ADDSS: a tool for regional aquaculture developmentdirectory.umm.ac.id/Data...

Aquacultural Engineering 23 (2000) 181–202

ADDSS: a tool for regional aquaculturedevelopment

Omar F. El-Gayar a,*, PingSun Leung b

a Department of Information Technology, Institute of Graduate Studies and Research,Uni6ersity of Alexandria, 163 El-Horreya A6e., El-Shatby, Alexandria, Egypt

b Department of Biosystems Engineering, Uni6ersity of Hawaii at Manoa, 3050 Maile Way,Gilmore 111, Honolulu, HI 96822, USA

Received 10 June 1998; accepted 14 July 1999

Abstract

Aquaculture is considered as a viable source for providing high quality and cheap protein,particularly for developing countries with protein shortages. However, due to its complexi-ties, aquaculture development planning can be difficult without the aid of modern decision-making technologies such as decision support systems (DSS). DSS are widely used in thebusiness and manufacturing sectors, but their use in agriculture is limited and in aquacultureis almost nonexistent. This paper presents the design and implementation of an aquaculturaldevelopment decision support system (ADDSS) that would systematically aid the planner inmaking choices regarding the development planning of the aquaculture industry for a givenregion. The system is composed of three main components: a model base containing allrelevant models which are essentially multiple objective in nature, a database containing allrelevant data, and a dialog component providing a user interface to the other twocomponents of the system. The application of ADDSS is demonstrated through a case studyfrom Northern Egypt where aquaculture appears as a viable industry for supplying cheapand good quality protein, balancing the foreign exchange deficits, improving the standard ofliving, and creating employment opportunities. © 2000 Elsevier Science B.V. All rightsreserved.

Keywords: Decision support systems; Multiple criteria decision-making; Aquaculture

www.elsevier.nl/locate/aqua-online

* Corresponding author.E-mail addresses: [email protected] (O.F. El-Gayar), [email protected] (P. Leung)

0144-8609/00/$ - see front matter © 2000 Elsevier Science B.V. All rights reserved.PII: S0144 -8609 (00 )00043 -1

182 O.F. El-Gayar, P. Leung / Aquacultural Engineering 23 (2000) 181–202

1. Introduction

1.1. General background

Personal computers, computer networks, large databases, color graphics andcomputer-based models are among the technological developments which stimulateinterest in the use of computers to support decision-making. Such uses are called‘decision support systems’ (DSS). Decision support systems are used as aids toenhance the effectiveness of the decision-making process in organizations. Cur-rently, many organizations operating in various business domains utilize DSS.Aquaculture is one of the fields that are looked upon as a potential applicationdomain for advanced information technologies, including decision support systems.

The importance of aquaculture stems from the fact that worldwide demand forquality fish protein is increasing dramatically, while in the meantime, the naturalfisheries are near their maximum sustainable yield (MSY) levels and are in theprocess of depletion. In addition, when fish are compared to alternative sources ofprotein such as terrestrial livestock, the following can be deduced:1. Fish have better feed conversion ratios.2. Fish protein is better quality, as it is associated with a low content of calories,

a low content of saturated fatty acids, and a high content of polyunsaturatedfatty acids (V-3). This is a healthier diet with respect to its contribution inpreventing cancer, heart diseases, vascular diseases and rheumatic diseases.

The implications of such an increase in demand are twofold. First, the increasein demand has put tremendous pressure on limited resources, such as land andlabor, required for aquaculture development. As a result, in many regions, aquacul-ture is moving towards more intensive operations taking advantage of the improvedproduction technologies. Moreover, the decision-making process is further compli-cated because of the dynamic and stochastic nature of the biological, physical andeconomic environments, thereby emphasizing the need for developing decision aidssuch as DSS for better management of aquacultural facilities.

Second, more sincere effort is dedicated towards aquaculture development as analternative source of high quality cheap protein, particularly for developing coun-tries where protein shortages already exist. Such effort is led by local governmentsas well as several international organizations that are involved in planning andfinancing aquaculture development in various regions of the world. The WorldBank, the Food and Agriculture Organization of the United Nations, and theUnited States Agency for International Development are just a few examples ofsuch organizations. Due to the wide range of issues involved in regional aquacul-ture development and the inherent complexities of these issues, the developmentand use of decision aids such as DSS as well as the application of new informationtechnology techniques are inevitable.

