IDAF IDAF PROGRARAM,DANIDA IDAF PROGRAMME FOR INTEGRATED DEVELOPMENT OF ARTISANAL FISHERIES IN WEST AFRICA IDAF PROGRARAM, FAO LIBRARY AN: 347073-077 FOOD AND AGRICULTURE ORGANIZATION

-

DANIDA

IDAF

PROGRAMME FOR INTEGRATED DEVELOPMENT OFARTISANAL FISHERIES IN WEST AFRICA

IDAF PROGRARAM,

FAO LIBRARY AN: 347073-077

FOOD AND AGRICULTURE ORGANIZATION OF THE UNITED NATIONS

Mauritania

Senegal

Cape VerdeThe Gambia

Guinea Bissau

Guinea

Sierra Leone

Liberia

Côte d'Ivoire

Ghana

Togo

Benin

Nigeria

Cameroon

c; 4

Equatorial Guinea

Gabon

Sao Tome and Principe

Congo

Zaire

Angola

DEPARTMENT OF INTERNATIONAL DEVELOPMENT COOPERATION OF DENMARK

Report of the Working Group inArdis dial Fisheries Statistics for the

Weste ii Gulf of Guinea, Nigeria ,odC on

Cotonou, Benin 3-7 May 1993

Technical Report N° 49 November 1993

FISHERY COMMITTEE FOR THE EASTERN CENTRAL ATLANTIC

PROGRAMME FOR THE INTEGRATED DEVELOPMENT OFARTISANAL FISHERIES

FOOD AND AGRICULTURE ORGANIZATION OF THE UNITED NATIONS

Cotonou, November 1993

Technical Report N° 49 November 1993

Report of the Working Group inArdis al Fisheries S'afisties for the

Weste i Gulf of Guinea, Nigeria ,odC on

Cotonou, Be n 3-7 May 1993

The designations employed and the presentation of the material in thispublication do not imply the expression of any opinion whatsoever on thepart of the Food and Agriculture Organisation or the financing agencyconcerning the legal status of any country or territory, city or area, or of itsauthorities, or concerning the delimitations of its frontiers or boundaries.

For bibliographic purposes this documentshould be cited.as follows:

Anon., - Report of the Working Group on Artisanal Fisheries Statistics for the Western Gulf1993 of Guinea, Nigeria and Cameroon, Cotonou, Benin 3-7 May 1993. Cotonou,

Programme for the Integrated Development of Artisanal Fisheries in WestAfrica, 126 p., IDAF/WP149.

IDAF ProjectFAO

P.O.Box 1369Cotonou, Republic of Benin

Telex: 5291 FOODAGRI Fax: (229) 33.05.19 Tel: (229) 33.09.25

Table of contents

Opening of the meeting 1

Adoption of the agenda

General discussion on statistical surveys with specific reference to fishingeffort and associated basic data

Definition, level of detail and purpose in measuring fishing effort 2

Current status of national fishery statistical surveys 3

Methodological and operational aspects of fishery statistical surveys 4

Fishing effort as a source of variability in catch/effort assessment surveys . . . 5

Statistical scenarios 5

Part A: complete coverage of fishing effort from all landing sites 5

Part B: partial coverage of fishing effort from all landing sites 5

Part C: sampling fishing effort and landings in space and time 6

Computer and implementation aspects 6

Conclusions and recommendations 7

Annex 1 List of participants 9

Annex 2 Agenda 10

Annex 3 List of documents presented to the Working Group 11

Annex 4 Pêche artisanale maritime en Côte divoire par J. Konan et R. Dedo 12

Annex 5 Artisanal fisheries in Cameroon by L. Nkumbe 27

Annex 6 Artisanal fisheries in Nigeria by M.O. Okpanefe 30

Annex 7 Methodologies used in .the collection, analysis and dissemination of artisanalfisheries statistics in the Western Gulf of Guinea (Benin, Côte d'Ivoire, Ghanaand Togo) by K.A. Koranteng 34

Annex 8 Methodological and operational aspects in catch/effort assessment surveys byC. Stamatopoulos 74

L Ope ng of the

Following a recommendation of the Twelfth session of CECAF (Accra, Ghana April-May 1992) a special Working Group on Artisanal Fisheries Statistics for the Western Gulfof Guinea, Nigeria and Cameroon was held in Cotonou, Benin, jointly by the FAO FisheryCommittee for the Eastern Central Atlantic (CECAF) and the programme for the IntegratedDevelopment of Artisanal Fisheries (IDAF) from 3 to 7 May 1993. Experts from Benin,Cameroon, Côte d'Ivoire, Ghana, Nigeria and FAO participated (Annex 1).

In his introductory remarks, Dr. B.P. Satia, Coordinator of IDAF welcomed theparticipants and stressed the need for the Working Group to suggest ways and means ofimproving the data collection systems in the subregion to enhance the development of thesub-sector.

Adoption of the agenda

The Agenda for the meeting which appears as Annex 2 was adopted.

Documents presented to the Working Group are listed in Annex 3.

Ms Amélie Gbaguidi-Aziable (Benin) and M.O. Okpanefe (Nigeria) were unanimouslychosen as chairpersons.

Mr. C. Stamatopoulos of FAO was the resource person for the Working Group.

Mr. K.A. Koranteng (Ghana) and Ms. H. Stokholm (IDAF Programme) were chosenas raporteurs assisted by Mr M. Ansa-Emmim (FAO).

General discussion or. statisticaul sureys with specific iiefenrnce to fishingeffort and associated basic data

The Working Group noted that expertise in methodology and operation of fisheriessurveys existed in several African countries due to technical assistance programmes (CECAFand national projects) and intensive training received locally and abroad.

A major problem facing all fisheries statistical programmes, established with externalfunding, is the sustainability of data collection after a project has ended. This problem henoted, is particularly acute in African countries where some governments have had difficultiesto continue with such programmes. In the life of the projects sufficient funding was madeavailable to supply all needs and to pay salaries and unless the recipient government incontinuing to provide regular funds to meet these needs, the system immediately broke downwhen the project functionally ceased. In some cases the projects established systems that weretoo complex to maintain when external funding ceased, or when foreign experts werewithdrawn. Another cause was that sometimes inadequate training was given to nationalexperts to be able to continue the programme. Often, provisions were not made for a smooth

IDAF Technical Report N° 49

IL eeting

transition and running of the programme. For example national experts were often notsufficiently involved in the management of the project and programmes.

Experiences gained in this area in Nigeria and Benin were presented. In Nigeria, astatistical and economic data collection system was put in place about 10 years ago throughan externally funded project. Good results were obtained but after the end of the project, thesystem collapsed mainly because of lack of funds to retain field staff or to maintain vehiclesand equipment. Consequently the institution that took over the programme was forced todrastically reduce the operation.

In the case of Benin, the Working Group noted that staff was not the problem but thatof lack of materials and funds to continue data collection after the operational phase of theproj ects.

In the discussion that followed, it became evident that several governments do not givefisheries statistics the priority it deserves and therefore do not see the need to finance fisheriesdata collection from national budgets.

Some participants were of the opinion that before a project ended, recommendationscould be made on minimum levels at which data collection systems could be operated andstill be able to produce statistically valuable results when the project functionally come to anend. Furthermore, in many countries Fisheries is only a Department or Division within theMinistry of Agriculture and sometimes does not receive the attention that it deserves.

Participants felt that the creation of possibly imperfect but robust self-sustainingsystems would offer more advantages than the introduction of complex systems that could notbe sustained.

The Working Group finally stressed the necessity for governments to endeavour tosupport data collection programmes. It noted the efforts made in the past and difficulties facedby governments in the subregion in implementing structural adjustment programmes, butunderscored the need to accord priority to statistical data collection. In view of thesedifficulties the Working Group recommended that all future fisheries development projectsor programmes should have as part of its immediate objectives the improvement of fisheryinformation systems.

40 Defi tion, level if detail 0id p eastuing fishing effort

The Working Group was informed of basic estimation process by means of which totalcatch is estimated. The process involves determining the Catch Per Unit of Effort (CPUE)(from sample data) and then multiplying it by the effort (obtained through a census). A cleardistinction was made between the weighted CPUE, a magnitude resulting from dividing totalsampled catch by total sample effort and mean catch rate, which is the arithmetic mean ofindividual CPUEs associated with fishing units. Several numerical examples were presentedand discussion focused on: measuring fishing effort in standard units by gear type, variabilityin CPUE and effort and, application of the approach of weighted averages in other cases suchas prices and unit values of fish production.

2 IDAF Technical Report N° 49

si se in tI

5. Cwnnt status of national fishery statistical sweys

Mr K.A. Koranteng presented the results of his mission which was aimed at examiningthe systems for statistical data collection on artisanal fisheries in the four countries of theWestern Gulf of Guinea, Benin, Côte d'Ivoire, Ghana, and Togo. The report is given in Annex4. After the presentation, the Working Group commended Mr Koranteng for the workundertaken. National reports for Côte d'Ivoire, Cameroon, Benin and Nigeria were alsopresented.

The participant from Benin complemented the information already in Mr Koranteng'sreport. She noted that since 1991, the total landings have reduced. For example it was only6,800 mt in 1991 as against the previous average landings of 8,000-9,000 mt. This was partlydue to the fact that the fishermen had abandoned fishing and using their canoes for nonfishery activities which they find to be more profitable business than fishing.

On catch assessment at the port of Cotonou, she noted that the sampling systemrecommended by CECAF is used except that canoes for examination are not selected inaccordance with any pre-determined order. Recorders are instructed to sample five canoes perfishing gear with 50% of the sample to be taken from canoes with high catches and the other50% from those with low catches. The total production of Benin is the sum of the estimatefrom the port and that from outside the port. Since 1993 the Centre National Océanographique(CNO) within the framework of a fisheries management programme, is collecting data alongthe coast. The Working Group noted that this method of selecting canoes for examination maynot produce a representative sample.

With regard to Cameroon it was noted that the 40,000 mt of fish produced annuallyby the sub-sector is about four times bigger than that of the industrial sector. There are about20,000 fishermen nearly 90% of whom are foreigners mainly operating in the swampy areasusing mainly gillnets, beach seines, long lines and purse seines. There is also a very importantshrimp fishery.

A frame survey in Cameroon was conducted in 1983. A Catch assessment survey, asexplained in Samba' (1986), was initiated. Unfortunately, due to financial constraints, thesystem is no longer in operation. The Department of Fisheries and the Limbe Research Stationare the two institutions concerned with data collection.

With regard to Nigeria a multi-stage probability sampling scheme was initiated in 1975a National Committee. Presently, the Federal Department of Fisheries is responsible for thecollection of artisanal fishery statistics in Nigeria. The Nigerian Institute for Oceanographyand Marine Research (NIOMR) also produces statistics which are supplementary to those ofthe Federal Department of Fisheries.

In the discussion that followed the presentations, the Working Group noted withconcern the problem of non-collaboration among national institutions engaged in the collection

'Samba, Alassane (1986): Collecte et traitement des statistiques de Oche artisanale au Gabon, auCameroon et au Togo. CECAF/TECH/86/77, CEAF Programme, Dakar, Senegal 68p.

IDAF Technical Report N° 49 3

of fisheries statistics. The Working Group therefore underscored the need for nationalinstitutions charged with the responsibility of collecting, processing and dissemination ofartisanal fishery statistical data to collaborate in order to avoid duplication of effort and moreimportantly to optimise the use of resources available for data collection (human, material andfinancial.

6. Methodological uud operational aspects of fishery statistical surveys

This Agenda item was introduced on the basis of the document prepared by Mr.Stamatopoulos (Annex 5).

The main objective of the document was to enhance the exchange of views and ideason major schemes for statistical data collection (statistical scenarios), rather than seeksolutions to specific survey problems.

The Working Group noted that from the methodological and operational viewpointalmost all the statistical systems that have been implemented in Africa deal with basic sampledata (catch, effort, prices) for the derivation of important sample-based magnitudes such asCatch Per Unit of Effott (CPUE) and Gear Activity Coefficient (GAC), and the estimation oftotal effort and fish production at various stratification levels such as minor stratum, andglobally. Census approaches are only used in specific sectors of the fishing industry whereregular recording of all fishing activities is in place. Examples of census-based data collectionschemes are logbooks of industrial fisheries and observer programmes.

Although sample-based surveys differ from country to country in terms of species,fishing gear, fishing methods, types of water bodies, etc., it has nevertheless been possible toidentify common elements in the formulation of statistical scenarios.

For each statistical scenario methodological notes have been provided in the document(Annex 5) as a background for an easier understanding of the statistical aspects involved, andhave been supplemented with examples based on computer-generated catch and effort dataappropriate for each specific survey approach.

7. Fishing effott a soun e of variability in catch/effort assess

The Working Group considered examples that illustrated the basic estimationprocedures involving Catch Per Unit of Effort (CPUE) and fishing effort. Special emphasiswas placed on the importance of adequately classified fishing gears for statistical purposes,in order to obtain more harmonized and meaningful expressions for fishing effort and, at thesame time, reducing the variability in collected effort and CPUE data.

The Working Group recognized that in the basic estimation process involving CPUEand fishing effort, the latter is the determining factor in the accuracy and cost/effectivenessof a survey system. It was noted that the three major statistical scenarios presented at the


II e t suu-veys

meeting all had a common component, the sample CPUE, and differed only in the mannerfishing effort was obtained.

8. Statistical sce arios

Pii A: complete coverage of fishing effort from all landing sites

The first major statistical scheme (Scenario A) was presented by means of which totalproduction is estimated on the basis of a sample CPUE and a complete enumeration of effortfor each gear type. In this manner fishing effort is censused in space and in time.

The Working Group noted that this type of approach provides the most accurateestimates for total production but it is also the most demanding in staff resources since itrequires that all 'landing sites are covered on a daily basis for observing gear activities.However, it was also noted that this approach would be both feasible and advantageous inspecific sectors of an artisanal fishery and as a component of a large-scale fishery surveywhere different statistical scenarios are integrated into one system. A number of numericalexamples were also presented and discussed by the Working Group.

art 13: p 'al cove e of fishing effort from all landing sites

In this type of sample-based surveys fishing effort is completely enumerated from alllanding sites (beaches) but only during a Ihnited period of randomly selected sÁnple days.Thus collection of data on fishing effort is based on census in space (since all beaches arecovered) and sampling in time (since only a limited number of days is involved).

In this manner the fishing effort over the entire period (i.e a month) is estimated byfirst determining the mean daily effort on a by-gear basis and then raising to a monthly totalby applying a time raising factor.

In this approach, as in Statistical Scenario A, no frame surveys are required since thefishing effort is censused in space and then directly estimated on the basis of the number ofsample days during a month.

The Working Group noted that this approach, when compared to Scenario A, wouldbe more feasible in terms of staff resources. However, close inspection of the results obtainedfrom its application on the same data set used for scenario A, revealed a loss of accuracy dueto the fact that fishing effort was estimated, rather than censused.

The problem of time raising factors for estimating fishing effort was discussed atlength. The Working Group noted the importance of applying correct raising factors by takinginto consideration empirical knowledge concerning patterns of fishing activities during thesurvey period.


Part C: sampling fishing effmt and landings in space and time

In this type of sample-based surveys both fishing effort and catch are sampled in spaceand time and frame survey data are used in the estimation process. Only a limited number ofselected beaches participate in the samples and only for a limited period of sample days.

In this manner the fishing effort over the entire period (i.e. a month) and for allbeaches is estimated by first determining the mean daily effort on a by-gear basis and thenraising to a monthly total by applying two raising factors; one referring to time and thesecond to gear units. The time rasing factor is determined in the same manner as in scenarioB.

The gear raising factor is provided by data from frame surveys, by means of whichthe number of gears at all beaches has been recorded and assumed to be valid over a longperiod of time. In fact, the use of frame survey data assumes that the gear propoiionsbetween sampled beaches and all beaches remain constant during one or two or more years,rather than that the actual number of gears has remained constant. Thus, the major sourcesof errors are: (i) migration of fishing units from/to sampled beaches; (ii) unequal proportionsof gears added to or removed from registered beaches and (iii) new beaches that remainunregistered and are therefore excluded from the survey.

The Working Group noted that this methodological and operational approach is themost commonly used since it is much more economical in staff resources than the twoscenarios, A and B, discussed earlier. However, the Working Group also noted the clear lossin accuracy when fishing effort is estimated using many assumptions with respect to gearproportions in the survey areas. Concern was expressed regarding sampling systems that makeuse of outdated frame surveys.

A number of numerical examples were presented and discussed. The Working Grouprealised the drastic drop in accuracy and reliability of estimates when survey systems changeoperational mode from A, B to C, that is they become dependent on frame surveys.

9. Co putter e ttion aspects

The Working Group was presented vvith a schematic approach indicating the maincomponents of recently developed statistical systems. Such systems are computer-based andtheir structure consists of databases for handling primary CPUE and effort data, statisticalstandards (classifications of gears, species, beaches, etc), frame survey data (if and whenapplicable) and a set of computer modules for data verification, data management, estimationprocedures and production of outputs.

The Working Group noted that although each national statistical system would requirea set of functions and services specific to national needs, there are certain commoncharacteristics in all systems which could become the subject of a study with the purpose ofproducing guidelines and systems standards in the form of manuals. Such products, which areurgently needed, are not available at present.


di pie Ii

The Working Group stressed the need for adequate training of national experts invarious aspects of applied fishery statistics and computing, with the purpose of takingmaximum advantage of the existing information and computer technology and also takingactive part in the development and operational phases of statistical systems.

10. Conclusions

A. Methodological and Operational Aspects

Based on the discussion on major sampling schemes (statistical scenarios) a.nd theirimplications in terms of data reliability, accuracy and survey costs, the Working Groupconcluded that:

i. In several cases the importance of timely and reliable fishery information and statisticsis given fower priority than it really deserves and this sometimes reflects the lowerpriority given to the fishery sector in general.

One of the major factors affecting the reliability of estimates in sample-based fisherysurveys is the fishing effort.

Estimation of fishing effort should be based on a minimum number of assumptionsregarding its uniformity over time and space.

Estimation of fishing effort involving frame survey data is the most complex approachsince it is the one with most assumptions with regard to the uniformity of effortfishing. Reliability problems are more acute in the cases of outdated frame surveys.

The accuracy of fishery surveys can be improved if these become independent offrame survey data. This would necessitate alternative ways for mobilizing field stafffor collecting effort data from all landing sites. If available, such personnel could bedravvn from other Institutions, since collection of more complete data on fishing effortwould require a lower level of expertise on the part of the recorders.

In general, improvements of the methodological and operational aspects of majorsampling schemes (scenarios), have significant implications in terms of staff resourcesand any new deployment of personnel for the collection of data should be a majorconsideration of the national fishery authorities.

Based on the above conclusions the Working Group recommended that:

High priority be given to the improvement of fishery information systems in the sub-region.

2. Catch Assessment Surveys based on unreliable or outdated frame survey data, bereviewed. For more complete statistical coverage of fishing effort, alternative ways andmeans for mobilizing sufficient numbers of data collectors be considered.


4 FId reco II II endations

3. Collaboration between Governmental Institutions responsible for data collection in thefield be strengthened, with the purpose of achieving maximum utilization of availablehuman resources already involved in data collection.

B. Systems development and t *ning aspects

The Working Group noting that computer zation is an integrated component in afishery statistical system, concluded that:

There exist several constraints in the development of computer-based surveys in thesub-region and some of these can be removed with-external assistance.

Guidelines, manuals and technical documents providing information on mostcommonly used statistical and computing techniques and methods are urgently needed.

There is an insufficient number of national staff adequately trained in the sector ofapplied fishery statistics and computing. Current situation does not permit active andeffective participation in the development of computerized fishery surveys.

Computer-based fishery systems should, above all, be self-sustaining, that is regularlyoperated and supported by national staff and with minimum dependence on externaladvice.

Based on the above major conclusions, the Working Group recommended that:

Pilot statistical projects with specific objectives and short duration be implemented inthe sub-region with the purpose of evaluating current fishery surveys, advise onmethodological and operational aspects and upgrading the level of national skills inthe sectors of survey design, applied statistical techniques and computing methods.

2. A series of manuals and technical documents providing information on major samplingschemes, system prototypes and computer standards, be prepared by FAO.


Names

List of particip ts

Adresses

Annex 1

Countries

1. ANSA-EMMIM,Michaël

Fishery Liaison OfficerFIPL, FAO Rome

ITALY

2. DEDO, René Centre de Recherches COTE-DIVOIREGnégoury Océanologiques (CRO)

B.P. V18, Abidjan

3. DEMUYNCK, Katlijn IDAF, B.P. 1369, Cotonou BENIN

4. GBAGUIDI-AZIABLE,Amélie

Direction des PéchesB.P. 383, Cotonou

BENIN

5. KAMPHORST, Bert IDAF, B.P. 1369, Cotonou BENIN

6. KORANTENG, K. A. Fisheries Department GHANAP.O. Box B-62, Tema

7. NKUMBE, Lucy Fisheries Research Centre CAMEROONP.M.B. 77 Limbe

8. OKPANEFE, M. O. N.I.O.M.R. PMB 12729, Lagos NIGERIA

9. SATIA, B.N.P. IDAF, B.P. 1369, Cotonou BENIN

10. STAMATOPOULOS,Constantine

Senior Fishery Data OfficerFIDI, FAO Rome

ITALY

11. STOKHOLM, Hanna IDAF, B.P. 1369, Cotonou BENIN


Annex 2

Agenda

Opening of the Meeting.

Adoption of the agenda.

General discussion on statistical surveys with specific reference to fishing effort andassociated basic data (landings and prices).

Definition, level of detail and purpose in measuring fishing effort.

Current status of national fishery statistical surveys. Size of artisanal fleet, gear typesand fishing methods, principal species, human resources aspects in data collection andprocessing.

Current status of national fishery statistical surveys (continued).

Methodological and operational aspects of fishery statistical surveys. Census-basedsurveys. Sample catch/effort assessment surveys. Frame surveys.

Fishing effort as a source of variability in catch/effort assessment surveys.Differentiation between statistical approaches and scenarios according to ways ofcollecting data on fishing effort.

Statistical scenarios. Part A: Complete coverage of fishing effort from all landing sites.No need for frame surveys. Presentation of examples and group exercises.

Statistical scenarios. Part B: Partial coverage of fishing effort from all landing sites.No need for frame surveys. Presentation of exarnples and group exercises.

Statistical scenarios. Part C: Sampling fishing effort and landings in space and time.Need for frame surveys. Presentation of examples and group exercises.

Conclusions on topics discussed. Statistical and computing aspects. Training needs.Staff constraints. Mobility of data collectors. Institutional infrastructure aspectsregarding fishery information and statistics. Suggestions and ideas for short/medium-term plans for upgrading national capabilities in the sector of fishery statistics andcomputing.

Adoption of the Report.


Annex 3

List of docwirenls presented to the WoriiTng G

Methodological and operational aspects in Catch Effort Assessment Surveys by C.Stamatopoulos.

Methodologies used in the collection, analysis and dissemination of artisanal fisherystatistics in the Western Gulf of Guinea (Benin, Côte d'Ivoire, Ghana and Togo) byK.A. Koranteng.

Background information on Nigerian Ar-tisanal fishery sector by M. O. Okpanefe.

Artisana1 Fisheries in Cameroon by Lucy Nkumbe.

Schemes for collecting catch and effort data for the estimation of fish production inthe marine fisheries sector in Ghana by K.A. Koranteng.

Pêche artisanale maritime en Côte d'Ivoire by J. Konan and R. Dedo.


Péche artisanale It

Worki fig Group onArtisanal Fisheries Statistics

for i e Weste i Gulf of Guinea,Nigeria . id Cameroon

ariti

3-7 May 1993

'It e en Coke divoire

par

Konan et R. Dedo

Centre de Recherches Océanographique,B.P. V19, Abidjan, Côte divoire

Annex 4


Cotonou, Be I

1. Description genérale

En Côte d'Ivoire il existe en mer une pêche artisanale florissante qui malheureusementn'est pas animée par les ivoiriens. Elle s'exerce sur toute la cote depuis le Ghana jusqu'àTabou, grace aux pêcheurs étrangers. Mais c'est surtout à Abidjan et dans le Sud-Ouest quela Oche artisanale est la plus animée et la plus suivie.

Pour mieux cerner, il nous faut décrire les facteurs de production tant matériels quehumains.

1.1 Les embancations

Les embarcations en usage sont de deux types:

La pirogue monoxyle qui est un tronc d'arbre évidé. Elle est longue de 6 A. 8 m. Elleest mue à la pagaie et a la voile. Elle est surtout utilisée par les pêcheurs Nanacrous(Libériens) pour la Oche à la palangrote. Elle ne peut transporter que deux personnesau maximum.

La grande pirogue ghanéenne mesure 9 A. 18 m de long, 1,3 à 2,2 m de large et 0,9a 1,2 m de creux. C'est généralement une grande pirogue monoxyle rehaussée debordage. Selon l'utilisation qu'on en fait, on dénombre trois modèles:

le modèle "Senne tournante" utilisée pour la grande pêche pélagique. Il est engénéral équipé d'un moteur hors bord de 25 A. 40 cv et transporte 12 a. 18personnes.

le modèle "Palangrote" toujours motorisé. Il est équipé d'une caisse a. glace,transporte 8 personnes pour une longue marée. Il est utilisé pour la grandeOche à la palangrote.

le modèle "Béninois" est de dimensions plus réduites. Il n'est pas motorisé.est utilisé pour larguer les sennes de plage et poser les petits filets maillants.

De la grande pirogue ghanéenne &rive une autre pirogue aux dimensions nettementréduites: c'est la pirogue moyenne, elle mesure 7 à 8 m, 0,95 m de large et 0,52 m decreux. Elle transporte 5 personnes et est utilisée pour la petite pêche pélagique pendantla mauvaise saison de péche.

Il est à noter que la motorisation des pirogues est de plus en plus poussée, et le moteurYamaha est le plus apprécié pour sa robustesse.

L2 Les engins de Oche

Cinq types d'engins (Tableau 1) sont utilisés sur le plateau continental ivoirien, soitcollectivement, soit individuellement. Ce sont: la senne toumante, la senne de plage, le filethadi, le filet maillant et la palangrote.


La senne tournante (Seef, Essi, Watcha) est un filet de grandes dimensions. Elle visele petits pélagiques en partiplier les sardinelles.

La senne de plage cible les poissons côtiers A. la fois démersaux et pélagiques. Ellenécessite beaucoup d'hommes pour sa manipulation.

Le filet hadi est un filet maillant toumant. C'est donc une senne toumante danslaquelle se maille le poisson.

Les filets maillants: Ils sont de différents types et sont donc différemment utilisés,soit au fond, soit en surface suivant les espèces ciblées. Contrairement aux sennes, cesont des engins individuels.

La palangrote est un engin spécifique pour Ocher sur les fonds rocheux, les poisson"nobles" (daurade, mérous, lutjans...). Les Nanacrous, les sénégalais et les Gans(ghanéens) sont pratiquement les seuls à l'utiliser.


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1.3 Les Pescheurs

C'est connu l'ivoirien n'est plus pêcheur car autrefois certains l'étaient. Ainsi la Ochemaritime est - elle aux mains des &rangers dans sa totalité (Tableau 2). Les plus nombreuxsont les ghanéens: Fanti (senne tournante et filet maillant), Awrouan (senne de plage et sennetournante) et les Gans (palangrote). Puis viennent pèle mêle: les Nanacrous (libériens) quidepuis l'avènement de la guerre civile dans leur pays sont de plus en plus nombreux cheznous. Les sénégalais (palangrote), les béninois et les togolais (senne de plage).

