JOURNAL OF THE WORLD AQUACULTURE SOCIETY

Vol. 34, No. 1 March, 2003

Management Strategies for Seabream Sparus aurata Cultivation in Floating Cages in the Mediterranean Sea and

Atlantic Ocean EUCARIO GASCA-LEYVA

Center for Research and Advanced Studies, Me'rida, Apdo. Postal 73-CORDEMEX, 9731 0 Me'rida, Yucatdn Me'xico

CARMELO J. LEON Department of Applied Economic Analysis, Univ. de Las Palmas de G. C. 3501 7

Las Palmas de G. C. Spain

JUAN M. HERNANDEZ Department of Quantitative Methods for Economics and Management,

Univ. de Las Plamas de G. C. 3501 7 Las Palmas de G. C. Spain

Abstract For decades, research and development efforts have focused on fish cultivation in floating

cages. Despite this focus, and the considerable private and public sector resources invested in these systems, there is a scarcity of economic data derived from the great number of variables present in these systems. A reliable way of generating economic studies is through systems modeling that relates biological, environmental, technical, and economic variables. This study presents results from a computer simulation of seabream production in floating cages under two scenarios: one representing conditions in the Mediterranean Sea, and the other in the Atlantic Ocean. A company operating in the Atlantic (Canary Islands, Spain) provided the majority of data, though some of them were provided by the University of Las Palmas, also in the Canary Islands. Given the assumptions in the simulation case study, the production cost for 1 kg of seabream in floating cages in the described systems is US$2.64 in the Mediterranean and US$2.90 in the Atlantic, and the internal rates of return were 27% and 59%, respectively. The results of model sensitivity analysis for both scenarios show that this cultivation system is more sensitive in the Mediterranean than in the Atlantic. This means that changes in system variables convey lower effects on the production and economic results in the Atlantic scenario than in the Mediterranean. In both scenarios, the feed ration size was one of the variables that improved feed conversion rates. It also improved costs and profitability in greater proportions than the other variables. The reduction of the feed ration below the levels recommended in feed tables lowered the feed conversion rate but increased the number of days in the seabream cultivation cycle, leading to higher production costs and lower internal rates of return. The analysis also showed that greater benefits and profitability could be obtained by raising pro- duction capacity through increases in the final stocking density of the system.

In recent years, seabream Sparus aurata and seabass Dicentrarchus labrax have be- come the most cultivated fish species in the Mediterranean Sea. In 2000, the joint pro- duction of these two species was approxi- mately 100,000 t, with seabream accounting for 59%. However, the drastic decrease in price for these two species during the same period has also motivated producers to im- prove their management decisions, tech-

niques, and cultivation strategies to keep generating profits.

There is a great diversity of seabream production systems in the Mediterranean, with firms operating in a wide range of en- vironmental conditions, diverse markets, and geographical settings. The combination of these elements creates unique character- istics for each production company, making it difficult to provide a general and accurate

0 Copyright by the World Aquaculture Society 2003

29

30 GASCA-LEYVA ET AL

representation. In the development of these production systems, they have had to adapt to changing production conditions. Produc- tion began in coastal lagoons using exten- sive methods in which juvenile fish were confined and captured after reaching com- mercial sizes (Arias 1976). The move to semi-intensive production in hand-made cages installed in protected areas did not increase production enough to satisfy the upward demand. The limitations of these systems, coupled with the low water cir- culation at these sites, prevented their fur- ther intensification (Rayner et al. 1977; Ber- mddez et al. 1989). Responses include that of France, which has placed emphasis on developing tank cultivation, though this system is currently not very profitable (De La Pomelie 1995). Different countries have had to look for technical solutions to their particular environmental conditions, and not always at a cost reduction.