During the last decade, a tremendous increase in the number of decision supportsystems (DSS) applications is reported in the literature (Eom and Lee, 1990),thereby acknowledging DSS as a useful technology that can improve decision-mak-ing in organizations. Moreover, while DSS applications are increasingly used in the

183O.F. El-Gayar, P. Leung / Aquacultural Engineering 23 (2000) 181–202

effective management of natural resources as seen in recent conferences (ASAE,1993; WASWC, 1995), aquaculture has not yet had its share. However, with therapid increase in the number of models developed for aquaculture, together withtheir associated data requirements, interest is rising in the development of inte-grated DSS that pool models and data into one easy to use computer systemsuitable for practical applications, either operational or strategic (Leung, 1993).

In that respect, Muench et al. (1986) developed a commercial computerizedpackage for managing pond data (Pond Manager©) and evaluating optimal har-vesting strategies (Harvest Optimizer©) for pond aquaculture. This marks thebeginning of a simple aquaculture decision support system. More recently, Ernst etal. (1993) developed a DSS for finfish aquaculture that is entirely concerned withoperational level issues. The DSS consists of an integrated set of computerizedtools, including mathematical programming and logical algorithms, simulationmodels, expert systems and graphical user interface that provide applied andintegrated expertise in fish genetics, biology and culture, aquatic chemistry, engi-neering, and ecosystem processes. On the other hand, in regard to DSS targeted forstrategic and planning purposes in aquaculture, none have been reported in theliterature until this point in time.

The application of multi-level and multi-objective analysis in aquaculture is veryfew. In fact, Sylvia and Anderson (1993) is the only study to date using such ananalytical framework. They developed a bio-economic model for developing infor-mation for private and public salmon aquaculture policy strategies when environ-mental issues are important. The two levels of analysis refer to the two actors —salmon producers and policy makers. While the producers are assumed to maximizeprofits, the public policy makers are faced with four policy objectives includingrevenue, benthic quality, profits, and tax revenues. The policy instruments in theirstudy include the number of allowable sites and an effluent tax.

On the other hand, multiple criteria decision-making (MCDM) techniques arealready in use in agriculture on an operational as well as on a strategic level. Withregard to operational models, Romero and Rehman (1989) provided a comprehen-sive survey of agricultural MCDM models. However, only a few models aredeveloped for strategic purposes. One of the early models developed in that respectis the multi-regional, single time period linear goal programming model by Bazaraaand Bouzaher (1981) for agricultural planning in a developing economy. Theeconomic aspects are addressed through the incorporation of several objectives asopposed to the conventional approaches which stress maximization of ‘economicwelfare’, defined as efficiency through maximum social product without consideringincome distribution. The policy objectives considered are regional employment,foreign exchange expenditures, and regional demand satisfaction. Regional employ-ment reflects the emphasis on income distribution while regional demand satisfac-tion reflects the importance of limiting foreign trade deficits and providing basicnutrition for the population. The constraints considered are for land, labor, water,machinery, fertilizer, and capital resources. The decisions suggested by the solutioninclude acreage allocated to different crops, sequences of crop rotations, fertilizerlevels, and transportation and distribution of crops and livestock among regions.The model is illustrated using data from the agricultural sector in Egypt.


Tapia (1990) developed a multiple objective linear programming model foragricultural planning. The model seeks to determine which types of crops to plant,in what months of the year and in what different ratios for a given farm in orderto maximize net total income, to maximize total yield per unit land area, tomaximize land usage, and to minimize production costs. The constraints consideredare land constraints, vegetable family constraints indicating that no vegetable typesbelonging to the same family can be planted in immediate succession, seasonalityconstraints as not all vegetables can grow in certain months, and market availabil-ity constraints which ensure a significant harvest of vegetables belonging to aspecific family. The model is solved as a multi-objective mathematical programmingformulation.

Tabucanon (1990) suggested MCDM as a framework for an analytical inquiryinto policy issues on food commodities. The main motivation behind his choice liesin the various criteria inherent in food policy issues as they relate to socio-economicand political goals. Tabucanon overviewed MCDM, its process, techniques andapplications as it relates to food policy analysis.

