Mechanization and technical interactions in multi-species Indian fisheries: implications for economic and biological sustainability

ARTICLE IN PRESS

Marine Policy 30 (2006) 237–248 www.elsevier.com/locate/marpol

Mechanization and technical interactions in multi-species Indian fisheries: implications for economic and biological sustainability Mahadev G. Bhat, Ramachandra Bhatta a

Environmental Studies Department, Florida International University, Miami, FL 33199, USA Fisheries Economics Department, College of Fisheries, University of Agricultural Sciences, Mangalore 575002, India

b

Received 19 February 2004; received in revised form 14 January 2005; accepted 15 January 2005

Abstract Joint capture of multiple species by multiple fleets results in technical and economic interactions between fleets. This paper develops a bioeconomic model that incorporates interrelationships between fleets for a representative fishery in India. The study analyzes the impacts of overcapitalization on bio-economic sustainability. Results show that a continuation of the current fishing intensity would deplete most commercially important species in the near future. However, an optimal effort re-allocation between fleets would increase fishery profits substantially, although some species would remain unsustainable. The study demonstrates how the newly emerging, large bodies of fishery data can be used for management decisions in developing countries. r 2005 Elsevier Ltd. All rights reserved. Keywords: Multi-species fisheries; Multi-gear; Technical interactions; Sustainability; India

1. Introduction Most fisheries around the world today possess three types of pluralism: the capture of multiple species (resources), the co-existence of multiple fleets (technology), and the coexistence of small-scale and large-scale fisheries in a given fishing ground (user groups). The fisheries literature has long recognized that interactions occur within and between these three fishery components [1–5]. For instance, biological interactions such as predator–prey or competition relationships may exist between multiple species [2,6]. Due to these relationships, excessive harvesting of one or more species may cause unsustainable changes in the stocks of related species. Economic and technical interactions occur when multiple fleets (e.g., trawlers and purse seines) compete for the same stock of one or more species. A failure to consider biological and technical interactions in fishery Corresponding author. Tel.: 305 348 1210; fax: 305 348 6137.

E-mail addresses: bhatm@fiu.edu (M.G. Bhat), [email protected] (R. Bhatta). 0308-597X/$ - see front matter r 2005 Elsevier Ltd. All rights reserved. doi:10.1016/j.marpol.2005.01.001

management may give rise to the problems of by-catch, stock unsustainability, and economic inefficiency [7]. Finally, social interactions take place between user groups. It is common in most fisheries for both smalland large-scale fishers to co-exist and to compete for resource stocks and space [8]. Small-scale fishers with limited access to credit and, in turn, limited access to advanced fishing technology are normally competed out by the more mechanized cost-efficient fishing fleets. Fishery management regimes in many, if not most, tropical developing countries have paid little attention to biological, technical and social interactions. Biological interactions are often poorly understood. Technical and social interactions are sometimes ignored for political and social reasons. For whatever reason, the net effect of failing to consider these interactions is for multi-species fisheries to be exploited at a sub-optimal level [9,10]. Such fisheries generally experience an increase in number of users as well as number of user conflicts. Multi-species and multi-fleet fisheries have complicated fisheries management particularly in India. The

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extraction of fishery resources has undergone major changes in the last three decades. Prior to the 1970s, the fisheries around the country were small scale with fixed fishing gears such as shore seines. The total catch was low, and the fish caught were destined for local markets. However, since the 1970s, the management emphasis has shifted toward more mobile fishing gears and fish export. Individual coastal states have promoted schemes to construct and distribute mechanized trawlers and purse seines through state and district organizations. Governments have also helped build ports and processing plants. As a result, from the 1970s to the early 1990s, fish catch has increased at a record rate and became highly diversified in terms of species landed. Currently, more than 80 species are commercially harvested throughout the country. The fishing technology also is highly diversified. The fishery modernization in India has had mixed results. On the one hand, despite the growing size of the fishing fleet, the industry has not attained its full potential. For instance, in the state of Karnataka, the average annual fish landing has fluctuated around 142,000 tonne in the last 10–15 years, much below its estimated total annual potential of 425,000 tonne. On the other hand, there are growing signs of biological and socio-economic un-sustainability that threaten the coastal fisheries. The introduction of trawlers are claimed to have adversely affected other shore seines. Also, trawlers often interfere with the fishing rights of traditional, small-scale fishers operating near shores, leading to rising social rifts between the two groups. The traditional fishing nets, which once accounted for 50–60% of the annual catch, have almost disappeared [11]. The common government responses to localized unsustainable harvests and users conflict are season and area restrictions. The state laws require licensing of fishing vessels but fail to impose restriction on the number or size of gears. The combination of season restrictions and unregulated growth in the number of vessels is believed to have led to intense harvesting activities during open seasons. Such strategies basically ignore the underlying technical and social interactions in fisheries. The lack of proper understanding of these interactions limits the ability of fishery agencies to assess the biological, economic and social implications of any management policy. Ill-informed policy may ultimately fail the Indian fishery industry, by negatively influencing the long-term biological stocks and the livelihood of the very people who are dependent on it. The main purpose of this paper is to provide a better understanding of the technological interactions in Indian fisheries and to show how this knowledge can be used in assessing the long-term impacts of alternative management strategies. A multi-species multi-gear effort allocation model is designed to test the fundamental

