1.2.1 A general description of the development of a fishery1.3 The structure and operation of the fishing industry
1.2.2 The use of models
1.2.3 Statistics of catch
1.2.4 Statistics of fishing effort and catch-per-unit-effort
1.2.5 Other statistics needed for stock assessment
1.3.1 The uses of statistics by economists1.4 The institutional framework for data collection and compilation
1.3.2 The uses of statistics by gear and food technologists
The need to collect data arises when attempts are made to manage the economic and social activities of a society. For example data on population size and distribution are used in planning the building, of houses, schools, roads and hospitals. The raw data which appear on the source document (e.g., a census form filled in by the head of a household) are collected and processed to provide statistics of, among other things, the number of children of primary school age in each district of each town. Instead of the wealth of individual detail which exists on the source document about age, family size, occupation, income and activities we may, for the purposes of the types of planning listed above, devise a simplified description or model of the population. In our model there may be only four age classes pre-school, school age, child bearing age and post-child bearing and we may use this as a basis for estimating the number of school places or hospital beds needed in five years time, given certain information about numbers of children born per family per year and rate of hospitalization for different age groups. This model describes the age structure of the population in a simple way which helps us to make predictions about the changes likely to take place in the population.
The collection of such data is not strictly essential - it would be possible to provide enough schools and hospitals simply by building a very large number, or by waiting until the existing ones were obviously overcrowded but either of these courses of action would have greater costs than if the right number could be predicted. Collecting date, producing statistic, and developing a model costs money and the cost of collection and modelling must be related to the benefits expected, whether they are expressed as money saved or more general social well-being.
The construction of models which describe some aspect of population, economics or industrial development requires research into the relationships which exist within the system being studied. How, for example, is family size related to income level? How does the likelihood of going to hospital change with age? Such research is usually based on statistics collected over a long period of time and these long-term statistics are essential for predictive modelling.
Once these simplified relationships are established it is possible to use the model to test the effects of particular courses of action and thereby to make a rational decision. For example, how much would the provision of old, people's homes, with certain nursing facilities, reduce the demand for mo expensive hospital treatment? If a model were being constructed for this particular purpose it might be necessary to collect additional, short term, information by means of a special survey on, say, the frequency of different types of illness among elderly people. As a result of the study it may be decided to make this information part of the routine census in future and to build up long-term statistics of incidence of illness.
The distinction between short-term and long-term statistics is a useful one when discussing the collection of data even though it is not clearest. Short-term statistics can be collected during the lifetime of the project in hand. They may be useful only to that particular project and they are not kept in the form of permanent records. In the example given above the statistics of illness frequency became long-term when it was decided to store them, presumably because it was felt that records of this kind would be useful to future studies.
1.1. Management of fisheries requires data
The aim of this manual is to outline the types of statistics required for fisheries management, with a general introduction to the methods of collection and processing which can be used in different situations. The emphasis will be on the routine, long-term statistics, which have wide application in the many disciplines used to provide practical guidance to fishermen, fisheries administrators and fishing companies. The primary users of the statistics are fisheries biologists, economists, development planners and gear and food technologists. They are the people who specify what data they think will be needed in order to plan, monitor, control and predict the operation of the fishing industry in a rational way.
The importance of collecting data and the amount of time and money worth spending on it can only be assessed if the objectives of fisheries management are fairly clearly de fined. The ultimate objectives of management of a fishery resource and of the fishing industry based on it are often vague, or there may be several which are not fully compatible, e.g., employment of the largest possible number of people, efficiency of labour utilization, maximization of physical yield (Lawson, 1974). The ultimate objective of management can often be divided into or replaced by simpler proximate objectives and the importance of collecting data evaluated in relation to them. Rothschild (1971) discusses this type of approach to fishery management and points out the dangers of suboptimization - when optimality in the chosen proximate objectives does not lead to optimality of the entire system.
