be expected.
10.4.
STATISTICAL QUALITY
Estimation:
Missing data is estimated, usually by the CGAs. It is unclear to which extent estimations are done and
how they are derived.
Data quality evaluation:
Coefficients of variation have not been presented in the TR 2012.
The mission has seen the data stored in the Access database of DPMA. Statistical quality could not be
evaluated.
It should be noted that the involvement of INSEE in the collection of aquaculture data seems rather
limited and that the INSEE certification developed for the catching sector does not apply to
aquaculture.
51
11. ECONOMIC DATA - PROCESSING INDUSTRY
The main institution responsible for the collection of data on fish processing is FranceAgriMer which
has sub-contracted the actual data collection to AND International.
FranceAgriMer is then in charge of ;


11.1.
Data provision
Yearly technical and financial reporting
PROGRAMME MONITORING
Organisation of the data collection
FranceAgriMer has sub-contracted the data collection to AND International. The data is collected
with paper questionnaires sent to the firms by postal mail. In order to increase response, several
weeks after the mailing the contact persons are called by phone, in case they did not yet respond.
About 2-3 persons are involved in data collection on fish processing on part time basis. Further 1-2
staff members of FAM
Compliance with methods and procedures
For the purposes of FranceAgriMer, the population of fish processors is stratified in a greater detail
than required by DCF. The 2010 stratification is presented in Table 8.
Table 8. Stratification of fish processing designed by FAM / AND International
Source: AND International, Translation of a table regarding the collection of economical and financial data related
to fish processing industries. Methodological Annex, April 2012
Staff
Three persons at AND International and one person at FranceAgriMer are directly involved on part
time basis with the elaboration of the data on fish processing.
52
11.2.
DATA STORAGE AND ACCESS
Primary data are stored in an access database at AND International only.
AND is extracting primary data from this access database in an excel format to FranceAgriMer as well
as the aggregated tables to be provided for the DCF call by emails. Lots of calculations are done in
Excel.
Access database
The analysis of the Access database provided with anonymous data after the mission, showed that:


A database is probably created every year.
The motivation of using Access was mainly to have a tool facilitating the mailing to the firms
and the capture of questionnaire information (receipt of answers and questionnaire data).
The form used for the capture of the data do not contains embedded interfiled consistency
tests.
But concerning the database itself:






No documentation on the database exists.
The database is not a relational database, its structure is then not normalised.
The tables do not have unique key.
The database seems to contain duplication or unused tables:
o several lists are available :
- liste600 containing company details and follow up of the sending to the
countries. It is very similar to poisson2011 table,
- list2010 AND (initialised with 2008 data) containing company details,
- list DGAL (authorisation),
- AND & INSEE.
o several tables of questionnaires: questionnaire and questionnaire 2010,
The database contains also other source of information like the “donnée comptable 2009”
containing accounting data from annual report 2009 and “export diane 30sept 2010”:
companies and number of employees information.
No query is predefined for the preparation of the excel data. It is assumed that most of the
calculations are done in excel, but the excel file containing the formulas was not provided
(ex: the one containing the calculation of the cv).
A print screen of the Access tables is presented below.
53
Figure 23. Access tables
Source: AND International
Excel files database
The Excel files contain all the primary data from champ 1 and 0 as well as aggregation formula to
compute the calculated DCF variable based on the detailed information. The result table is presented
bellow and completed with indication on the formula applied:
Table 9: Results of the computation of the DCF variables
Values in K €
Formula based on
information collected
in the questionnaires
Calculated DCF
Variables
French label
Turnover champ1
(total)
Chiffre d'affaires
total
Turnover champ 0
based on
percentage of
processing
Chiffre d'affaires en
transformation de
PPA
Subsidies
Subventions
Total produits
exploitation - Subsidies
- Turnover
Other income
Autres produits
d'exploitation
Cout personnel
exterieur + cout
personnel salarie
Wages and salaries
of staff
Coût de la main
d'oeuvre salariée
54
Total 305
companies
Including
extrapolated
Representativeness
4 507 268
221 421
95%
3 702 847
221 029
94%
47
99%
27 988
3 211
89%
655 674
33 312
95%
5 463
Imputed value of
unpaid labor
Coût de la main
d'oeuvre non
salariée
3 258
Energy costs
Achats d'énergie
Purchase of fish
and other raw
material
Achats de matière
première
Total charges
exploitation -achat
energie - Achat
matières premieres Cout personnel
exterieur - Cout
personnel salarie
Other operational
costs
Autres charges
d'exploitation
Actifs immobilises nets
- Actifs immobilises
nets 2008
Depreciation of
fixed assets
Amortissement des
actifs immobilisés
nets
79 817
Financial costs, net
Résultat financier
- 12 036
Extraordinary
costs, net
Résultat
exceptionnel
- 9 714
Total value of asset
Total du bilan
Net investments
Investissement net
Amortissement des
actifs immobilisés
nets+ Dotation
amortissements
Debt
Dettes
Average numbers
of persons
employed
Effectif moyen
59
98%
271 151
26 779
90%
1 754 647
66 546
96%
1 697 895
92 322
95%
251
100%
-
847
93%
-
144
99%
2 109 888
96 990
95%
159 188
3 982
97%
1 211 025
61 945
95%
475
97%
15 633
Note: In the result tables, we should expect that the field “Turnover champ 0 based on percentage of
transformation “is the turnover of the companies of champ 0. According to the formula, the value found in the
“Turnover champ 0 based on percentage of transformation“ is the percentage of Turnover champ 1 based on
percentage of transformation. The value related to companies of champ 0 seems not included in the final table.
Recommendation: having at least all primary data centralised at FAM, preferably in a database system
allowing FAM to work with the data more easily than in excel. It would also be recommended to build
a relational database and a system were the calculation can be performed automatically clarifying the
process and limiting the risks of errors, and allowing test automation.
Technical support/maintenance: not relevant as only the excel file with no formula are provided to
FranceAgriMer.
Security: All DCF partners transmit the data to DPAM using the secured channel of the DCF web
page. The security is ensured even if only aggregated data are transmitted to DPMA.
55
Nevertheless, it is to be noted that the transmission between AND. International and the institute in
charge of the DCF data collection regarding the primary data is not secured.
Recommendation: the transmission of the files should be more secured (crypting of the mails, setting
up of secured ftp…)
Data storage: the mission did not visit the premises of the contractor. At FranceAgriMerlevel, the
excel files are stored on a server in a shared drive dedicated to the unit (15 persons) whereas only 2-3
persons are working with the primary data.
Recommendation: the access to the excel file should be more restricted.
Backups and archives: AND archives the questionnaires.
AT FranceAgriMer, daily backup on external drives are ran.
Recommendation: questionnaire should be scanned and provided to FAM
Documentation: the methodological documentation is well done, but no technical documentation is
available regarding the database and how the corrections are done, how the coefficient of variation
is calculated.
11.3.
DATA PROCESSING
Data Collection
1.
Update of the enterprises list and preparation of the questionnaires (October –November)
The list of enterprises to be surveyed was originally prepared on the basis of NACE numbers 1020
(fish and snails) and 1085 (ready meals) and is updated annually with Registry of commercial court
(Greffes des tribunaux de commerce) and completed the public health certification DB.
The list of fish processing firms is split into two parts:

category 1: (champ1) containing firms for which fish products represent more than 50% of
their turnover : for these firms all variables are requested.