Tableau 2 Péche maritimes 1992: Nationalité des opérateurs de San-Péclro (nombre etpourcentage)

Cated'Ivoire

Ghana Libéria Sénégal Mali Togo Guinée Bénin Total

Pécherset aides

0 121394,3%

58

4,5%15

1,2%0 0 0 0 1286

Fumeuse 0 55997.9%

0 0 12

2,1%0 0 0 571

Mareyeur 254, 60,7%

9723,2%

0 0 4410,5%

15

3,6%8

2,0%0 418


1.4 Les espèces chées

Les espèces les plus abondants sont évidemment les petits pélagiques et notammentles sardinelles (Tableau 3, 4, 5 et 6).

Tableau 3 Espèces d'importance économique capturées par la Oche maritimes artisanales

Senne tournantes

Sardinelle rondeSardinelle platePlat-plat mussoPlat-plat médailleMaquereauBoniteThonineFriture A &alliesChinchardJaponRasoir,

Sardinella auritaSardinella maderensisSe/ene dorsalisChloroscombrus chrysurusScomber japonicusSarda sardaEuthynnus alletteratusBrachydeuterus auritusTrachurus sp. et Decapterus sp.Caranx crysos, carangus et hipposIlisha africana

Senne de plage

Sardinelle rondeSardinelle plateAnchoisChinchardCarpe blancheOmbrine

Sardinella auritaSardinella maderensisEngraulis encrasicolusTrachurus sp. et Decapterus sp.Pomadasys sp.Pseudotolithus sp.

Filets maillants de fond

RequinsOmbrineCapitaineBrochetRaiesLangousteCigale rouge

Pseudotolithus sp.Galeoides decadactylusSphyraena sp.Raja sp. et Dasyatis sp.Panulirus regiusScyllarides herklotsii

Lignes

MérousLutjans ou carpe rougeDaurades

Epinephelus guasa et aeneusLutjanus goreensis, agennes et fulgensDentex gibbosus et canariensis, Pagruspagrus, Sparus caeruleostictus

Filets maillants A. grands pélagiques

RequinsMantes ou Diable de merEspadonVoilierMarlin bleu

Manta birostrisXiphias gladiusIstiophorus albicansMakaira nigricans


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Tableau 6 Production annuelle des principales espèces débarquées de la !Ache artisanalemaritime Côte d'Ivoire 1990-1992.


Espèces Année 1990 1991 1992

Sardinella aurita 3613 3472 4773

Sardinella maderensis 4905 2143 2388

Scornber japonicus 316 765 265

Engraulis encrasicolus 871 - 1057 910

Plat-plat 1682 1076 1200

Brachydeuterus auritus 971 923 746

Ilisha africana 505 735 812

Sarda sarda 424 769 941

D vers 3713 3829 4974

Total 17 000 14 769 17 009

1.5 Les oints de débarquements

Le littoral ivoirien (Figure 1) peut être divisé en trois parties; la partie Ouest allantd'Abidjan à Tabou, la region du grand Abidjan et la partie Est, d'Abidjan à Assinie.

A l'Ouest il existe des villes côtières et c'est dans ces villes qu'ont lieu les principauxdébarquements. Entre les villes on note d'autres points mais moins importants. Il fautnoter dans cette zone le cas particulier du littoral Alladjan allant du canal de VridiGrand-Lahou. Ce littoral comporte une soixantaine de senne de plage et presque autantde points de débarquements.

A Abidjan il existe quatre principaux points de débarquements dont Vridi. Il est suivipar le Centre de Recherches Océanographique.

01.1 A l'Est il existe six points de débarquement importants.

Pour l'ensemble, il existe 48 points de débarquements dont une quinzaine de pointsmportants.

2. 1 s irétrodes de conecte des do es stalistiques

2.1 ts unités des péehes et les maim

Si dans votre centre de Oche vous avez 174 ou 90 ou 45 unites de Oche, ou mêmemoins, qui pêchent régulièrement, séparez les en métiers ou types de Oche. C'est A. dire:

= ...._,...-_,,_ .-- e.= ...-a= =.--....7.,-.- :-.--, =,-- = --...= .......-_, -..- a -,.. ..,-._ -...,

OCT;,---- -°' D I VOIRE.=,..,seCrrsiE .0.00.0 Vf Idi ,,Irn0Jof.,,c non

S''..I Z u211DJAN,c,... s<....,..,L,,,,, vf..13. poI-B.ct .,,,..,,,a<I^ r- , ) ,,- D_D:-- , 1.1,1dg7(1,u.cv +11,,,,,/ Onf r

,00epoirn

i

''.1Coon?f'.4'...?, F»

!cc i7i).-

A100.0G00i'Szo Fooo

to

El Points de de:borquement échontillonnésPoints de déborquernent non échontillonnés

Figure i Carte de peche artisanale maritime de Côte d'Ivoire.

Oche pélagiques avec senne tournante et filets maillants de surfacepêche à la senne de plageOche A. la liguepêche de fond avec filets maillants de fond.

Repartissiez ces unités de chaque type en 1, 2, 3,...6 groupes de facon qu'elles puissentétre toutes enquêtées 1 a 6 fois par semaine suivant le cas.

21 Types d'enquéte

L'enquête cadre, l'enguate d'activité et l'enquéte de production s'exécutent suivant deuxmodes (Tableau 7): le mode exhaustif ou tout est enquête et le mode par échantillon oul'enquête porte sut une partie représentant le tout: il est: soit simple, soit stratifié.

L' enquête cadre s'effectue sur le mode exhaustif. Elle vise a. répertorier tous lesfacteurs de production, matériels et humains (tableau 8).

L'enquête d'activité consiste à dénombrer chaque jour (ou suivant une periodo plusgrande) toutes les unités de Oches en activité ce jour ou cette période là; en les classant partype.

Elle donne l'effort de peche, c'est A. dire les activités mises en jeu pour capturer lespoissons. L'unité qui l'exprime peut étre: les heures de Oche, les coups de senne, les sortiesetc...

C'est pourquoi il vous est expressément demandé de noter pour chaque UP enquêtée:lieu de Ochedateheures (départ et retour)


Zone Ce ZoneZone Oues I ad Laddu Rvdde., Allodion d'Abdion

nombre de coups de sennenombre de nappes maillants utiLséesnombre de lignes utiliséesnombre de sorties

La sortie est l'unité chois e et utilisée pour le moment.

A la fin de le journée vous aurez un nombre total de sorties journalières. Soit parexemple 34 sorties le 5 Août 90. Il faut en plus determiner le nombre de pirogues rentréesbredouilles.

Pour le moment une enquête exhaustive de Factivité est requise.

Tableau 7 Princ paux points de débarquement et les modes d'enquête en Côte d'Ivoire

Centre de pêche Points de débarquement

Tabou PopoTabouBoulélé

Grand Béréby Grand BérébyRoc-B érébyDawa

San Pedro

Sassandra

Fresco

Grand Lahou

Abidjan

Bassam

MonogagaTakyPort de pêche

WharfGrand Drewin

FrescoPalmindustrie

Lahou plage

Vridi IVridi IIVridi IIIVridi SIR

AzurettieAhounianti (Bassam)

Mode d'enquétes

Éxhaustif

Éxhaustif

txhaustif

txhaustif

txhaustif

txhaustif

Stratifié

txhaustif


2.3 L'enquête de production

2.3.1 L'échantillonnage des unités des l'.'ches

Quand les pirogues sont un peu nombreuses ou quand les agents commis A. cette tdchesont nombreux et disponibles, le temps que durent les débarquements, alors l'enquête sur lemode exhaustif est faisable et judicieux. Dans le cas contraire il faut échantillonner, c'est direengater une partie seulement des débarquements. Mais attention, cette partie doit être l'imagefidèle de la pêche.

Si la plage de débarquement est peu &endue, on fait un échantillonnage simple: on tireau sort les pirogues A. enguêter. Si la plage est &endue on fait un échantillonnage stratifié: Ondivise la plage en plusieurs parties et le sort désigne l'ordre dans lequel chaque morceau doitêtre enquêté.

Tablean 8 Récapitulatif du potentiel de pêche artisanale maritime en Côte d'Ivoire (1989)

Abidjan Est Total

298 30 474

17 9 78

35 1493 5051

7 2 380

250 12 858

362 20 668

2.3.2 L'estimation de la production

Les pirogues débarquent leur captures en tas. Pour avoir la production d'une pirogueil suffit de connaitre le nombre total de tas et le poids moyen de tas. Pour avoir la productiondes pirogues échantillonnées ou engates, il suffit d'additionner la production de chacune despirogues.

Ouest GrandLahou

Jville

Nombre de pêcheurs 2584 258 1847

Equipes 374 24 60

Petite pirogue 193 16 0

Moyenne pirogue 153 8 14

Grande pirogue 127 14 54

Senne tournante 119 17 10

Senne de plage 7 1 44

Filet maillant 2969 328 226

Filet maillanttournant

355 11 5

Ligne 596 o

Moteur 255 24 7


Pour avoir la production totale de toutes les pirogues il suffit d'avoir la productionmoyenne de chaque pirogue que lion multiple par l'ensemble des pirogues ayant pechées (d'oul'importance de l'activité journalière des pirogues).

Il faut absolument séparer la production par type de métier.

23.3 Le poids moyen du tas

Le débarquement se fait en tas moyen d'espèces ou de groupes d'especes. Pour chaqueespèce donnée ou groupe d'espèces il faut prendre le poids moyen du tas chaque quinzainede mois sur 10-20-30 tas suivant l'abondance de respèce, ou chaque fois que visiblement voussentez que ce poids à change notablement.

2.3.4 Le tui

Quand les prises sont multispecifiques, il faut determiner la proportion relative dechaque espèce. Cela revient a. trier le poisson. En general trier un tas est suffisant et cela surchaque pirogue enquetée.

Le tri achevé il faut peser chaque lot d'espèces. Quand le mélange est homogène unseau-échantillon preléve sur un tas est suffisant. Quand le mélange n'est pas homogène ouquand on veut plus de precision il faut trier le tas clans son entier. Il faut systématiquementtrier, sinon rimportance d'une espece risque de vous échapper.

2.4 Mensuration des es ces déb uées

Le but visée est d'une part de c,omparer les structures de taille des espèces capturéespar la péche industrielle et la Oche artisanale, d'autre part de suivre revolution des talliesmoyennes des espèces ciblées.

Pour le moment il vous est demandé de mesurer les deux especes de sardinelles: auritaet maderensis et le maquereau. Plus tard d'autres espèces seront designees. Les mesures sefont au centimetre inférieur sur un échantillon de cinquante (50) individus au moins. Pour queles mensurations puissent être bien exploitées par les scientifiques il faut absolument donner:le poids de l'échantillon, le poids total de respèce d'ou l'échantillon à été prelévé et au fin dumois la production de respèce mesurée.

3. Stnictr des services de pêche

Dans les principales localités du littoral notamment au Sud-Ouest, sont implantés desCentres de Pêche qui relèvent du Ministere de la Production Animale. Leur rble est:

de récolter les statistiques de production et les facteurs de productiond'encadrer les pecheurs (au plan technique et administratif)de veiller sur la salubrité des produits halieutiquesde prêter main-forte aux chercheurs.


Ces Centres de Pêche dependent directement du Directeur Départemental et A. traverslui du Directeur Regional de la Péche Artisanale. C'est A. ce dernier qu'appartient de faire laliaison avec la Direction des Peches soit directement soit l'intermédiaire du Directeur Generalde la Production Animale.

Ainsi donc la Sous-Direction des Péches éprouve beaucoup de difficultés à faire passerses consignes. Auparavant, les Centres de Péche dépendaient directement de la Sous-Directiondes Péches.

Direction Régionale

DirectionDépartementale

Centre des Pêches

Direction Genéralede la ProductionAnimale

Ces Centres de Pêche ont malheureusement peu de personnel pour faire face au travaildemandé.

4. Precision des statistiques et tioyens pour les a éliorer

Il est difficile en peche artisanale à l'heure actuelle d'avoir des chiffres très précis. Eneffet la precision depend des hommes qui sont plus ou moins honnêtes et compétents, desinstruments et des méthodes utilisées plus ou moins mal appliquées. En un mot on estconfronté A. de nombreux biais en Oche artisanale. Il faut en etre conscient pour lesminimiser. Ces biais proviennent d'une estimation visuelle des captures, d'un mauvais comp-tage des tas de poissons, de pesées malfaites, d'échantillons inadéquats (trop réduits), demauvaises réponses aux enquétes (questionnaires, interviews) etc...

Pour améliorer la qualité des enquêtes, il faut dans un premier temps former et recyclerles agents commis A. cette tâche. Dans un deuxième temps il faut les equiper en instrumentsde mesure et en moyens de déplacement. Enfin il fait réduire la pénibilité du travail quidemande une presence continuelle sur les lieux de débarquement en instituant des roulements.

Direction desPêches

Sous-Direction desPêches

Service des Pêchesartisanale


Pour clore le tout, il est nécessaire de superviser le travail accompli et de le redresser au caséchéant. Et c'est cela le plus difficile.

5. Co clusio

La pêche artisanale peut encore beaucoup progresser mais les conditions économiqueslui sont défavorable. Cependant elle se modernise de plus en plus par la motorisation parl'utilisation de la glace et bientôt par l'adoption des écho-sondeurs. Malgré les tentatives del'améliorer, la pirogue ghanéenne est toujours là, égale à elle môme et cela faute d'avoirsuffisamment associé les pécheurs et utilise les matériaux locaux. Mais, quelque soit lesprogrès enregistrés, il demeure un problème crucial, celui l'ivoirisation des acteurs de laOche. On se demande encore par quel bout le prendre.

En dehors de cela il faut aussi considérer les problèmes poses par les systèmesd'enquéte statistiques:

couverture incomplete (cas de l'est de la Côte d'Ivoires)omissions et double comptagemigrations des p'èchersrelations pécheurs - enquêteurs pas toujours au beau fixemanque de moyensmanque de personnel qualifiémanque de supervisionmauvais échantillonnagemauvaise evaluation des captures etc...

Bref la pêche artisanale nous donne du pain sur la planche.


WorIcing Gmup onArtis lal Fisieties Statistics

for the Western Gulf of GM lea,Nigeria d Ca II eroon

3-7 May 1993

Artis al fisheries in Came on

by

Lucy Nicumbe

Fisheries Research CentreP.M.B. 77 Limbe

Annex 5


1. Ifltfl)dUCtîOu to àe rtisn.aI fisheries in Cameroon

3. The shri

This sector plays a very important role in Cameroon both socially and economically.Marine artisanal fishery produces an estimate of 40,000 tons/yr of fish which is four timeswhat the industrial fishery pro duces. Despite such importance very little in the form ofstatistical data is known. There are about 20,000 fishermen of which about 90% areforeigners. Most of the fishing villages are in the large mangrove areas thus makingaccessibility very difficult. The Fisheries Research Centre carried out some studies in thissector. The first attempt in 1983 was a frame survey of the entire coast of Cameroon. Due tothe lack of funds this exercise has not been done again.

2. Methods for the collection of sLta

The Research Centre collected data or effort through a frame survey and on catchthrough catch assessment of data on, see Annex 1 (Elaborate). The most commonly usedfishing gears are gillnets for pelagic and demersal fishing, the beach seines (draishain), longlines and purse seines (awatsha). From the data collected you can then analyze the fishingeffort by gear.

The artisanal shrimp fishery is based on the species Palaemon hastatus. The gear usedis a conical shaped net known as the "ngotto". There are two types of "ngottos" namely thesmall (2.5 m long) and the big (6.5 m long) "ngotto". These nets are planted at the mouth ofthe estuary against the water current. The total catch was estimated as the number of basinsper day. 'While the fishing effort was estimated as the number of canoes that went fishing perday (boat/days).

From this study, the bias was reduced because the numerator was stationed at thefishing village, This study was carried out in only one of the many areas which shrimp fisheryis the main occupation. Due to the financial strain we could not extend our study.

4. Co ts

The major constraint is that of finances. Since 1986, the Research Centre has nomoney to carry out any research. The workers have been for 13 months without asalary. The situation is a very desperate one. Also, there is lack of personnel andinfrastructure.

There is the Ministry of Livestock, Fisheries and Animal Industry (MINEPIA) whichis different from the Ministry of Research to which the Fisheries Research Centre isattached. These two bodies have something in common in that they both have to dealwith the fisheries sector. In the case of MINEPIA, they are more interested in thecollection of taxes. MINEPIA has numerators at most of the major landing sites. They


111 p fish ry

stand a better chance of collecting statistical data on catch and effort which could beused by the Research Centre. But the main problem is that there is no goodrelationship between MINEPIA and the Research Centre.

c) Most of the fishing villages are in swamp areas thus making it inaccessible. In theNorth of the coastline (Ndian division) which is inaccessible has 60% of thefishermen. (Elaborate using coastline map - Annex 3).90% of the fishermen are foreigners which makes it difficult to gat accurateinformation from them.Finally, the Research Ministry has to undergo restructuring and the Government isworking on it at the moment. So we don't know what will become of the FisheriesResearch Centre.

5. Òffices responsible for fis ery statistics

At the provincial level, MINEPIA has fisheries posts located at major landing sites.The numerators at these posts estimate the catch visually while there is a record of the canoesand fishermen. He also collects taxes which is proportionate to the size of the catch. TheResearch Centre has not been able to carry out similar exercise due to lack of funds. If therewas some good relationship between the two ministries, then the numerators of MINEPIAcould be trained by the researches so that they collect reliable data which the Research Centrecould use.

The Government has created two self-managing bodies namely: The Marine FisheryDevelopment Fund (CDPM) and the Mission of Marine Artisanal Fishery Development(MIDEDECAM). These bodies are responsible for the development of this sector mainly tosupport the fishermen to improve on their wellbeing. They are not concerned with thecollection of statistical data but we hope that in the near future they will be able to help inthe collection of statistical data.


Woiing Gmup onAriisanal Fisheries Statistics

for iie Wes m G f of Gui ea,Niiia and Ca it eroon

Cotonou, Benin 3-7 May 1993

Arfisa al fisheries in Nigeria

by

M. O. Okpanefe

Nigerian Institute of Oceanography and Marine ResearchPMB 12729, Lagos NIGERIA

Annex 6


itmduction

The preparation of this paper is in accordance with the guidelines set by thecoordinator of GCP/RAF/192/DEN and received on 28/4/93. In 1975, a committee under thechairmanship of Mr, J, G, Tobor with members drawn from NIOMR, Federal Department ofFisheries (FDF), Federal Office of Statistics, University of Ife and then Kainji Lake ResearchInstitute, was mandated to conduct a National Fisheries Statistical Survey in Nigeria. Theproject was funded by the National Accounts Survey headed by Professor Aboyade who wasmandated to provide input/output information for all the Nation's economic sector includingfishery. The survey was executed in 1975/76 and the final report was published by NIOMRin 1976.

The exercise not only provided useful fisheries statist cal information for the countrybut also laid to rest the then frequent and varied estimated fish production figures for thenation. Li also established methodological statistical system for collection and analysis of data,which method were to be used by the succeeding FDF until another up-dating survey wouldbe conducted, ten years later.

Methodology for data collection

We believe now as then that the methods used for data collection for the artisanalsector are still valid and useful. For the benefit of those who had no access to the publication,the methods are briefly described here.

By artisanal fisheries sector, we meant fish productions and activities of less well-organised fishing units such as individual fishermen or canoes over the whole rangeof the coast line, brackish water creeks, rivers and lakes of the entire country. Previousexperience informed us that fishing units differ between the coastal states and theentirely inland states and this fact was reflected in the design. It was also realised thatthey covered a wide range involving numerous fishermen and fishing canoes scatteredall over the coast line, creeks, rivers and lakes. A complete coverage was unrealisticand the decision was to cover this sector on a sample basis. Since there was noexisting frame from which a sample could be drawn, a Frame Survey had to beconducted to establish a frame for each state, such that a raising factor could beobtained and enable sample estimates to be raised to population parameters.

Frame Survey of Coastal States:Experience showed that many of the coastal states possess marine, brackish and freshwater. There was no clear distinction between marine and brackish water activities. Itwas further noted that fishing in the coastal states was based on the use of canoesoperating from a recognisable site.A frame was then built up for each Division in a state by compiling a list of fishlanding sites with supplementary data of the number of the canoes operating at thesite.



In some of the inland states are to be found, large lakes as Chad Lake and Kainji aswell as numerous rivers. Besides the large lakes where fishing is carried out by theuse of canoes, fishing in the rivers is based on individual fishermen, sometimes usinga goard as a boat.A frame was built up for each division of a state by compiling a list of fishing villageswith the supply of the number of fishermen in the village. For the big lakes, a list oflanding sites was compiled.

Selection of Primary Sampling Units (PSU):In all cases PSU (i.e. landing sites or fishing units) was based on probabilityproportional to size (PPS) method and selection was independent from division todivision. This selection method not only simplified the estimation procedure but alsoenabled state estimates to be obtained by simple summation over divisions weightingbeing done on division level.

Selection of Secondary Sampling Unit (SSU):From the design of two types of survey units were involved. The ultimate survey unitin the case of the coastal states and large lakes is the fishing canoe operating at aselected site while in the case of inland states the survey unit is the fishermenoperating in a selected fishing village.Selection of the numbers of canoes or fishermen whose catches are to be examinedis based on a simple random sampling method. This selection is made by theenumerators at the landing site or fishing village on each day of data collection byusing a pre-determinate table. The maximum number of survey units to be examinedwas fixed at five (This table is to be found in the publication).

Rotation of Sample:From the point of view of sampling theory, it is essential to rotate the sample. For thisparticular design the sample of landing site or fishing village was fixed for a periodof one year. Thus once a landing site or fishing village was selected, it is retained fora period of one year and an enumerator was posted there for data collection during theyear. This "fixed sample" design was necessitated by the need to reduce survey costarising from travelling expenses.On the other hand the sample size of five selected canoes or fishermen was automaticrotational due to the selection method adopted since fresh selection was made eachday of data collection.

Survey Results:This survey was geared towards the provision of needed catch and effort data atlanding sites or fishing villages. Catches of selected canoes or fishermen were sortedinto species and weighed, value of species, gear used and crew size were all recorded.However, there was no analysis of catch by gear or fishing method in the final report.

3. Status of the ards al fisheries sector

The result of the 1975-1976 survey report showed that fish production from theartisanal sector in 1976 was 482,000 metric tons. Recent figures published by the FDF, which

took over from the committee showed that in 1990 the artisanal production was 290,800metric tons, 1991 recorded 295,568 and provisional estimated 230,082 metric tons for 1992.With these figures it has become obvious that the contribution from the artisanal sector is onthe decline. Whereas the artisanal sector accounted for 97.6% of the total production in 1976,the recent information indicated that contribution from this sector was 92%, 84% and 60%in 1990, 1991, and 1992 respectively.

On effort, no recent information on number of canoes or fishermen is available. In thisregard, tho latest information was up to 1989 whereas my interest is limited to the past threeyears 1990-1992.

se of data

]he form.in which the data was presented in the survey report is still retained by thesuccessor, FDF. The information provided for fish supply by sector, production by stated,effort in terms of number of trawlers, their catches number of fishing crafts as canoes,production by species etc... The usage varied from user to user.These related to providinginformation for planning fisheries development project, researching into the resources andmanagement of the resource, administration and so on.

National offices for productiofi of statistics

Essentially, the FDF is the responsible organisation for production of fisheries statisticsfor Nigeria. Other institutions as NIOMR and Fresh Water Research institute, New Buesaproduce fisheries statistics which are supplementary to those of FDF. Most of such statisticsrelate to fish resources and management.

Cons Is

From experience it is found that collection of data from the artisanal sector is a verydifficult exercise.requiring a long period of socialising with the fishing communities andfishermen before acceptance could be gained and reliable data were obtained.

There is the obvious problem of adequate and well trained staff for the field work andproject supervision. The problem of inaccessibility of some of the selected sites and fishingvillages obviously increase survey costs. There is a problem of taxonomy if the staff are notexposed to different types of fish found in the field. Another very big problem is that of thefishermen accepting their fish to be weighed. All these and some others particulary inadequatefunding makes production of fisheries statistics from the artisanal sector a complex and anunpalatable exercise.


by

K. A. Koranteng

Senior Fisheries Research OfficerFisheries Research and Utilization Branch

Fisheries Department, Tema, Ghana

FAO LIBRARY AN: 347075

Annex 7


Working Gro p onArlisial Fisheries Statistics

for the Western Gulf of GuineaNigeria d eroon

Cotonou, Be 'n 3-7 May 1993

Methodologies used in the collection, analysis anddisse 11: nation of artisanal fishery statistics in

the Western Gulf of Guineae 119 Cite divoire, Gh la and Togo)

Table of contents

Introduction 36General description of artisanal fisheries in the sub-region 362.1 Côte divoire 362.2 Ghana 362.3 Togo 372.4 Benin 37Frame surveys 373.1 Côte divoire 383.2 Ghana 383.3 Togo 393.4 Benin 40Catch and effort assessment surveys 404.1 Côte d'Ivoire 40

4.1.1 Data collection at Vridi II (Abidjan) 404.1.2 Estimation of catch 414.1.3 Data collection outside Abidjan 41

4.2 Ghana 414.2.1 Sampling Units 414.2.2 Data collection and process ng 424.2.3 Data processing and estimates 42

4.3 Togo 434.4 Benin 43

4.4.1 Data collection by the Model Project, Benin 44Existing structure and functions of national fishery offices with regard tostatistical data collection 455.1 Côte divoire 455.2 Ghana 465.3 Togo 475.4 Benin 47Problems affecting accuracy and reliability of results and suggestions forimprovement 476.1 Côte d'Ivoire 486.2 Ghana 486.3 Togo 486.4 Benin 48Summary 49References 50

Appendix I Information Report - Number 22 51


Introduction

The Working Group on Artisanal Fisheries Statistics is to examine issues concerningfishery statistical surveys with specific reference to data on fishing effort. This paperassembles information on the present status of artisanal fishery statistics in the Western Gulfof Guinea.

The situation in each of the four countries, Côte d'Ivoire, Ghana, Togo and Benin isgiven. The information on Ghana and Côte d'Ivoire were assembled during my mission, butinformation on Togo and Benin were put together from various reports. It is expected thatparticipants from Togo and Benin will provide additional information on the subject.

General description of artis al fisheries in the sub-region

Artisanal fisheries in all four countries in the sub-region are similar in nature. Themain fishing craft is the dugout canoe. Many different types and sizes of fishing gears areutilized in these fisheries. Many of the gears, as well as the fishing methods, originate fromGhana and are diffused throughout the sub-region by migrant fishermen. Migration offishermen is a characteristic feature of artisanal fisheries in the western Gulf of Guinea.Ghanaian fishermen dominate the fishery.

The main fish species exploited are small pelagics (sardinellas, anchovy andmackerels) but some of the gears target demersal fishes.

2.1 Côte divoire

There are 48 landing centres located along the entire length of the 550 km coastlineof the country. However, the fishery is important only in a few places. The fishermen aremainly foreigners, notably from Ghana, and also from Senegal, Togo, Benin and Liberia.Since the civil war in Liberia, the number of Liberian fishermen in Côte d'Ivoire hasincreased, especially around Tabou in the west.

Various sizes of canoes are used. These range in sizes from large Ghana type ones thatcarry 12-18 fishermen to small Ivorian type ones that are operated by 1 or 2 fishermen.

Various types of fishing gears are used (Appendix 1). For statistical purposes the gearsare grouped into five; purse seine, beach seine, gillnet, encircling gillnet, and lines.

The main species caught by each of the main gears are given in Appendix 1. In thelast three years (1990-1992) the average total production by the artisanal fleet was 16 000 mt.

2.2 Ghana

The marine fishing industry in Ghana is one of the most important sectors of theeconomy and the artisanal fleet is by far the most important of all fishing fleets. The artisanalsector contributes between 65 and 80% of the annual total Ghanaian marine fish landing of


around 300,000 mt. About 100,000 full-time fishermen are engaged in this sector with nearlyone and a half million other people depending on them as their wives, children and otherrelations. It is the largest in the sub-region.