In order to establish more clearly what the objectives might be and what data are needed in relation to them, we need to look at the nature of the fisheries resources and then at the structure and operation of the fishing industries based on them. Because moat fisheries are common property resources one must make a distinction between the objectives of managing the resource as a whole and the objectives pursued by individual countries, fishing companies or fishermen. This distinction and the problem of common property re sources in general have been discussed by Gordon (1954) and Gulland (1974). If a resource is common property then the level of exploitation will increase until the actual yield from each additional unit of exploitation equals the cost of the additional unit. The aim of an individual country or fishing, enterprise in such a situation will be to secure as large a part of the total yield as possible, since anything which it does not take may go to a competitor. The total yield from the resource may then be reduced below the level which could be maintained if the level of exploitation were controlled. Control of the level of exploitation can only be achieved, if all the participants agree to it and the international and regional fisheries regulatory bodies have been established to provide the institutional framework for this kind, of resource management. Subject to the regulation on catch, gear etc. imposed by these regulatory bodies, individual countries and fishing enterprises will have further objectives which give rise to national and local fisheries management measures. There is thus a hierarchy of objectives and arising from them a hierarchy of management measures which are summarised in Table 1.
The table gives only an indication of the kinds of objectives which exist at each level and of the data required to study them and to evaluate me measures. A further breakdown of objectives and of the relationships between then is given by Kesteven (1973). The objectives of resource management are evolving in response to changes in the way that fisheries are exploited (e.g., an increase in large mobile fleets) and to change in the concepts of ownership which are applied. The biological basis of resource management and its objectives are discussed fully by Gulland, (1974). The treatment of these subjects in Section 1.2 of the present work is intended only to serve as a basis for examining the data requirements.
It will be seen from Table 1 that a wide variety of data are required for resource management and management of the fishing industry. Each discipline (e.g., economic, biology, sociology, engineering) which is used to study the fish resource and the industry will have a different approach to the overall objectives and will require different kinds of data. For example, a sociologist may look at the safety records of different group of trawlers and relate then to the structure of authority and the patterns of ownership of the vessels. A technologist or engineer looking into safety would be concerned with the use of machinery on board and the seaworthiness of the boats. This example is also intended to show the need to integrate different approaches in an overall objective such as safety at sea. The present work will nevertheless follow broadly discipline oriented lines in considering data requirements, rather than organising them according to objectives as baa been done in Table 1. It would be impossible for a work of this kind to aim at a comprehensive coverage of the data required by all users and in any case new requirements arises as the methods of study and objectives evolve.
Table 1: A hierarchy of objectives in fisheries management, some of the measure used to achieve them, and the requirements for data, assessments and forecasts
What we shall aim to cover are the most important kinds of data used, by the main disciplines-biology, economics, development planning, gear and food technology, business management - with the emphasis on the data required for resource management rather than industrial management, although there is a large common ground between the two.
Section 2 lists the basic data requirements with a brief discussion of the qualities the data should have. Section 3 deals with problems of collection and with the relative coats of collection in different types of fishery (industrialized, small scale). The processing and production of statistics are dealt with in Section 4 and Section 5 tries to cover some of the shortcomings which result from imposing a fairly simple structure onto a complex subject. It also tries to anticipate some of the development in fishery statistics.
The definition of objectives and setting up of a system of collection of statistics for studying them is not the end of the process. Once a collection and processing system has been established it limits and influences the kind of question which can be answered by its use. It is therefore essential to keep the objectives themselves under constant review, particularly at times of rapid change in the structure of the industry and in the Inter national law and resource management practices. Following from this it may be necessary to revise the collection system.
Biologists, economists etc. are the immediate users of fisheries statistics, but the justification for collecting them comes from the ways in which the analyses by biologists or economists are used by administrators and fishery managers to make decisions about the fisheries. Close cooperation among immediate users and between immediate users and administrators is needed in setting out the uses and objectives and to avoid duplication.
1.2. The nature of the fish resource and the methods for studying it
The fishing industry is based on a natural resource not controlled directly by man, except in fish farming. Because of the lack of direct control and because the sea is a foreign Environment, there is a relatively greater need to study and monitor its changes and -to collect data routinely than exists on land. Underlying any planned development, management or economic study there must be some understanding of the biology and population dynamics of the resource.