Innovation in seabream and seabass cul- tivation systems has been driven by diverse factors. One of these factors has been com- petition for coastal use from sources such as tourism, which has resulted in many pro- duction activities being moved offshore into the open sea (Barbato et al. 1993). Cur- rently, a large proportion of seabream pro- duction takes place in floating, flexible cag- es, a recently developed technology that is applied in semi-exposed areas and with greater profitability than land-based inten- sive systems (Blakstad et al. 1996). In re- sponse to factors such as market demand, increasing touristic development and envi- ronmental conditions in the Mediterranean, this technology was adapted from hand- made cages used in the estuaries of Asia as an ideal approach to sustainable production in open sea settings.

Economic studies for seabream cultiva- tion are scarce because of the difficulty of acquiring data from companies in this in- dustry (De La Pomelie 1995; Gasca-Leyva et al. 1998). However, this research, and es- pecially bioeconomic modeling of aquacul- ture systems, is fundamental to the study of

economic behavior based on biological, en- gineering, environmental, and systems management factors. Modeling can be used to carry out a number of analyses, with sen- sitivity being an essential methodology for understanding these systems.

Sensitivity analysis involves the modifi- cation of a system’s parameter values in or- der to study the behavior of the system it- self. This allows us to determine the ro- bustness of the submodels in the system and to evaluate the effects of each control pa- rameter. If these values are modified within actual ranges taken from research and pro- duction data, they can generate valuable re- sults. Thus, sensitivity analysis is useful for several purposes: 1) to understand the bio- logical, technical, and economic (costs, benefits and profitability) behavior of the system; 2) to reflect advances made via fu- ture research (e.g., higher animal growth rates); 3) to determine research needs for the industry; and 4) to reflect alternative values in an uncertainty interval. All these analyses could improve technical, biologi- cal, and economic returns, thereby provid- ing useful information for strategic man- agement decisions (Allen et al. 1984).

The present study focused on seabream cultivation system sensitivity in two sce- narios: 1) the Mediterranean Sea and 2) the Canary Islands (in the Atlantic Ocean). The Mediterranean was included as it is the area of greatest commercial seabream produc- tion. The Canaries, where seabream culti- vation is more recent, are included due to the particular environmental, geographic and market characteristics present there. Sensitivity analysis allowed evaluation of system control variables under the two sce- narios, determining which one was the most sensitive, and which variables, when mod- ified, had the greatest effect on profitability. Economic viability limits were also deter- mined according to the effects of the most relevant variables.

Cultivation System Model A simulation model of floating cage cul-

tivation system was developed utilizing a

MANAGEMENT STRATEGIES FOR SEABREAM 31

system dynamics approach and simulation software (PowersimB Constructor 2.5) (Powersim Corporation, Herndon, Virginia, USA). System dynamics (Forrester 1961) can be conceived as a graphical represen- tation of the dynamics inherent in any sys- tem. The most basic structures are the pos- itive or negative causal feedback loops that represent cyclical dependency between var- iables in a way similar to what occurs in nature (Aracil 1992).

Within the simulation model, the four ba- sic components (biology, environment, management, and economy) are interrelat- ed. The biological submodel contains a growth model based on seabream physiol- ogy, as well as experimental and commer- cial cultivation data. This model is central to the system, generating daily growth re- sults, feed consumption, and feed conver- sion indices according to water tempera- ture, ration, and fish weight from juvenile to commercial sizes. Some of the input and output variables for the analyses in this study are described herein (for a more de- tailed description of the mathematical mod- el, see Le6n et al. 2001). A general descrip- tion is also given of the cultivation system as it provides the structure to model and simulate seabream production.

Floating Cage Cultivation System

Most of seabream farms in the Mediter- ranean use circular cages ranging from 5 to 30 m diameter, with 12 and 16 m being the most common sizes. Nets are polyamide, with dimensions varying depending on wa- ter depth of the farm’s location. Net depth normally ranges from 7 to 10 m, the latter being most frequent. Anchoring systems are flexible in order to resist waves as high as 7 m and winds up to 120 km/h. A farm with 600 t/yr production capacity requires be- tween 24 and 36 cages, depending on cage size, with an average of 48,000 m3 capacity. Supplementary equipment, such as boats, net cleaning machines, fish counters, grad- ers, pumps, is also necessary for proper functioning of this system.