1.2. Objecti6es

The main objective of this work is to design and implement an aquaculturaldevelopment decision support system (ADDSS) that aids the decision maker or theplanner in making choices regarding the planning and development of the aquacul-ture industry in a given region. Given the inherent multiple objective nature ofaquaculture planning, and due to the lack of multiple criteria decision-making(MCDM) models for regional aquacultural development, it is also the objective ofthis work to develop such a model. The applicability of the system is thendemonstrated through a case study from Egypt.

2. Methods

2.1. ADDSS system components

ADDSS includes three main components: a modeling, a database and a dialogcomponent as shown in Fig. 1. The arrows in Fig. 1 denote possible interactions, inwhich the user, through the dialog component, can access the modeling component(for analysis purposes) and the database component (for data-entry/query). Fur-thermore, the modeling component can interact with the database component foraccessing data and storing results.

2.1.1. The dialog componentThe dialog component provides a user interface to the other two components of

ADDSS: the database component and the modeling component. In ADDSS, thedialog component is a graphical user interface (GUI) with a pull-down menustructure that operates under Microsoft’s Windows© environment as shown in Fig.


2. The dialog component takes full advantage of Windows multi-tasking capabilityas well as the multitude of user interface objects available under Windows.Multi-tasking is particularly useful as it allows the user to switch between varioustasks (programs) in an easy manner rather than the traditional sequential approach,thereby mimicking the way people normally think. It should be noted, however,

Fig. 1. ADDSS framework (adapted from Sprague and Carlson, 1982).

Fig. 2. The database menu popup.


Fig. 3. The modeling menu popup.

that while multi-tasking capability is available under Windows, its introduction andutilization significantly complicates the design and implementation of the dialogcomponent. The dialog component is implemented using the screen and menusub-systems of Microsoft’s FoxPro© development environment together with Fox-Pro’s programming language.

The two primary menu pads in ADDSS’s user interface are the database and themodeling menu pad. The database menu pad (Fig. 2) provides an interface to thedatabase component while the modeling menu pad (Fig. 3) provides an interface tothe modeling component which includes model specification, building and running.

2.1.2. The database componentThe database component is composed of a database management system

(DBMS) and a database containing all relevant data as shown in Fig. 4. TheDBMS provides the interface between the database and the other ADDSS compo-nents. The database, in turn, is a pool of all relevant data pertaining to the policyobjectives and the issues involved when planning for regional development foraquaculture. The database contains region-, species- and feed-specific data asshown in Fig. 4. Region-specific data include climatic conditions, populationstatistics and market statistics such as fisheries production, aquaculture production,


other protein sources such as beef and poultry, current protein consumption percapita, projected export demand, etc. Species-specific data include data regardingthe optimum culture environment and the different culture technologies. Feed-spe-cific data include ingredients’ analysis, feed formulas, and manufacturingtechnologies.

The relational database structure is designed in a top down fashion independentof the modeling component. Moreover, particular emphasis is given to datanormalization to eliminate data redundancy. In the conceptual database design(Fig. 5), each aquaculture growout production system has several productiontechnologies, e.g. a sea bream monoculture production system can be grown usinga semi-intensive or an intensive production technology. Growout production sys-tems data include the name of the system, while growout production technologydata include information on resource requirements such as water, land, capital andlabor.

To allow for polyculture, the simultaneous cultivation of several species, eachgrowout production technology is assumed to grow multiple species. Data includedrepresent the fry requirements for each species as well as the production per unitarea. Other data pertinent to the cultivated species include designated usage, e.g.whether it is designated for human consumption or not, domestic and export prices,domestic and export market demands, amount of fry available in the wild, etc.

Besides the possible availability of fry in the wild, fry may be produced incaptivity using hatchery production technologies. Hatchery production technologydata include information on resource utilization similar to those for the productionsystems technologies.

Fig. 4. The database component.


Fig. 5. Database conceptual design.