Man's influence on fish stocks is exerted almost entirely through fishing and there is little attempt, at least in the oceans, to regulate species composition, eliminate disease and predators, control migrations or fertilize selectively, such as has taken place for thousands of years on land. In the sea man is a hunter, not a farmer, but he can and should be a prudent hunter, giving proper thought to the long -term effects of his actions. The resource is renewable, but limited in size and in many fisheries the upper limit of exploitation has been overshot.
The way in which the average yield in weight from a fish stock changes at different steady levels of exploitation (= fishing effort) is shown in Figure 1. Obviously there will be no yield if there is no fishing effort but equally if the fishing effort remains at a very high level the yield will be very low because the fish do not have time to grow or reproduce before they are caught at some intermediate level of exploitation there will be a maximum average yield (known as the maximum sustainable yield or MSY).
To a first approximation the total gross revenue from the resource is directly related to the yield in weight and the total cost of fishing is directly related to the amount of fishing effort. The line in Figure 1 shows where the costs and revenues are balanced and at the point B where it cuts the yield (= revenue) curve, the revenue from one wilt of fishing effort exactly equals the cost of that unit. This is the state towards which a common property fishery will move and at this point the total gross yield from the resource is lower than it would be if the amount of fishing effort (and hence the cost of fishing) were reduced (e.g., to MSY at point A). The difference between cost and revenue is greatest at Point C (maximum net economic yield.), to the left of MSY.
Fig 1: Steady yield at different levels of effort
(A = maximum physical yield,
B = zero economic yield,
C = maximum economic yield).
Without going further into the biological and economic arguments, which can be found in Gulland (1974), Clark et. al. (1973) and Roedel (1975), it is clear that MSY cannot be used as an objective of mage but it may be useful as a tool. It provides an indication of the greatest sustained physical yield which can be expected from the resource, and it is useful in explaining the dangers of over fishing and the need to reduce fishing effort.
The simple theoretical concept of MSY ignores the fact that fish resources are not static and unchanging. There are fluctuations in the number of young fish entering the fishery each year and in the species composition of catches in an area. It is the aim of fisheries biology to predict and if possible explain such changes and also to predict the effects of man's activities on the fisheries. Rational management decisions can only be taken if the consequences of such decisions can be predicted.
The development of many fisheries has followed a similar pattern and a generalised description is shown in Figure 2. The first stage begins with either no fishery or a small subsistence fishery and the characteristic, of the stage are increasing investment, modernisation of equipment and increased total catch. The second stage is characterised by a levelling off in total catch followed by a decline. In the third stage the catch is moderate and erratic unless regulation is introduced to rebuild the stock.
An analysis of the biological and economic changes in the fishery shows why things have gone wrong and what steps can be taken during each stage to ensure that the development of the fishing industry is balanced and sustained. At the beginning of the first stage the fish are abundant and even relatively inefficient fishing methods will produce a reasonable catch, i.e., the catch per unit effort is high. Improvements in the gear used and introduction of new boats will increase the catch, but at the same time reduce the abundance. The reduced abundance will be reflected in a decline in the catch per unit effort, but improvements in fishing power may conceal this. The critical period in preventing over fishing occurs towards the end of the first stage. If there are no data on the total catches and fishing effort, then no assessment can be made of the upper limit of the sustainable catch from the fishery and of the trend in catch per effort. The profitability of a fishing vessel depends largely on the catch per effort and if the downward trend is not recognised then future investment and ship building programmes will be based on the high catch per effort during the early years of the fishery and thus lead to over investment in boats. There is a strong optimistic bias among fishermen in a developing fishery, which leads them to gamble on the continuation or return of high catch rates, when it is in fact their own activity which is preventing it. The common property problem enters here. As well as over-investment in boats there may be over-investment in port facilities, processing equipment etc. The investor has risked money on poor information, where the means of improving the information are quite clear and the cost of collecting it can be specified. In a few cases the opposite happens and investors are unwilling, in the absence of information, to risk money on what is in fact a good prospect, i.e., the stock remains under-exploited.