The canoes number about 8,000 (Table 1) of which around 50 percent are motorised.The canoes operate from 306 landing centres in 189 fishing villages (FRUB data, 1993)located along the entire coastline of Ghana.

Many types of fishing gears are used by the fleet but for statistical purposes these havebeen grouped into five; ali/poli/watsa, beach seines, drifting gill nets ("Nifa nifa''), set nets(surface, mid-water and bottom), and hooks on lines. Many of the nets target specific fishspecies.

The total annual catch of artisanal fisheries depends mainly on the catch of sardinellasand anchovies.

2.3 Togo

Togo, with a coastline of only 48 km, has an important art sanal fishery which isdivided into two parts; the fishing port of Lome and the areas outside the port. The activitiesin the two sectors are somewhat different.

In the port the sector is dominated by m grant Ghanaian fishermen. Along the rest ofthe coast, fishing is done by relatively sedentary fishermen based in about 18 fishing villages.

The fishing gears are similar to those used in Ghana. Annually, the canoes land about10 000 mt of fish.

2.4 Benin

There are about 600 canoes operating from about 50 fishing centres. Annual fishlandings is between 8,000 and 9,000 mt. The gears are classified as follows: purse seine,encircling gillnet, beach seine, bottom-set gillnet, shark gillnet (flouting or bottom-set),driftnet (surface), and lines.

There are three coastal provinces in Benin. In the Western province of Mono, thefishermen are mainly Ghanaians; they operate encircling and purse seine-type nets withmotorised canoes. In the Atlantique Province, the important gears are hook-and-line andencircling nets. Many of the fishermen are Ghanaians based at the Cotonou port. At Ouéméon the East, the fishermen are mostly Beninese and they use mainly set nets.

3. Fla

The number and types of canoes and gears in use in the industry are obtained throughperiodic frame surveys. Table 1 gives the number of canoes obtained in such, or similar,surveys in each country. The frame surveys also provide the necessary data for the design ofcatch assessment surveys.


IIe s eys

Table 1 Total number of canoes counted in frame and other surveys

Sources: CECAFfECAF Series 91/56 and national databases' Very small-sized canoes are not included2 Years of frame survey

3.1 Cite d'Ivoi

The last frame survey in Côte d'Ivoire was carried out in 1989; a new frame surveyis to be conducted sometime this year. Table 2 gives the summary results of the 1989 survey.

3.2 Ghana

In Ghana, canoe frame surveys are conducted at irregular intervals: Since 1969, eightsuch surveys have been conducted, the last one was in 1992 (Table 3). The usual forms usedin these surveys are given in Appendix 2.

Since 1987 monthly censuses, done on the last non-fishing day of the month, havebeen carried out at all sampling centres.


Year Côte d'Ivo re Ghana2 Togo Benin

1972 5591973 8238 5501974 5401975 603 5001976 2181977 8472 2591978 346 32419791980 235 3411981 69381982 202 4271983 677' 456 5701984 673' 6091985 679' 5111986 706' 8210 3141987 2561988 769' 277 6541989 762' 8050 267 557199019911992 8688

Table 2 The maritime artisanal fisher es in Côte d'Ivoire

Table 3 Summary of canoe/gear statistics; 1989 Canoe Frame Survey

3.3 Togo

The last frame survey conducted by Direction des Productions Animales was in 1982.In October 1983, a team of ORSTOM socio-economists and biologists carried out anothersurvey.

Zones Ouest GrandLahou

J'ville Abidjan Est Total

Number of fishermen 2584 258 1847 4422 867 9987

Small canoes 193 16 0 62 60 331

Middle sized canoes 153 8 14 107 6 288

Big canoes 127 14 54 325 42 562

Purse seine 119 17 10 298 30 474

Beach seine 7 1 44 17 9 78

Gillnet 2969 328 226 35 1493 5051

Encircling gillnet 355 11 5 7 2 380

Lines 596 0 0 250 12 858

Gear Region Volta Great Accra Central Western Total

Ali 453 521 138 1112

Poli 753 408 20 1181

Watsa 85 513 450 343 1391

Beach seine 417 176 129 130 852

Set net 33 176 732 359 1300

Lobster net 2 90 253 229 574

Line 5 860 172 77 1114

Drift gillnet 3 94 24 245 366

One man canoe 1 39 28 94 162

Total 546 3154 2717 1635 8052


3.4 Benin

The last frame survey was conducted in 1988 and Table 4 gives some of the results.

Table 4 Frame survey, 1988, Benin

4. Catch id effort assessment su eys

4.1 Côte d'Ivoite

The coastal area is divided into three sectors; namely East (i.e. between Assinie andAbidjan), Grand Abidjan, and West (i.e. between Abidjan and Tabou). The Centre deRecherches Océanographiques (CRO) collects catch and effort data at Vridi II, one of the 4main landing centres in Abidjan. The Direction des Pêches collects some statistics at 7 centresin the West. No statistics are collected from the East.

4.1.1 Data collection at Vridi II (Abidjan)

Due to the vastness of Vridi II, the centre is divided into three strata. Only onerecorder (field enumerator) works at the landing centre. He works in one stratum every dayand a different stratum is sampled on every sampling day. The enumerator works on 5 of the6 fishing days in the week; there is no fishing on Sundays. He is to follow a sampling schemeprepared by CRO (Appendix 1).

The two gears at the centre, purse seine and set nets may be put together in the samplefor the day even though the Enumerator is expected to record the two gears separately. Thepurse seines outnumber the set nets at the centre.


Gear Sector Queme Port deCotonou

Atlantique Mono Total

Purse seine 0 24 26 22 72

Ene rcling gillnet 23 69 80 43 215

Beach seine 0 0 49 49 98

Bottom-set gillnet 705 700 2175 1122 4702

Shark gillnet 0 8 pir 0 0 8

Driftnet 0 214 0 0 214

Lines 0 18 pir 0 0 18

Canoes 61 225 211 157 654

Motors 24 138 100 26 288

N° of fishermen 223 553 1281 1154 3211

4.1.2 Estimation of catch

On the sampling day, the enumerator examines 10-12 canoes if a large number ofcanoes return from sea; otherwise he is expected to examine all canoes that land.

The fish landed by the canoes are put into heaps on the beach. The enumeratorestimates the average weight of a heap of fish and counting the number of heaps, he uses thisto assess the total weight of fish landed by the sampled canoe and for the sampling day.Canoes that arrive at Vridi II without fish are not included in the sample as they may havesold their catch at Port Bouet, one of the other fish landing centres in Abidjan.

The data are processed at CRO.

4.1.3 Data collection outside Abidjan

Outside Abidjan, the enumerators of Direction des Pêches collect statistics on fishlandings. Some of the enumerators have received some training at CRO and they try to usethe sampling methods used by the CRO enumerator at Vridi. The enumerators who have notbeen through this training do not follow any defined scheme.

4.2 Ghana

Forty enumerators take catch, effort and price data at 53 landing centres in all fourregions. These sampling sites have about 42.6% of the total number of canoes in Ghana.

4.2.1 Sampling Units

A three-stage sample survey system (Banerji, 1974) is in use in Ghana since 1972. Thestages are: primary unit: sampling centre, secondary unit: sampling day, and tertiary unit:canoes.

Primary Sampling Units: Sampling centres

The coastline (536 km long) is divided into four strata corresponding to the fourmaritime administrative regions of Ghana; namely Volta, Greater Accra, Central and Western.Within each region, a number of fishing villages are selected with Probability Proportionalto the Size (PPS) of units (number of canoes; PPS sampling). Large centres are purposefullyincluded in the sample. The number of centres selected for sampling in each region dependson the number of enumerators in the region which has been determined in accordance withthe number of canoes in the region compared to the national total.

Secondary Sampling Units: Sampling days

Sampling days depend on the number of days that fishing is done at the centre. Atmost fishing villages in Ghana, one day in the week is observed as a non-fishing day(reserved for communal activities and maintenance of fishing gear and canoes. Some of theEnumerators do not work on Saturdays and Sundays. Consequently, each target gear at the

IDAF TechnIcal Report N° 49 41

sampling centre is observed for at least 4 days in a week and two weeks in a month. Two oreven three gears may be observed at the same centre.

Tertiaty Sampling Units: Canoes

A number of canoes are systematically selected for observation as a function of thenumber that go fishing for the day. Enumerators are provided with charts to guide them in theselection of canoes for observation.

Each gear is examined separately. In general, each of the five categories of fishinggears is operated by one type of canoe. The canoes are put in the category for which they aremost frequently used. For beach seines, the sampling unit is the canoe.

4.2.2 Data collection and processing

The enumerator estimates the total landing of a sampled canoe by weighing some ofthe fish which are normally put into standard boxes or other kind of container. To make theenumerators work easy, they have been provided with a table which gives the average weightof a standard-sized box or container of selected fish species.

The enumerator also records the price of the fish as well as the number of hours thatthe sampled canoe had been at sea

Every enumerator has two or three gears to observe and a maximum of seven canoesper gear may be examined on each sampling day. When canoes make two or more trips inthe day, especially during the main fishing season, each trip is considered separately.Nationally, about 500 canoes are examined every day.

The forms used in the collection of the data are given in Appendix 2.

4.23 Data pmcessing and estimates

Processing of catch and effort data is done on monthly basis and follows the followingformat:

Within Region -estimate for each gear by sampling centre-landing site estimates are combined for each gear-total catch; all gears combined

National -all regional estimates are combined by gear and for all gears

Following the notation of Barter] (1974) the estimation procedure is as follows:

indices: a = regionh = geari = landing centrej = dayk = canoe


variables: M = total number of tripsm = number of trips examinedy = catchD = number of fishing days in the monthd number of days with samplesN = total number of canoes in the region (fixed between frame surveys)n = number of sampling centres in the region (fixed until new centres areselected)

The total catch/day sampled/sampling centre/gear is given by:

mail ijM...,.. .: '

2- = ai.l...Ljah. ij -Yah. ijkniah.ij k=

Total catch/month/sampling centre/per gear is g ven by

kah. i

Total catch/month/region/gear is given by

Yah. ij

The estimation of total fishing effort and price are done simultaneosly with the catch.

The sampling and estimation procedures are summarised in Appendix 2.

4.3 Togo

The data collection system used in Togolese artisanal fisheries has been described bySamba (1986). The coastal area of the country is divided into three strata; namely areas Westof Lomé Port (stratum 1), the Lomé Port (stratum 2) and East of the port (stratum 3). Dataare collected only in stratum 1 (on Mondays and Wednesdays) and stratum 2 (every day).

The enumerators record the number of trips made, determine the catch per trip, anda simple ratio (number of canoes examined to total number of trips) is used to estimate thetotal catch. The gears are not separated.

4.4 Benin

For statistical purposes, the 120 km coastline of Benin is divided into four sectors;namely Mono, Atlantique (excluding the Cotonou port), Cotonou Port and Ouémé. Thecollection of fish catch statistics at the Cotonou port and the Province of Ouémé is the


responsibility of the Direction des Pêches and for sometime the Projet Modèle Benin collectedstatistics in the Mono and Atlantique Provinces.

4.4.1 Data collection by the Model Project, Benin

From October 1984 to the end of 1988, the erstwhile the Model Project Benin of IDAFhad put in place a data collection system which involved five villages in two provinces. Theprincipal objectives of the activity were to assess, inter alia, fishing effort, fish production andthe employment situation in the villages. The enumerators recorded the landings of a sampleof the canoes and beach seines.

Data processing and estimates

Follow ng the no at on of Senouvo (1991) the estimation procedure is as follows:

i. mean catch per trip for village v in month m is given by

where

Pm=

t_Ks

L., Pis=s,

. mean catch per trip for the 5 v llages for month m is g ven by

tv Pisi L * P

v Sy m,vv1 y=y,

=

mean catch per tr p for the 5 villages for the year is

sts= P 's1

llÌ S, y=y, v Sy L S*m=mi S, m=m ,

Sa Sa

P, = Individual catch in village v for month mPnv,---= Mean catch/launching in village v the month mS, = Number of launching in village v for month mS. Number of launches in month m for all villagesSa = Number of launches per year for all villages.


This estimate for the five villages is then used to calculate a figure for the provinces(excluding the Port). For each gear, the total catch Pt, is calculated from:

5. Existing struc ii and fstatistiCal data,collection

5.1 Côte divoire

Two government institutions are responsible for the production of fisheries statisticsin Côte d'Ivoire. These are the Centre de Recherches Océanologiques (Ministère deL'Enseignement et de la Recherche Scientifique) and the Direction des Pêches (Ministère del'Agriculture et des Ressources Animales).

The charts below depict the flow of information from the landing centres, where thedata are collected, to the processing centres.

There is very little interaction between the two institut ons in this task and data fromone institution may be supplied to the other only on request.

Centre de Récherches Oceanologiques

Centre de Recherches Océnologiques Centres des Pêches


where P = Monthly catch per gear typeN = Total number of a gear type in a sectorn = Number of a gear type in the 5 villages1,25 = Constant - representing self consumption not registered

I clions of natioiial fisheiy offices vv* gard

Direction des Pêches

Direction Generale de la Production An male

5.2 Ghana

In Ghana the collection, processing and storage of marine fishery statistics is the soleresponsibility of the Fisheries Department and its Branches (the Marine Fisheries Branch,MFB and the Research and Utilization Branch, FRUB). All field enumerators belong to MFBand are directly supervised by the Regional Fisheries Officers (RFO's) of the Branch. Thesampling scheme, sampling procedures and schedule of work of all enumerators are, however,determined by FRUB.

All the data are forwarded to FRUB either directly or through the RFO's forprocessing. Staff of FRUB occasionally visit all recording centres to interact directly with theenumerators.

Training of field enumerators is done jointly by FRUB and MI13 however, alltechnical matters are handled by FRUB.

The chart below shows the flow of fisheries data in the Ghanaian .system.


DirectionRegionale

Direction des Pêches

<->

1

DirectionDepartementale

ServiceStiatistique

Sous-Direction desPêches

Centre des Pêches Service des PêschesArtinasale

FISHERIES DEPARTNIENT(Ministry of Food and Agriculture)

5.3 Togo

Two government institutions are responsible for the collection of fisheries statistics inTogo. These are the Division Halieutique de Direction des Productions Animales and theDirection des Pêches. The duties and functions of these two institutions have not been clearlydefined in the literature and it appears that there is very little interaction between the twoinstitutions.

5.4 Benin

Since the end of The Projet Mod&le Benin, fisheries statistical data collection,processing and analysis are the sole responsibility of the Direction des Pêches.

6.1 Ciite °Noire

There is no elaborate system of data collection in the artisanal fisheries of Côted'Ivoire. It appears also that none of the enumerators (of CRO or Direction des Pêches)follows the required scheme of data collection.

A definite sampling scheme needs to be instituted for the whole artisanal fisheriessector of Côte d'Ivoire. The multi-stage sampling scheme which has been in operation in

affecting accuracy d reliability of results d suggestions for

IDAF Technical Report NI' 49 47

Administration Inland Marine Research &Fisheries &Aquaculture

fisheries Utilization (Canoestatistics Unit)

A

RegionalOffices

DistrictOffices

LandingCentres

6. Prob:e I I

improvement

Ghana for about two decades could be tried here. Infact this is the scheme recommended byCECAF for member countries.

All enumerators (of CRO or Direction des Pêches) must follow the same trainingprogramme organised jointly by the two institutions. It is essential that all enumerators followthe same recording scheme and be able to perform equal tasks.

6.2 Ghana

One of the major problems associated with artisanal fishery statistics in Ghana is thehigh rate of migration of fishermen. The daily, short- and-long-term migrations change thenumber of canoes at sampling centres thereby affecting the raising factors used in theestimation. The effect of such migrations on catch assessment surveys is currently beinginvestigated through a Ghana/Côte d'Ivoire/ORSTOM Joint Sardinella Programme.

Another major problem is the use of more than one gear on a canoe. Canoe ownerswho have more than one gear interchange the gears at will depending on the season. It isdifficult to accordingly adjust the number of canoes per gear obtained in the frame surveys.This could lead to double counting an hence possible overestimation of the total catches bygear types.

One possible solut on of this problem is to give an identification number to everycanoe and "register" it for one particular type of gear. This information should be providedto all enumerators and this would them in the selection of canoes for examination.

The amount of data collected by the enumerators is a funct on of the number of canoesthat they examine per sampling day. This has been limited by the processing capacity atFRUB. This bottleneck is, however, being gradually removed as FRUB has now acquiredadditional computers.

6.3 Togo

A sampling scheme for the entire marine artisanal fishery needs to be instituted. Forthis to succeed it is essential that Direction des Péches and Direction des ProductionsAnimales pull their resources together because of the inadequate material and humancapacities in either of the two departments.

6A Benin

In view of the small size of the artisanal fleet in Benin, it appears that the presentsystem of data collection is good and only needs to be expanded. In 1987 for example, thenumber of canoes at the 5 villages covered by PMB was 12.6% of the total number of canoesin the country, excluding the Cotonou Port. Their total production was about 5% of thenational total or 11.2% if the Port is excluded, these ratios are probably too low to form thebasis for calculating the intended production. It may also be a better idea to integrate therecording system at the Cotonou port with the rest.


7. S III il. ary

The situation of artisanal statistics in the western Gulf of Guinea is summarised below:

Abbreviations as in text.


Côte d'Ivoire Ghana Togo Benin

Frame survey- Frequency- Last survey

?

1989Variable1992

Variable1984?

Variable1988

Catch assessment survey- Coverage- Selection of sampling centres- Measure of effort

- New scheme- Problems

IncompletePurposefulTripsOperationsRequiredLogisticsManpower

CompletePPSTripsOperations0.KLogistics

IncompleteConvenienceTripsOperationsRequiredLogisticsManpower

IncompletePurposefulTripsOperationsExpansionLogistics

Responsible institutions CROD.P.

FRUB D.P.A.D.P.

D.P.P.M.B.

8. Referrnces

AMEGAVIE, K. (1982): Organisation de collecte des données statist ques au Togo. InCECAF/TECH/84/52, pp. 68-77.

BANERJI, S.K. (1974): Fishery Statistics in West Africa. FAO, Rome, (mimeo).

BERNACSEK, G.M, et al (1987): Profil des ressources halieutiques du Togo.CECAF/TECH/87/82.

BERNACSEK, G.M & A. AZIABLE (1986): Profil des ressources halieutiques du Benin,CECAF/TECH/86/72.

CECAF (1984): Seminar on frame and catch assessment surveys for the CECAF coastalcountries. CECAF/TECH/84/52.

KONAN, J. & R. DEDO (1993): Pêche artisanal maritime en Côte d'Ivoire (mimeo) (Annex1 of the present document).

KORANTENG, K.A. (1989): Schemes for collecting catch and effort data for the estimationof fish production in the marine fisheries sector in Ghana. INF. REP. NO. 22; FRUB, TEMA,GHANA.

SAMBA, A. (1986): Collecte et traitement des statistiques de pêche artisanale au Gabon, auCameroun et au Togo. COPACE/TECH/86/77.

SENOUVO, P.A. (1990): Statistique de pêches des villages du Projet Modèle, annee 1987.PMB/WP/13.

SENOUVO, P.A. & A. GBAGUIDI AZIABLE (1991): Recueil des statistiques des pêchesmaritimes au Benin, period de 1984 a 1989. PMB/WP/17.


FORIN

INFO 'I ON REPO T

NU UER22

SCHE I S FOR COLLEC I G CATCHAND EFFORT DATA

IION IF FISH P OWL CTIONE S SECTOR

by

K A. KORANTENG

FLSRE S DE ART INTRESEARCH & UHL TION BRANCH

TE G ANA

Appendix 1

1989

IDAF Technical Report N° 49 5 1

OCTOBE 111

ACKNOWLEDGEMENTS

Acknowledgements are due to Dr. Trevor White and Mr. Oliver Nmashie for readingthe script and making very valuable suggestions that have, undoubtedly, improved the qualityof this report.

TABLE OF CONTENTS

Introduction 53Description of the fishing fleets 53Data collection and catch estimation 553.1 Canoe fleet statistics 55

3.1 .1 Canoe Frame Surveys (CFS) 553.1.2 Primary Sampling Units (PSU): Sampling sites 553.1.3 Secondary Sampling Units (SSU): Sampling days 563.1.4 Tertiary Sampling Units (TSU): Canoes 573.1.5 Required information and data collection 573.1.6 Canoe data processing 583.1.7 Estimating the total catch and value 58

3.2 Inshore fleet statistics 593.2.1 Information collected, data process ng and catch estimation . 59

3.3 Industrial fleet statistics 603.3.1 Statistics of commercial shrimpers 60

3.4 Tuna fleet statistics 60Publication of statistics 61Outlook for the future 61References 62

TABLE OF APPENDIXES

Al FORM 1A: Canoe fisheries monthly activities 63A2 FORM 1D: Catch, effort and value 64B1 FORM 2A: Monthly record of landed motor fishing vessels 65B2 FORM 2B: Records of catch value and effort of inshore motor fishing vessels

(trawl net/purse seine/line/ali net) 66Cl FORM 3A: Industrial vessels monthly activities 67C2 FORM 3B: Industrial vessels catch and effort data for the month of 19. . . 68

FORM 4: Tuna catch statistics 69El FORM 5A: Shrimp vessels monthly activities 70E2 FORM 5: Catch and effort data for commercial shrimping for the month 19.11

Format for reporting marine fish production in Ghana 72


Introduction

It is often said that statistics provide the backbone for policy and planning. The needfor accurate statistics, therefore, is extremely essential if economists, biologists, planners,industrialists and policy maker, the primary users of fisheries statistics, are to derive anybenefits from them.

In a way, the users of statistics determine the type of data that need to be collectedin a survey. Like any kind of statistics, it is very expensive and sometimes time consumingto collect fishery production statistics. The quality of the statistics produced is a function ofthe quality of the survey design, the availability and dedication of the personnel that carry outthe survey and the manner in which it is implemented.

Ghana has a highly developed marine fishing industry. It is one of the most importanteconomic activities in the country. Between 1981 and 1988, annual production from the seaaveraged around 246,000 tonnes. In 1988, for example, the total production by Ghanaianvessels was about 303,000 tonnes valued at over 80 billion cedis (about 300 million U.S. $).

For nearly half a century, the Fisheries Department in Ghana has collected and keptrecords on fishing activities. Most of the fish landings in the nation's ports and harbours andat the over 200 fishing villages along the coast are enumerated. In 1972, the processing of thisdata was transferred from the Department's Head office in Accra to the Research & UtilizationBranch at Tema.

In this report, the methods used in the collection and analysis of the data andproduction of the statistics are presented. It is intended that this document be as simple aspossible hence statistical details have been left out. It is my hope that this document willsatisfy the numerous enquiries that have been, and continue to be made to the FisheriesDepartment on this subject.

Desuiption of e fishing fleets

There are four sectors in the industry; namely artisanal (on canoe), inshore (or semindustrial), distant water (or industrial) and the tuna fleets.

The artisanal fleet is made up of over 8,000 dug-out canoes about 53.5% of which aremotorised, using outboard motors of up to 40 RP (Koranteng & Nmashie, 1988). A numberof fishing gear types are used in the artisanal sector. In general each type of gear is operatedby a type of canoe which is suitable for the operation. For statistical purposes, the gears havebeen put into five (5) main categories, namely, Ali/Poli/Watsa (three separate gears that aregrouped together for this purpose because they are operated from the same type of canoe),Beach Seine, Set Net, Hook and Line and Drift Gillnet. This does not mean, however, thata line canoe, for example, cannot be used for set net fishing. The strict division here is basedon the most frequent usage of each type of gear on a specific type of canoe.


The number of canoes operating each of these gear types are shown in Table 1, whichis based on the 1986 frame survey in Ghana (Koranteng & Nmashie, 1988). The small canoes,called "one-man canoes" (OMC), were classified separately for the first time in this survey.They are used mainly for set net and line fishing very close inshore.

Table 1 Statistics on landing beaches, fishing villages and numbers of canoes operatingthe indicated fishing gears.

Source: 1986 Ghana canoe frame survey

Most canoes undertake daily trips. In the last five years or so, however, the line canoesare be ng fitted with ice boxes to allow them to stay at sea for up to about three days.

The inshore fleet is comprised mostly of Ghana-built wooden trawler/purse seiners ofbetween 8 and 37 m overall length. There are two categories of these inshore vessels; namely,those of length 12 m and below and the 12.3-37 m vessels. The smaller vessels do purseseining mainly during the sardinella season and turn to trawling for the rest of the year. Mostof the larger vessels, on the other hand, purse seine almost all the year round and occasionallya few of them go trawling. The small vessels undertake daily trips while the bigger ones maystay at sea for a week or more when trawling. When purse seining these larger vesselsnormally do not stay at sea for more a day.

At the beginning of this year (1989), there were about 300 such fishing craftsoperational in Ghana, out of a total number of around 600 (Koranteng, 1988).

All industrial vessels (30 m and above) are of steel hulls and constructed outsideGhana. Many of them, belonging to large fishing companies, were built to operate in moreproductive foreign waters but are now fishing exclusively in Ghanaian waters since the adventof the 200 nautical mile EEZ regime. Presently there are about 14 of these vessels is this fleet(Koranteng, 1988).

Commercial shrimping was resumed in Ghana about three years ago after a break ofover ten year. The Department of Fisheries, however, does not encourage this fishery becauseshrimp resources in Ghanaian water are very low and the operation of shrimp trawls leads todestruction of large numbers of immature fish. Presently only four shrimp vessels are licensedto operate in Ghana.

54 IDAF Techn cal Report N° 49

REGION Fishingvillage

L,andingbeach

Beachseine

Ali/Poll/Watsa

Set net Line Driftgillnet

One-mancanoe

Total(canoes)

Volta 32 48 389 88 23 17 12 0 529

Crreat Accra 44 62 175 1841 198 658 126 24 3016

Central 40 78 121 1557 926 139 96 25 2866

Westem 72 88 112 481 705 190 222 93 1803

TOTAL 188 276 797 3969 1852 1004 456 142 8214

The fleet of shrimpers is considered as part of the industrial fleet although theircatches are treated separately.

Commercial tuna fishing began in Ghana in 1959. The tuna fleet now (October, 1989)is made up of 25 tuna bait-boats, 5 purse seiners, and (since September, 1989) 1 long-liner.All these vessels fly the Ghanaian flag and are operated by Ghanaian registered companies;some of them through joint-venture arrangements with overseas partners. Most of the vessels,however, have Korean and Japanese nationals in their crew.

3, lata collectio and catch esti III atio

The system for collecting and processing catch data differ from fleet to fleet. Whereasonly a sqmple of fishing crafts in the artisanal and part of the semi-industrial fleets have theircatches examined, industrial and tuna fleet statistics are based on a complete enumeration ofthe landings of all vessels in these categories.

3.1 Canoe fleet s tistics

The three-stage sample survey system in use since 1972 follows Banerji (1974). Inbrief, the coastline of Ghana, which is about 550 km long, has been divided into four (4)strata. These correspond to the country's four coastal administrative regions. the tour regionsare Volta, Greater Accra, Central and Western.

3.1.1 Canoe Frame Surveys (CFS)

A census of all canoes operating out of the country's fishing villages is takeperiodically. These frame surveys are extremely essential and provide the necessarybackground data for the assessment of fish produced by the artisanal fleet.

CECAF (1982) summarises the purpose of a frame survey as:

prepare a complete list of fish landing sites (frame) for the catch assessment survey.(A catch assessment survey is a statistical survey for collecting the catch and effortstatistics).

to collect basic statistics on the number of fishing canoes, fishermen, types of fishinggear, etc.