Fish are divided and distributed into lots subsequently placed in different cages as a function of size, water characteristics and cage capacity. Initial stocking densities vary from <1 to 3 kg/m3 (Barnabe 1991), and fish growth rates determine the mean duration of a complete cultivation cycle. Fish growth varies depending on environ- mental conditions at the farm location, wa- ter temperature being the most influential factor.

Fish begin weight gain between 10 and 15 g, with a first grading when they reach 80 g, and a second at 120 g. After 120 g, growth is more homogeneous than in the earlier sizes, and further gradings are rarely necessary. If conditions allow, one of the cultivation strategies involves increasing initial stocking density so that at first grad- ing there are enough fish of sufficient size to split the lot into two cages. This is then repeated during successive gradings.

Feeding is completely artificial, consist- ing of extruded andfor pelletized feed. Feed rate is adjusted monthly according to the feeding tables provided by the feed supply companies.

Final market weight or size at which the fish is harvested depends on marketing plans and strategy. Cultivated seabream is usually marketed in size intervals ranging between 100 and 1,000 g, with the inter- mediate sizes being more commonly found in the market (Datsolopoulos 1996). Mar- keting decisions depend on the demand for different products. Final densities at harvest vary between 10 and 15 kg/m3.

Model Variables

Parameter estimates and variables for the simulation model were obtained from the seabream production company, Alevines y Doradas S.A., Grand Canary, Spain. This company has maintained a successful sea- bream production system in floating cages from July 1994 to present, with an approx- imate 200 t/yr production in 2001. The use of these data enhances the bioeconomic model by reproducing scenarios that re-

32 GASCA-LEYVA ET AL.

TABLE 1. Principal parameters used for bioeconomic analysis, used as base for the two studied scenarios.

Ouantitv

Parameter Mediterranean Measurement unit Canaries

Ratio

Survival rates: Normal Extra large

Juvenile size Stocking density:

Initial Final

Substandard fish (SSF): Normal Extra large

Water temperature Personnel Technical direction

cost of Juveniles Feed

Marketing cost

Commercial price (C.P.): Normal (400 g) Extra large (700 g)

Commercial price SSF Discount rate Annual production

Cage capacity

%

% %

g

kg/m3 k g / d

% %

C employeeslfarm employees/farm

PTNunit mmg P T m g

P T m g P T m g

%

UY r m'lfarm

100

99 99

13

0.5 13

8 12

i17.241 19

1

100 100

150h

1900, 1.0001 [ 1,800, 2,0001

50% (C.P.) 6

600

48,000

- a -

92 Not produced

- -

0.6 15

- - Not produced

t12.261 - - - -

70 92

85

[900, 1,0001 Not produced

- - - - - -

a Indicates equality in parameter quantity for both scenarios. Cost government subsidized at 100 PTA.

spond to real culture practices. Biological, technical, and economic data are those for floating cages in operation from 1994 to 1996.

Table 1 summarizes the principal biolog- ical, technical, and economic assumptions used for the two scenarios considered here. These data were obtained from primary sources, commercial data, and published re- search literature. Both water temperature and commercial price were modeled ac- cording to random variables distributed uni- formly throughout the production process using the minimum and maximum intervals in this study. For the Canaries scenario, monthly water temperature data were ob-

tained from experiments carried out in cag- es located in Melenera Bay, Gran Canaria, and from published data for the Mediter- ranean scenario (Barnabe 1991; Sahin 1995). The figures assumed for the latter scenario do not represent an average across this vast area, but convenient assumptions that can be found in some farms located in the Central Mediterranean.