On the other hand, each growout production technology uses more than one feedtype, which in turn can be produced by utilizing various levels of technologies usingmultiple feed ingredients as inputs. Feed data include import and export prices,


while feed production technology data include details on resource requirementssuch as labor, land, water, and capital. With multiple feed ingredients used in theproduction of a particular feed, feed ingredients data include the feed ingredientrequirements for every feed production technology. Other data pertinent to feedingredients include the quantities available domestically as well as their importprices. Finally, resource information such as the amounts available domestically ofeach resource type is also stored in the database.

Microsoft’s FoxPro DBMS provides all required data management capabilitiesincluding commands for adding, deleting, updating, browsing, listing, retrieving,and sorting records as well as indexing, importing, exporting and creating databasefiles. Moreover, programs operate in a multi-tasking fashion allowing more thanone program to operate concurrently, thereby greatly facilitating the switch fromone database to the other. Fig. 6 is a sample data entry screen for the speciesdatabase.

2.1.3. The modeling componentThe modeling component is composed of a model base management system

(MBMS) and a model base containing all relevant models as shown in Fig. 7. TheMBMS provides the interface between the model base and the other ADDSS

Fig. 6. Species database.


Fig. 7. The modeling component.

components. The model base is a pool of all relevant models pertaining to thepolicy objectives and issues involved when planning for regional development foraquaculture. In this version of ADDSS, the role of the model base managementsystem reduces to the management of the MCDM model and techniques developed,by providing provisions for model specification, building, and execution (Fig. 3).

In aquaculture development, the planners are often confronted with a multitudeof policy objectives that they seek to realize through the development of theregion’s aquaculture industry. Examples of such policy objectives include producinghuman food, improving natural stock, improving the standards of living, andincreasing foreign exchange earnings (Pillay, 1977). In realizing such policy objec-tives, the planners have to make decisions regarding the level of activities (decisionvariables) that would strike an acceptable balance among these conflicting policyobjectives. Examples of such decision variables include what species to grow, whattechnology to use, how much to grow of each species and/or technology, etc.Moreover, the planners are constrained by the resources available in the regionsuch as land, labor, water, etc., as well as other external constraints such asdomestic market demand, export market demand, and pollution restraints. TheMCDM model thus seeks to assist the planners in identifying feasible (i.e. satisfyingresources and external constraints) alternatives that balance the multiple policyobjectives encountered when planning for regional aquaculture development.

Several techniques are available for handling MCDM problems (Zeleny, 1982;Tabucanon, 1988; Romero and Rehman, 1989). Such techniques vary in theirsuitability to handle different decision situations. For example, when the plannerfaces a situation in which it is desired to optimize a set of objectives, then therecommended approach can be either multiple objective programming (MOP) orcompromise programming (CP) depending on the number of objectives considered.


The MOP technique attempts to generate the efficient set, which is a subset of thefeasible solutions in which no other feasible solution can achieve the same or betterperformance for all policy objectives under consideration and strictly better for atleast one objective. It is then left to the planner to choose from such sets. However,as the number of objectives increases, the size of the efficient set grows exponen-tially thereby overwhelming the planner with a large number of efficient solutionsthat render it extremely difficult for the planner to compare, contrast, and finallychoose from the set of efficient solutions. MOP is thus recommended only whentwo objectives are considered. This not only results in an efficient set of manageablesize, but also allows for graphing the efficient solutions in the objective functionspace thereby exemplifying the tradeoffs between the objectives under consider-ation. The main purpose of MOP is to establish the set of efficient solutions. Thiscan be accomplished by several methods which include the constraint method, theweighting method, and the non-inferior set estimation method (NISE) (Romero andRehman, 1989). In ADDSS, the weighting method is used to obtain an approxima-tion of the efficient set while the NISE method is used to obtain the exact set.

When the number of objectives is greater than two, CP is recommended. CPsearches for a best compromise solution among the efficient solutions and presentsonly that solution to the planner, so avoids overwhelming the planner with a largenumber of efficient solutions to choose from. Given the efficient set, CP seeks thebest compromise solution based on some information regarding the preferences ofthe planner with respect to the objectives under consideration. CP assumes that anyplanner seeks a solution as close as possible to the ideal point (vector) comprised ofthe ideal values for all the relevant objectives. The distance function is thusintroduced as a proxy measure for human preferences regarding the closeness to theideal point (Romero and Rehman, 1989).