The methodology of such a survey is given by Banerji (1974), CECAF (1984), andKoranteng & Nmashie (1988). Field enumerators visit all existing landing sites along theentire coastline and record various at-tributes of the canoes, artisanal gears and the artisanalfishery generally.

3.11 Primary Sampling Units (PSU): Sampling sites

Within each region a number of fishing villages are selected on the basis of thenumber of fishing units (canoes) present. They are selected with probability proportional to


the size (PPS) of sampling unit, canoes. These constitute the primary sampling units (PSU).One of the major advantages in selecting the PSU this way is that villages with large numbersof canoes get better chances of being included in the sample. The PPS method also simplifiesthe method of catch estimation (Banerji, 1974).

Of the 188 fishing villages and 276 landing beaches recorded in the 1986 canoe framesurvey, 53 landing beaches (sites) were selected as sampling sites (Table 2). These aremanned by 35 field enumerators (Technical Assistants).

Table 2 Numbers of sampling sites for catch assessment surveys, starting March 1987.One-man canoes are there on experimental basis only.

At each selected landing beach, the canoes are substratified into the types of gears thatare operated on them. Some of the gears at the selected sampling sites are examined. In thisselection process, the importance and consistency of operation of each gear at the selected siteare taken into consideration. It is ensured also that Technical Assistants are not unnecessarilyburdened with field work since this will reduce their efficiency. Where there are more thanone Technical Assistant (especially at the large centres) all gears are sampled.

Presently (1989) the artisanal catch assessment survey is based on the gears andnumber of sampling sites in each region as shown in Table 2. Sampling of one-man canoescommenced in 1987 and on an experimental basis.

3.1.3 Secondaty Sampling Units (SSU): Sampling days

Every month, each target gear at a recording site is covered in two weeks. Where twogears are handled by one Technical Assistant, he records one gear each week in alternateweeks. This means that he records gear A in weeks 1 and 3 and gear B in weeks 2 and 4.This pattern is reversed in the following month. Banerji (1974) recommended one week permonth for each gear at every sampling site.

For each gear records are taken for at least four days in a sampling week. This isbecause Saturdays and Sundays are non-working days for all except a few of the enumerators,and almost every fishing village has one day in the week observed as a non-fishing day.Various communal activities as well as the repair and maintenance of fishing gear and fishingcrafts take place on this day (Koranteng and Nmashie, 1988).


REGION N° ofsites

Beachseine

Ali/Poli/Watsa

Set net L ne Driftgillnet

One-mancanoe

Total no.of gears

Volta 8 5 3 2 0 1 0 11

GreatAccra

15 4 6 5 5 3 0 23

Central 13 4 6 4 4 2 1 22

Western 17 4 4 5 3 4 1 21

TOTAL 53 17 19 16 12 II 2 77

3.1.4 Tertiary Sampling Units (TSU): Canoes

For any gear, the canoes (or gears in the case of beach seines) at the landing centreconstitute the tertiary sampling units (TSU). The recorder selects some of the canoes thatreturn from fishing and enumerate their landings. It is assumed here that on any day thecatches and landings of canoes at the centre are likely to be similar, thus the recorder doesnot need to examine every canoe operating a particular type of gear. Selection of canoes forexamination is based on the numbers (of canoes) that actually operate at the centre on thesampling day. They are then selected according to the order of arrival of the canoes from thefishing grounds. Each Technical Assistant is provided with a chart which guides him in theselection of the canoes to be examined. The number of canoes to be sampled on any samplingday is thus variable.

The sampling design is summarised in Table 3.

Table 3 Stages showing characteristics of the canoe sampling

3.1.5 Required information and data collection

On the selected sampling day, the enumerator must first find out how many canoeswent fishing. This number determines the number of canoes to be sampled and the order bywhich they are to be selected for sampling. He also records the actual number of canoes thatlanded (that is returned from fishing) that day. For every selected canoe, he records:

duration of fishing tripthe number of crewquantity (weight) and value of each species in the catch.

Forms for recording the data are given as Appendices Al and A2. Appendix Al (Form1A) gives a record of the month's activities (including that of the Recorder) at the landingbeach. Appendix A2 (Form 1B) is for recording the catch and effort data.


Sampling Stage 1st stage sampling 2nd stage sampling 3rd stage sampling

Sampling Unit Landing site Fishing day Canoe landing

Stratification Fishery regionLarge and small

By gear used

Method of sampling PPS Systematic Systematic

Size of sample All in the large landingsites stratum, severalin the small landingsites

At least 4 days in theweek; two weeks in themonth in alternativeweeks

Maximum for eachtype of gear

Place of sampling At the head office(R & U) in advance

At the head office(R & U) in advance

By enumerating oneach sampling day

3.1.6 Canoe data processing

Processing of catch and effort data, which is done on monthly basis, is as follows:

Within region

estimate for each gear for every sampling centreestimate for each gear in the region (landing centre estimates are combined)estimate for total catch (all gear combined).

Overall estimate

all regional est mates are added to produce national est mate (by gear and onmonthly basis)national estimate of total catch (all gears) in the year.

From 1987, the processing of catch and effort data is being done on a microcomputerusing the LOTUS 1-2-3 spreadsheet. Previously processing was carried out manually usinghand and desktop calculators.

3.1.7 Estimating the total catch and value

The aim of the whole exercise is to estimate how much fish is landed in the countryby the canoes. At the sampling site, the estimated catch by any sampled gear for the monthis simply given by:

Where D the number of days in the month, excluding non-fishing daysd = the number of days on which samples were obtainedX = the estimated total catch at the centre for the d days.

This figure is then divided by the number of units (canoes operating the gear type) atthe recording centre to provide a mean catch for canoe for the centre. The means from allcentres in the region where the same gear is sampled are then combined to provide.an overallmean of means for the region. This is then multiplied by the number of similar units in the-region as recorded in the frame survey. A national estimate is obtained by combining theregional estimates. The catch values and fishing effort are similarly processed. The estimationprocedure is summarised in Table 4.


Table 4 Stages showing character stics of the canoe sampling

3.2 Inshom fleet statistics

Data are collected by the field enumerators at the landing centres from which thesevessels operate. These centres are Tema, in the Greater Accra Region; Winneba, Apam,Murnford and Elmina in the Central Region; Shama, Sekondi, Takoradi and Axim in theWestern Region. In 1986 about 126 vessels operated from Tema, 70 from the centres inCentral region and 63 from the centres in Western region (Mensah and Koranteng, 1988).

Except for the large vessels that operate only from Tema and Takoradi because of theavailability of adequate berthing facilities, most inshore vessels land their catches at any ofthe above-mentioned centres. From the positional analysis data collected by the FisheriesDepartment, it is evident that these vessels change their landing points very frequently.

Inshore vessels are sampled on every fishing day. During the sardinella season whena large number of vessels operate and also at large landing centres like Tema, Elmina,Sekondi and Takoradi, a sample of vessels may be examined on the sampling day.

Where a sample of vessels are examined, the method of selection of vessels is similarto the selection of canoes for sampling.

3.2.1 Information collected, data processing and catch estimation

The data are collected at the time of landing. Because of the high rate of mobilityamong these vessels, it is essential to record the exact number of vessels that land on anysampling day. Appendices B1 and B2 give the forms used for recording monthly activity andcatch and effort data respectively of the inshore fishery. These forms are similar to those usedfor canoe statistics. Data from the two categories of inshore vessels are collected andprocessed separately.

The information obtained from these vessels is the same as with the canoes.


Estimation Daily estimation Monthly estimation Regional estimation

Method of estimation Simple estimation Simple estimation Ratio estimation

Sample data Observed catch of sampledcanoes

Estimated daily catch ofsampling days

Estimated monthly catchof sample landing sites

Ra s ig factor Lauding raising factor Time raising factor Frame raising factor

Data for calculatingraising factor

Number of canoes landingand number of canoessampled

Number of fishing daysand number of samplingdays

Number of active canoesin the stratum and at thesampling sites

Estimated total catch Estimated daily catch ofgear on each sampling dayat landing site

Estimated monthly totalcatch at each sampledlanding site

Estimated monthly totalcatch in the stratum

Estimates of catch, value and fishing effort are available by:

vessel categoryfishing gear typemonthlanding centreregion, and then national total.

If a sample of vessels had been examined for the day, the appropriate raising factors(which depend on the sampling fraction) are used to arrive at the day's landing. The month'sproduction is obtained from the addition of the daily estimates.

3.3 Industrial fleet statistics

Al! compan es that operate industrial vessels send their data directly to the FisheriesDepartment. Every such company (14 of them at the moment) is required, under Ghana'sfisheries regulations, to report their activities and all landings to the Director of Fisheries. Thecompany must report on the activities of every vessel that it owns (both operational and non-operational). Forms for this purpose are provided by the Fisheries Department; samples ofwhich are given in Appendices Cl and C2.

Since the data are obtained by complete enumeration method, estimates of landings areobta ned by:

computing monthly landings of all companiessumming these to produce the production for the entire fleet.

The companies are also to report any part of their production that they export. In allcases data exist by species of fish caught.

3.3.1 Statistics of commercial shrimpeis

Until the beginning of 1989, catches made by this fleet (which until October, 1988comprised of only two vessels) were considered with those of the inshore fleet. They are nowprocessed and treated separately. The relevant catch and effort data forms are shown inAppendices El and E2.

The methods used to obtain and process the data are the same as used in the otherindustrial fleet.

3.4 Tuna fleet statistics

Tuna catch data collection and processing follow strictly the system adopted by theInternational Commission for the Conservation of Atlantic Tunas, ICCAT, based in Madrid,Spain. A sample form for recording catch and effort data is given in Appendix D.

Just like the industrial fleet, all operators of tuna vessels in Ghana are required by thefisher es regulations to submit all catch data to the Director of Fisheries. Processing of the


data follows the same format as that for the industrial vessels, except where specific ICCATrequirements are to be met.

Publicatio of statistics

Fisheries statistics may be published in many ways. In Ghana the estimated landingsmade by all fleets are published annually. Appendix F, which gives Ghanaian marine fishproduction for 1987 and 1988, shows the format for reporting these catches. An officialstatistical bulletin is now being prepared by the Research & Utilization Branch of theFisheries Department.

Outlook for the fu

It is essential that data collection systems be reviewed periodically. In so doing,however, one must ensure that any underlying sequence (time series) in the data is notinvalidated. This is because the value of most statistical time series lies in their reliability andcontinuity in time and any modification to the data collection system must be take this intoaccount.

The Ghana fisheries data acquisition systems were initiated with the assistance of theFood and Agriculture Organisation (FAO) of the United Nations Organisation. Since theirinception, a number of statisticians, including some from FAO, have evaluated the systemsand found them to be still adequate. The canoe data sampling scheme has received the mostcritical evaluation. Consequently, no major changes to the marine fisheries data collectionsystems are envisaged in the very near future. The following are, however, planned:

1 A separation of the three artisanal gears Ali, Poli and Watsa. This is in line withrecommendations made by working groups on small pelagic species in the WesternGulf of Guinea (CECAF, 1988).

Separation of catches made by artisanal lobster set nets and those of the other set nets(mainly the "Toga").

Improvement of species by species reporting by industrial vessel operators. Foreconomic and operational reasons, a substantial amount of fish sold by industrialvessel operators are classified as "mixed". The Fisheries Department has had to resortto a sampling scheme that enables the data processors to separate the mixed fish intospecies. Unfortunately this sampling programme has not operated satisfactorily and itis hoped that it could be improved in the future.


6. Reformes

Banerji, S.K. (1974): Fisheries statistics in West Africa. Work undertaken during the periodSeptember, 1971 - February, 1973. FAO, RUMIE WS/E7100

CECAF (1984): Report of the seminar on frame and catch assessment surveys for CECAFcoastal countries. Banjul, The Gambia 4-13 October, 1982. CECAF/TECH/84/52.

CECAF (1988): Report of the Technical consultation on small pelagic species of the WesternGulf of Guinea Statistical Division, Abidjan, 9-12 December, 1987.CECAF/TECH/88/89.

Koranteng, K.A. (1988): A preliminary report on the 1988 census of motor fishing vessels inGhana, (mimeo).

Koranteng, K.A. & 0.0. Nmashie (1987): A report on the 1986 Ghana canoe frame survey.Information Report N° 21 Fish. Res. Unit. Tema

Mensah, M.A. & K.A. Koranteng (1988): A review of the oceanography and fisheriesresources in the coastal waters of Ghana 1981-1986. Fish. Res. Unit. Marine Res.Reports N° 8


Data collections sheets


64

1A: CANOE FISHE MON LY ACTIVITIES

NAME OF RECORDER:

If fishing, no observation, fishing holiday, or no fishing, state reasons in remarks column.

MONTHLY CANOE CENSUS AT LANDING CENTRES

N.B. The census should be done on the first Fishing Holiday of every month.

APPENDIX Al


LandingCentre

Ali/Poli/Watsa

Beachseine

Set net Line Drift GilLnet One mancanoe

TOTAL N° of OutboardMotors

AREA: MONTH & YEAR

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N° of Inshore Motor Fishing Vessels Landed

Purse Seine

27' - 39'

Purse Seine

40' - 120'

Trawl

27' - 39'

Trawl

40' - 120'

Other Total

REMARKS

If there is no fishing of particular unit write NF.If no observations are taken on a day write NO.In the remark column, mention significant facts like "fishing holiday" rough sea, etc. if any,against a particular date.

APPENDIX B1

FORM 2A: MONTHLY RECO OF LANDED MOTOR FISHING VESSELS

AREA: MONTWYEAR:

CENTRE: RECORDED-


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Note: if vessel is at sea indicate so in remarks column.

APPENDIX Cl

FO 3A: INDUSTRIAL VESSELS MONTHLY ACTIVLIIES

FISHING COMPANY:MONTH & YEAR:NAME OF RECORDING OFFICER:

REG. NO. OFVESSEL,

VESSEL'S NAME PORT OFDISCHARGE

DATE VESSELBERTHED

REMARKS


APPENDIX C2

FORM 3B: INDUSTRIAL VESSELS' CATCH AND EFFORT DATAFOR THE MONTH OF 19

1. NAIVLE OF COMPANY2. NAME AND REGISTRATION NO. OF VESSEL3. EXPIRY DATE OF VESSEL'S LICENCE.4. TYPE OF FISHING VESSEL (eg. TRAWLER, P. SEINER, CARRIER)5. FISHING GROUNDS (eg. OFF SHANIA DEPTH: 40 m)

OFF DEPTH (b) OFF DEPTH....... .............(c) OFF DEPTH (d) OFF DEPTH(e) OFF DEPTH (f) OFF DEPTH

6. (a) DATE VESSEL LEFT PORT FOR FISHINGDATE VESSEL RETURNED TO PORTNO. OF DAYS ABSENT FROM PORT (b-a)NO. OE DAYS USED IN FISHING--

7. PORT OF DISCHARGE8. QUANTITY OF FISH LANDED (i.e NO. OF CRATES/CARTONS/SACKS)9. AVERAGE WEIGHT PER CRATE/CARTON/SACK (in kg)

10. TOTAL TONNAGE OF FISH LANDED-

NO. OF DAYS IN PORT PRIOR TO NEXT TRIPREMARKS:

11. SPECIES OF FISH CARTONS/CRATES/SACKS/TONS VALUE (0)

iiiiiiv

a)b)c)d)

e)

vi

RED PANDORA (Yiyiwa)SEA BREAMS (Sikasika)CUTTLEFISHSOLEOTHERS (Specify)

MIXED

12. FISH EXPORTS CARTONS/CRATES/SACKS/TONS VALUE (0)

.4114040=011

ii

iiiiva)b)c)d)vi

CUTTLEFISHSOLESEA BREAIVISOTHERS

MIXED


Fra/14/12/87/

FO : NA CATCH STATISTICSDATE-

NAME OF COMPANY.NAME OF VESSEL- REGISTRATION N°.TRIP N° PERIOD-N° OF CREW: FOREIGN- GHANAIAN- TOTAL.PORT OF DEPARTURE-DATE OF DEPARTURE-DATE OF ARRIVAL - ABIDJAN-DATE OF ARRIVAL - TEMA.N°. OF DAYS AT SEASN°. OF DAYS AT BAITING.N°. OF DAYS TUNA FISHING.TOTAL LANDING (m/t).

TOTAL EXPORT - ABIDJAN.TOTAL EXPORT - TEMA.TOTAL LOCAL MARKET - TEMA-

BREAK-DOWN BY SPECIES

APPEND IX Elp

SPECIES EXPORT- EXPORT- MARKET PRISE TOTALABIDJAN TEMA

YF GG LiSYF RIYF R2YF R3

SJ JSJ RISJ R2SJ R3

BE GGBE R1BE R2 & R3

BSJ

MIXEDTUNA

DOCTORFISH

TOTAL:


FO 5A: S VESSELS MON


r I I'

NOTE: If vessel is at sea indicate so in remarks column.

LY A 1 IES

APPENDIX El

FISHING COMPANY.MONTH & YEAR.NAME OF REPORTING OFFICER:

REG. NO. OFVESSEL

VESSEL'SNAME

PORT OFDISCHARGE

DATE VESSELBERTHED

REMARKS

71

13. FISH EXPORTS

14. N° OF DAYS IN PORT PRIOR TO NEXT TRIP

APPENDIX E2

FO 5. CATCH AND EFFORT DATA FOR CO ERC1AL S l' I INGFOR E MONTh OF 19....

1. NAME OF COMPANY2. PARTICULARS OF VESSEL

NAMEREGISTRATION N°TOTAL LENGTH (m) d) GRT

3. EXPIRY DATE OF VESSEL'S LICENCE4. FISHING EFFORT ASSESSMENT.

DATE VESSEL LEFT PORT FOR FISHING (SHRIMPINGDATE VESSEL RETURNED TO PORT.N° OF DAYS ABSENT FROM PORT (b - a)-N° OF DAYS USED IN SHRIMPING-

5. PORT OF DISCHARGE.6a FISHING GROUNDS (eg. OFF SHAMA Depth: 40 m)

(a) OFF Depth (b) OFF Depth(e) OFF Depth (d) OFF Depth

(e) OFF Depth (f) OFF Depth6b (a) DATE VESSEL LEFT PORT FOR FISHING

(b) DATE VESSEL RETURNED TO PORT(c) N° OF DAYS ABSENT FROM PORT (b-a)(d) N° OF DAYS USED IN FISHING

7. QUANTITY OF SHRIMPS CAUGHT (HEADS-ON) (IN GRATES/PACKETS)8. AVERAGE WEIGHT PER CRATE/PKT OF SHRIMPS (WITH HEADS) (in kg)9. AVERAGE WEIGHT PER CRATE/PKT OF SHRIMPS (HEADLESS) (in kg).10. QUANTITY OF SHRIMPS LANDED (GRATES/PKTS)

a) HEADS-ON b) HEADLESS11. QUANTITY OF SHRIMPS EXPORTED (CRATES/PKT/TONS)

a) HEADS-ON b) HEADLESS

12. WEIGHT/QUANTITY OF BY-CATCH (i.e other types of fish caught)

SPECIES CRATES/CTNS/TON VALUE (0)

iiiii

iv

v

vi

RED PANDORA (YiyiwaSEA BREAM (Sikasika)CUTTLEFISHSOLEOTHERS (Specify)a)b)c)d)

Mixed

CTNS/CRATES/SACKS/TONS VALUE (0)

i

ii

iii

vi

CuttlefishSoleSea BreamsOthers (Specify)a)b)c)Mixed


APPENDIX F

FO T FOR POR E FISH PRODUCHON IN Gil NA


1987 1988

CATCH IN M/T VALUE x 01000 CATCH IN M/T VALUE x 01,000

1. CANOE FISHERYj. Round Sardine 45,670.7 75,851.5

Flat Sardine 25,479.2 10,450.4Chub Mackerel 397.3 7,423.5

iv. Anchovy 87,984.4 75,902.3v. Frigate Mackerel 4,689.3 6,382.5vi. Sea Breams 9,737.5 13,039.9vii. Burrito 13,516.4 8,434.2viii. Others 73,976.5 46,557.9

TOTAL (CANOES) 261,451.3 49,675,747 244,242.2 67,111,605

2. INSHORE VESSELS

1,732.1 576.2a. Purse seinei. Round Sardine'

Flat Sardine 1,681.8 95.7Club Mackerel 35.1 157.7

iv. S cad Mackerel 13.9 15.5v. Others 1,540.4 1,035.8

TOTAL 5,003.3 950,627 1,880.9 517,248

b. Trawlers472.2i. Sea Breams -

ii. Cassava fish 239.4Burrito 916.9

iv. Trigger fish 2,412.0v. Others 1,492.8

TOTAL 9,928.1 1,886,339 5,533.3 1,521,658

TOTAL INSHORE VESSELS 14,931.4 2,836,906 7,414.2 2,038,906

3. DISTANT WATER VESSELS1,071.9 498.6i. Sea Bream

Trigger Fish 213.2 36.3Cuttle Fish 1,688.1 1,580.7

iv. Herrings (SardineIlas) 8,643.5 9,618.4v. Others 8,554.6 4,308.0

TOTAL (DISTANT WATER) 20,171.3 3,832,551 16,042.0 4,411,550

4. TUNA FISHERY

4,155.8 2,770.0a. Ghana flagi. Yallowfm

Fig Eye 123.7 51.9Skipjack 26,576.0 29,936.4

iv. Black Skipjack 270.5 289.3v. Others 2,339.1 2,385.7

TOTAL (GHANA FLAG) 33,465.1 4,685,114 35,433.3 8,124,378

b. Foreign flag

TOTAL TUNA CATCH 33,465.1 4,685,114 35,433.3 8,124,378

5. TOTAL DOMESTIC CATCH 330,019.1 61,030,378 302,931.7 81,686,439

6. TUNA FISH TRANSHIPPED 26,290.1 29,571.9

7. TUNA SOLD LOCALLY 7,175.0 5,861.3

Cotoiou, Be iln 3-7 May 1993

World ig Gro ,p onArdisanal Fisheries Statistics

for rifle Western Gulf of G 7nea,Nigeria d Ca u eroon

C. Sta

by

ill to 11 ulos

Senior Fishery Data OfficerFishery Information, Data and Statistics Service

FAO Fisheries Department

FAO LIBRARY AN: 347077

Annex 8


e odological id operational aspects incatch/effott assess ¡lent surveys

A 11; Iiexes I - W: ex

Contents

Introduction 76

Statistical scenario A 78

2.1 Methodological aspects 782.2 Operational aspects 792.3 Summary of characteristics 792.4 Example 80

Statistical scenario B 83

3.1 Methodological aspects 833.2 Operational aspects 83

3.3 Summary of characteristics 843.4 Example 84

Statistical scenario C 85

4.1 Methodological aspects 854.2 Operational aspects 864.3 Summary of characteristics 874.4 Example 87

Computer aspects 88

Implementation aspects 89

illpies 4 IId exercises

Annex I Records of fishing activities 90Annex II Statistical scenario A 108Annex III Statistical scenario B 116

Annex IV Statistical scenario C 122


L INTRODUCTION

This note was prepared with specific reference to the collection and processing ofcatch/effort data from artisanal fisheries in the Gulf of Guinea. The paper takes intoconsideration current national needs for basic fishery statistical data and suggests thatthe following three issues be considered and discussed by the Working Group:

Basic methodological and operational concepts in the collection andprocessing of data on catch and fishing effort;

Computer aspects related to the collection/processing of basic data;

Training requirements and upgrading of national skills in the sector ofapplied fishery statistics and computing.

Most of the views expressed in the document are based on experience gained over thepast years in the design and implementation of catch/effort assessment surveys in Africancountries. During this period significant changes have occurred in the approaches usedin the design and implementation of statistical surveys and particularly in the sectors ofupgrading national capabilities and institutional infrastructures, integration of statisticaland computing methods, and implementation of self-sustaining statistical programmeswith minimum or no dependence on external assistance.

From the methodological and operational viewpoint almost all of the statistical systemsthat have been implemented in Africa deal with basic sample data (catch, effort, prices)for the derivation of important sample-based magnitudes such as Catch Per Unit ofEffort (CPUE) and Gear Activity Coefficient (GAC), and the estimation of total effortand fish production at various stratification levels such as minor stratum, and region.Census approaches are only used in specific sectors of the fishing industry where regularrecording of all fishing activities is in place. Examples of census-based data collectionschemes are logbooks of industrial fisheries and observer programmes.

Although sample-based surveys differ from country to country in terms of species, fishinggear, fishing methods, types of water bodies, etc., it has nevertheless'been possible toidentify common elements in the formulation of statistical scenarios. Sections 2, 3 and4 of the document each examine three of the most commonly used scenarios for thecollection of data on fishing effort and catches. These are briefly referred to asSCENARIO A, SCENARIO B and SCENARIO C and are illustrated in summary formin Table 1.1. For each statistical scenario methodological notes have been provided asa background for an easier understanding of the statistical aspects involved, and aresupplemented with examples based on computer-generated simulated catch and effortdata appropriate for each specific survey approach.


Computer aspects are discussed separately in Section 5 of the document. The principleof an integrated "statistics-computing" approach is introduced by means of whichmicrocomputers become an inseparable part in the design and implementation of fisherystatistical surveys and programmes. The discussion concerns storage and processing ofprimary fishery data (effort, landings, prices) and analytical and reporting/publishingfunctions. Also discussed are new needs in terms of computer equipment, software andstaff skills which have significant impact on the structures and functions of nationalfishery institutions, research centres and statistical offices.

Section 6 concerns training aspects and upgrading of national skills required for theimplementation of self-sustained fishery surveys and programmes. This issue has alwaysbeen and continues to be a principal one, as fishery surveys, irrespective of theirstatistical merits,, can be ineffective without the presence of adequately trained staff andthe availability of essential operational resources and means.

Table 1.1

Summary of methodological an e operational characteristicsof fishery surveys also indicating the order of accuracy of resulting estimates

TYPE OF CENSUS CENSUS SAMPLING SAMPLINGSURVEY IN IN IN IN SURVEY

SPACF, TIME SPACE TIME REQUIRED1=Most accurate

(ALL (ALL (SOIVIE (SOME4= Least accurate BEACHES DAYS BEACIIES DAYS

COVERED) COVERED) COVERED) COVERED)

1. CENSUS oncatch andeffort

YES YES NO NO NO

2. SCENARIO A

Catch NO NO YES YES NO

Effort YES YES NO NO NO

3. SCENARIO B

Catch NO NO YES YES NO

Effort YES NO NO YES NO

4. SCENARIO C

Catch NO NO YES YES YES

Effort NO NO YES YES YES


2. STATISTICAL SCENARIO A.

2.1 Methodological aspects

In this type of sample-based surveys fishing effort is completely enumerated from alllanding sites (beaches) and for the entire survey period, usually a calendar month.Depending on the precision required for data on fishing effort, records on fishingoperations are regularly collected from all fishing units that have been active during thefishing period.

For example, effort data for fishing by beach seine may include: net size, number ofhauls made, number of fishermen and duration of each haul or, simply the number ofhauls made. Likewise, effort information for gillnets might include number of sets of netunits, number of fishermen, duration, etc. or simply number of net sets. Evidently, amuch refined definition of fishing effort will require a significant amount of data to becollected and used for an overall estimate of the effort exerted by all fishing units duringthe entire period. In most cases detailed data on fishing effort are obtained only throughsub-sampling and used for specific purposes and studies, whereas large-scale datacollection systems focus on those data items that are considered indispensable for areasonably accurate estimate of total effort and total fish production.

In statistical scenario A no frame surveys are required since the fishing effort is censusedand used directly for the estimation of total catch by means of the formula:

Total est. catch = (Total effort) x (Sample CPUE) (2 . 1)

where

Sample CPUE = (sum sample catches)/(sum sample effort) (2 . 2)

The sample Catch Per Unit of Effort (CPUE) is formulated for each gear type and usessample catch and effort data obtained from beaches during the sampling days. Samplecatch and effort data should be as representative as possible and of sufficient size so asto provide good estimates for the sample CPUE. To be noted that the only source ofvariability in the estimation of total catch is the CPUE, which makes the computationof confidence limits for estimated catch a straightforward operation. Figure 2.1 illustratesan example of the relationship between variability and sample size for CPUE's frombeach seines and gillnets.