Intermediate input and marketing costs are considered to be higher in the Canaries than in the Mediterranean as feed suppliers and principal markets are on the European continent. Marketing costs in the Canaries are subsidized at 40%. Final cultivation density values are those published by Blak-

MANAGEMENT STRATEGIES FOR SEABREAM 33

stad et al. (1996) and have been adjusted for the hydrological and production char- acteristics of each scenario. In both scenar- ios, the currents and water quality are as- sumed to be adequate for seabream growth.

Seabream production strategy is deter- mined to a large extent by demand and product marketing viability, meaning there is no single valid production strategy. For the present study, it is assumed continuous production with specific goals (600 tlyr), which depends on farm size, and with suf- ficient demand for the final product. For the Canaries scenario, we assumed a final prod- uct combination of 80% for the normal size (400 g) and 20% for the extra large size (700 g). The Mediterranean scenario con- siders only 100% normal size (400 g) be- cause this is at present the only economi- cally viable size in this environment.

Sensitivity Analysis A farm using floating cage cultivation is

sensitive to factors both internal and exter- nal to the system. Internal factors include biological processes and the management decisions adopted within a particular com- pany. External factors include environmen- tal processes and market conditions. A sen- sitivity analysis was carried out to deter- mine the parameters with the greatest influ- ence on profitability, in both the Canaries and Mediterranean scenarios. The farm size is assumed 48,000 m3, with a production of 600 t/yr, since this has been shown to be the most efficient level of production (Ste- phanis 1995; Gasca-Leyva 1999).

The sensitivity analysis was conducted for each of seven chosen variables allocated in biological, technical, environmental, market, and economic groups. These vari- ables were modified lo%, one each time, in relation to the base scenario (Table 1). In most cases the variables were modified so as to improve farm profitability.

The mortality rate used in the base anal- ysis was raised because its reduction would be clearly unrealistic. Water temperature was also raised, though only by 1 C for the

TABLE 2. Sensitivity analysis results for Mediterra- nean and Canaries scenarios.

9% change in IRR

Modified variable Mediterranean Canaries

Market price 41 31 Mortality 30 29 Ration 15 12 Juvenile cost I 1 9 Feed cost I 1 5 Water temperature 7 5 Number of employees 4 1

total annual cycle. The ration size was re- duced 20% from the most current practice, approaching the levels recommended in ta- bles provided by feed suppliers. The num- ber of employees was reduced by a single unit in order to avoid a fractional number of employees. The changes in economic re- sults were evaluated using the percentage variation in the Internal Rate of Return (IRR) in relation to the base scenario (Table 2). The IRR value is generated when pro- duction and economic benefits are stable for both the base and sensitivity analyses.

The overall results indicate that the sys- tem is somewhat more sensitive in the Med- iterranean scenario than in the Canaries. In what follows, we present the results of the analyses for the different variables accord- ing to their class in the management sys- tem.

Biological Variables

The bioeconomic model was unexpect- edly more sensitive to the biological vari- ables of mortality and feeding rate than to all the other variables in the system, except for the commercial price. The mortality rate was increased by lo%, leading to a reduc- tion in production from 600 to 540 t/yr. The generated IRR estimator was 19% for the Mediterranean and 42% for the Canaries, descending substantially in relation to the levels of the base scenarios (27% and 59%, respectively). Proportionally, the mortality rate influences the IRR in the Canaries sce- nario more acutely than the rest of vari-

34 GASCA-LEYVA ET AL.

ables, except for commercial price. Its in- fluence on IRR in the Mediterranean sce- nario was less marked (Table 2). However, the mortality rate had a major effect in the Mediterranean than in the Canaries because of the larger number of fish required to at- tain the production goal. Thus, this variable has greater effect on net benefits, and con- sequently on returns.

The simulation analyses for higher mor- tality rates show that even when changes are made in all production costs, the great- est change occurred in feed costs, which led to a rise in average costs. The mortality generating function in the bioeconomic model assumes higher mortality during the first days after stocking. It is important to note, though, that the fish losses in the later stages of the production cycle have greater effects on production costs because they in- volve higher feed consumption.