So far we have stressed. the need for collecting data on total catch and fishing effort in order to be able to predict trends in catch per effort and the maximum yield from the resource. These statistics, directed towards "resource objectives" (See Table 1), must underlie any development programme, but an investor will need a wider rang of information in order to plan the strategy (e.g., analysis of demand and sales prices, the technical feasibility of landing, processing and marketing, and the financial feasibility). Some idea of the scope of these needs can be obtained from Table 2, which is discussed in Section 1.3.1.
Figure 2: Showing diagrammatically the development of a fishery. In the first stage of development the aim in to increase investment. Total catches rise while catch per effort falls. In the second stage catches start to decline but effort continues to increase, although at a declining rate. The aim is to limit the total effort and stabilize the fishery. In the third stage effort is lower, catch per effort is higher, and the total catch is stable.
If the need to limit the level of fishing effort was not foreseen, either because the data were not collected at all or because they were not acted on, then the signs of stress within the fishing industry begin to show. Total catch declines, the price of fish may rise so that earnings are not greatly affected at first there are demands for the banning of foreign fishing and finally requests for subsidies to maintain the livelihood of fishermen.
The options open to management at all levels can again he evaluated only if sufficient information exists. At the international resource management level the effect of various reductions in total catch or other restrictive measures on the rate of recovery of the stock can be assessed; at the national level the cost of different types of subsidy and of increasing fish imports must be examined., and so on, The severity of the regulatory measures which are needed depends largely on how late these have been left. The history of the regulation of catches of whales, for example, is one of successive regulations coming too late to prevent the situation which they were intended to prevent.
Having analysed rather broadly the underlying biological and economic changes which take place in our general model of development we can clearly recognise the data which are needed to evaluate the options at each stage. One cannot argue that a successful and sustained fishery is only possible if these data are collected, since very few fisheries have to date been developed in this way and some continue to be successful. There are however two important points which should be borne in mind. (1) Fisheries can develop much more quickly now than they have done in the past, particularly when large mobile fleets are involved. (2) The catching power of modern vessels with all their technological side is enormous. The risks involved in waiting to see what will happen are not worth taking, since the means of avoiding them are known.
The data needed initially come from a wide variety of sources. The existing subsistence fishery may furnish some written or oral records of species available, fishing grounds, seasonal fluctuations and types of gear which are effective. Exploratory surveys of various kinds (discussed in FAO Manual on Marine Fishery Resource Surveys, in preparation) can be used to find the best fishing grounds, estimate the likely catch rates and assess, at least approximately, the annual sustainable yield. As the new fishing industry develops the system for collecting information on the landings must be built up, since total catch and fishing effort are the most important statistics of the fishery. The Vital role which statistics of catch and effort from a new fishery play in assessment models will be explained in Section 1.2.2. Another reason for organising the data collection system as early as possible is that the obligation to collect data and the acceptance of management control should be established in the fishing community from the start. The landing information collected from the developing fishery can be used to improve and up-date the original assessments of catch rates and yield and hence to adjust the rate of new investment.
By the time the limits of the resource are reached a good deal should be known about the biological features of the populations involved and more detailed information may be needed on the distribution of catches and effort in order to sake resource- management effective and efficient. The kinds of problems being investigated may include the effectiveness of particular area or seasonal closures as a management measure or the effect of mesh size regulation on a mixture of species. Fish populations are subject to long term and short term changes due -to natural causes as well as man's influence and the demands of the fishing industry also change. Maintenance of the stocks therefore needs continuing monitoring.
We have already described briefly the use of a model for giving advice about the building of hospitals and schools. The model provided a means of simplifying and describing various features of a population of identifying the relationships between these features which needed to be studied, and hence of predicting the outcome of various possible actions. Thus a model links the data available and the advice on a problem, and at the ease time indicates what further data might be, useful to achieve the chosen objective. It may also be used to calculate, at least roughly, the amount of money or time worth spending on the collection of data.