Formulae (2.1) and (2.2) indicate that the main estimation process does not involvedetails on species composition. In fact, estimated catcheS by species are the lastcomputational step and based on species proportions found in the samples. For exampleif species X is Y% of the total sample catch by a specific gear, the total estimated catchof species X by this gear is given by:

Catch of species X = Y x (Total est. catch) / 100 (2 . 3)


2.2 Operational aspects

Statistical scenario A is the most accurate amongst sample-based surveys (but also themost costly), because it requires that all beaches are covered on a daily basis (sincefishing effort is censused in space and time). Its feasibility depends on a combination offactors such as:

Number of beaches and mobility of data collectors;

Pattern of fishing operations in terms of time of landings;

Number of daily landings;

Willingness on the part of fishermen to co-operate and respond to questionsconcerning their activities.

The effectiveness of this type of survey depends primarily on the reliability andcompleteness of the obtained information. In some instances, the number of visits tobeaches that are not easily accessible is reduced to once a week and data on fishingeffort for the previous days are obtained by asking the fishermen to remember detailson earlier fishing activities. Or, due to the timing of landings of boats of a paricular gear,the data collector is systematically omitting the recording of these fishing operations, thusintroducing a negative bias into the system.

2.3 Summary of characteristics

All beaches must be covered on a daily basis for the collection of data onfishing effort by each type of gear;

Sample catch and effort data for the formulation of sample CPUE's bygear must be collected from all beaches during the sample days.

No frame surveys are required.


2.4 Example

Table 2.1 illustrates a hypothetical example of a fishery operating two types of gear(beach seine and gillnet) in ten landing sites (beaches). The number of gillnets refers togillnet units of 100 m each and of about the same mesh size. For beach seines it hasbeen assumed that all are of the same type and operated in the same manner.

Figure 2.1

elationship between variability of CPUE and sample sizeComputer-generated (simulated) data for beach seine and gillnet

80

25

20

15

10

5

o5

STATISTICAL SCENARIO A

Var.of CPUE as a function of sample size

Coeff. var. (5)

10 15 20 25 30 35 40 45 50

Sample sin in %

BEACH SEINE GILLNET


Table 1.1 in Annex I illustrates all simulated fishing activities that took place on the tenbeaches during one month. The complete data set of fishing activities consists of 791records each representing a single activity of a fishing unit (i.e a boat) operating one typeof fishing gear. The last two columns of the table indicate the fishing effort exerted andthe resulted total catch (Kg) of all species. These records will be the basis of all samplingfunctions used by each of the three statistical scenarios discussed in the document. In thismanner all effort and catch estimates resulting from each approach can be compared tothe total actual figures obtained from the entire population of fishing activities. Theseare shown in Table 2.2.

Table 2.1

Statistical Scenario A

A hypothetical fishery with two gear types.Computer-generated data based on empirical parameters.


BEACH GEAR NO. OFUNITS

Begadi Beach seine 1

Begadi Gillnet 27

Chiswa Beach seine 2

Chiswa Gillnet 20

Ganza Beach seine 1

Ganza Gillnet 8

Kamuzi Beach seine 3

Kamuzi Gillnet 34

Kopodoma Beach seine 3

Kopodoma Gillnet 11

Mbema Beach seine 3

Mbema Gillnet 20

Membwe Beach seine 2

Membwe Gillnet 31

Ngoa Beach seine 4

Ngoa Gillnet 15

Salimi Beach seine 6

Salimi Gillnet 46

Upiri Beach seine 2

Upiri Gillnet 11

Total - Beach seines 27

Total - Gillnets 223

Table 2.2

Total effort and catch by gear for all beaches during a month.Results obtained from Table 1.1 in Annex I

Table 11.1 in Annex II illustrates the sample data used for the application of scenario A.The first column of the table indicates the order of the sample in the original Table 1.1in Annex I containing all fishing operations.

Table 11.2 in Annex II shows catch estimates by beach and by gear resulting from theapplication of scenario A to the sample data. These estimates are summarized in Table11.3 in the same Annex. To be noted that the estimated total catch by beach seine(43,176 Kg) provides a satisfactory estimate of the actual total (41,950 Kg). For gillnetsthe estimated total is 22,331 Kg which, again, is very close to the actual catch figure forthis gear (22,132 Kg).


BEACH GEAR EFFORTSets orhauls

CATCHKg

All beaches Beach seine 379 hauls 41 950 Kg

All beaches Gillnet 1690 sets 22 132 Kg

3. STATISTICAL, SCENARIO B.


In this type of sample-based surveys fishing effort is completely enumerated from alllanding sites (beaches) but only during a limited period of randomly selected sampledays. Thus collection of data on fishing effort is based on census in space (since allbeaches are covered) and sainpling in time (since only a limited number of days isinvolved).

In this manner the fishing effort over the entire period (i.e a month) is estimated by firstdetermining the mean daily effort on a by-gear basis and then raising to a monthly totalby applying a time raising factor,

In this approach, as in Statistical Scenario A, no frame surveys are required since thefishing effort is censused in space and then directly estimated on the basis of the numberof sample days du' ring a month. Estimation of the total effort for each gear is achievedby means of the formula:

Total est. effort = (Effort in n ciays) x (R/n) (3. 1)

where

R = Time raising factor.

The definition of the time raising factor R is crucial in this approach. If there is noreason to assume that fishing activities are reduced or increased during a month, thenR is set to 30 or 31 or 28 or 29, depending on the number of calendar days in the month.

The same technique would be used even if it is known that Sundays (or Fridays) are daysof little or no fishing and the sample days do not exclude the possibility that such lowactivities are included in the sample. This, however, is applied when the sample size indays is large enough, i.e 14 or 15 or 16 days which allows for the inclusion of "atypical"fishing days.

When operational constraints do not allow frequent visits to beaches or when recorderscannot be employed during non-working clays then the raising factor must be adjustedto represent total number of active fishing days during the month, i.e 26 (excludingSundays or Fridays).

The sample Catch Per Unit of Effort (CPUE) is formulated for each gear type and usessample catch and effort data obtained from beaches during the sampling days. Samplecatch and effort data should be as representative as possible and of sufficient size so asto provide good estimates for the sample CPUE. Estimated total catch by gear is thenobtained by means of the formula:

Est. catch = Sample CPtJE x Est. effort (3 . 2)


4. STATISTICAL SCENARIO C.


In this type of sample-based surveys both fishing effort and catch are sampled in spaceand time and frame survey data are used in the estimation process. Only a limitednumber of selected beaches participate in the samples and only for a limited period ofsample days.

In this manner the fishing effort over the entire period (i.e a month) and for all beachesis estimated by first determining the mean daily effort on a by-gear basis and then raisingto a monthly total by applying two raising factors: one referring to time and the secondto gear units.

In this approach, as opposed to Statistical Scenarios A and B, frame surveys are requiredsince the fishing effort must be estimated for all beaches by assuming that the GearActivity Coefficient (GAC) in the samples is not significantly different from that at theuncovered beaches. Estimation of the total effort for each gear is achieved by means ofthe formula:

Total est. effort = (Effort in n days with g gears) x (R/n) x (G/g) (4 . 1)

where

R = Time raising factor and G = gear raising factor.

The definition of the time raising factor R is crucial in this approach. If there is noreason to assume that fishing activities are reduced or increased during a month, thenR is set to 30 or 31 or 28 or 29, depending on the number of calendar days in the month.

The same technique would be used even if it is known that Sundays (or Fridays) are daysof little or no fishing and the sample days do not exclude the possibility that such lowactivities are included in the sample. This, however, is applied when the sample size indays is large enough, i.e 14 or 15 or 16 days which allows for the inclusion of "atypical"fishing days.

When operational constraints do not allow frequent visits to beaches or when recorderscannot be employed during non-working days then the raising factor must be adjustedto represent total number of active fishing days during the month, i.e 26 (excludingSundays or Fridays).

To be noted that the time raising factor may be gear-specific, that is depending on thegear used and the fishing method.

The gear raising factor G is provided by data from frame surveys, by means of which thenumber of gears at all beaches has been recorded and assumed to be valid over a longperiod of time. In fact, the use of frame survey data assumes that the gear proportionsbetween sampled beaches and all beaches remain constant during one or two or more


Formulae (3.1) and (3.2) indicate that the rnain estimation process does not involvedetails on species composition. In fact, estimated catches by species are the lastcomputational step and are based on species proportions found in the samples. Forexample if species X is Y% of the total sample catch by a specific gear, the totalestimated catch of species X by this gear is given by:

Catch of species X - Y x (Total est. catch) / 100 . 3)

3.2 Operational aspectsStatistical scenario B is the most popular amongst sample-based surveys because itprovides good estimates of total effort and catch and does not require frame surveys.However, although much less costly than scenario A, it still assumes that all beaches arestatistically covered during the sample days. Its effectiveness depends on a combinationof factors such as:

1)- Number of beaches and mobility of data collectors;Pattern of fishing operations in terms of time of landings;Number of daily landings;Willingness on the part of fishermen to co-operate and respond to questionsconcerning their activities.Knowledge of the intensity of fishing activities during certain days for theformulation of time raising factors.


1 All beaches must be covered during a limited number of sample days forthe collection of data on fishing effort by each type of gear;

Sample catch and effort data for the formulation of sample CPUE's bygear must be collected from all beaches during the sample days.

No frame surveys are required.

A Time Raising Factor must be appropriately defined. It can be gear-specific.

3.4 ExampleAnnex III gives an example of such an approach using the computer-generated(simulated) data illustrated in Annex I and applied for days 5, 10, 15, 20 and 25. Allbeaches were covered during these 5 days and 112 sample records were analyzed forbeach seine and gillnet.

Table 111.2 summarizes the estimating process applied to fishing effort and catches foreach of the two gears in the study, i.e beach seine and gillnet. To be noted that theestimated total catch by beach seine (48,445 Kg) is a rather good approximation to theactual catch figure (41,950 Kg) and so is the estimated catch by gillnet (18,569 Kg) whencompared to the actual catch by this gear (22,132 Kg). Table 111.4 provides confidencelimits and an index of variation for both estimates.


years, rather than that the actual number of gears has remained constant. Thus, themajor sources of errors are: (i) migration of fishing units from/to sampled beaches; (ii)unequal proportions of gears added to or removed from registered beaches and (iii) newbeaches that remain unregistered and are therefore excluded from the survey.

The sample Catch Per Unit of Effort (CPUE) is formulated for each gear type and usessample catch and effort data obtained from sampled beaches during the sampling days.Sample catch and effort data should be as representative as possible and of sufficient sizeso as to provide good estimates for the sample CPUE. Estimated total catch by gear isthen obtained by means of the formula:

Est. catch = Sample CPUE x Est. effort (4 . 2)

Formulae (4.1) and (4.2) indicate that the main estimation process does not involvedetails on species composition. In fact, estimated catches by species are the lastcomputational step and are based on species proportions found in the samples. Forexample if species X is Y% of the total sample catch by a specific gear, the totalestimated catch of species X by this gear is given by:

Catch of species X = Y x (Total est. catch) / 100 (4 . 3)

4.2 Operational aspects

Statistical scenario C is the least accurate amongst sample-based surveys because itmakes several assumptions regarding CPUE's, proportions of active gears on each beach,and effort exerted. However, it is also the least costly and in many occasions the onlyalternative for surveys involving limited personnel operating in large areas with manyand/or difficult-to-access landing sites. Its effectiveness depends on factors such as:

Representativeness and number of beaches selected as samples;

Updateness and accuracy of frame survey data;

Number of daily landings;

Willingness on the part of fishermen to co-operate and respond to questionsconcerning their activities.

Knowledge of the intensity of fishing activities during certain days for theformulation of time raising factors.



A number of representative beaches must be covered during a limitednumber of sample days for the collection of data on fishing effort by eachtype of gear;

Sample catch and effort data for the formulation of sample CPUE's bygear must be collected from sample beaches during the sample days.

Frame surveys are required.

A Time Raising Factor must be appropriately defined. It can be gear-specific. A Gear Raising Factor must also be defined for each gear typefrom' data from frame surveys.

4.4 Example

Annex IV gives an example of such an approach using the computer-generated(simulated) data illustrated in Annex I and applied in days 5, 10, 15, 20 and 25 for threebeaches only (Chiswa, Kopodoma and Ngoa). A data set of 24 samples was analyzed forbeach seine and gillnet. These are given in Table IV.1.

Table IV2 summarizes the frame survey data referring to all beaches of the survey.Table IV.3 provides total number of gears for the three beaches selected. Table IV.4provides guidelines for the estimation approach which involves time and gear raisingfactors. To be noted that the estimated total catch by beach seine (39,096 Kg) is a rathergood approximation to the actual catch figure (41,950 Kg), whereas the estimated catchby gillnet (13,492 Kg) is a rather poor estimate of the actual figure (22,132 Kg).


5. COMPUTER ASPECTS

5.1 Databases

The principle of an integrated "statistics-computing" approach has resulted from thegradual expansion of low-cost microcomputers which have become an inseparablecomponent in the design and implementation of fishery statistical programmes.Computing units of varying data storage capacity and processing power are now an activepart in survey design, decentralized storage and processing of primary data (effort,landings, prices) and centrally performed analytical and reporting/publishing functions.The introduction of this new technology changed significantly the essential qualificationsand experience requirements on the part of both survey designers and nationalexperts/users, thus significantly affecting the training and skill upgrading requirements.

Primary fishery data (raw data obtained directly from beaches during catch/effort andframe surveys), are best organized and processed by means of well-defined databases andcomputer procedures that make maximum use of database concepts and techniques. Thisapproach constitutes the cornerstone of medium/large-scale statistical programmes sinceit offers a number of major advantages such as:

Flexibility in increasing/decreasing the system scope in terms of species, gears,beaches and stratification schemes;

Controlled data inputting with automatic validation of data and performance ofroutine operations, thus drastically reducing the risks of manual errors;

( ) Automatic estimation of effort and catch;

(iv) Flexibility in obtaining a wide variety of reports from a single set of collectedfishery data.

5.2 Electronic spreadsheets and other software

Electronic spreadsheets and other types of applications software are best suited forsecondary fishery data (primary data that have already been computer-processed).Secondary data are usually of low volume and are used for a variety of purposes such aspreparation of technical documents and summary reports, tabulations and graphicalpresentations. Most of current software products allow easy transfer of summarizedprimary data directly from source databases into electronic spreadsheets and documents,thus speeding up the preparation and improving the quality of the final system outputs.

The present document is an example of integrating primary data from databases (alltables with sample data were directly "imported" from a single database), with text (usinga wordprocessor) and graphics (Figure 2.1 was prepared by a graphics package).


6. IMPLEMENTATION ASPECTS

The self-sustaining nature of a statistical system has always been and continues to be aprincipal issue, during both survey design and implementation. In this respect experienceshows that the results obtained were sometimes below expectation, either because ofmethodological drawbacks (high degree of complexity and/or lack of adequate trainingof national experts), or because of operational constraints (lack of sufficient national staffand/or operational funds). The introduction of computers increased the computationalcapability of national administrations for the editing and storage of the collectedinfoi ¡nation but, on the other hand, also increased the training needs since technicalexpertise in statistical aspects had to be supplemented with similar skills relating tocomputer-based concepts and techniques.

Past experience has shown that fishery statistical surveys are best implemented within thecontext of a Fishery Statistical Programme with the objective of integrating variousspecific Surveys and studies into a single fishery information system. In almost all casesthis is a lengthy and costly process that should be undertaken in a stepwise mannerinvolving the following major activities:

Identification of long-term staff requirements for data collection, supervision andstorage/processing of basic fishery data and the dissemination of fisheryinformation and statistics to national, regional and international users;

Identification of short-term staff requirements for the implementation of pilotfishery surveys, later to be expanded at national level;

Identification of computing needs and operational means (transportation,recording materials, etc.) for the pilot surveys;

Training of national staff at various levels (data collectors, supervisors, computeroperators, statisticians, researchers, etc.), in basic aspects of applied fisherystatistics and computing;

Implementation of pilot surveys with built-in data processing functions;

Evaluation of pilot surveys and revision (where and when appropriate) of theirmethodological., operational and computational functions;

Stepwise expansion of pilot surveys to other fishery sectors and regions. Trainingof new staff and acquisition of additional computing and operational resources.

For medium/large-scale fisheries the implementation of such a modular approach wouldrequire from 3 to 5 years, depending on the variety, size and complexity of the artisanalfisheries. To be noted that there are no international standards for the implementationof fishery surveys for artisanal fisheries and even within the same country certain fisherysectors may justify the application of different survey approaches in both methodologicaland operational terms, as already discussed in the preceding sections dealing withalternative statistical scenarios.


CCi 1TER-GENECATCFI AN EFFOR I LkIA

l'r-X I


III A TED r1-7 ATED

Table 1.1

Simulated catch and fishing effort data over a periodof 31 fishing days covering 10 landing sites

and two types of fishing gear

SAMPLENO.

DAY FISHINGUNIT

BEACH GEAR EFFORT(SETS/HAULS)

TOTALCATCH(KG)

1 1 FU-47 Begadi Gillnet 2 562 1 FU-50 Begadi Gillnet 2 383 1 FU-51 Begadi Gil1net 4 140

L.1 FU-45 Chiswa Beach seine 1 135

5 1 FU-46 Chiswa Beach seine 1 2716 1 FU-41 Chiswa Gillnet 1 127 1 FU-42 Chiswa Gillnet 5 408 1 FU-66 Ganza Gillnet 1 119 1 FU-67 Ganza Gillnet 1 10

10 1 FU-34 Kamuzi Beach seine 1 9311 1 FU-28 Kamuzi Gillnet 4 5212 1 FU-29 Kamuzi Gillnet 6 7213 1 FU-39 Mbema Beach seine 1 11814 1 FU-58 Membwe Gillnet 3 5115 1 FU-61 Membwe Gillnet 4 8016 1 FU-62 Membwe Gillnet 2 4617 1 FU-5 Ngoa Beach seine 2 20418 1 FU-6 Ngoa Beach seine 2 21219 1 FU-2 Ngoa Gillnet 1 7

20 1 FU-3 Ngoa Gillnet 1 721 1 FU-4 Ngoa Gillnet 2 622 1 FU-11 Salimi Gil1net 3 1223 1 FU-12 Salimi Gillnet 2 1024 1 FU-15 Salimi Gillnet 2 6

25 1 FU-54 Upiri Gillnet 2 3226 1 FU-55 Upiri Gillnet 4 8427 2 FU-49 Begadi Gillnet 4 13628 2 FU-51 Begadi Gillnet 3 4529 2 FU-69 Ganza Beach seine 2 12630 2 FU-65 Ganza Gillnet 2 2031 2 FU-28 Kamuzi Gillnet 4 4832 2 FU-24 Kopodoma Gil1net 3 1833 2 FU-38 Mbema Beach seine 1 14034 2 FU-36 Mbema Gillnet 7 5635 2 FU-37 Mbema Gillnet 5 4536 2 FU-64 Membwe Beach seine 1 12937 2 FU-61 Membwe Gil1net 4 8838 2 FU-62 Membwe Gillnet 2 2839 2 FU-8 Ngoa Beach seine 1 12640 2 FU-1 Ngoa Gil1net 3 1841 2 FU-20 Salimi Beach seine 2 9442 2 FU-13 Salimi Gillnet 2 14


SAMPLE DAY FU BEACH GEAR EFFORT CATCH

43 2 FU-16 Salimi Gillnet 5 4044 2 FU-9 Salimi Gillnet 4 2045 2 FU-56 Upiri Beach seine 1 10546 2 FU-57 Upiri Beach seine 1 18647 2 FU-54 Upiri Gillnet 2 3048 3 FU-47 Begadi Gillnet 3 6649 3 FU-48 Begadi Gillnet 3 6650 3 FU-49 Begadi Gillnet 4 6051 3 FU-46 Chiswa Beach seine 1 18652 3 FU-41 Chiswa Gillnet 2 2453 3 FU-43 Chiswa Gil1net 4 3654 3 FU-44 Chiswa Gillnet 3 2755 3 FU-66 Ganza Gillnet 1 1156 3 FU-67 Ganza Gillnet 1 17

57 3 FU-68 Ganza Gillnet 2 3458 3 FU-34 Kamuzi Beach seine 1 13059 3 FU-35 Kamuzi Beach seine 2 25260 3 FU-29 Kamuzi Gillnet 4 4061 3 FU-32 Kamuzi Gillnet 4 4462 3 FU-24 Kopodoma Gillnet 4 9263 3 FU-40 Mbema Beach seine 1 10564 3 FU-36 Mbema Gillnet 6 3665 3 FU-37 Mbema Gillnet 5 3566 3 FU-63 Membwe Beach seine 1 13567 3 FU-58 Membwe Gillnet 5 6568 3 FU-60 Membwe Gillnet 5 5569 3 FU-3 Ngoa Gil1net 1 7

70 3 FU-4 Ngoa Gillnet 3 1271 3 FU-19 Salimi Beach seine 2 10672 3 FU-22 Salimi Beach seine 2 13873 3 FU-12 Salimi Gillnet 3 1574 3 FU-14 Salimi Gillnet 5 1075 3 FU-56 Upiri Beach seine 1 24176 3 FU-57 Upiri Beach seine 1 20477 3 FU-54 Upiri Gillnet 3 4878 4 FU-47 Begadi Gillnet 2 3279 4 FU-49 Begadi Gillnet 3 4580 4 FU-50 Begadi Gillnet 2 6681 4 FU-51 Begadi Gillnet 3 11782 4 FU-52 Begadi Gillnet 4 6883 4 FU-69 Ganza Beach seine 1 91

84 4 FU-66 Ganza Gillnet 1 1385 4 FU-67 Ganza Gillnet 1 1286 4 FU-68 Ganza Gillnet 2 3087 4 FU-33 Kamuzi Beach seine 2 26288 4 FU-34 Kamuzi Beach seine 2 24889 4 FU-29 Kamuzi Gillnet 7 7090 4 FU-31 Kamuzi Gillnet 3 4291 4 FU-63 Membwe Beach seine 1 101

92 4 FU-58 Membwe Gillnet 5 14593 4 FU-59 Membwe Gi1lnet 3 57



SAMPLE DAY FU BEACH GEARt

EFFORT CATCH

94 4 FU-5 Ngoa Beach seine 2 25895 4 FU-7 Ngoa Beach seine 2 32496 4 FU-22 Salimi Beach seine 2 18497 4 FU-13 Salimi Gillnet 2 298 4 FU-15 Salimi Gillnet 3 399 4 FU-56 Upiri Beach seine 1 245

100 4 FU-57 Upiri Beach seine 1 75101 4 FU-54 Upiri Gillnet 3 60102 5 FU-51 Begadi Gillnet 4 108103 5 FU-45 Chiswa Beach seine 1 249104 5 FU-46 Chiswa Beach seine 1 194105 5 FU-41 Chiswa GAllnet 1 13106 5 FU-43 Chiswa Gillnet 5 95107 5 FU-44 Chiswa Gil1net 2 16108 5 FU-65 Ganza Gillnet 2 26109 5 FU-66 Ganza Gillnet 1 15110 5 FU-67 Ganza Gillnet 1 11111 5 FU-28 Kamuzi Gillnet 5 45112 5 FU-32 Kamuzi Gillnet 5 45113 5 FU-23 Kopodoma Gil1net 2 20114 5 FU-40 Mbema Beach seine 1 133115 5 FU-64 Membwe Beach seine 1 143116 5 FU-58 Membwe Gillnet 5 115117 5 FU-7 Ngoa Beach seine 2 308118 5 FU-20 Salimi Beach seine 2 180119 5 FU-10 Salimi Gillnet 3 3

120 5 FU-11 Salimi Gillnet 4 28121 5 FU-13 Salimi Gillnet 1 5

122 5 FU-9 Salimi Gillnet 4 28123 5 FU-56 Upiri Beach seine 1 225124 5 FU-55 Upiri Gillnet 5 90125 6 FU-48 Begadi Gillnet 3 78126 6 FU-50 Begadi Gilinet 2 32127 6 FU-45 Chiswa Beach seine 1 75128 6 FU-46 Chiswa Beach seine 1 251129 6 FU-43 Chiswa Gillnet 3 33130 6 FU-65 Ganza Gillnet 2 28131 6 FU-67 Ganza Gillnet 1 17132 6 FU-28 Kamuzi Gillnet 4 44133 6 FU-30 Kamuzi Gillnet 2 24134 6 FU-32 Kamuzi Gillnet 8 88135 6 FU-24 Kopodoma Gillnet 4 108136 6 FU-39 Mbema Beach seine 1 133137 6 FU-36 Mbema Gillnet 6 36138 6 FU-63 Membwe Beach seine 1 77139 6 FU-60 Membwe Gillnet 3 87140 6 FU-61 Membwe Gillnet 3 54141 6 FU-6 Ngoa Beach seine 1 107142 6 FU-8 Ngoa Beach seine 2 206143 6 FU-10 Salimi Gillnet 5 40144 6 FU-11 Salimi Gillnet 4 8


SAMPLE DAY FU BEACH GEAR EFFORT CATCH I

145 6 FU-15 Sa1imi Gillnet 1 6146 6 FU-16 Salimi Gillnet 2 10147 6 FU-9 Salimi Gillnet 5 35148 6 FU-56 Upiri Beach seine 1 181149 6 FU-57 Upiri Beach seine 1 235150 6 FU-54 Upiri Gillnet 2 38151 6 FU-55 Upiri Gillnet 6 48152 7 FU-51 Begadi Gillnet 3 42153 7 FU-46 Chiswa Beach seine 1 290154 7 FU-41 Chiswa Gil1net 1 17155 7 FU-42 Chiswa Gillnet 3 57156 7 FU-44 Chiswa Gillnet 3 48157 7 FU-69 Ganza Beach seine 1 119158 7 FU-66 Ganza Gillnet 1 12159 7 FU-67 Ganza Gillnet 1 19160 7 FU-68 Ganza Gillnet 2 36161 7 FU-33 Kamuzi Beach seine 2 180162 7 FU-35 Kamuzi Beach seine 1 106163 7 FU-31 Kamuzi Gi1lnet 5 70164 7 FU-25 Kopodoma Beach seine 1 155165 7 FU-27 Kopodoma Beach seine 1 62166 7 FU-24 Kopodoma Gillnet 3 81167 7 FU-38 Mbema Beach seine 1 106168 7 FU-39 Mbema Beach seine 1 98169 7 FU-40 Mbema Beach seine 1 138170 7 FU-37 Mbema Gillnet 6 24171 7 FU-59 Membwe Gillnet 3 84172 7 FU-60 Membwe Gillnet 4 88173 7 FU-61 Membwe Gillnet 3 81174 7 FU-62 Membwe Gillnet 2 56175 7 FU-5 Ngoa Be'ach seine 1 50176 7 FU-7 Ngoa Beach seine 1 39177 7 FU-1 Ngoa Gillnet 2 4178 7 FU-2 Ngoa Gillnet 1 4179 7 FU-11 Salimi Gillnet 5 25180 7 FU-15 Salimi Gillnet 2 2181 7 FU-54 Upiri Gillnet 2 10182 7 FU-55 Upiri Gillnet 6 132183 8 FU-48 Begadi Gillnet 2 46184 8 FU-49 Begadi Gillnet 4 144185 8 FU-50 Begadi Gillnet 2 26186 8 FU-51 Begadi Gillnet 4 88187 8 FU-52 Begadi Gillnet 4 88188 8 FU-46 Chiswa Beach seine 1 239189 8 FU-41 Chiswa Gillnet 1 17190 8 FU-42 Chiswa Gillnet 4 72191 8 FU-43 Chiswa Gil1net 5 50192 8 FU-66 Ganza Gillnet 1 10