Feed rationing is a management decision in which employee training and experience are decisive, making it difficult to ration strictly according to the feed tables. Sea- bream farmers offer rations which range from 10 to 20% above those recommended in feed tables provided by feed suppliers. Therefore, the base analysis in this study assumes ration sizes 20% above these val- ues. If the ration size is reduced to the lev- els suggested by feed tables, then the IRR rises from 27 to 31% (15% change) in the Mediterranean scenario, and from 59 to 66% (1 2% change) in the Canaries scenario (Table 2). The greatest effect on IRR in the Mediterranean is related to the differences in the Feed Conversion Rate (FCR) for both scenarios. Maintaining the ration 20% above the feed table values, FCR is 2.23 in the Mediterranean and 1.82 in the Canaries, whereas when using the feed table values, FCR is 1.83 for the Mediterranean and 1.52 for the Canaries. These results highlight the need for seabream producers to put more emphasis on managing feed rationing, since it is a critical variable for optimizing eco- nomic returns.

Technical and Environmental Variables

The reduction of the number of employ- ees by one single individual raises produc- tivity. The results for each scenario are mi- nor impacts of a 4% change in IRR (from 27 to 28%) in the Mediterranean and a 1% change in the Canaries (from 59 to 59.8. This factor leads to the least sensitivity on the returns of the cultivation system.

Water temperature is an important vari- able in the system because it has a direct effect on fish growth. A 1 C rise in water temperature in the Mediterranean scenario raised production by 24 t/yr, leading to a two points above base analysis for the IRR. The 2% difference observed between the Canaries and Mediterranean scenarios shows that this rise in water temperature has a larger effect in the latter scenario. This is the result of the lower winter tem- peratures in the Mediterranean (1 2 C; lower limit for growth) (Muller-Feuga 1990).

Market and Economic Variables

The study of commercial price is relevant because it has been showing a declining tendency in the markets in recent years. Among all the variables in the simulation model, market price had the greatest impact on the system returns, especially in the Mediterranean scenario. A rise of 100 Spanish Pesetas (PTA.) in commercial sale price increased IRR by 11 points (41%) from the base scenario in the Mediterra- nean. These effects are lower for input pric- es. That is, a reduction in juvenile and feed costs caused only 11% rise in IRR for the Mediterranean scenario and 5% for the Ca- naries (Table 2).

The Canaries scenario is more robust to changes in the system variables because of the lower sensitivity of economic returns. However, this robustness could be affected by the fact that the Canaries scenario ben- efits from a competitive advantage due to the combination of the extra large (700 g) and the normal (400 g) sizes. If the Canar- ies scenario was restricted to producing

MANAGEMENT STRATEGIES FOR SEABREAM 35

TABLE 3. Sensitiviry results for seabream cultivation under the Canary Islands scenario (normal and extra large sizes), according to different management strategies.

IRR Production Average cost Factors Measurement unit Quantity WO) Wyr) (Ptas.kg)

Base analysis

Final stocking density kg/m' 2.5 3.5 7

I 5a 1 8 3

Ration % 70 8 0 h 90

Personnel employees/farm 17 21

Survival % 88 75 67 65

Price for extra large size PTAkg 1,400, 1,6001 [1,200, 1,4001 t I ,ooo, 1,2001

Water temperature C + I - 1

59

Neg. 15 42 70 75

55 66 61

60 58

39 30 1 1

Neg.

41 31 20

62 58

600

150 200 400 800 930

540 577 593

600 600

528 450 400 390

600 600 600

620 565

579

753 696 616 576 550

575 559 566

574 584

627 718 770 782

579 579 579

677 613

a 25 employees/farm. According to recommended feed ration tables provided by feed suppliers.

only the normal (400 g) size, then the IRR would be 26%, which is much lower than the IRR obtained from the combination of both sizes, and also lower than the one gen- erated for the Mediterranean scenario (Gas- ca-Leyva et al. 2002).