An example of a fisheries model with very simple data requirements is the production model developed by Schaefer. The data needed are the total fishing effort and a measure of the catch per unit effort, which is used as an index of stock size or biomass. If there is no fishing the stock size will be high and will not increase from year to year, because the stock will be at the limit that the environment can support. If fishing effort is very high the stock will be reduced to a small size or eliminated and there will be little or no increase from year to year. In between these levels of fishing effort the stock size will increase from year to year and this is where fishing can take place (see Figure 3 which is just like Figure 1).
The model can be used to predict the yield which will be obtained at different levels of fishing effort, but it is limited because it does not take the age structure of the population into account and because it requires several years of catch and effort information to start with. In particular it needs data from the start of the fishery, when the fishing effort is low and stock size is high. Models which try to explain and predict biological changes in the resource and economic models of the primary phase of the fishing industry are often based on the same or very similar data series as will become apparent in Section 2.
At a simple level the data requirements for such models are:
1. A measure of the catch in weight or value, i.e., the output.
2. A measure of the level of effort put into the fishery which may be in terms of number of fishing boats, hours worked, labour or cost, i.e., the input.
3. A measure of the performance, i.e., catch-per-effort. Since this is simply the output divided by the input, it is obvious that it can be obtained from 1 and 2, and that in general if any two of the three data requirements are met, the third can be obtained from them.
More complex models, which incorporate the age and size structure of the population, are needed in order to assess the immediate and long term effects of regulatory measures such as mesh size restrictions, minimum landing sizes, or catch and effort regulation. Statistics of age and size structure are therefore second in importance to catch and effort for models which test the effect of different management options on the resource.
Statistics of the total weight and the total value of the catch are fundamental to all studies of fisheries dynamics. The total output from the fishery must be known and the catch statistics should therefore be complete. Additional information on the breakdown of the catch by species and areas is required for many purposes and biological studies also require a breakdown by age. In practice the statistics which are easily available are of landed weight of fresh, frozen, gutted or filleted fish, because these are recorded by the fish markets or merchants at the time of first sale (sales notes) and are often kept by the fishermen or producer organizations. In order to have a standard unit for all fish pro ducts, these landed weights have to be converted back to the equivalent live weights, by means of conversion factors, to give the "nominal catch". The actual catch or "gross catch" which case up in the net is greater than the "nominal catch", because some fish are discarded and others may be eaten on board or lost in handling.
Figure 3: Annual production at different stock levels
If the need to limit the level of fishing effort was not foreseen, either because the data were not collected at all or because they were not acted on, then the signs of stress within the fishing industry begin to show. Total catch declines, the price of fish may rise so that earnings are not greatly affected at first there are demands for the banning of foreign fishing and finally requests for subsidies to maintain the livelihood of fishermen.
1.2.4 Statistics of fishing effort and catch-per-unit-effort
The measurement of fishing effort is one of the most difficult and important tasks in fishery research. Confusion is caused because different groups of people use the concept in different ways: (a) in general terms fishing effort is the amount of time, money, labour, technology and skill applied to catching fish, i.e., the work done or scarce resource used, and it is therefore of great interest to economists and technologists; (b) statistics of effort provide the biologist with a measure of the proportion of fish being caught, of the relative abundance and of the mortality due to fishing; (c) catch per boat or catch per landing is perhaps the most widespread index of performance used by practising fishermen. They often remember that in former times catch rates were higher, and many scientific investigations have had their origin in the need to explain declining catch rates. Cooperation in collecting statistics will usually be greatly improved if the information can be processed rapidly and given back in a form which is of interest to fishermen and the industry. Examples of this are the biweekly or monthly summaries of catches, catch rates and value/hr.fished/HP prepared for the trawl fishery off the Ivory Coast (Figure 4, from Fontaneau and Troadec, 1969).