193 8 FU-67 Ganza Gillnet 1 12

194 8 FU-33 Kamuzi Beach seine 1 149

195 8 FU-32 Kamuzi Gillnet 7 98196 8 FU-25 Kopodoma Beach seine 1 57


197 8 FU-26 Kopodoma Beach seine 1 46198 8 FU-24 Kopodoma Gillnet 4 44199 8 FU-40 Mbema Beach seine 1 120200 8 FU-37 Mbema Gillnet 6 72201 8 FU-58 Membwe Gillnet 4 92202 8 FU-60 Membwe Gillnet 3 63203 8 FU-61 Membwe Gillnet 3 69204 8 FU-7 Ngoa Beach seine 1 149205 8 FU-8 Ngoa Beach seine 1 81206 8 FU-3 Ngoa Gillnet 1 6

20? FU-4 Ngoa Gillnet 2 820'8 8 FU-17 Salimi Beach seine 2 154209 8 FU-19 Salimi Beach seine 2 194210 8 FU-20 Salimi Beach seine 2 160211 8 FU-22 Salimi Beach seine 2 108212 8 FU-12 Salimi Gillnet 5 35213 8 FU-16 Salimi Gillnet 3 15214 8 FU-9 Salimi Gillnet 5 45215 8 FU-57 Upiri Beach seine 1 169216 8 FU-54 Upiri Gillnet 3 48217 8 FU-55 Upiri Gillnet 6 132218 9 FU-46 Chiswa Beach seine 1 148219 9 FU-69 Ganza Beach seine 1 171220 9 FU-67 Ganza Gillnet 1 14221 9 FU-68 Ganza Gillnet 2 38222 9 FU-33 Kamuzi Beach seine 2 196223 9 FU-29 Kamuzi Gillnet 6 72224 9 FU-30 Kamuzi Gi1lnet 3 39225 9 FU-31 Kamuzi Gillnet 4 52226 9 FU-24 Kopodoma Gillnet 4 40227 9 FU-63 Membwe Beach seine 1 70228 9 FU-64 Membwe Beach seine 1 116

229 9 FU-62 Membwe Gillnet 2 60

230 9 FU-7 Ngoa Beach seine 2 306231 9 FU-21 Salimi Beach seine 2 88232 9 FU-14 Salimi Gillnet 5 30233 9 FU-16 Salimi Gillnet 2 8234 9 FU-56 Upiri Beach seine 1 118235 10 FU-53 Begadi Beach seine 1 170236 10 FU-49 Begadi Gillnet 4 120237 10 FU-50 Begadi Gillnet 2 50238 10 FU-52 Begadi Gillnet 4 156239 10 FU-42 Chiswa Gillnet 3 24240 10 FU-43 Chiswa Gillnet 4 32241 10 FU-65 Ganza Gi1lnet 2 20242 10 FU-67 Ganza Gillnet 1 16243 10 FU-68 Ganza Gillnet 2 34244 10 FU-33 Kamuzi Beach seine 2 276245 10 FU-28 Kamuzi Gi1lnet 4 44246 10 FU-31 Kamuzi Gillnet 5 45247 10 FU-24 Kopodoma Gi1lnet 4 60



248 10 FU-39 Mbema Beach seine 1 91249 10 FU-37 Mbema Gillnet 7 56250 10 FU-63 Membwe Beach seine 1 79251 10 FU-64 Membwe Beach seine 1 159252 10 FU-58 Membwe Gillnet 5 45253 10 FU-61 Membwe Gillnet 3 42254 10 FU-4 Ngoa Gil1net 2 14255 10 FU-17 Sa1imi Beach seine 2 180256 10 FU-18 Salimi Beach seine 2 182257 10 FU-19 Salimi Beach seine 2 114258 10 FU-21 Salimi Beach seine 2 204259 10 FU-10 Salimi Gillnet 4 12260 10 FU-13 Sa1imi Gillnet 1 5

261 10 FU-14 Salimi Gillnet 2 6

262 10 FU-56 Upiri Beach seine 1 124263 10 FU-57 Upiri Beach seine 1 98264 10 FU-55 Upiri Gillnet 4 36265 11 FU-47 Begadi Gillnet 3 90266 11 FU-48 Begadi Gi1lnet 2 40267 11 FU-49 Begadi Gillnet 4 76268 11 FU-45 Chiswa Beach seine 1 108269 11 FU-46 Chiswa Beach seine 1 162270 11 FU-69 Ganza Beach seine 2 250271 11 FU-65 Ganza Gillnet 2 38272 11 FU-66 Ganza Gillnet 1 13273 11 FU-68 Ganza Gillnet 2 36274 11 FU-34 Kamuzi Beach seine 2 140275 11 FU-28 Kamuzi Gillnet 3 39276 11 FU-29 Kamuzi Gillnet 4 48277 11 FU-30 Kamuzi Gillnet 2 18278 11 FU-32 Kamuzi Gillnet 6 78279 11 FU-25 Kopodoma Beach seine 1 144280 11 FU-24 Kopodoma Gillnet 3 36281 11 FU-40 Mbema Beach seine 1 138282 11 FU-63 Membwe Beach seine 1 118283 11 FU-59 Membwe Gillnet 3 30284 11 FU-5 Ngoa Beach seine 1 104285 11 FU-3 Ngoa Gillnet 1 2

286 11 FU-18 Salimi Beach seine 2 174287 11 FU-19 Salimi Beach seine 2 124288 11 FU-10 Salimi Gillnet 4 20289 11 FU-12 Salimi Gillnet 5 15290 11 FU-14 Salimi Gillnet 4 20291 11 FU-15 Salimi Gillnet 1 6

292 11 FU-16 Salimi Gillnet 2 10293 11 FU-54 Upiri Gillnet 3 45294 11 FU-55 Upiri Gillnet 5 45295 12 FU-53 Begadi Beach seine 2 450296 12 FU-47 Begadi Gillnet 2 44297 12 FU-48 Begadi Gillnet 2 52




298 12 FU-51 Begadi Gillnet 3 87299 12 FU-42 Chiswa Gillnet 4 32300 12 FU-65 Ganza Gillnet 2 26301 12 FU-67 Ganza Gillnet 1 10302 12 FU-68 Ganza Gillnet 2 32303 12 FU-34 Kamuzi Beach seine 1 74304 12 FU-32 Kamuzi Gillnet 4 48305 12 FU-26 Kopodoma Beach seine 1 145306 12 FU-40 Mbema Beach seine 1 134307 12 FU-36 Mbema Gillnet 5 15308 12 FU-37 Mbema Gil1net 6 24309 12 FU-63 Membwe Beach seine 1 136310 12 TU-64 Membwe Beach seine 1 92311 12 FU-6 Ngoa Beach seine 2 88312 12 FU-7 Ngoa Beach seine 1 30313 12 FU-4 Ngoa Gillnet 2 12314 12 FU-17 Salimi Beach seine 2 120315 12 FU-10 Salimi Gil1net 4 24316 12 FU-11 Salimi Gillnet 3 24317 12 FU-12 Salimi Gil1net 4 8

318 12 FU-9 Salimi Gillnet 3 12319 12 FU-54 Upiri Gillnet 3 24320 13 FU-47 Begadi Gil1net 3 87321 13 FU-46 Chiswa Beach seine 1 135322 13 FU-65 Ganza Gillnet 2 32323 13 FU-66 Ganza Gi1lnet 1 15324 13 FU-67 Ganza Gillnet 1 10325 13 FU-34 Kamuzi Beach seine 1 75326 13 FU-35 Kamuzi Beach seine 2 224327 13 FU-29 Kamuzi Gillnet 6 54328 13 FU-30 Kamuzi Gillnet 4 52329 13 FU-32 Kamuzi Gillnet 6 48330 13 FU-26 Kopodoma Beach seine 1 50331 13 FU-23 Kopodoma Gil1net 2 38332 13 FU-39 Mbema Beach seine 1 123333 13 FU-36 Mbema Gilinet 6 30334 13 FU-37 Mbema Gillnet 6 60335 13 FU-63 Membwe Beach seine 1 76336 13 FU-62 Membwe Gillnet 2 42337 13 FU-8 Ngoa Beach seine 2 130338 13 FU-18 Salimi Beach seine 2 196339 13 FU-19 Salimi Beach seine 2 118340 13 FU-10 Sa1imi Gillnet 3 6341 13 FU-11 Salimi Gillnet 2 6342 13 FU-13 Salimi Gillnet 2 6343 13 FU-57 Upiri Beach seine 1 213344 13 FU-55 Upiri Gillnet 4 24345 14 FU-49 Begadi Gillnet 3 84346 14 FU-43 Chiswa Cillnet 5 60


347 14 FU-44 Chiswa Gillnet 3 24348 14 FU-69 Ganza Beach seine 2 162349 14 FU-65 Ganza Gillnet 2 24350 14 FU-68 Ganza Gillnet 2 20351 14 FU-33 Kamuzi Beach seine 2 256352 14 FU-28 Kamuzi Gillnet 4 52353 14 FU-29 Kamuzi Gillnet 5 60354 14 FU-30 Kamuzi Gillnet 3 33355 14 FU-31 Kamuzi Gillnet 3 42356 14 FU-32 Kamuzi Gillnet 8 88357 14 FU-25 Kopodoma Beach seine 1 136358 14 FU-24 Kopodoma Gillnet 4 92359 14 FU-36 Mbema Gillnet 6 18360 14 FU-64 Membwe Beach seine 1 66361 14 FU-58 Membwe Gillnet 3 57362 14 FU-59 Membwe Gil1net 3 45363 14 FU-60 Membwe Gillnet 4 68364 14 FU-62 Membwe Gillnet 2 58365 14 FU-6 Ngoa Beach seine 1 141366 14 FU-17 Salimi Beach seine 2 84367 14 FU-19 Salimi Beach seine 2 184368 14 FU-20 Salimi Beach seine 2 120369 14 FU-11 Salimi Gillnet 3 3

370 14 FU-14 Salimi Gillnet 3 24371 14 FU-16 Salimi Gillnet 6 24372 14 FU-56 Upiri Beach seine 1 198373 15 FU-53 Begadi Beach seine 2 562374 15 FU-49 Begadi Gillnet 4 64375 15 FU-51 Begadi Gillnet 4 152376 15 FU-45 Chiswa Beach seine 1 194377 15 FU-46 Chiswa Beach seine 1 80378 15 FU-42 Chiswa Gillnet 4 76379 15 FU-44 Chiswa Gillnet 4 44380 15 FU-34 Kamuzi Beach seine 1 157381 15 FU-29 Kamuzi Gillnet 6 60382 15 FU-39 Mbema Beach seine 1 141383 15 FU-36 Mbema Gillnet 5 30384 15 FU-37 Mbema Gillnet 6 24385 15 FU-58 Membwe Gillnet 5 115386 15 FU-61 Membwe Gillnet 3 60387 15 FU-1 Ngoa Gillnet 2 12388 15 FU-3 Ngoa Gillnet 1 5389 15 FU-18 Salimi Beach seine 2 78390 15 FU-22 Salimi Beach seine 2 206391 15 FU-10 Salimi Gillnet 2 16392 15 FU-12 Salimi Gillnet 6 24393 15 FU-15 Salimi Gillnet 1 3

394 15 FU-56 Upiri Beach seine 1 209395 15 FU-57 Upiri Beach seine 1 235


SAMPLE DAY FU BEACH,

GEAR EFFORT1

CATCH

396 16 FU-47 Begadi Gillnet 3 75397 16 FU-50 Begadi Gillnet 2 34398 16 FU-52 Begadi Gillnet 4 48399 16 FU-41 Chiswa Gillnet 1 14400 16 FU-42 Chiswa Gillnet 3 54401 16 FU-43 Chiswa Gillnet 5 50402 16 FU-44 Chiswa Gillnet 2 30403 16 FU-65 Ganza Gillnet 2 20404 16 FU-66 Ganza Gillnet 1 19405 16 FU-33 Kamuzi Beach seine 1 158406 16 FU-28 Kamuzi Gillnet 3 24407 16 FU-29 Kamuzi Gillnet 4 52408 16 FU-30 Kamuzi Gillnet 2 28409 16 FU-24 Kopodoma Gil1net 4 84410 16 FU-40 Mbema Beach seine 1 130411 16 FU-63 Membwe Beach seine 1 155412 16 FU-58 Membwe Gillnet 4 48413 16 FU-60 Membwe Gillnet 5 45414 16 FU-61 Membwe Gillnet 4 88415 16 FU-5 Ngoa Beach seine 2 152416 16 FU-6 Ngoa Beach seine 2 86417 16 FU-18 Salimi Beach seine 2 166418 16 FU-20 Salimi Beach seine 2 94419 16 FU-21 Salimi Beach seine 2 146420 16 FU-10 Salimi Gillnet 3 21421 16 FU-16 Salimi Gillnet 6 12422 16 FU-57 Upiri Beach seine 1 200423 17 FU-47 Begadi Gillnet 2 58424 17 FU-48 Begadi Gil1net 3 54425 17 FU-49 Begadi Gillnet 4 100426 17 FU-51 Begadi Gillnet 4 96427 17 FU-52 Begadi Gillnet 5 155428 17 FU-42 Chiswa Gillnet 4 68429 17 FU-69 Ganza Beach seine 2 388430 17 FU-30 Kamuzi Gillnet 3 30431 17 FU-23 Kopodoma Gillnet 2 28432 17 FU-24 Kopodoma Gillnet 4 20433 17 FU-36 Mbema Gillnet 7 35434 17 FU-63 Membwe Beach seine 1 94435 17 FU-64 Membwe Beach seine 1 78436 17 FU-60 Membwe Gillnet 4 44437 17 FU-61 Membwe Gillnet 4 72438 17 FU-5 Ngoa Beach seine 1 90439 17 FU-6 Ngoa Beach seine 1 154440 17 FU-3 Ngoa Gi1lnet 1 2441 17 FU-4 Ngoa Gillnet 3 9

442 17 FU-10 Salimi Gillnet 3 12443 17 FU-15 Salimi Gillnet 3 24444 17 FU-16 Salimi Gillnet 6 18



445 17 FU-56 Upiri Beach seine 1 188446 17 FU-55 Upiri Gillnet 6 66447 18 FU-47 Begadi Gi1lnet 2 74448 18 FU-51 Begadi Gillnet 4 88449 18 FU-45 Chiswa Beach seine 1 282450 18 FU-41 Chiswa Gillnet 2 24451 18 FU-44 Chiswa Gillnet 4 64452 18 FU-69 Ganza Beach seine 1 40453 18 FU-65 Ganza Gillnet 2 22454 18 FU-68 Ganza Gillnet 2 20455 18 FU-29 Kamuzi Gillnet 7 98456 18 FU-31 Kamuzi Gillnet 4 44457 18 FU-24 Kopodoma Gillnet 4 124458 18 FU-36 Mbema Gillnet 5 60459 18 FU-63 Membwe Beach seine 1 127460 18 FU-64 Membwe Beach seine 1 101461 18 FU-60 Membwe Gillnet 5 145462 18 FU-61 Membwe Gillnet 3 36463 18 FU-5 Ngoa Beach seine 1 109464 18 FU-1 Ngoa Gillnet 2 14465 18 FU-2 Ngoa Gillnet 1 6

466 18 FU-17 Salimi Beach seine 2 174467 18 FU-19 Salimi Beach seine 2 136468 18 FU-21 Salimi Beach seine 2 172469 18 FU-11 Salimi Gillnet 5 10470 18 FU-13 Salimi Gillnet 2 16471 18 FU-14 Salimi Gillnet 2 18472 18 FU-16 Salimi Gillnet 5 30473 18 FU-9 Salimi Gil1net 5 5474 18 FU-56 Upiri Beach seine 1 143475 18 FU-57 Upiri Beach seine 1 151476 19 FU-47 Begadi Gillnet 2 62477 19 FU-49 Begadi Gillnet 3 96478 19 FU-45 Chiswa Beach seine 1 205479 19 FU-43 Chiswa Gillnet 4 64480 19 FU-65 Ganza Gillnet 2 32481 19 FU-28 Kamuzi Gillnet 4 32482 19 FU-31 Kamuzi Gillnet 5 60483 19 FU-25 Kopodoma Beach seine 1 83484 19 FU-37 Mbema Gillnet 6 36485 19 FU-64 Membwe Beach seine 1 91486 19 FU-61 Membwe Gillnet 4 72487 19 FU-62 Membwe Gillnet 2 62488 19 FU-7 Ngoa Beach seine 1 118489 19 FU-8 Ngoa Beach seine 2 332490 19 FU-3 Ngoa Gil1net 1 6

491 19 FU-17 Salimi Beach seine 2 200492 19 FU-21 Salimi Beach seine 2 104



493 19 FU-22 Salimi Beach seine 2 146494 19 FU-11 Salimi Gillnet 5 45495 19 FU-12 Salimi Gillnet 4 20496 19 FU-13 Salimi Gil1net 1 6

497 20 FU-45 Chiswa Beach seine 1 144498 20 FU-66 Ganza Gillnet 1 15499 20 FU-28 Kamuzi Gillnet 3 36500 20 FU-32 Kamuzi Gillnet 4 52501 20 FU-25 Kopodoma Beach seine 1 122502 20 FU-38 Mbema Beach seine 1 107503 20 FU-37 Mbema Gillnet 6 48504 20 FU-63 Membwe Beach seine 1 107505 20 FU-64 Membwe Beach seine 1 131506 20 FU-59 Membwe Gillnet 3 30507 20 FU-17 Salimi Beach seine 2 154508 20 FU-19 Salimi Beach seine 2 132509 20 FU-21 Salimi Beach seine 2 172510 20 FU-10 Salimi Gillnet 3 15511 20 FU-9 Salimi Gillnet 3 9512 20 FU-57 Upiri Beach seine 1 117513 21 FU-48 Begadi Gi1lnet 2 24514 21 FU-51 Begadi Gillnet 3 63515 21 FU-41 Chiswa Gillnet 1 19516 21 FU-65 Ganza Gillnet 2 32517 21 FU-67 Ganza Gillnet 1 12518 21 FU-28 Kamuzi Gillnet 3 30519 21 FU-31 Kamuzi Gillnet 5 60520 21 FU-36 Mbema Gillnet 6 36521 21 FU-37 Mbema Gillnet 5 55522 21 FU-59 Membwe Gillnet 4 92523 21 FU-61 Membwe Gillnet 4 88524 21 FU-62 Membwe Gillnet 2 38525 21 FU-7 Ngoa Beach seine 1 66526 21 FU-3 Ngoa Gillnet 1 4527 21 FU-4 Ngoa Gillnet 3 21528 21 FU-17 Salimi Beach seine 2 206529 21 FU-21 Salimi Beach seine 2 184530 21 FU-22 Salimi Beach seine 2 142531 21 FU-16 Salimi Gillnet 3 24532 21 FU-9 Salimi Gillnet 3 15533 21 FU-56 Upiri Beach seine 1 95534 21 FU-55 Upiri Gillnet 6 96535 22 FU-53 Begadi Beach seine 1 198536 22 FU-49 Begadi Gil1net 3 78537 22 FU-50 Begadi Gillnet 2 62538 22 FU-52 Begadi Gillnet 4 88539 22 FU-45 Chiswa Beach seine 1 156540 22 FU-43 Chiswa Gillnet 5 55



541 22 FU-67 Ganza Gillnet 1 18542 22 FU-33 Kamuzi Beach seine 2 244543 22 FU-35 Kamuzi Beach seine 1 86544 22 FU-25 Kopodoma Beach seine 1 104545 22 FU-26 Kopcdoma Beach seine 1 105546 22 FU-39 Mbema Beach seine 1 107547 22 FU-37 Mbema Gil1net 7 49548 22 FU-6 Ngoa Beach seine 2 174549 22 FU-7 Ngoa Beach seine 1 79550 22 FU-1 Ngoa Gillnet 2 12551 22 FU-3 Ngoa Gillnet 1 6552 22 FU-4 Ngoa Gillnet 3 9553 22 FU-21 Sa1imi Beach seine 2 186554 22 FU-22 Salimi Beach seine 2 210555 22 FU-12 Salimi Gillnet 6 54556 22 FU-14 Salimi Gillnet 3 3557 22 FU-15 Salimi Gillnet 1 9


559 22 FU-56 Upiri Beach seine 1 215560 22 FU-54 Upiri Gillnet 3 30561 23 FU-48 Begadi Gillnet 2 48562 23 FU-49 Begadi Gillnet 4 128563 23 FU-41 Chiswa Gillnet 1 16564 23 FU-42 Chiswa Gillnet 4 40565 23 FU-35 Kamuzi Beach seine 1 139566 23 FU-28 Kamuzi Gillnet 5 60567 23 FU-31 Kamuzi Gillnet 4 32568 23 FU-32 Kamuzi Gillnet 8 64569 23 FU-26 Kopodoma Beach seine 1 137570 23 FU-27 Kopodoma Beach seine 1 116571 23 FU-23 Kopodoma Gil1net 2 38572 23 FU-24 Kopodoma Gillnet 4 92573 23 FU-37 Mbema Gillnet 6 60574 23 FU-64 Membwe Beach seine 1 157575 23 FU-61 Membwe Gillnet 5 100576 23 FU-5 Ngoa Beach seine 2 92577 23 FU-6 Ngoa Beach seine 1 67578 23 FU-2 Ngoa Gillnet 1 5579 23 FU-4 Ngoa Gillnet 3 21580 23 FU-18 Salimi Beach seine 2 192581 23 FU-20 Salimi Beach seine 2 144582 23 FU-22 Salimi Beach seine 2 164583 23 FU-9 Salimi Gillnet 3 12584 23 FU-57 Upiri Beach seine 1 108585 23 FU-54 Upiri Gillnet 2 16586 24 FU-48 Begadi Gillnet 2 64587 24 FU-49 Begadi Gillnet 3 81588 24 FU-50 Begadi Gillnet 2 72



589 24 FU-46 Chiswa Beach seine 1 153590 24 FU-66 Ganza Gillnet 1 10591 24 FU-30 Kamuzi Gillnet 3 24592 24 FU-27 Kopodoma Beach seine 1 88593 24 FU-23 Kopodoma Gillnet 2 58594 24 FU-24 Kopodoma Gillnet 4 84595 24 FU-38 Mbema Beach seine 1 115596 24 FU-37 Mbema Gillnet 6 42597 24 FU-63 Membwe Beach seine 1 107598 24 FU-64 Membwe Beach seine 1 69

599 24 FU-59 Membwe Gillnet 3 33600 24 FU-62 Membwe Gillnet 2 28601 24 FU-7 Ngoa Beach seine 2 282602 24 FU-1 Ngoa Gillnet 3 12603 24 FU-16 Salimi Gil1net 3 3

604 24 FU-57 Upiri Beach seine 1 217605 24 FU-55 Upiri Gi1lnet 6 72606 25 FU-47 Begadi Gillnet 2 50607 25 FU-51 Begadi Gillnet 4 48608 25 FU-46 Chiswa Beach seine 1 289609 25 FU-68 Ganza Gillnet 2 38610 25 FU-35 Kamuzi Beach seine 2 302611 25 FU-29 Kamuzi Gillnet 7 56612 25 FU-30 Kamuzi Gillnet 4 56613 25 FU-27 Kopodoma Beach seine 1 129614 25 FU-23 Kopodoma Gillnet 2 38615 25 FU-60 Membwe Gi11net 4 96616 25 FU-62 Membwe Gillnet 2 62617 25 FU-7 Ngoa Beach seine 2 312618 25 FU-8 Ngoa Beach seine 1 81619 25 FU-19 Salimi Beach seine 2 154620 25 FU-20 Salimi Beach seine 2 182621 25 FU-22 Salimi Beach seine 2 208622 25 FU-11 Salimi Gillnet 4 32623 25 FU-14 Salimi Gi11net 3 9624 25 FU-16 Salimi Gil1net 3 21625 25 FU-55 Upiri Gillnet 6 120626 26 FU-53 Begadi Beach seine 1 98627 26 FU-48 Begadi Gillnet 2 40628 26 FU-49 Begadi Gil1net 3 81629 26 FU-51 Begadi Gillnet 3 69630 26 FU-52 Begadi Gil1net 4 108631 26 FU-41 Chiswa Gillnet 1 16632 26 FU-42 Chiswa Gillnet 5 80633 26 FU-69 Ganza Beach seine 1 166634 26 FU-65 Ganza Gillnet 2 24635 26 FU-29 Kamuzi Gil1net 5 45636 26 FU-31 Kamuzi Gillnet 3 33



637 26 FU-26 Kopodoma Beach seine 1 105638 26 FU-27 Kopodoma Beach seine 1 84639 26 FU-36 Mbema Gillnet 6 36640 26 FU-63 Membwe Beach seine 1 94641 26 FU-58 Membwe Gillnet 5 60642 26 FU-59 Membwe Gillnet 3 33643 26 FU-1 Ngoa Gillnet 2 10644 26 FU-2 Ngoa Gillnet 1 3645 26 FU-18 Salimi Beach seine 2 62646 26 FU-22 Salimi Beach seine 2 130647 26 FU-9 Salimi Gillnet 2 18648 26 FU-54 Upiri Gillnet 2 10649 26 FU-55 Upiri Gillnet 6 132650 27 FU-53 Begadi Beach seine 1 258651 27 FU-51 Begadi Gil1net 3 81652 27 FU-42 Chiswa Gillnet 3 33653 27 FU-43 Chiswa Gillnet 4 36654 27 FU-69 Ganza Beach seine 2 448655 27 FU-34 Kamuzi Beach seine 2 224656 27 FU-35 Kamuzi Beach seine 2 164657 27 FU-30 Kamuzi Gillnet 3 33658 27 FU-25 Kopodoma Beach seine 1 63659 27 FU-23 Kopodoma Gillnet 2 40660 27 FU-24 Kopodoma Gillnet 4 108661 27 FU-37 Mbema Gillnet 7 21662 27 FU-64 Membwe Beach seine 1 78663 27 FU-59 Membwe Gillnet 3 84664 27 FU-61 Membwe Gil1net 3 90665 27 FU-62 Membwe Gillnet 2 32666 27 FU-1 Ngoa Gillnet 3 12667 27 FU-2 Ngoa Gillnet 1 4668 27 FU-3 Ngoa Gillnet 1 5669 27 FU-4 Ngoa Gillnet 2 14670 27 FU-18 Salimi Beach seine 2 84671 27 FU-20 Salimi Beach seine 2 128672 27 FU-55 Upiri Gillnet 4 72673 28 FU-48 Begadi Gillnet 2 26674 28 FU-45 Chiswa Beach seine 1 241675 28 FU-43 Chiswa Gillnet 3 33676 28 FU-65 Ganza Gillnet 2 30677 28 FU-29 Kamuzi Gillnet 7 63678 28 FU-30 Kamuzi Gillnet 3 33679 28 FU-31 Kamuzi Gillnet 3 30680 28 FU-27 Kopodoma Beach seine 1 172681 28 FU-23 Kopodoma Gillnet 2 18682 28 FU-36 Mbema Gillnet 7 84683 28 FU-64 Membwe Beach seine 1 153684 28 FU-59 Membwe Gillnet 3 81685 28 FU-60 Membwe Gillnet 4 44