Finally, it can be noted that market and environmental variables (e.g., feed cost, ju- venile cost and water temperature) led to quite close percentage changes in economic returns for both scenarios (Table 2). The management decisions involving the ration size show some the largest effects on the system. This variable can be controlled, leading to higher returns just by requiring employees to follow strict rationing guide- lines.

Management Strategies The modification of variables in the mod-

el allows us to search for decisions leading to greater competitiveness, thereby deter-

mining the ranges where production is eco- nomically viable. This can be approached by altering parameters according to farm- er's experience and published data, to gen- erate maximum and minimum IRR, and their impact on average costs. The purpose is to analyze the sensitivity for a range of alternative values of the variable of interest. The basic assumptions are the same as for the sensitivity analysis. The analysis is con- ducted only for the Canaries scenario, since this region offers larger competitive oppor- tunities because of its favorable water tem- perature for seabream production.

The modified parameters and the results of the analysis are shown in Table 3. Stock- ing density was modified according to rec- ommendations from seabream producers. Biological, environmental, and external pa- rameters were varied according to biblio- graphic references. Final market price was modified only for the extra large (700 g)

36 GASCA-LEYVA ET AL.

I

-r

OVyr OAverage cost

- 750

~ 710 8 - 670

- 630 3 - 5 9 0 fj

P 550

80

m - h

2.5 3.5 7 I 1 15 18.5

Final stoking density (kg/m3)

1100

900

Q 700

1 5 0 0

300

100

Q

2.5 3.5 7 11 I5 18.5 Final stoking dcnsity (kg/m’)

FIGURE I . Effect of ,final stocking density on return (IRR), production (Uyr), and average cost.

size. Estimators include internal rate of re- turn, annual production and average cost, which were compared with the base results.

Regarding the effects of raising stocking density, it can been seen that it has a large influence on economic returns in the Ca- naries scenario, resulting in the highest IRR and lowest average costs (Table 3). If the final density is raised to an average level of 18.5 kg/m3, average costs decrease substan- tially (0.29 Ptas./kg) and the IRR increases more than 15 points (Fig. 1). The good wa- ter quality allows stocking densities greater than 15 kg/m3, thereby increasing the re- turns to investment.

If the cultivation density is reduced to 3.5 kg/m3, then total production would be 200 t/y, with a surprisingly commercially ac- ceptable IRR. Figure 1 shows the results for this variable, and clearly indicates the wide

range within which cultivation is still prof- itable.

Due to its proximity to the point of feed efficiency, a 70% rationing generates a feed conversion index lower than the base anal- ysis, with a decrease from 1.82 to 1.4 for normal size. However, it also retards growth more than 1 mo, having the same effect on the weight gain cycle, which reduces re- turns (Table 3). The economic results are presented in Fig. 2, showing that the max- imum IRR and minimum average cost are obtained at an 80% ration value. This sug- gests that this value is optimal based on the assumptions made.

For very similar production levels, dif- ferent authors have reported diverse labor requirements. Most of these discrepancies can be explained by variations in labor costs and in the degrees of automation in

70 80 90

~

Ration (%)

0 Averagecost *Production 596 1 r 610

70 80 90 100 Ration (%)

FIGURE 2. Effect of ration on returns (IRR), average cost. and annual production.

MANAGEMENT STRATEGIES FOR SEABREAM 37

99 88 75 67 65 survival (%) I

FIGURE 3. Effect of mortality on returns, production, and average cost,

different geographical areas. However, there are no published data that allow com- parisons within the same area (Stephanis 1995; Prickett and Iakovopoulos 1996). To investigate this variable in the present study, the number of employees was low- ered, assuming a subsequent increase in worker productivity. The number of em- ployees used in this study was based on ob- servations of seabream and seabass farms in Spain, deciding upon an appropriate number for a farm of 600 t/yr. As expected, the IRR increases with lower employee numbers, a situation possible with increas- ing employee specialization and acquisition of equipment, allowing increased automa- tion (Hill and Ingersent 1982).