A further discussion of the concept of fishing effort and its measurement is given in Section 2.2.2. Fishing effort is much more difficult to define end to measure than catch, as it is not a simple physical unit. For this reason biologists have developed techniques which do not require the measurement of fishing effort and many management schemes rely on catch regulations rather than effort regulations. Fishing effort data should nevertheless be collected because they are used in economic and technological studies and because, in spite of imperfections, they are the moat accessible measure of abundance and mortality.
Figure 4: example of monthly tabulations of values and profitability for the Ivory Coast trawl fishery . (Transcribed from a computer listing reproduced in Fontaneau and Troadec, 1969).
1.2.5 Other statistics needed for stock assessment
The biologist studying the population dynamics of stocks is trying to determine what level of mortality due to fishing will give the optimum yield in the long term, how the size of the stock is changing due to the currant fishing regime and due to the recruitment of young fish into the stock and how the number of young fish produced each year is affected by changes in the number of adult, spawning fish. All these studies need basic data on the number of fish of different sizes and ages in the stock collected routinely over a number of years, and this basic data comes mainly from sampling of the commercial landings. Except in very new fisheries, which are still in the exploratory stages, the commercial landings will be far greater in volume and wider in coverage than one could expect from research surveys and the cost of obtaining length frequency and age composition data by market sampling will be far lower.
The need for trained staff to carry out such sampling is briefly discussed in Section 1.4 but first the data requirements for a study of the fishing industry will be examined in relation to possible uses.
1.3 The structure and operation of the fishing industry
The distinction made in Table 1 between resource management and national or local industry management is in many ways artificial. The need to distinguish, between them arose because of the problem of common ownership, i.e., the objectives of each individual may not be consistent with the objectives of the group as a whole. If the resource is owned by one individual or management unit then there is no need for the distinction because there will be only one act of objectives. In any case we need to understand the structure and operation of the industry in order to evaluate the possibilities for resource management as well as for-national and local management of the industry.
The fishing industry may be divided into three fairly distinct phases:
Primary phase - catching and landing
Secondary phase - processing
Tertiary phase - marketing and distribution
Within each of these phases one may considers (a) the structure of the sector, for example the fishing fleets, processing plants, and distribution networks; and (b) the operation, divided in-to inputs of fishing effort, labour etc., and outputs of fresh fish, processed fish etc. The inputs are equated with the costs of the operation and the outputs with the revenues. In general, output from the primary phase is one of the inputs to the secondary phase and so on (Figure 5). This kind of analysis can be extended to looking at the connections of the fishing sector with all other aspects of the economy, e.g., labour, exports etc. For example, how would an increase in domestic production of fishmeal reduce the import bill and could such an increase be need to reduce unemployment in particular regions of the country?
In some cases all three of the phases described may be carried out by one individual, e.g., a fisherman who dries fish and sells it to the consumer or by one firm which owns ships, processing plants and distribution networks. Particularly during development of the industry its structure will be dynamic, and one of the aims of planning will be to en sure balanced growth throughout the industry (e.g., -to avoid having too little processing or ice producing capacity for the catching power of the fleet).
In addition to the vertical division into three phases the differences between large scale, industrialised fisheries and small scale, artisanal fisheries are sufficiently important to be included in the scheme of classification. Although the ultimate objectives and hence data,
Figure 5: Showing the movement of the raw material through the industry ( ->) and the points of routine (->) and occasional (-->) data collection, mainly for stock assessment
equirements for small scale and large scale fisheries may he quite similar, the problems of data collection are different and they will be dealt with separately in Section 3.
The fishing industry has a number of features which set it apart from other types of industry and from agriculture. The primary phase is a high risk activity in terms of physical safety and: in terms of return on investment. It is often seasonal and a very high proportion of the product is exported, so that there is a well developed world trade. There is direct international competition for limited renewable resources and this gives rise to international control of the activities of the industry. These and other features mean that the methods of studying the fishing industry and the data requirements are in many ways different from those needs in manufacturing and agriculture.
Reviews of the uses of statistics on North Atlantic fisheries by business and government are given by Parrish (1962). A few examples of the uses made of short term and long-term statistics may help to show how varied these are.