On the other hand, when the planner faces a situation in which multiple policyobjectives exist, i.e. aspiration levels or target values for all relevant policyobjectives are known a priori, then the recommended technique is weighted goalprogramming (WGP). WGP optimizes multiple goals (policy objectives) simulta-neously by minimizing the deviations among the desired levels of targets and theactual goal values through the addition of positive and negative deviation variablespermitting either under- or over-achievement of each goal. WGP considers all thegoals simultaneously in a composite objective function which minimizes the sum ofall the deviations among the goals from their respective aspiration levels (targets).The deviations are weighted according to the relative importance of each goal tothe planner (Schniederjans, 1995).

In ADDSS, model specification includes assigning weights and targets to thepolicy objectives in the model (Fig. 8), selecting resources and external constraintsto incorporate in the model, and selecting the decision variables (activities) toconsider when solving the model (Figs. 9 and 10). Building the model involvescompiling the model specifications, acquiring the required data from the database,and generating a GAMS© (the acronym stands for General Algebraic ModelingSystem) program file for solving the specified model. Finally, model executioninvolves selecting the desired MCDM solution technique and activating the GAMS


system with the corresponding program file (Fig. 11). A detailed description of theMCDM model and the different MCDM techniques can be found in El-Gayar(1995).

2.2. De6elopment issues and system features

Each of ADDSS’s components has its own processing requirements as well as itsown development environment. With regard to the dialog component, a window-based graphical user interface represents the state of the art. As such, the choice ofthe development environment should facilitate the design and implementation ofsuch interfaces. Moreover, the flexibility desired from the proposed system requireseasy modifications of the user interface. On the other hand, the database compo-nent requires powerful data management capabilities that are provided by special-ized commercial data management systems and with less emphasis on processingefficiency. Such capabilities realize desired design features such as flexibility, lowcost and generality. In addition, the modeling component requires the use of eitherspecialized solvers or an implementation language that results in efficient code, dueto the processing requirements involved.

Fig. 8. Weight and target assignments for aquaculture development policy objectives.


Fig. 9. Selection of a set of decision variables.

The challenge thus imposed, is the choice of development tools that meet suchrequirements and realize the desired design features. The development environmentwhich satisfies the basic requirements of the dialog and database components isFoxPro© for Windows version 2.6 of Microsoft Inc. — FoxPro© belongs to theapplication category known as Relational DataBase Management Systems(RDBMS). Likewise, GAMS© is used to develop and implement the MCDMmodel and techniques representing the core of ADDSS. GAMS© is designed tofacilitate the construction and solution of large and complex mathematical pro-gramming models.

Special features and characteristics of ADDSS include user friendliness, general-ity, low cost, speed, and flexibility. For ADDSS to be efficiently used, the systemshould possess a friendly user interface. This is attained through the use of awindow-based graphical user interface. The use of FoxPro© with its powerfuldesign tools (such as the screen generation utility) facilitated the design of the userinterface as well as any future modification of the interface.

Besides international development agencies, the primary beneficiaries of ADDSSare local governments in developing countries with limited financial and technolog-ical capabilities. Therefore it is important to reduce the cost of the system by


designing it to operate on an IBM PC and compatible machines instead ofspecialized expensive workstations, mini-computers or mainframes. Furthermore,the modular design of the system allows for easy addition of models to the modelbase, thereby reducing the need for in-house model development and its associ-ated costs. Moreover, the use of a dedicated database development system with itsassociated high-level programming language (Xbase dialect) for system implemen-tation significantly reduces development times and cost.

Another feature of ADDSS is generality. ADDSS can accommodate variousregions by simply incorporating the relevant data and model specification. Theuser-friendly interface enables the user to easily enter the relevant data and modelspecification. Alternatively, the user can utilize FoxPro’s powerful file import andexport capabilities to load the relevant data directly into the system. Otherfeatures that are pertinent to the MCDM model include provisions for the choiceof policy objectives, the choice of constraints, the choice of variables to beincluded in the model, and the choice of an appropriate MCDM technique.