686 28 FU-7 Ngoa Beach seine 1 96687 28 FU-2 Ngoa Gillnet 1 7

688 28 FU-4 Ngoa Gillnet 2 6689 28 FU-18 Salimi Beach seine 2 78690 28 FU-10 Salimi Gillnet 3 21691 28 FU-12 Salimi Gillnet 5 25692 28 FU-16 Salimi Gillnet 2 16693 28 FU-55 Upiri Gillnet 4 88694 29 FU-50 Begadi Gillnet 2 38695 29 FU-51 Begadi Gillnet 4 76696 29 FU-52 Begadi Gillnet 5 105697 29 FU-45 Chiswa Beach seine 1 160698 29 FU-46 Chiswa Beach seine 1 107699 29 FU-42 Chiswa Gillnet 5 40700 29 FU-43 Chiswa Gillnet 4 76701 29 FU-29 Kamuzi Gillnet 5 65702 29 FU-30 Kamuzi Gillnet 4 36703 29 FU-31 Kamuzi Gillnet 5 45704 29 FU-25 Kopodoma Beach seine 1 126705 29 FU-23 Kopodoma Gillnet 2 10706 29 FU-24 Kopodoma Gillnet 3 99707 29 FU-39 Mbema Beach seine 1 97708 29 FU-64 Membwe Beach seine 1 87709 29 FU-58 Membwe Gillnet 3 51710 29 FU-5 Ngoa Beach seine 1 109711 29 FU-8 Ngoa Beach seine 2 130712 29 FU-20 Salimi Beach seine 2 80713 29 FU-21 Salimi Beach seine 2 146714 29 FU-22 Salimi Beach seine 2 72715 29 FU-12 Salimi Gillnet 3 21716 29 FU-57 Upiri Beach seine 1 168717 29 FU-54 Upiri Gillnet 2 34718 29 FU-55 Upiri Gillnet 5 60719 30 FU-47 Begadi Gillnet 2 52720 30 FU-50 Begadi Gillnet 2 62721 30 FU-52 Begadi Gillnet 5 60722 30 FU-69 Ganza Beach seine 1 201723 30 FU-65 Ganza Gillnet 2 32724 30 FU-66 Ganza Gillnet 1 13

725 30 FU-68 Ganza Gillnet 2 34726 30 FU-35 Kamuzi Beach seine 1 73727 30 FU-29 Kamuzi Gillnet 6 66728 30 FU-31 Kamuzi Gillnet 4 56729 30 FU-32 Kamuzi Gil1net 7 70730 30 FU-63 Membwe Beach seine 1 101731 30 FU-58 Membwe Gillnet 5 130732 30 FU-59 Membwe Gillnet 3 84733 30 FU-60 Membwe Gillnet 4 76


SAMPLE DAY FU BEACH GEAR_

EFFORT CATCH

734 30 FU-6 Ngoa Beach seine 2 324735 30 FU-7 Ngoa Beach seine 2 240736 30 FU-1 Ngoa Gillnet 3 9737 30 FU-19 Salimi Beach seine 2 98738 30 FU-21 Salimi Beach seine 2 136739 30 FU-15 Salimi Gillnet 1 3740 30 FU-56 Upiri Beach seine 1 234741 30 FU-57 Upiri Beach seine 1 90742 30 FU-55 Upiri Gillnet 6 72743 31 FU-53 Begadi Beach seine 2 410744 31 FU-47 Begadi Gillnet 2 34745 31 FU-48 Begadi Gillnet 3 69746 31 FU-51 Begadi Gillnet 4 48747 31 FU-52 Begadi Gillnet 4 64748 31 FU-45 Chiswa Beach seine 1 195749 31 FU-46 Chiswa Beach seine 1 102750 31 FU-41 Chiswa Gillnet 2 26751 31 FU-42 Chiswa Gillnet 3 33752 31 FU-44 Chiswa Gillnet 3 27753 31 FU-65 Ganza Gillnet 2 34754 31 FU-66 Ganza Gillnet 1 15755 31 FU-67 Ganza Gillnet 1 19756 31 FU-68 Ganza Gillnet 2 26757 31 FU-33 Kamuzi Beach seine 1 123758 31 FU-35 Kamuzi Beach seine 1 149759 31 FU-29 Kamuzi Gillnet 4 52760 31 FU-30 Kamuzi Gillnet 4 52761 31 FU-31 Kamuzi Gillnet 3 42762 31 FU-32 Kamuzi Gillnet 4 36763 31 FU-25 Kopodoma Beach seine 1 91764 31 FU-26 Kopodoma Beach seine 1 47765 31 FU-23 Kopodoma Gillnet 2 20766 31 FU-40 Mbema Beach seine 1 122767 31 FU-37 Mbema Gillnet 6 60768 31 FU-63 Membwe Beach seine 1 138769 31 FU-58 Membwe Gillnet 5 60770 31 FU-61 Membwe Gillnet 4 36771 31 FU-62 Membwe Gillnet 2 46772 31 FU-5 Ngoa Beach seine 1 109773 31 FU-6 Ngoa Beach seine 1 38774 31 FU-7 Ngoa Beach seine 2 128775 31 FU-8 Ngoa Beach seine 2 122776 31 FU-1 Ngoa Gillnet 3 12777 31 FU-2 Ngoa Gillnet 1 2778 31 FU-17 Salimi Beach seine 2 60779 31 FU-20 Salimi Beach seine 2 98




780 31 FU-22 Salimi

_

Beach seine 2 182








788 31 FU-56 Upiri Beach seine 1 197

789 31 FU-57 Upiri Beach seine 1 134

79Q 31 FU-54 Upiri Gi1lnet 3 72

791 31 FU-55 Upiri Gillnet 4 68

ANNEX IA


CENSUS FOR EFFORT

r,:nA7rinLING FOR CPUE'S

NO FP E SURVEY


Table 11.1


Sample data (20% of the total), taken from Table 1.1 in Annex Iand used for the definition of sample CPUE's

SAMPLEfDAY FISHING BEACH 1 GEAR

i

EFFORT TOTAL

626 26 FU-53 Begadi Beach seine 1 98650 27 FU-53 Begadi Beach seine 1 258

3 1 FU-51 Begadi Gillnet 4 14082 4 FU-52 Begadi Gi1lnet 4 68

183 8 FU-48 Begadi Gillnet 2 46184 8 FU-49 Begadi Gi1lnet 4 144187 8 FU-52 Begadi Gillnet 4 88267 11 FU-49 Begadi Gillnet 4 76297 12 FU-48 Begadi Gillnet 2 52345 14 FU-49 Begadi Gillnet 3 84562 23 FU-49 Begadi Gillnet 4 128586 24 FU-48 Begadi Gillnet 2 64606 25 FU-47 Begadi Gillnet 2 50607 25 FU-51 Begadi Gillnet 4 48628 26 FU-49 Begadi Gillnet 3 81629 26 FU-51 Begadi Gillnet 3 69721 30 FU-52 Begadi Gillnet 5 60

4 1 FU-45 Chiswa Beach seine 1 135188 8 FU-46 Chiswa Beach seine 1 239218 9 FU-46 Chiswa Beach seine 1 148589 24 FU-46 Chiswa Beach seine 1 153674 28 FU-45 Chiswa Beach seine 1 241

52 3 FU-41 Chiswa Gi1lnet 2 24154 7 FU-41 Chiswa Gillnet 1 17191 8 FU-43 Chiswa Gi11net 5 50240 10 FU-43 Chiswa Gi1lnet 4 32378 15 FU-42 Chiswa Gillnet 4 76402 16 FU-44 Chiswa Gillnet 2 30479 19 FU-43 Chiswa Gillnet 4 64652 27 FU-42 Chiswa Gillnet 3 33270 11 FU-69 Ganza Beach seine 2 250348 14 FU-69 Ganza Beach seine 2 162

30 2 FU-65 Ganze Gillnet 2 2084 4 FU-66 Ganze Gillnet 1 13

110 5 FU-67 Ganz6 Gillnet 1 11192 8 FU-66 Ganza Gillnet 1 10220 9 FU-67 Ganza Gillnet 1 14221 9 FU-68 Ganza Gillnet 2 38302 12 FU-68 Ganza Gillnet 2 32349 14 FU-65 Ganza Gillnet 2 24498 20 FU-66 Ganza Gillnet 1 15

590 24 FU-66 Ganza Gil1net 1 10724 30 FU-66 Ganza Gillnet 1 13

87 4 FU-33 Kamuzi Beach seine 2 262325 13 FU-34 Kamuzi Beach seine 1 75


SAMPLE DAY FISHING BEACH GEAR EFFORT TOTAL

655 27 FU-34 Kamuzi Beach seine 2 224656 27 FU-35 Kamuzi Beach seine 2 164726 30 FU-35 Kamuzi Beach seine 1 7361 3 FU-32 Kamuzi Gillnet 4 4489 4 FU-29 Kamuzi Gillnet 7 7090 4 FU-31 Kamuzi Gillnet 3 42

112 5 FU-32 Kamuzi Gillnet 5 45132 6 FU-28 Kamuzi Gillnet 4 44133 6 FU-30 Kamuzi Gillnet 2 24163 7 FU-31 Kamuzi Gillnet 5 70328 13 FU-30 Kamuzi Gillnet 4 52407 16 FU-29 Kamuzi Gillnet 4 52408 16 FU-30 Kamuzi Gillnet 2 28591 24 FU-30 Kamuzi Gillnet 3 24678 28 FU-30 Kamuzi Gillnet 3 33727 30 FU-29 Kamuzi Gillnet 6 66357 14 FU-25 Kopodoma Beach seine 1 136501 20 FU-25 Kopodoma Beach seine 1 122613 25 FU-27 Kopodoma Beach seine 1 129763 31 FU-25 Kopodoma Beach seine 1 91113 5 FU-23 Kopodoma Gillnet 2 20331 13 FU-23 Kopodoma Gillnet 2 38358 14 FU-24 Kopodoma Gillnet 4 92431 17 FU-23 Kopodoma Gillnet 2 28571 23 FU-23 Kopodoma Gillnet 2 38

13 1 FU-39 Mbema Beach seine 1 118136 6 FU-39 Mbema Beach seine 1 133248 10 FU-39 Mbema Beach seine 1 91410 16 FU-40 Mbema Beach seine 1 130

65 3 FU-37 Mbema Gillnet 5 35249 10 FU-37 Mbema Gillnet 7 56307 12 FU-36 Mbema Gillnet 5 15

334 13 FU-37 Mbema Gillnet 6 60384 15 FU-37 Mbema Gillnet 6 24227 9 FU-63 Membwe Beach seine 1 70251 10 FU-64 Membwe Beach seine 1 159335 13 FU-63 Membwe Beach seine 1 76360 14 FU-64 Membwe Beach seine 1 66411 16 FU-63 Membwe Beach seine 1 155662 27 FU-64 Membwe Beach seine 1 78

15 1 FU-61 Membwe Gillnet 4 8068 3 FU-60 Membwe Gillnet 5 55

116 5 FU-58 Membwe Gillnet 5 115171 7 FU-59 Membwe Gillnet 3 84201 8 FU-58 Membwe Gi1lnet 4 92361 14 FU-58 Membwe Gillnet 3 57364 14 FU-62 Membwe Gillnet 2 58461 18 FU-60 Membwe Gillnet 5 145524 21 FU-62 Membwe Gillnet 2 38616 25 FU-62 Membwe Gillnet 2 62


SAMPLE DAY FISHING BEACH GEAR EFFORT TOTAL

642 26 FU-59 Membwe Gillnet 3 33665 27 FU-62 Membwe Gillnet 2 32

17 1 FU-5 Ngoa Beach seine 2 20495 4 FU-7 Ngoa Beach seine 2 324

337 13 FU-8 Ngoa Beach seine 2 130439 17 FU-6 Ngoa Beach seine 1 154601 24 FU-7 Ngoa Beach seine 2 282710 29 FU-5 Ngoa Beach seine 1 109711 29 FU-8 Ngoa Beach seine 2 130772 31 FU-5 Ngoa Beach seine 1 109

70 3 FU-4 Ngoa Gillnet 3 12177 7 FU-1 Ngoa Gillnet 2 4285 11 FU-3 Ngoa Gillnet 1 2464 18 FU-1 Ngoa Gillnet 2 14465 18 FU-2 Ngoa Gillnet 1 6

641 26 FU-1 Ngoa Gillnet 2 10668 27 'FU-3 Ngoa Gillnet 1 5118 5 FU-20 Salimi Beach seine 2 180255 10 FU-17 Sa1imi Beach seine 2 180256 10 FU-18 Salimi Beach seine 2 182258 10 FU-21 Sa1imi Beach seine 2 204287 11 FU-19 Salimi Beach seine 2 124367 14 FU-19 Salimi Beach seine 2 184418 16 FU-20 Salimi Beach seine 2 94466 18 FU-17 Salimi Beach seine 2 174507 20 FU-17 Sa1imi Beach seine 2 154529 21 FU-21 Salimi Beach seine 2 184621 25 FU-22 Salimi Beach seine 2 208689 28 FU-18 Salimi Beach seine 2 78

22 I FU-11 Sa1imi Gillnet 3 1242 2 FU-13 Salimi Gillnet 2 1497 4 FU-13 Salimi Gillnet 2 2

145 6 FU-15 Salimi Gillnet 1 6147 6 FU-9 Salimi Gillnet 5 35180 7 FU-15 Salimi Gillnet 2 2259 10 FU-10 Salimi Gillnet 4 12260 10 FU-13 Salimi Gillnet 1 5261 10 FU-14 Salimi Gillnet 2 6288 11 FU-10 Sa1imi Gillnet 4 20291 11 FU-15 Salimi Gillnet 1 6421 16 FU-16 Salimi Gillnet 6 12495 19 FU-12 Salimi Gillnet 4 20511 20 FU-9 Salimi Gillnet 3 9

558 22 FU-9 Sa1imi Gillnet 5 5

692 28 FU-16 Salimi Gillnet 2 16781 31 FU-10 Salimi Gillnet 4 2846 2 FU-57 Upiri Beach seine 1 18675 3 FU-56 Upiri Beach seine I 24199 4 FU-56 Upiri Beach seine I 245

343 13 FU-57 Upiri Beach seine 1 213445 17 FU-56 .Upiri Beach seine 1 188


SAMPLE DAY FISHING BEACH GEAR EFFORT TOTAL.

512 20 FU-57 Upiri B,:ach seine 1 11726 1 FU-55 Upiri Gillnet 4 84

181 7 FU-54 Upiri Gillnet 2 10293 11 FU-54 Upiri GIllnet 3 45672 27 FU-55 Upiri Gillnet 4 72717 29 FU-54 Upiri Gillnet 2 34718 29 FU-55 Upiri Gillnet 5 60


Table 11.2


Data from census and estimated productionby beach and by gear.

Sample data taken from Table 11.1

Table 11.3


Total production by gear based on data of Table 11.2

BEACH GEAR EFF-

ORT

GEN-

SUS

CATCHIN KG

CENSUS

CPUE

CENSUS

CPUEFROM

SAMPLEDDATA

CATCHIN KG

ESTIMATE

COEFF.OF

VARIA-TION

(1) (2) (2)/(1) (3) (1)X(3) (%)

Begadi. Beach seine 10 2146.0 214.60 178.00 1780.00 37.98Begadi Gillnet 228 5448.0 23.89 23.96 5462.88 7.25Chiswa Beach seine 26 4751.0 182.73 183.20 4763.20 11.47Chiswa Gillnet 139 1747.0 12.57 13.04 1812.56 9.44Ganza Beach seine 16 2162.0 135.13 103.00 1648.00 19.32Ganza Gillnet 83 1187.0 14.30 13.33 1106.39 5.83

Kamuzi Beach seine 39 4480.0 114.87 99.75 3890.25 10.60Kamuzi Gillnet 302 3349.0 11.09 11.42 3448.84 4.25Kopodoma Beach seine 23 2367.0 102.91 119.50 2748.50 7.55Kopodoma Gillnet 80 1490.0 18.63 18.00 1440.00 11.55Mbema Beach seine 20 2396.0 119.80 118.00 2360.00 7.25Mbema Gillnet 168 1183.0 7.04 6.55 1100.40 17.85Membwe Beach seine 31 3365.0 108.55 100.67 3120.77 15.97Membwe Gi1lnet 214 4112.0 19.21 21.28 4553.92 8.35Ngoa Beach seine 63 6353.0 100.84 110.92 6987.96 10.66Ngoa Gillnet 70 335.0 4.79 4.42 309.40 14.75Salimi Beach seine 120 8612.0 71.77 81.08 9729.60 6.66

Salimi Gillnet 278 1347.0 4.85 4.12 1145.36 12.29Upiri Beach seine 31 5318.0 171.55 198.33 6148.23 8.70Upiri Gillnet 128 1934.0 15.11 15.25 1952.00 13.62

GEAR EFFORT CATC1I CPUE SAMPLE TOTALS CVFROM FROM FROM CPUE

CENSLJS CENSUS CENSUS (FROM (96)TABLE

(1) (2) (2)/(1) (3) E2)

Beach seine 379 41950 111169 113.92 43176 11.02%

Gillnet 1690 22132 13.10 13.21 22331 8.83%


Exercise 11.1

The purpose of this exercise is to manually verify the results shown in Table 112. Usewill be made of the sample data illustrated in Table ILL For instance, the first two linesof Table 111 provide the data for the definition of the sample CPUE for beach seine inlanding site Begadi.

Sample CPUE for beach seine in Begadi (98 + 258)/(1+ 1)=178 kg/haul.

This figure is then multiplied by the known total effort in hauls which is 10, thusresulting in an estimated total catch of 1780 kg for this beach and this type of fishinggear.

A similar approach is to be used for each beach and gear type in order to re-constructthe results given in Table 11.2.

Table 11.4

STATISTICAL SCENARIO A - Exercise 11.1

Manual verification of results given in Table 11.2

BEACH GEAR EFF-

ORT

GEN-SUS

CATCHIN KG

CENSUS

CPUE

CENSUS

CPUEFROM

SAMPLEDDATA

CATCHIN KG

ESTIMATE

COEFF.

OFVARIA-TION

(1) (2) (2)/(1) (3) (1)X(3) (%)

Begadi Beach seine 10 2146.0 214.60 37.98Begadi Gillnet 228 5448.0 23.89 7.25Chiswa Beach seine 26 4751.0 182.73 11.47Chiswa Gillnet 139 1747.0 12.57 9.44Ganza Beach seine 16 2162.0 135.13 19.32Ganza Gillnet 83 1187.0 14.30 5.83Kamuzi Beach seine 39 4480.0 114.87 10.60Kamuzi Gillnet 302 3349.0 11.09 4.25Kopodoma Beach seine 23 2367.0 102.91 7.55Kopodoma Gillnet 80 1490.0 18.63 11.55Mbema Beach seine 20 2396.0 119.80 7:25Mbema Gillnet 168 1183.0 7.04 17.85Membwe Beach seine 31 3365.0 108.55 15.97Membwe Gillnet 214 4112.0 19.21 8.35Ngoa Beach seine 63 6353.0 100.84 10.66Ngoa Gillnet 70 335.0 4.79 14.75Salimi Beach seine 120 8612.0 71.77 6.66Salimi Gillnet 278 1347.0 4.85 12.29Upiri Beach seine 31 5318.0 171.55 8.70Upiri Gillnet 128 1934.0 15.11 13.62


Exercise 11.2

The purpose of this exercise is to manually verify the results shown in Table 11.3. Usewill be made of the results illustrated in Table 11.2.

First step involves the summing-up of all estimated catches for beach seine, to be placedin the appropriate row in Table 11.5 under TOTALS. This will be followed by a similaroperation for gillnet.

The final step involves the derivation of two overall CPUE's for the two gear types andfor the entire region of the ten beaches. These are formulated by dividing the totalestimated catch by the total effort for each gear type.

DISCUSSION: Why this method is the most accurate when enough samples are takenfrom all beaches. Alternative approaches with small samples. The CPUE as the onlysource of variability. Coefficient of variation. Why this exercise assumes a stratifiedpopulation. About statification techniques in general.

Table 11.5

STATISTICAL SCENARIO A - Exercise 11.2

Manual verification of the results shown in Table 11.3

GEAR EPPORT CATO 1 CPLTE SAMPLE TOTALS cvFROM FROM FROM CPLJE

CENSUS CENSUS CENSUS (FROM (%)TABLE

(1) (2) (2)/(1) (3) 112)

Beach seine 379 41950 110.69 11.02%

Gillnet 1690 17132 13.10 8.83%


AN EX III

STATISTICAL SCENARIO B

CENSUS IN SPACE FOR EFFORT

SAMPLING IN TIME FOR EFFORT

LANG IN SPACE/TIME FOR CPUE

NO FRAT 3URVEY


Table 111.1


Sample data for 5 days, taken from Table 1.1 in Annex 1and used for the estimation of effort and total catch.

SAMPLENO.

DAY FISHINGUNIT

BEACH GEAR EFFORT(SETS/HAULS)

TOTALCATCH(KG)

102 5 FU-51 Begadi Gillnet 4 108103 5 FU-45 Chiswa Beach seine 1 249104 5 FU-46 Chiswa Beach seine 1 194105 5 FU-41 Chiswa Gillnet 1 13

106 5 FU-43 Chiswa Gillnet 5 95107 5' FU-44 Chiswa Gillnet 2 16108 5 FU-65 Ganza Gillnet 2 26

109 5 FU-66 Ganza Gi1lnet 1 15

110 5 FU-67 Ganza Gillnet 1 11111 5 FU-28 Kamuzi Gillnet 5 45

112 5 FU-32 Kamuzi Gillnet 5 45113 5 FU-23 Kopodoma Gillnet 2 20114 5 FU-40 Mbema Beach seine 1 133115 5 FU-64 Membwe Beach seine 1 143


117 5 FU-7 Ngoa Beach seine 2 308118 5 FU-20 Sa1imi Beach seine 2 180119 5 FU-10 Salimi Gillnet 3 3

120 5 FU-11 Salimi Gil1net 4 28



123 5 FU-56 Upiri Beach seine 1 225124 5 FU-55 Upiri Gillnet 5 90

235 10 FU-53 Begadi Beach seine 1 170236 10 FU-49 Begadi Gillnet 4 120237 10 FU-50 Begadi Gillnet 2 50

238 10 FU-52 Begadi Gillnet 4 156

239 10 FU-42 Chiswa Gillnet 3 24

240 10 FU-43 Chiswa Gillnet 4 32



243 10 FU-68 Ganza Gillnet 2 34244 10 FU-33 Kamuzi Beach seine 2 276245 10 FU-28 Kamuzi Gillnet 4 44

246 10 FU-31 Kamuzi Gi1lnet 5 45247 10 FU-24 Kopodoma Gillnet 4 60

248 10 FU-39 Mbema Beach seine 1 91

249 10 FU-37 Mbema Gillnet 7 56

250 10 FU-63 Membwe Beach seine 1 79

251 10 FU-64 Membwe Beach seine 1 159252 10 FU-58 Membwe Gillnet 5 45




254 FU-4 Ngoa Gillnet 2 14255 FU-17 Salimi Beach seine 2 180256 FU-18 Salimi Beach seine 2 182257 FU-19 Sa1imi Beach seine 2 114258 FU-21 Salimi Beach seine 2 204259 FU-10 Salimi Gillnet 4 12260 FU-13 Salimi Gillnet 1 5

261 FU-14 Salimi Gillnet 2 6

262 FU-56 Upiri Beach seine 1 124263 FU-57 Upiri Beach seine 1 98264 FU-55 Upiri Gillnet 4 36373 FU-53 Begadi Beach seine 2 562374 FU-49 Begadi Gillnet 4 64375 FU-51 Begadi Gil1net 4 152376 FU-45 Chiswa Beach seine 1 194377 FU-46 Chiswa Beach seine 1 80378 FU-42 Chiswa Gillnet 4 76379 FU-44 Chiswa Gillnet 4 44380 FU-34 Kamuzi Beach seine 1 157381 FU-29 Kamuzi Gillnet 6 60382 FU-39 Mbema Beach seine 1 141383 FU-36 Mbema Gil1net 5 30384 FU-37 Mbema Gillnet 6 24385 FU-58 Membwe Gillnet 5 115386 FU-61 Membwe Gillnet 3 60387 FU-1 Ngoa Gillnet 2 12388 FU-3 Ngoa Gillnet 5

389 FU-18 Salimi Beach seine 2 78390 FU-22 Sa1imi Beach seine 2 206391 FU-10 Salimi Gillnet 2 16392 FU-12 Salimi Gillnet 6 24393 FU-15 Salimi Gil1net 1 3

394 FU-56 Upiri Beach seine 1 209395 FU-57 Upiri Beach seine 1 235497 FU-45 Chiswa Beach seine 1 144498 FU-66 Ganza Gillnet 1 15499 FU-28 Kamuzi Gillnet 3 36500 FU-32 Kamuzi Gillnet 4 52501 FU-25 Kopodoma Beach seine 1 122502 FU-38 Mbema Beach seine 1 107503 FU-37 Mbema Gillnet 6 48504 FU-63 Membwe Beach seine 1 107505 FU-64 Membwe Beach seine 1 131506 FU-59 Membwe Gillnet 3 30507 FU-17 Salimi Beach seine 2 154508 FU-19 Salimi Beach seine 2 132509 FU-21 Salimi Beach seine 2 172510 FU-10 Salimi Gil1net 3 15511 FU-9 Salimi Gillnet 3 9

512 FU-57 Upiri Beach seine 1 117606 FU-47 Begadi Gillnet 2 50607 FU-51 Begadi Gillnet 4 48608 FU-46 Chiswa Beach seine 1 289609 FU-68 Ganza Gillnet 2 38610 FU-35 Kamuzi Beach seine 2 302

611 25 FU-29 Kamuzi Gillnet 7 56612 25 FU-30 Kamuzi Gillnet 4 56613 25 FU-27 Kopodoma Beach seine 1 129614 25 FU-23 Kopodoma Gillnet 2 38615 25 FU-60 Membwe Gillnet 4 96616 25 FU-62 Membwe Gillnet 2 62617 25 FU-7 Ngoa Beach seine 2 312618 25 FU-8 Ngoa Beach seine 1 81619 25 FU-19 Salimi Beach seine 2 154620 25 FU-20 Salimi Beach seine 2 182621 25 FU-22 Salimi Beach seine 2 208622 25 FU-11 Salimi Gillnet 4 32623 25 FU-14 Salimi Gil1net 3 9

624 25 FU-16 Salimi Gillnet 3 21625 25 FU-55 Upiri Gillnet 6 120


Table 111.2

STATISTICAL SCENARIO BTotal production by gear based on data from Table 111.1

NOTE: From Table 11.3 in Annex II it is easy to compare the estimated catch and effortwith the real values which are:

TOTAL EFFORT FOR BEACH SEINETOTAL CATCH BY BEACI I SEINECPUE FOR BEACH SEINE

TOTAL EFFORT FOR GILLNETTOTAL CATCH BY GILLNETCPUE FOR GILLNET

Exercise 111.1

The purpose of this exercise is to manually verify the results shown in Table 111.2. Usewill be made of the sample data illustrated in Table 111.1. Work should be carried outon a by-gear basis. Effort and catches should be summed up for each of the two gearsand the sums placed under columns (1) and (2). The sample CPUE should then beformulated by dividing column (2) by column (1). The column of the estimated effortshould then be completed by multiplying (1) by 31 and dividing by 5 (number of sampledays). Finally, estimates of total catch by gear is obtained by multiplying the sampleCPUE in column (3) by the estimated effort in column (4).