Short-term statistics:
(a) Marketing - currant price information is used by vessel owners when directing the activities of their fleets and by processors and distributors planning their buying. Governments may provide such reports on current prices to help fishermen choose where and when to sell and the government may intervene with price controls or import tariffs.
(b) Current fisheries management - already in many fisheries there is regulation based on daily reporting of catches. This is likely to spread as catch and effort regulations are introduced and the activities of fishing fleets are more strictly directed.
Long-term statistics:
(a) Investment planning - this may include studies of market trends and predictions of catch rates expected from different gears and types of vessel.
(b) Government research - studies of employment and income levels in the fishing industry. Contributions to the work of the international and regional regulatory bodies.
1.3.1 The uses of statistics by economists
Up to now economic studies have been concerned mainly with management at the national and local level rather than with international regulation, pertly because of the difficulty of applying economics in international contexts and obtaining agreement. The result has been that the data compiled by the international regulatory bodies have been almost exclusively biological and even at the national level the collection of biological data has been given greater emphasis. Of course catch and effort data can be used in both disciplines and many of the statistics needed for economic studies can be collected ad hoc, but there is a strong case for extending the routine collection to include data series used for economic modelling.
Economic models are used to study, among other things, (a,) how productively the industry uses inputs such as labour, capital and supplies, and (b) what the connections are between the fishing industry and the rest of the economy. They need statistics of the inputs, which include all the costs of the industry, and of the outputs, which include all the revenues. Some of the costs and benefits, particularly social costs, are intangible, but neglecting them limits the validity of the analysis. Inputs may be converted into terms which reflect their usefulness in other sectors of the economy, i.e., opportunity costs. The use of "shadow costs" for this purpose is discussed by Engtrom (1974). A difficulty which is encountered to a greater extent in the collection of economic data than biological data is that fishermen and companies often wish to conceal or distort figures of costs and earnings, and careful checking of data from a number of sources may be needed to detect this. For example, the monthly revenue figure quoted by a company may be compared with the quantity of various species landed at prevailing prices. Since a certain level of confidentiality about current operations seems to be necessary for many businesses this must be respected in the collection of statistics.
Some details of the data on the structure and operation of the fishing industry which are needed far assessing economic performance and for charting flows are given in Section 2.3. To show that the overall data requirement for management of the industry and for fishery development planning may be much wider than this, we can take as an example part of the table of contents of a paper on the preparation of fishery investment projects (Table 2, after Engstrom, 1974).
The fisheries statistics needed for development planning are discussed by Banerji (1975), but much of the information needed for an understanding of the fishing industry is less specific than this. Among the general information used may be population figures and projections, food consumption, gross national product, investment level, interest rates arid exchange rates. More specific information on fisheries may include taxes and tariffs on fish products and on the equipment and supplies used by the industry, subsidies, special loan rates, local labour availability and wage levels. All this information will come from a wide variety of sources and does not need the kind of routine collecting system with which this manual is mainly concerned. What is perhaps needed is an inventory of such requirements and a catalogue of their availability in different countries.
1.3.2 The uses of statistics by gear and food technologists
The objectives on the technological side of the industry are concerned with improving efficiency rather than simply measuring it. Research and development is carried out in order to improve the methods of finding, catching, handling, processing and marketing fish, to assess the technological requirements of different fisheries or countries, and to look for ways of applying technology towards the overall objectives of fisheries management. For example technical improvements in storage of the catch on board, which reduce spoilage, will give the fisherman more saleable fish of better quality. Better use is made of the gross catch and this may help to increase the income of fishermen or to reduce pressure on the stock.
Many of the routine statistical requirements of gear and food technologists are the same as those of biologists, but usually there is also a far more detailed short-term requirement for particular projects. The rout in requirement is for the usual catch and effort data, plus statistics on quantities of each species condemned, and possibly also information on other quality categories. Details of vessel, technological aids and gear are needed, as is information on the availability of ice and the capacity of storage and processing facilities.