Category 2: (champ 0): for which fish processing is less than 50% (champ 0). Only income is
requested for this category.
Table 10. Number of enterprises
Champ 1:
Champ 0:
Number of
firms in 2009
243
32
9
27
41
NAF
Activity
1020Z
1085Z
4638A
Other
4638A
Processing and preserving of fishes, crustaceous and molluscs
Ready meals with fish
Whole sale trade of fishes, crustaceous and molluscs
Whole sale trade of fishes, crustaceous and molluscs
Source: FranceAgriMer
An exhaustive survey is performed annually excluding nevertheless very small firms whose contact
details are unknown. This considered important to achieve high coverage, because some large food
processors process and sell large quantities of fish although this is not their main activity. At the
56
same time some firms are eliminated, e.g. because the NACE code 1020 also contains producers of
snails. For 2011, 305 firms in champ 1 and 111 firms in champ 0 were identified. A contact person has
been identified within each firm for the purpose of the survey.
2.
Sending of the first questionnaire (end of December)
A common empty paper questionnaire is sent by postal mails to the firms of champ 1 and 0.
Companies belonging to champ 0 stops the filling of the questionnaire earlier.
The results of the previous survey are included in the introduction letter explaining the purpose of
the survey.
The questionnaire explains clearly how to fill in the cells.
At the same time AND int. collects the annual report of the previous year DIANE and Registry of
commercial court) and update the internal database with the administrative information collected.
3.
Sending of the second questionnaire (January -15 February)
For the non-answering companies, a prefilled questionnaire with the available information is
collected in Diane and Greffe. In this way the respondent only need to check the correctness of the
figures (see Annex 4).
Finally firm which had not yet responded are contacted by phone.
In the meantime, received data is entered in an Access database and combined with data obtained
from other sources as explained in data validation step.
4.
Data validation and estimation (15 February – end of February)
Various sources are used to check the consistency of the data:
o
o
o
o
o
o
DIANE database – providing economic information on French firms 10
Registry of commercial court (Greffes des tribunaux de commerce) – providing annual
reports11
public health certification DB
Previous survey
Enterprise websites
Professional press
Before being captured, the questionnaire are checked visually by AND Int’ expert regarding;

The consistency of information written by the company

The consistency with DIANE data and previous year data
If inconsistency is found, the company is called for clarifications.
For the remaining missing values, data which has not been filled in the questionnaires and is not
available in the annual report, is first of all completed from secondary sources, such as the company
websites and professional associations (ADEPALE, regional associations and specialized press).
10
https://diane.bvdinfo.com/version-2012917/Home.serv?product=diane2006
11 http://www.greffes.com/
57
Remaining missing values are estimated in two ways:
1.
2.
Based on change in comparison with previous year. E.g. if turn-over increased by x%, and
energy costs are not available, than energy costs of previous year are increased by x%.
Using the average of the corresponding segment. In this respect it is important to stress
that the segments are distinguished by size, namely under and over turn-over of 5 mln euro.
In order to evaluate the quality of the data, the references ‘completeness’ distinguishes three levels:



1: questionnaires fully completed or information found in the yearly annual report,
2: data fully completed based on preceding year annual reports or partially completed but
with relevant turnover and number of employee
3: no data or no recent reliable data. For 2010, this concerns 33 small companies covering
less than 5% of the total turnover of the sector, whose data were extrapolated using the
averages calculated for each of the variable of the small companies having answered the
questionnaire.
The origin of the information is also indicated: it can be 1. Answers to the questionnaires, Annual
reports or Extrapolations
Recommendation: The two parameters give only a general level of quality of the declaration but do
not allow identifying which variable was corrected or extrapolated and what was the original value in
case of correction.
The FranceAgriMer received different version of output file but not the Access database.
Recommendation: FAM should also receive the access database which should allow the FAM
responsible to more easily check the data.
5.
Data publication (since March)
AND International produced DCF deliverables in March.
Apart from that, data collected by AND International is used in various studies by FranceAgriMer12.
The DCF data on fish processing is not on-line accessible.
12
E.g. Fisheries and aquaculture sector in France, April 2012, p.18
58
Figure 24. Collection and processing of data on fish processing
Source: AND International, Collecte de données économiques et financières concernant les entreprise de mareyage
de l’industrie de transformation des produit de la mer, Annexe méthodologique, Avril 2012
59
11.4.
STATISTICAL QUALITY
Data quality
All DCF required information is available.
Level of response rate was in 2011 at 81%, while the coverage rate (in terms of turn-over) reached
94%. This means that the survey results approach census.
The number of observations indicated in the annual report was found in the detailed Excel sheet.
Coefficient of variation has been presented in the AR 2012. However, it was suspected that the
estimated values were included in the calculation when they should not be. The procedure for the
calculation of the CV is not done in the Access database, but done manually in an Excel file.
Nevertheless, the AND’s file contain only values, no (formula).
Recommendation: Formula applied should be made visible to FAM.
Comparison with ESANE survey
The results of the survey are also compared to the results of the ESANE13 survey by INSEE, which is
done in the context of the Structural Business Survey. The comparison with ESANE can be only done
after the DCF has been submitted to the EC, as the ESANE results become available only with a
significantly longer delay. In addition, the data provided by INSEE are only provided at aggregated
level for confidentiality reasons.
Table 11 shows that the differences between the two surveys are in the order of 1% in relation to total
turnover and full-time employment. The difference in the number of surveyed firms is due to
different reasons:



ESANE is surveying firms with code 1020Z meaning including snails firms.
Some problem of misclassifications like inclusion of the MORPOL France commercial
companies in NACE 1020Z
55 micro enterprises not registered in financial databases, which cannot be reached DCF.
Table 11. Comparison of the results of DCF and ESANE survey for NACE 1020 firms
Source: AND International, April 2012
13 Élaboration des
statistiques annuelles d'entreprise / ESANE,
http://www.insee.fr/fr/methodes/default.asp?page=definitions/esane.htm
60
Revision of 2006-2008
2008 as well as DCR 2006-2007 data were considered very incomplete and not reliable and were
revised by AND international in August 2011. The revision methodology is described in the “Annex
methodologique DCF avril 2012” documents.
Recommendation : FranceAgriMer indicated that it would be relevant to have the possibility to send
revised data as some correction may be applied on the data by comparing the results provided to DCF
with the ESANE survey or if specific revisions were applied on the data for previous years.
61
12. VARIABLES ON THE EFFECTS OF FISHERIES ON THE MARINE ECOSYSTEM
Organisation for the production of related data
Table V.1. of the TR include the first 8 indicators to be collected. Ifremer is responsible for the
calculation of indicators 1 to 8 while DPMA calculates indicator 9.
The first four indicators are calculated using data from the scientific surveys stored in the
“Harmonie” database. Indicators 5, 6 and 7 are calculated using VMS data (vessels over 15 meters,
Recopesca (vessels under 15 meters) and the activity calendar described in chapter 7.1 (Calendrier
d’activité). Indicator 8 regarding the volume of discards is calculated using métier related data
coming from ObsMer sampling scheme (onboard data collection). Indicator 9 for fuel efficiency is
calculated by DPMA using economic data collected by Lemna and Ifremer.
Achievement of objectives
For the calculation of indicators 1 to 4, an R-based application has been created, called “R sufi”.
These indicators are calculated using data from the research surveys. There have been problems
concerning the calculation of indicator 4. Scientific surveys do not cover all the target species
spawning seasons and consequently surveys are not sufficient to collect all the required primary data
for the calculation of this indicator.
Indicators 5 to 7 have been successfully calculated using an algorithm to evaluate the distribution of
fishing activities using VMS data, Recopesca data and the activity calendar (Calendrier d’activité).
Indicator 8 is calculated using Obsmer data collected during the onboard sampling. The discarding
rates are estimated with an interval of confidence using bootstrap.
Indicator 9 is not reported in table V.1. DPMA is responsible for its calculation. The indicator is directly
obtained combining economic data on landings and fuel cost.
Compliance with methods and procedures
The methodologies used to calculate the different indicators are coherent and correctly approached.
62
13. CONCLUSIONS BY CHAPTER
13.1.
Biological data
General comments
Ifremer possess a more detailed classification of métiers than that required by the DCF. In
consequence, in order to define the métier sampling range, original métiers are merged to form the
métier level 6 defined in the EC Decision 2010/93/EU. The selection of métiers to sample has
successfully been carried out.
The sampling scheme is aimed in higher strata than the métier level 6 merging different métiers by
port as one single sampling target. This concludes in better correlation between the expected
number of samples and achieved number of samples but fails to proportionate a level of accuracy at
the ”DCF métier” level. The fact that the sample design is aimed in wider strata than the métier level
6 may generate important deviations from the plans at the métier level.
As for the stock-related data, sample sizes are based on tradition and the sampling allocation for
species measured on-shore is correlated to the fishing effort during the year. Sample achievement
rates are most of the time successful. In the case of the maturity variable, a bad planning of the
sampling has led to a null data collection.
AR tables foreseen in DCF regulations (tables III.C.1 up to III.C.6 for métier -­­
related
variables
and
III.E.1
to
III.E.3
for
stock-­­
related variables) are filled in trying to follow the specific rules but with some mistakes
and missing data:



Precision levels in table III.E.3, column S “achieved number of trips on-shore” in table
III.C3 and values for the landings in the Mediterranean are not reported.
Important discrepancies arise between tables III.C.3 and III.C.4 in figures at the métier
level.
Some measurements have not been reported in table III.C.3.
Recreational fisheries
Data collection on Bluefin tuna, glass eel and salmon are based on the report of catches done by the
fishermen. The non-reported catches are not estimated so the total catches might be underestimated.
The sampling strategy on yellow eel has recently been restructured to use a similar methodology to
the salmon.
Sea bass recreational fishery catches estimation is based on telephone survey and voluntary logbooks of the fishermen interviewed. A new large-scale survey based on the sea bass methodology is
in progress in order to estimate total catches of different target species such as cod and
elasmobranches for the first time for the MS.
There are no measures of precision provided for this type of fishery.
63
13.2.
Research surveys at sea
France takes part in 5 international research surveys financed by the DCF. All these surveys follow
internationally arranged methodologies and coverage rates are very good.
13.3.
Evaluation of the effects of the fishing sector in the maritime ecosystem
Indicators 1 to 8 are calculated by Ifremer while indicator 9 is responsibility of DPMA.
First four indicators are calculated using data from the research surveys. The first three are correctly
managed while the forth indicator, concerning the size at maturation, have planning problems since
research surveys do not cover all the spawning seasons of the different species.
Indicators 5, 6 and 7 are calculated combining data from the VMS and Recopesca (for vessels under
15 metres) with the activity calendar (calendrier d´activité). Indicator 8 is calculated using data from
the sea sampling.
Indicator 9 is calculated combining economic data on landings and fuel costs. It is not reported in
table III.V.1.
13.4.
Economic data
General conclusions on economic data
Compilation of large part of the economic data takes place in three stages: DPMA sub-contracts
Lemna and Ifremer who in their turn sub-contract private accountants. The primary data submitted
by the accountants cannot be directly verified.
Catching sector
An INSEE -approach to data collection on the catching sector will be implemented as of 2013, which is
an important guarantee for the quality of the produced statistics.
Aquaculture sector
Collection of data on aquaculture sector has started relatively recently. France pursues an ambitious
survey on detailed stratification.
Fish processing sector
The data collected on the fish processing almost reach census level with coverage of 94% of turnover
in 2010.
64
14. RECOMMENDATIONS BY CHAPTER
The system in place is well developed and mastered internally by the institutions. From the IT point
of view as mentioned in the document, the following general recommendations could be indicated:




To develop some light, automated tools for the checking of the data when it does not exist
and develop the corresponding documentation.
When not done, to keep trace of the original data and on the changes applied.
To develop a few pages on the DCF web site and to envisage the possibility to promote the
dissemination of fisheries data to the public.
In the complex centralised system, the final user interface to be developed for extracting
the data should be easy to use for non-IT experts. User should need the help of IT specialists
only in exceptional extractions cases.
14.1.
Biological data
Métier-related sampling
The sample design should be re-arranged to achieve better coverage levels at métier level. It could
be useful to use the “Calendrier d’activité” to plan the sampling scheme.
Stock-related variables
Data collection on maturity should re-planned for some species. It is foreseeable that certain species
are not measurable during certain scientific surveys due to the season.
Concerning the unreported precision levels, an automatisation to obtain all the CV values in one
single R output could be built. This way it would not be so time consuming the elaboration of the
report.
14.2.
Recreational fisheries
It is recommended to estimate the total number of anglers targeting yellow eel in order to obtain
reliable total catch estimations.
Precision measures for all recreational fisheries should be reported (as they are calculated by the
ONEMA Directorate for Research).
14.3.
Research surveys at sea
No recommendations
14.4.
Economic data
Catching sector
No recommendations. In view of the new approach to be introduced in 2013, certified by INSEE,
recommendations are not considered relevant.
Aquaculture sector
Procedures for validation of the data should be formalized.
65
Approaches to estimations of missing data should be well described.
Fish processing sector
No recommendations.
14.5.
Transversal variables
No recommendations.
14.6.
Evaluation of the effects of the fishing sector in the marine ecosystem
No recommendations.
66