'fable 111.3

STATISTICAL SCENARIO BManual verification of results shown in Table 111.2

= 379 hauls= 41950 Kg= 110.69 Kg/haul

= 1690 net sets= 22132 Kg= 13.10 Kg/net set


GPAR

Month:31 days

SAMPLE1,..1,1,10RI'

SAMPLECATO 1

SAMPLECPUE

ESTIM.1,..F110RT

PSIIM.GATC11

Samp1e:5 days (1)x (3)x(4)(2)/(1) 31/5

(1) (2) (3) (4) (S)

I3each seine 62 7814 126.03 384.40 48445

Gillnet 232 2996 12.91 1438.40 18569

GEAR

Month:31 days

SANIPLElif7FORT

SAMPLEC_ATC11

SANLPLECPUE

ESTIM.EFFORT

E511M.C-ATC.11

Sample:5 days (1)x (3)x(4)(2)/(1) 31/5

(1) (2) (3) (4) (5)

Beach seine

Gil!net

Variability of estimates

In statistical scenario B the formulation of confidence limits for the estimated catch andeffort is more complex than that applied in scenario A. This is due to the fact that theoverall variance for the estimated catch depends on two variates: the sample CPUE andthe sample effort. The most practical approach is to work on a by-gear basis and derivemonthly estimates of catch for each sample day and then formulate confidence liinits forthe mean catch. This approach cannot be easily verified manually; however the resultsare given below for demonstration purposes.

Table 111.4


Variability of estimated catch in Statistical Scenario B

DISCUSSION: Comparison with Statistical Scenario A in terms of accuracy and costs.Why frame surveys are not required. Stratification techniques. The concept of samplingin time. Adjusting the time raising factors according to known patterns of fishingactivities.


Beach seine 48445 41975 54918 6.82 %

Gillnet 18569 12806 24344 15.85 %

GEAR Est. catch (Kg) Lower Upper Coeff. Var (%)limit limit

ANNEX IV

STAT!STICAL SCENARIO C

SAMPLING IN SPACE AND TIME FOR EFFORT

SA.L17'1.1_. NG IN SPACE A D TIME FOR CPUE

SE OF FRAME IA If2VEYS


Table IV.1

STATISTICAL SCENARIO C

Sample data for 5 days and 3 beaches, taken from Table 1.1 in Annex Iand used for the estimation of total effort and catch.

The total number of sample records is 24 since only three beaches have been covered(Chiswa, Kopodoma and Ngoa). The number of sample days is 5 as samples were takenduring days 5, 10, 15, 20 and 25. Since there is no indication as to what has happenedin the other beaches, frame survey data are required in order to proportionally estimateeffort and catches in all ten beaches. These data are summarized by the following twotables.


Sampleno.

D

AY

FishingUnit

Beach Gear Effort(sets/hauls)

CatchKgs

103 5 FU-45 Chiswa Beach seine 1 249.0104 5 FU-46 Chiswa Beach seine 1 194.0105 5 FU-41 Chiswa Gillnet 1 13.0106 5' FU-43 Chiswa Gillnet 5 95.0107 5 FU-44 Chiswa Gillnet 2 16.0113 5 FU-23 Kopodoma Gillnet 2 20.0117 5 FU-7 Ngoa Beach seine 2 308.0239 10 FU-42 Chiswa Gillnet 3 24.0240 10 FU-43 Chiswa Gillnet 4 32.0247 10 FU-24 Kopodoma Gillnet 4 60.0254 10 FU-4 Ngoa Gillnet 2 14.0376 15 FU-45 Chiswa Beach seine 1 194.0377 15 FU-46 Chiswa Beach seine 1 80.0378 15 FU-42 Chiswa Gillnet 4 76.0379 15 FU-44 Chiswa Gillnet 4 44.0387 15 FU-1 Ngoa Gillnet 2 12.0388 15 FU-3 Ngoa Gillnet 1 5.0497 20 FU-45 Chiswa Beach seine 1 144.0501 20 FU-25 Kopodoma Beach seine 1 122.0608 25 FU-46 Chiswa Beach seine 1 289.0613 25 FU-27 Kopodoma Beach seine 1 129.0614 25 FU-23 Kopodoma Gillnet 2 38.0617 25 FU-7 Ngoa Beach seine 2 312.0618 25 FU-8 Ngoa Beach seine 1 81.0

Table IV.2

Statistical Scenario C

Results of a frame survey conducted on all beaches

Table IV.3

Statistical Scenario C

Number of gear units in selected beaches.Data taken from frame survey (Table IV.2)



Begadi Beach seine 1

Begadi Gillnct 27


Chiswa Gillnet 20Ganza Beach seine 1

Ganza Gillnet 8

Kamuzi Beach seine 3

Kamuzi Gillnet 34


Kopodoma Gillnet 11

Mbema Beach seine 3

Mbema Gillnet 20Membwe Beach seine 2

Membwe Gillnet 31Ngoa Beach seine 4

Ngoa Gillnet 15

Salimi Beach seine 6

Salimi Gillnet 46

Upiri Beach seine 2

Upiri Gillnet 11

Total - Beach seines 27Total - Gillnets 223



Chiswa Gillnet 20


Kopodoma Gillnet 11

Ngoa Beach seine 4

Ngoa Gillnet 15

Total - Beach seines 9

Total - Gillnets 46

COMPUTATIONAL STEPS

(1) No. of sample days: 5

(2) Time raising factor: 31

(a) Sample effort from thethree selected beaches

Sample catch from thethree selected beaches

Sample CPUE: (4)/(3)

No. of units in sampledbeaches

No. of units in all beaches

Estimated effort:

(3) x (2)/(1) x (7)/(6)

(9) Estimated catch: (5) x (8)

ActualeffortOOOOO 0 OOOOO 0*

ActualOOOOOO I

Actual

BEACH SEINE

13 hauls

2102 Kg

161.69 Kg/haul

9 beach seines

27 beach seines

241.8 hauls

39096 Kg

379 hauls

41950 Kg

110.69 Kg/haul

GILLNET

36 net sets

449 Kg

12.47 Kg/net set

46 gillnet units

223 gillnet units

1082.03 net sets

13492 Kg

1690 net sets

22132 Kg

13.10 Kg/net set

Table IV.4

STATISTICAL SCENARIO C

Estimation of effort and catches from the sampledata given in Table IV.1.


Exercise FV.1

The purpose of this exercise is to manually verify the results shown in Table IV.4. Usewill be made of the sample data illustrated in Table IV.1. Work should be carried outon a by-gear basis and as indicated by the column: COIVIPUTATIONAL STEPS. Forcomparison purposes actual values for catch anf effort are also provided.

Table IV.5

Manual verification of results given in Table IV.4.

DISCUSSION: Comparison of approach with Statistical Scenarios A and B. Assumptionsmade. Variability as a fiinction of sample CPUE, sample effort and Gear ActivityCoefficient (GAC). Complexity in determining confidence limits for estimated effort andcatch. Updateness of frame surveys and consistency of proportions in gear units.Problems arising from migrating fishing units. Frequency of frame surveys. Historicalrecords of frame surveys and their use.


COMPUTATIONAL STEPS BEACH SEINE GILLNET

(1) No. of sample days: 5

(2) Time raising factor: 31

(3) Sample effort from thethree selected beaches

(4) Sample catch from thethree selected beaches.

(5) Sample CPUE: (4)/(3)

(6) No. of units in sampledbeaches

(7) No. of units in all beaches

(8) Estimated effort:

(3) x (2)/(1) x (7)/(6)

(9) Estimated catch: (5) x (8)

Actual effort........................... ...... 379 hauls 1690 ne't sets

Actual catch.................................. 41950 Kg 22132 Kg

Actual CPUE................ ................ 110.69 Kg/haul 13.10 Kg/net set

Jorion, P.J.M.1985

Jorion, P.J.M.1985

Tandberg, A.,1986

LIS'I'E DES RAPPORTS DIPA - LIST OF IDAF REPORT

Documents teclutiques / Technical documents

De Graauw, M.A., Etude de préfactibilité technique de l'aménagement d'abris pour la péche maritime1985 artisanale au Benin. Cotonou, Projet DIPA. 55 p., DIPA/WP/1.

Black Michaud, Mi., Mission d'identification des communautés littorales de pecheurs artisans au1985 Bénin. Cotonou, Projet DIPA, 24 p., DIPA/VVP/2.

Gulbrandsen, 0.A., Preliminary account of attempts to introduce alternative types of small craft into1985 West Africa. Cotonou, IDAF Project, 51 p., IDAF/WP/3.

Gulbrandsen, 0.A., Un compte-rendu préliminaire sur les tentatives d'introduire des types alternatifs1985 de petites embarcations en Afrique de l'Ouest. Cotonou, Projet DIPA, 53 p.,

DIPAN/P/3.

, The influence of socio-economic and cultural structures on small-scale coastal fishe-ries development in Benin. Cotonou, IDAF Project, 59 p., IDAF/WP/4.

, L'influence des structures socio-économiques sur le développement des pêches artisa-nales sur les côtes du Benin. Cotonou, Projet DIPA, 59 p., DIPA/WP/4.

Preliminary assessment of the nutritional situation of subsistence fishermen's families.Cotonou, IDAF Project, 31 p., IDAF/WP/5.

Wijkstrom, O., Recyclage des personnels Oche en gestion et comptabilité. Cotonou, Projet DIPA,1986 25p., DIPA/WP/6.

Collart, A., Development planning for small-scale fisheries in West Africa, practical and socio-1986 economic aspects of fish production and processing. Cotonou, IDAF Project, 34 p.,

IDAF/WP/7.

CoHart, A., Planification du développement des peches artisanales en Afrique de l'Ouest; production1986 et traitement du poisson, ses aspects matériels,techniques et socio-économiques.

Cotonou, Projet DIPA, 67 p., DIPA/WP/7.

Van der Meeren, A.J.L., Socio-economic aspects of integrated fisheries development in rural fishing1986 villages. Cotonou, IDAF Project, 29 p., IDAF/VVP/8.

Haling, L.J., et O. Wijkstrom, Les disponibilités en materiel pour la Oche artisanale. Cotonou, Pro-1986 jet DIPA, 47 p., DIPANVP/9.

Akester, S.J., Design and trial of sailing rigs for artisanal fisheries of Sierra Leone. Cotonou, IDAF1986 Project, 31 p., IDAF/WP/10.

Vétillart, R., Rapport détude préliminarie sur l'aménagement d'un abri pour la Oche maritime artisa-1986 nale A Cotonou. Cotonou, Projet DIPA, 31 p., DIPA/WP/11.

Van Hoof, L., Small-scale fish production and marketing in Shenge, Sierra Leone. Cotonou, IDAF1986 Project, 36 p., IDAF/WP/12.

Everett, G.V., An outline of West African small-scale fisheries. Cotonou, IDAF Project, 32p., IDAF/1986 WP/13.

Anon., Report of the second IDAF liaison officers meeting; Freetown, Sierra Leone (11 - 14 No-1987 vember 1986). Cotonou, IDAF Project, 66 p., IDAF/VVP/15.

Anon., Compte-rendu de la deuxième reunion des officiers de liaison du DIPA. Cotonou, Projet1987 DIPA, 27 p., DIPA/WP/16.

Campbell, R.J., Report of the preparatory technical meeting on propulsion in fishing canoes in West1987 Africa (Freetown, 15-18 November 1986). Cotonou, IDAF Project, 88 p.,

IDAF/WP/17.

Davy, D.B., Seamanship, Sailing and Motorisation. Cotonou, IDAF Project, 85p., IDAF/VVP/18.1987

Anum-Doyi, B., and J. Wood, Observations on fishing methods in West Africa. Cotonou, IDAF Pro-1988 ject, 53 p., IDAFNVP/19.

Anon., Report of the third IDAF liaison officers meeting (Cotonou, 2 - 4 December 1987). Cotonou,1988 IDAF Project, 88 p., IDAF/WP/20.

Anon., Compte-rendu de la troisième reunion des officiers de liaison du DIPA (2-4 Décembre 1987).1988 Cotonou, Projct DIPA, 85 p., D1PA/WP/20.

Haakonsen, J.M. (Ed.) Recent developments of the artisanal fisheries in Ghana. Cotonou, IDAF Pro-1988 ject, 69 p., IDAF/VVP/21.

Everett, G.V., West African marine artisanal fisheries. Cotonou, IDAF Project, 41 p., IDAF/WP/22.1988

Les pêches maritimes artisanales en Afrique de l'Ouest. Cotonou, Projet DIPA, 44p., DIPA/WP/22.

.R., Observations on small fishing craft development in West Africa. Cotonou, IDAF1989 Project, 22 p., IDAFNVP/23.

Zinsou, J. et W. Wentholt, Guide pratique pour la construction el l'in roduction du fumoir "chorkor".1989 Cotonou, Projet DIPA, 33 p., DIPA/WP/24.

Zinsou, J. and W. Wentholt, A practical guide to the construction and introduction of the chorkor1989 smoker. Cotonou, IDAF Project, 29 p., IDAFTWP/24.

Chauveau, J.P., F. Verdeaux, E. Charles-Dominique et J.M. Haakonsen, Bibliographic sur les com-1989 munautés de pêcheurs d'Afrique de l'Ouest - Bibliography on the fishing communities

in West-Africa. Cotonou, Projet DIPA - IDAF Project, 220 p., DIPA-IDAF/VVP/25.

Small-scale fisheries development issues in West Africa. Cotonou, IDAF Project,47p., IDAF/WP/26.

Everett, G.V., Problèmes de développement de la Oche artisanale en Afrique de l'Ouest. Cotonou,1989 Projet DIPA, 49 p., DIPA/WP/26.

Haakonsen, J,M., et W. Wentholt, La Oche lacustre au Gabon. Cotonou, Projet DIPA, 36p., DIPA1989 /WP/27.

Anon., Report of the ad hoc technical meeting on artisanal fisheries craft, propulsion, gear and secu-1990 rity in the IDAF region; Cotonou, 25 - 26 September 1989. Cotonou, IDAF Project,

111 p., IDAF/WP/28.

Anon., Report of the fourth IDAF liaison officers meeting (Dakar, 21 - 23 November 1989).1990 Cotonou, IDAF Project, 135 p., IDAF/WP/29.

Anon., Compte-rendu de la quatrième reunion des officiers de liaison du DIPA. Cotonou, Projet1990 DIPA, 121 p., DIPA/WP/29.

B.R., D.E. Tempelman and A.M. Uff. Report of round table meeting on women'sactivities and community development in artisanal fisheries (projects) in West Africa.Cotonou, IDAF Project, 12 p. + annexes, IDAF/WP/30.

B.R., D.E. Tempelman et A.M. IJff, Rapport du séminaire sur les activités féininineset le développement communautaire dans les projets de péches artisanales en Afriquede l'Ouest. Cotonou, Projet DIPA, 14 p. + annexes, DIPA/WP/30.

IJff, A.M., Socio-economic conditions in Nigerian fishing communities. Based on studies along the1990 Benin and Imo river estuaries. Cotonou, IDAF Project, 113 p., IDAF/WP/31.

Okpanefe, M.O., A. Abiodun and J.M. Haakonsen, The fishing communities of the Benin River es-1991 tuary area: Results from a village survey in Bendel State, Nigeria. Cotonou, IDAF

Project, 75 p., IDAF/WP/32.

Everett, G.V.,1989

43

Everett, G.V.,1988

Coackley, A.D

Houndékon,1990

Houndékon,1990

Anon., Compte-rendu du cours "Analyse Quantitative des Aspects Sélectionnés de Développement".1991 Cotonou, Projet DIPA, 6 + xlvi p., DIPA/WP/33.

Anon., Report of the course on "Quantitative Analysis of Selected Aspects of Fisheries Develop-1991 ment". Cotonou, IDAF Project, 6 + xlv p., IDAFfWP/33.

Callerholm Cassel, E., Cost and Earnings and Credit Studics on Ghanaian Canoe Fisheries. Cotonou,1991 IDAF Project, 38 p., IDAF/WP/34.

Sheves, G.T., The Ghanaian dug-out canoe and the canoe carving industry in Ghana. Cotonou, IDAF1991 Project, 109 p., IDAF/WP/35.

Haakonsen, J.M. and Chimère Diaw, Fishermen's Migrations in West Africa. Cotonou, IDAF1991 Project, 293 p., IDAF/WP/36.

Haakonsen, J.M. et Chimère Diaw, Migration des Pecheurs en Afrique de l'Ouest. Cotonou, Projet1991 DIPA, 332 p., DIPA/WP/36.

Gulbrandsen, 0.A., Canoes in Ghana. Cotonou, IDAF Project, 82 p., IDAFNVP/37.1991

Anon., Artisanal Fisheries in West Africa, Report of the Fifth IDAF Liaison Officers Meeting.1991 Cotonou, IDAF Project, 140 p., IDAF/WP/38.

Anon., Les peches Artisanales en Afrique de l'Ouest, Compte-rendu de la Cinquième reunion des1991 Officiers de Liaison du DIPA. Cotonou, Projet DIPA, 122 p., DIPA/WP/38.

Beare, R.J. and P. Tanimomo, Purse seine and encircling net fishing operations in Senegal, Guinea,1991 Sierra Leone, Ghana and Benin. Cotonou, IDAF Project, 92p., IDAFTWP/39.

Everett, G.V. and G.T. Sheves, Recent trends in artisanal fisheries and report on alte atives to ca-1991 noes. Cotonou, IDAF project, 33 p., IDAF/WP/40.

Callerholm Cassel, E. and A.M. Jallow, Report of a socio-economic survey of the artisanal fisheries1991 along the atlantic coast in The Gambia. Cotonou, IDAF project, 97p., IDAF/WP/41.

Chimère D aw, M. et Jan M. Haakonsen, Rapport du séminaire sur les migrations de pêcheurs1992 artisans en Afrique de l'Ouest. Cotonou, projet DIPA, 36p., DIPA/WP/42.

Chinière Diaw, M. and Jan M. Haakonsen, Report on the regional seminar on artisanal fishermen's1992 migrations in West Africa. Cotonou, IDAF project, 35p., IDAF/VVP/42.

Houndékon, B. et L. Affoyon, Rapport du séminaire-atelier de sensibilisation sur la méthode accélérée1993 de recherche participative tenu à Libreville Gabon en Novembre 1992. Cotonou,

Projet DIPA, 56p., DIPA/WP/43.

Anon., Rapport de la sixième reunion des fonctionnaires de liaison Banjul, Gambie 1 - 5 février 1993.1993 Cotonou, Projet DIPA, 57 p., DIPA/WP/44.

Anon., Report of the sixth IDAF liaison officers meeting Banjul, Gambia 1 - 5 February 1993. Coto-1993 nou, IDAF Project, 60 p., IDAF/WP/44.

Horemans, B.1993

Horemans, B.1993

and B. Satia (eds), Report of the Workshop on Fisherfolk Organisations in West Africa.Cotonou, IDAF Project, 93 p., IDAF/VVP/45.

et B. Satia (éds), Rapport de l'atelier sur les organisations de pécheurs en Afrique del'Ouest. Cotonou, Projet DIPA, 102 p., DIPA/WP/45.

Kébé, M., Gallène J. et Thiam D.- Revue sectorielle de la Oche artisanale en Guinée Bissau.1993 Programme de Développement Intégré des Péches Artisanales en Afrique de l'Ouest

(DIPA), 32 p. + annexes, DIPA/WP/46.

Horemans B., - La situation de la Oche artisanale en Afrique de l'Ouest en 1992. Cotonou.1993 Programme de Développement Intégré des Pêches Artisanales en Afrique de l'Ouest,

36 p., DIPA/WP/47.

Kébé, M., Njock J.C. et Gallène J.- Revue sectorielle de la Oche maritime au Cameroun.1993 Programme de Developpement Integré des Péches Artisanales en Afrique de l'Ouest

(DIPA), 30 P. + annexes, DIPA/WP/48.

Anon., Report of the Working Group on Artisanal Fisheries Statistics for the Western Gulf of Guinea,1993 Nigeria and Cameroon. Cotonou, IDAF Project, 126p., IDAF/VVP/49

Satia, B.P., Ten years of Integrated Development of .Artisanal Fisheries in West Africa (Origin,1993 Evolution and Lessons Learned). Cotonou, IDAF Project, 37p., IDAF/WP/50

Satia, B.P., Dix ans de développement integré des pêches artisanales en Afrique de l'Ouest (Origine,1993 évolution et leçons apprises). Cotonou, Projet DIPA, 41p., DIPA/WP/50.

11. Manuels de terrain / Field Manuals

Johnson, J.P. et M.P. Wilkie, Pour un développement intégré des péches artisanales; du bon usage1988 de participation et de la planification. Cotonou, Projet DIPA, 157p. + annexes, Manuel

de Terrain N° 1.

Meynall, P.J., J.P. Johnson, and M.P. Wilkie, Guide for planning monitoring and evaluation in fishe-1988 ries development units. Cotonou, IDAF Project, 116 p., IDAF Field Manual N° 2.

HL LDAF Newsletter / La Lettre du DLPA

IDAF Newsletter/Lettre du DIPA, 1, October/Octobre 1985, 4 p.IDAF Newsletter/Lettre du DIPA, 2, Januarv/Janvier 1986, 14 p.IDAF Newsletter/Lettre du DIPA, 3, June/Jilin 1986, 40 p.IDAF Newsletter/Lettre du DIPA, 4/5, Sept./Dec. 1986, 76 p.IDAF Newsletter/Lettre du DIPA, 6, September 1987, 58 p.

IDAF Newsletter/LettreIDAF Newsletter/LettreIDAF Newsletter/LettreIDAF Newsletter/LettreIDAF Newsletter/LettreIDAF New sletter/LettreIDAF News letter/LettreIDAF Newsletter/LettreIDAF Newsletter/LettreIDAF Newsletter/LettreIDAF Newsletter/LettreIDAF Newsletter/Lettre

IDAF Nevvsletter/Lettre du DIPA, 7, June/Juin 1988, 84 p.du DIPA, 8, June/Juin 1989, 74 p.du DIPA, 9, October/Octobre 1989, 84 p.du DIPA, 10, August/Aoat 1990, 84 p.du DIPA, 11, January/Janvier 1991, 6 p.du DIPA, 12, April/Avril 1991, 8 p.du D1PA, 13, July/Juillet 1991, 6 p.du DIPA, 14, October/January 1992, 12 p.du DIPA, 15, September/Septembre 1992, 85p.du DIPA, 16, December/Décembre 1992, 31p.du DIPA, 17, March/Mars 1993, 39p.du DIPA, 18, June/Juin 1993, 38p.du DIPA, 19, September/Septembre 1993, 32p.

IV. Documents de travail du Pm jet Modèle, Bain / Woricing papers of the Model Project, Benin

Coackley, A.D.R., Report on installation of a diesel inboard motor in a Ghana canoe. Cotonou, Mo-1988 del Project, 7 p. + annexes, PMB/VVP/1 (En).

Coackley, A.D.R., Installation d'un moteur diesel "inboard" dans une pirogue ghanéenne. Cotonou,1988 Projet Modèle, 9 p. + annexe, PMB/WP/1 (Fr).

Zannou, L,FI., Etudes technico-économiques des fours améliorées pour le fumage de poisson en1988 Republique Populaire du Benin. Cotonou, Projet Modèle, 8 p. + 6 tableaux,

PMB/WP/2.

Atti-Mama, C., et M. Raïs, Etude démographique des communautés cibles du projet Modèle Benin.1988 Cotonou, Projet Modèle, 20 p. + 10 annexes, PMB/WP/3.

Jorion, P., Non-monetary distribution of fish as food in Beninois small-scale fishing villages and its1988 importance for auto-consumption. Cotonou, Model Project, 26p., PMB/WP/4.

Tanimomo, P.F., Catalogue des engins de Oche maritime artisanale du Benin. Cotonou, Projet1989 Modele, 46 P. + 3 annexes, PMB/WP/4, PMB/WP/5.

Tanimomo, P.F., Rapport de consultation sur la formation des jeunes pécheurs de l'UNICOOPEMA1989 à Lome. Cotonou, Projet Modele, 17 p. + 6 annexes, PMB/WP/6.

Atti Mama, C., Impact socio-économique de la piste Pahou-Kpota. Cotonou, Projet Modele, 10 p.1989 + 3 annexes, PMB/WP/7.

Ahouanmènou, C., C. Atti-Mama, B. Houndékon, D. Tempelman et D. Turcotte, Animation, gestion1989 et planification, séance de travail avec les agents de ten-ain. Cotonou, Projet Modele,

142 p. + annexes, PMB/WP/8.

Atti-Mama, C., D. Turcotte, et W. Wentholt, Evaluation interne des activités du projet modèle Benin1989 dans le secteur de Ouidah. Cotonou, Projet Modele, 36 p. + 7 annexes, PMB/WP/9.

Tempelman, D., The participatory approach in an integrated artisanal fisheries project; structuring1989 community development - womens activities. Cotonou, Model Project, 43 p.,

PMB/WP/10.

Landry, J., Cours d'alphabetisation fonctionnelle en calcul. Cotonou, Projet Modele, 59 P. + 3 1989annexes PMB/WP/11.

Landry, J., and D. Tempelman, Functional literacy, Training Guide for a numeracy course. Cotonou,1989 Model Project, 55 p. + 3 annexes, PMB/WP/11.

Atti-Mama, C., Systèmes traditionnels et modernes d'épargne et de credit en milieu pécheur au Benin.1990 Cotonou, Projet Modèle, 41 p. + annexes, PMB/WP/12.

Sènouvo, P., Statistiques de péches des villages du Projet Modèle Ann& 1987. Cotonou, Projet Mo-1990 dele, 33 p., PMB/WP/13.

Sheves, G.T., P.T. Holler and P.F. Tanimomo, Report on demonstration with echo-sounders, 1990compas ses and multimono gillnets in Ghana. Cotonou, Model Project, 22 p.,PMB/WP /14.

Coackley, A.D.R., and G.T. Sheves, A review of the experimental introduction of diesel inboard1990 motors to Ghana canoes. Cotonou, Model Project, 41p., PMB/WP/15.

IJff, A.M. et D.E. Tempelman, Etude sur les relations entre les captures de poisson et l'état nutri1990 tionnel des communautés de pécheurs dans la province du Mono, au Benin. Cotonou,

Projet Modèle, 27 p., PMB/VVP/16.

Sènouvo, A.P. et A.A. Gbaguidi, Recueil des données statistiques des peches maritimes au Benin.1990 Période de 1984 à 1989. Cotonou, Projet Modèle, 134p., PMB/WP/17.

Houndékon, B.R., Initiative locale et développement: Experience des communautés de pécheurs1990 marins du Benin. Cotonou, Projet Modèle, 17 p., PMB/WP/18.

Le Gurun, J.F., La section de technique des péches. Cotonou, Projet Modèle, 43 p., PMB/WP/19.1991

FAO/Government Cooperative Programme, Integrated Development of Small-Scale Fisheries in West1991 Africa, Model Project Benin, Project findings and recommendations. FAO, Rome,

FI:GCP/RAF/198/DEN Terminal Report, 34p.

Programme de Cooperation FAO/Gouvernements, Développement Intégré de la Oche artisanale en1991 Afrique de l'Ouest, Projet Modèle Benin, Conclusions et recommandations du Projet.

FAO, Rome, F1:GCP/RAF/198/DEN Rapport terminal, 40 p.

V. Documents occasionnels / Occasional Papers

Direction Nationale du Projet Modele Benin, Mise en place et plan d'exécution. Cotonou, Projet1985 DIPA43 p. + 3 annexes.

Sheves, G.T.1985

Sheves, G.T.1985

Paraiso, F-X.1985

Integrated small-scale fisheries projects: principles, approaches, and progress in thecontext of the Benin prototype project. Paper presented at the workshop on Small-scale Fisheries Developinent and Management, Lome, 20-29 November 1985, 33 p.

Projets intégrés de péches artisanales : approches et evolution dans le contexte du projetpilote. Document présenté à l'atelier regional sur le développement et l'aménagementdes peches artisanales, Lome, 20-29 Novembre 1985, 36 p.

, Rapport sur stages de recyclage en identification des poissons Cotonou, GCP/RAF/192/DEN, 24 p.

Collar, A. et M. Guidicelli, Développement des pécheries naarititimes et continentales de la piscicul-1985 ture au Gabon. Rome, FAO, GCP/RAF/192/DEN 77 p.