ADDSS modular design also facilitates the modification of the system toaccommodate various problems and/or situations as well as the inclusion or

Fig. 10. Selection of individual decision variables within a decision variable set.


Fig. 11. Selection of MCDM solution technique.

exclusion of data, models or analysis routines. In effect, the top down design of thedatabase in a fashion independent of the models in the model base, and the use ofa specialized database management system (DBMS) help realize much of thedesired flexibility and in particular, data–model independence. Also, with regard tothe dialog component, the choice of the integrated database development environ-ment facilitates the inclusion and exclusion of modules from the system. For themodeling component, GAMS© provides a high level language for the compactrepresentation of large and complex models thereby allowing changes to be made inmodel specifications simply and accurately. GAMS© also permits model descrip-tions that are independent of solution algorithms.

Finally, ADDSS processing speed is enhanced by the use of a specialized modeldevelopment system (GAMS©) and its accompanied solvers. Moreover, the choiceof the development environment allows for the utilization of assembly language andC programming language for model implementation, if necessary, thereby produc-ing efficient routines.


3. Results and discussion

3.1. Case study: application of ADDSS to Egyptian regional aquaculturalde6elopment

With the agriculture sector representing 20% of GDP, 18% of total exports, and36% of total employment, the sector is looked upon as a potentially importantvehicle for reaching the Government’s objectives of achieving balanced growth inthe rural economy of Egypt (World Bank, 1993). Aquaculture, which is consideredas part of agriculture, can play an important role in realizing these objectives. Thecase study is concerned with resolving issues pertaining to the planning foraquaculture development in Northern Egypt. Aquaculture is considered to be aviable industry in this region with significant potential for supplying cheap and goodquality protein, for helping to balance the foreign exchange deficit, for improvingthe standards of living and finally, for creating employment opportunities.

Northern Egypt is characterized by being able to accommodate a wide variety ofaquaculture production systems and technologies. Such production systems incorpo-rate a variety of species, primarily sea bream, sea bass, mullet, tilapia and carp thatare grown separately (monoculture) or in combinations (polyculture). Moreover,production systems can be employed at different technology levels ranging fromintensive to semi-intensive to extensive culture systems. With such a variety ofsystems and technologies which vary in yield, product quality, profit, and utilizationof resources, the choice of the system and the level of technology to employ wouldclearly affect the levels of the different policy objectives. Moreover, the possible needto compromise among these attributes further complicates the decision-makingprocess.

It should also be noted that the choice of production systems and technologies isnot the only controversial issue. Similar issues include the choice of fry and feedproduction systems and technologies. Export and import decisions also play a role;of particular interest is identifying how much to import and how much to export foreach species and feed type.

3.2. The results

In this case study we consider two policy objectives, namely, regional availabilityof protein and foreign exchange earnings1. Since no target values are known a priorifor both objectives, multiple objective programming is most suited to the case studyunder consideration.

By using MOP, the planner can inspect the entire efficient set and evaluate thetrade-off involved. While the efficient set (Fig. 12) represents the trade-off curvebetween regional availability of protein and foreign exchange earnings (Romero andRehman, 1989), the actual values of the trade-off (i.e. the opportunity costs) are

1 Income and employment are also important objectives that can be handled easily using ADDSS butare not considered for this illustrative case study.


represented by the slopes of the straight lines connecting the extreme efficientpoints. For example, the slope of the line segment AB in Fig. 12 indicates that inthis part of the trade-off curve, each thousand ton increase in regional availabilitydecreases foreign exchange earnings by 0.2 million Egyptian pounds (L.E.) In otherwords, the opportunity cost of increasing regional availability of protein by 1000tons is a sacrifice of 0.2 million L.E. of foreign exchange earnings.

In general, the planner is to choose the best-preferred plan from the set ofefficient solutions represented by the trade-off curve. The planner’s choice dependson the preferences that the planner attaches to each objective. However, inspectingFig. 12 reveals that with no significant trade-off between the two objectives to theleft of point B, point B can represent an appropriate policy alternative if foreignexchange earnings is highly preferred by the planner. This is referred to as the‘foreign exchange policy’. At the other extreme, point E can represent an appropri-ate policy alternative if regional availability of protein is highly preferred to theplanner while not exhausting available foreign reserves. This is referred to as the‘food security policy’. As a compromise, points C and D can represent anappropriate policy alternative if the planner seeks a policy that strikes a balancebetween the two objectives. Such policies are referred to as the ‘compromise policy’.Table 1 summarizes the solutions recommended under the different possible policyalternatives.