At the detailed, short-term level, day by day or haul by haul information on catch rates may be used in order to assess the capacity and work rate of processing equipment on board. Some idea of the potential for fish meal and oil production can be gained from occasional sampling of the proportion of guts, liver and heads in the processing stage. This can be done at the same time as the calculation of conversion factors for "landings" to "nominal catch". Such detailed, short-term statistics are not usually part of the general statistical collection system because they require special surveys or recording exercises. Because they are often of direct interest to the fishing industry they tend to be easier to collect than statistics whose value is indirect and obscure.
1.4 The institutional framework for data collection and compilation
Even when the objectives of planning, management and development are clearly defined and the importance of the collection of basic data is recognised, the problems of setting up and maintaining a collecting and processing system may be very great. If the need for collection is only recognised as a result of adverse changes in the fishery then speed in implementing such collection is paramount and it will often be too late to prevent a situation which could have been foreseen. There is increasing emphasis on the need for all those participating in a fishery to collect and report the basic data on catches and effort.
Table 2: Information needed for project formulation of fisheries development programme
PROJECT FORMULATION
a) Description of Project and Project Ares
b) Analysis of Fishery Resources and Catch Projections
c) Analysis of Demand and Sales Prices
d) Technical Feasibility
(i) General
(ii) Fishing operations
(iii) Landing facilities
(iv) Processing end. storage
(v) Marketing and distribution
e) Financial Feasibility
(i) Investment cost operating costs and cash flew
(ii) Financing of the. project
(iii) Financial profitability-internal financial return
f) Impact on national economy
(i) Economic profitability
(ii) Supply of food for domestic consumption
(iii) Employment creation
(iv) Income redistribution
(v) Foreign exchange
(vi) Regional development
(vii) Public finance
(viii) Other objectives
Most countries now have some form of collection of basic data on their fisheries, but the way in which the responsibility for collection and analysis is organised varies greatly. Figure 6 shows two extreme forms which the organization can take, one with all the functions performed within a single ministry, the other with each function separate. While there are disadvantages to having a single ministry with complete control over the sources and uses of information it is obvious that the advantages are very great. Fisheries administrators and planners must have some control over the kind of biological and economic research which is done and these immediate users of the statistics must in turn be directly involved in the setting up, development and evaluation of the data collecting systems. The organization of systems for data collection often has as much to do with the establishment of communication procedures as with direct recording and measuring. As well as basic data on the fishing industry and the resource itself, we have seen that many studies require much wider statistical information on the economy and the population as a whole. A full treatment of the general problems of setting up and maintaining a national statistical system are dealt with in a "Handbook of Statistical Organization" published by the United Nations, New York (Studies in Methods Series F. No.6). The particular problems of fishery statistical systems are dealt with by FAO, Fisheries Division (1965).
Even in the initial stages of setting up a statistical collection system for national and regional purposes, it is necessary to think ahead in order to design a system that will allow for subsequent growth and refinement. Not only can time, staff and funds be saved in this way, but the system should be capable of responding to change in a way that continues to make management on a rational basis possible. With the present rapid changes in jurisdiction and method of regulation, even the most sophisticated data collecting and processing systems in existence are frequently pushed beyond their capacities. The lag during which they catch up has to be filled with management based on guess rather than data. The scientific disputes which lie behind many recent failures in management are usually about which guess to adopt rather than about methods of work and models.
Because of limitations in the amount of money available for setting up and maintaining a statistical collection system and because it will take time for the organization, skills and facilities to develop in a country, there must be a system of priorities for the data to be collected. As well as specifying the priorities of the different categories of data, the qualities of accuracy, precision and timeliness must be considered. For example, how accurate and precise should the estimates of total catch be before it is worth allowing tires and money for the collect ion of effort data? In practice if a survey is to be carried out to collect catch data then effort and other data can be collected at the same time with little extra cost. Notes on priorities and collection of fishery statistical series for the use of less developed countries are given by FAO, Department of Fisheries (1975).