As it was observed in the sensitivity analysis, mortality is a factor with relevant repercussions on seabream production. A rise in mortality produced an increase in av- erage costs. For example, at 33% mortality

70 I

10 4 (18.20) (14, 16) (12, 14) (10, 12)

Price for extra large size (ptas. X 100)

FEURE 4. Effect of seabream commercial sale price on cultivation returns (IRR).

the bioeconomic model generated barely commercially acceptable returns, and at 35% the returns became unacceptable, that is, negative (Fig. 3).

Commercial price affects returns and has been steadily decreasing in recent years. Assuming a decline in the price for the ex- tra large size similar to the normal size, and with no change in the latter, the IRR re- mains positive above 6%. This is related to the use of an optimal farm size giving the maximum IRR and minimum average costs. Thus, results could be negative for subop- timal sizes. Figure 4 shows the evolution of economic returns as commercial sale price varies, proving a clear descending lineal tendency.

Water temperature was modified by 2 1 C in the bioeconomic model, a reasonable value for this geographic area, given his- torical records on temperature. With a I C temperature increase in the annual cycle, average costs decreased and the IRR in- creased 3 points (Fig. 5).

Conclusions

The present study has focused on evalu- ating the effects of variable modification on seabream cultivation in floating cages using a systems modeling approach. This was ini- tially used to carry out a sensitivity analysis of two substantially different cultivation scenarios and demonstrate which of the modified variables generated greater effects on profitability. The cultivation system sce-

38 GASCA-LEYVA ET AL.

FIGURE 5. Effect of water temperature variation on ar production.

nario for the Mediterranean was shown to be more sensitive than that for the Canary Islands, meaning that variable modifications have greater influence in the former con- text. However, all variables had a similar quantitative effect for each of the two sce- narios, with a lower impact in the Canaries scenario. Commercial sale price had the greatest effect on returns, followed by mor- tality. The number of employees showed to have the lower influence.

The sensitivity analysis was also used to determine the limiting values for profitabil- ity and viability in the Canaries scenario. The feed ration variable was found to have a strong effect on system costs, benefits, and profitability. It is this variable that can generate the lowest average cost and high- est profitability, compared to the biological variables, if feed is administered at the rate recommended in feed supplier tables. The feed conversion index is actually improved if feed levels are given lower than those in the tables, but system profitability is also reduced.

Finally, the stocking density is a variable which can be used to increase production without increasing farm size in the Canaries scenario. Also, the combined production of the normal and extra large sizes in the Ca- naries scenario strongly influences the com- mercial viability of the system as a result of the commercial price for the extra large size. It is concluded that, despite the higher input costs, the Canaries have an advantage

rerage cost. internal rate of return (IRR), and annual

for seabream cultivation because it allows greater production system flexibility.

Acknowledgments This study was supported by a Ph.D. fel-

lowship of the CONACYT Mexico for the first author. The authors acknowledge the support provided by the personnel of the Instituto Canario de Ciencias Marinas to this study. We also thank the anonymous reviewers for their insightful comments.

Literature Cited Allen, P. G., L. W. Botsford, A. M. Schuur, and W.

E. Johnston. 1984. Bioeconomics of aquaculture. Elsevier Scientific Publishing Company, Amster- dam, The Netherlands.

Aracil, J. 1992. Introducci6n a la Dinarnica de Siste- mas. Alianza Universidad Textos, Madrid, Spain.

Arias, A. 1976. Sobre la biologia de la dorada, Sparus aurata, L., de 10s esteros de la provincia de Cidiz. Investigaciones Pesqueras 40( 1):201-22.