Fig. 12. The efficient set for regional availability of protein and foreign exchange earnings using the NonInferior Set Estimation (NISE) method.


Tab

le1

Pol

icy

alte

rnat

ives

Pol

icy

BP

olic

yC

orD

Pol

icy

E

Pro

tein

:98

%;

exch

ange

:0%

Obj

ecti

ves

Pro

tein

:25

%;

exch

ange

:97

%P

rote

in:

60%

;ex

chan

ge:

56%

Pro

duce

for

dom

esti

cco

nsum

ptio

n.P

rodu

cefo

rex

port

.N

ofis

hor

feed

Bal

ance

sth

eal

loca

tion

ofre

sour

ces

Dec

isio

nim

port

s.Im

port

requ

ired

ingr

edie

nts

vari

able

sbe

twee

nex

port

san

ddo

mes

tic

Impo

rtca

rp.

Impo

rtfe

edan

din

gred

ient

sco

nsum

ptio

nR

ice

bran

;w

ildfr

y(b

ream

and

bass

);R

esou

rces

Ric

ebr

an;

wild

fry;

brac

kish

wat

er;

fres

hR

ice

bran

;w

ildfr

yw

ater

brac

kish

wat

erD

omes

tic:

sea

bass

and

mul

let.

Exp

ort

Mar

kets

Dom

esti

c:se

abr

eam

,se

aba

ssan

dm

ulle

t.E

xpor

tm

arke

tssa

tisfi

edm

arke

tssa

tisfi

edE

xpor

tm

arke

tssa

tisfi

ed


Under the foreign exchange policy (point B), foreign exchange earnings attains alevel of 101 million L.E. representing 97% of its ideal value, while regionalavailability of protein attains a level of 22 000 tons representing 25% of its idealvalue. Such relatively high foreign exchange earnings is realized through gearingdomestic production towards the production of exportable fish and feed as opposedto producing fish for domestic production. Moreover, imports are reduced toimporting feed ingredients such as soybean and fish meal that are essential for theproduction of exportable fish and feed. Only rice bran and wild fry supply arelimiting resources. Moreover, export market demands of fish and feed are satisfied.Thus, to further enhance foreign exchange earnings, it is not only important toexploit additional feed ingredients and fry resources or to better utilize thoseavailable, but also to increase marketing efforts aimed at promoting the demand fordomestic aquaculture products.

Under the food security policy (point E), regional availability of protein attainsa value of 86 000 tons representing 98% of its ideal value without utilizing availableforeign reserves. Such relatively high level of regional availability of protein isattained by gearing domestic production towards supplying domestic consumptionas well as by importing fish such as carp. The production of fish and feed for exportis maintained to finance the imports of fish for domestic consumption, and feed andfeed ingredients for supporting the domestic aquaculture industry. Fresh water,brackish water, wild fry, and rice bran (feed ingredient) are limiting resources. Withbrackish water used in the production of fish for domestic consumption and export,it is thus important to consider water-saving production technologies such asre-circulating systems. Moreover, the limited supply of rice bran emphasizes theneed to consider alternative ingredients or to secure additional supplies. At thedomestic front, only sea bream, sea bass, and mullet supplies satisfy their respectivemarket demands indicating that there is room for increased production of tilapiaand carp provided additional resources particularly fresh water, brackish water andrice bran are available. However, export markets are satisfied for both types ofexportable fish species and exportable feed types thereby indicating the potentialdifficulty in financing the additional imports of feed and feed ingredients requiredif regional availability of protein is to increase through the increase or betterutilization of limiting resources. Thus, to increase regional availability of proteinwithout utilizing available foreign reserves, it is not only important to seekadditional resources, but also to consider expanding the export demand fordomestic aquaculture products and feed.

Under the compromise policy (point C or D), regional availability of proteinranges from 52 to 54 000 tons representing 59 and 62% of its ideal value,respectively. On the other hand, foreign exchange earnings range from 61 to 57million L.E. representing 58 and 55% of its ideal value, respectively. The compro-mise policy thus strikes a balance between the two objectives under consideration.Such policy is realized through balancing domestic production between providingfish for domestic production and providing fish for export as well as eliminating theimport of fish. As is the case with the food security policy, brackish water and ricebran are limiting resources, while mullet and sea bass demands are satisfied.


In effect, MOP can present a number of policies to the planner to choose from.The final choice, however, depends on the planner’s preferences with regard to thetwo objectives. Nevertheless, upon inspecting the three policy alternatives, andirrespective of the policy alternative chosen, two observations are worth noting.First, wild fry for sea bream and sea bass are limiting resources thereby emphasiz-ing the importance of investigating the possible means of increasing wild fry supply.Means of increasing wild fry supply include, regulating the fishing of broodstock,regulating pollution levels in water ways and in the northern lakes which are fertilebreeding grounds for broodstock fish, and re-stocking the northern lakes with frythereby replenishing the depleted broodstock. Second, export market demand foraquaculture product is always satisfied indicating the importance of promotingdomestic production for the export markets. A detailed description of the solutionand ADDSS’s output can be found in El-Gayar (1995).

3.3. Discussion and suggestions for further impro6ement

Decision support systems (DSS) can provide the means for delivering aquaculturemodels to the end-user whether a farmer or a policy maker. Nevertheless, the useof DSS in aquaculture is still limited particularly when compared to other fields.ADDSS is one further step towards the adoption of DSS concepts in aquaculture.ADDSS’s strength lies in its user-friendliness particularly when allowing the user toenter the data and model specification for the problem under consideration. Thisfeature is useful when planning for regional aquaculture development where theuser is confronted by multiple and conflicting goals. A situation in which the stepsfor reaching an acceptable solution is often not clear, thereby requiring the user toexplore the problem at hand by changing the model specification to uncover theinterplay of the goals in the model.

However, there are limitations to the system. In effect, these limitations reflect alimitation of the underlying MCDM model. First, the model is static. The solutionthus reflects the steady state of the system. As such, the model does not capture thepath to this steady state, a feature that can be important in situations when goalsare not realized simultaneously.

Second, although aquaculture is a risky business and many parameters built intoaquaculture models are stochastic, this model is deterministic. Nevertheless,ADDSS’s user-friendliness and the ease of performing sensitivity analysis bychanging the desired parameters and exploring alternative scenarios mediate theuncertainty in the parameters.

Third, the solution of the MCDM model reflects the desirable allocation ofresources among competing activities (decision variables) that compromise amongmultiple and conflicting goals. Examples of these competing activities include whatspecies to culture, what production technology to utilize, whether to produce forexport or for local consumption, and so forth. However, unless we are dealing witha planned economy, the next step in a policy formulation setting is the translationof the optimal level of decision variables into appropriate policy instruments suchas taxes, subsidies, import/export controls, etc. This is where firm level DSS can


assist in exploring the response of the aquaculturist to government policyinstruments.

In addition to the aforementioned issues, the following directions and recommen-dations are suggested for further development:1. The expansion of the MCDM model to capture the quantity–price interactions

in the different fish and feed markets, both at the domestic and at the exportlevels. It should be noted, however, that such added complexity that increasesthe data requirements of the system and thus cost, does not necessarily entailadded benefit in terms of increased accuracy.

2. The incorporation of pollution as a decision attribute in the MCDM modelinstead of an external constraint thereby allowing the planner to explicitlyaddress the trade-off between environmental versus economic objectives which isan important issue facing aquaculture today.

In summary, the potential usefulness of ADDSS and similar types of decisionsupport models in a highly complicated and risky ‘free market environment’ thuslies in their application in exploring elements of the problem, revealing possiblerelationships, and illuminating the policy debate (Sylvia, 1997). Given the complica-tions of human behavior and the dynamics and uncertainties characterizing globalmarkets, it is impossible for policy makers to accurately realize policy goals. Whilesignificant understanding may help policy makers guide development, the complex-ities, uncertainties, and realities of this complex industry will limit their ability tosocially engineer a solution.

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