Barbato, F., A. Fanari, F. Meloni, and R. Savarino. 1993. Cage culture of gilthead sea bream (Sparus aurata) in two Sardinian coastal lagoons. Pages 95-104 in G . Barnab6 and P. Kestemont, editors. European Aquaculture Society, Special publica- tion 18. Proceedings of the International Confer- ence “Bordeaux Aquaculture ’92”. Bordeaux, France.

Barnabe, G. 1991. Acuicultura, volume 11. Omega, Spain.

Bermidez, L., G. B. Garcfa, 0. G6mez, M. J. Ro- sique, and F. Farco. 1989. First results of the ongrowing cages of Sparus aurata, Puntauo pun- t a u 0 and Litognatus rnormyrus in the Mar Menor (Murcia, S.E. Spain). Pages 27-28 in R. Billard and N. De Pauw, editors. European Aquaculture Society, Special publication 10. Short communi-

MANAGEMENT STRATEGIES FOR SEABREAM 39

cations and abstracts of Aquaculture Europe ’89- business joins science, volume 1 . Bordeaux, France.

Blakstad, F., A. F. Fagerholt, and D. Lisac. 1996. Cost of bass and bream production: comparisons between land based and cage facilities. Pages 245-248 in B. Chatain, M. Saroglia, J. Sweetnan, and F? Lavens, editors. Proceedings of seabass and seabream culture: problems and prospects. Euro- pean Aquaculture Society, Verona, Italy.

Datsolopoulos, A. 1996. Contemporary methodology of marketing at the development of aquaculture. Cahiers Options Mediterrantes 17: 195-201.

De La Pomelie, C. 1995. L tlevage du bar et de la daurade en France: viabilitt Cconomique des sys- ttmes de production. CIHEAM Cahiers Options Mtditerrantennes 14 79-89.

Forrester, J.W. 1961. Industrial dynamics. Productiv- ity Press, Cambridge, Massachusettis, USA.

Gasca-Leyva, E. 1999. Bioeconomia del cultivo de dorada (Sparus auruta). Doctoral thesis. Univer- sidad de Las Palmas de Gran Canaria, Espaiia.

Gasca-Leyva, E., C. Ldn, and J. Vergara. 1998. Economics of commercial guilthead seabream production. Page 166 in R. M. De Voe, editor. World Aquaculture Society, Book of Abstracts of Aquaculture ’98, Las Vegas, USA.

Gasca-Leyva, E., C. J. Ldn, J. M. HernBndez, and J. M. Vergara. 2002. Bioeconomic analysis of

production location of sea bream (Sparus aurata) cultivation. Aquaculture 213:219-232.

Hill, B. E. and K. A. Ingersent. 1982. An economic analysis of agriculture. Heinemann, London, Eng- land.

L e h , C. J., J. M. Hernhdez, and E. Gasca-Leyva. 2001. Cost minimization and input substitution in the production of gilthead seabream. Aquaculture Economics and Management 5, 3&4: 147-170.

Muller-Feuga, A. 1990. Modtlisation de la croissance des poissons en tlevage. Service de la Documen- tation et des Publications IFREMER, Brest, France. Rapports Scientifiques et Techniques de I’LFREMER 2 1.

Prickett, R. A. and G. Iakovopoulos. 1996. Potential gains through the careful management of current technology. Pages 206-21 1 in B. Chatain, M. Sar- oglia, J. Sweetnan, and F? Lavens, editors. Pro- ceedings of seabass and seabream culture: prob- lems and prospects. European Aquaculture Soci- ety, Verona, Italy.

Rayner, P., 0. Tsur, and H. Gordin. 1977. Cage culture of Spurus aurafa. Aquaculture 1 1 :285- 296.

Sahin, M. 1995. Sea bass and bream in floating cages in Turkey. CIHEAM Cahiers Options Mtditerra- ntennes 1457-63.

Stephanis, J. 1995. Economic viability of production systems seabadseabream in Greece (industrial scale). CIHEAM Cahiers Options Mtditerranten- nes 14:65-77.