hypothesis that the increasing size of fishing effort adversely affects the sustainability of multiple species and the economic profits. Further, the model is used to identify more sustainable management responses. In order to obtain precise, policy-relevant results, the model is applied to a single fishing ground rather than to the entire country or state. However, the underlying economic, biological and technical parameters of the model have been developed from a large amount of data collected from multiple fishing ports in the study state of Karnataka for a 10-year period. Our study provides broad estimates of excess fishing in terms of actual fishing hours spent by different fishing fleets. Fishery managers often require knowledge of the excess fishing capacity in terms of actual number of vessels. Kirkley et al. demonstrate an empirical approach for the estimation of the number of vessels to decommission in order to eliminate excess capacity in the case of the Malaysian purse seine fishery [12]. Such an exercise would require more in-depth data on the number and size of vessels, and variable inputs by fleet type; a fleet-specific excess vessel estimation is beyond the scope of this paper. Instead, our analysis conducts an empirical simulation of the long-term optimal mix of multi-fleet fishing effort and multiple species catch, demonstrating how a large set of newly emerging tropical fishery data can be utilized for management purpose in developing countries. The results of this study would aid resource managers in prioritizing their regulatory actions on different species and fleets.

2. Overview of the study area The coastal ecosystem of Karnataka is a mosaic of monsoon wetlands, beaches and mountains stretched along its 300 km long shoreline. The coastal ecoregion of the state is separated by the Western Ghats (ghats ¼ mountains) connected by a number of rivers that form vast estuaries. Coastal wetlands serve as an important source of economic livelihood for the local communities. The vast natural resource base of sea and estuaries generates income in millions of rupees (US $1.00ERs. 45.00). Traditional fishing using indigenous, non-motorized boats used to be very common. In the last 20 years, fishing technology has undergone widespread mechanization with the help of government patronage and the entry of multinational fishing corporations into Indian waters [13]. However, modern technology and capital have been accessible to only a small group of fishers. Large classes of traditional fishers either continued to operate with indigenous techniques or worked for modern fishing vessels, and thus have not reaped the benefits of fishery mechanization. Modern fishing vessels initially operated only in inshore waters and overexploited the fishery resources. This depletion

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adversely affected fishing opportunities for small-scale fishers. It is estimated that around 310 different marine fish species are available in the coastal waters of Karnataka apart from other migratory aquatic animals. The total fish production of the state increased from 75,793 tonne in 1969 to a high of 251,012 tonne in 1989; the production has suffered somewhat since then. The pelagic fish form the highest percentage of the total catch, followed by demersal and crustaceans. The major pelagic species include oil sardine, Indian mackerel, stolephorus and scads while important demersal species include threadfin breams, prawns and cephalopods. The annual rates of increase in catch for the period of 1969–98 are estimated for individual fish groups. The catch amounts of pelagic, demersal, mollusks and crustaceans increased annually by 1.35%, 4.05%, 25.04% and 6.68%, respectively. For the same period, the annual rate of increase in total production was estimated at 2.16%. In contrast to the decline in fishery production, the total number of mechanized fishing boats doubled during the last two decades. During this period, the number of non-mechanized boats also increased. Almost all of the boats are now motorized and fitted with outboard engines. The increase in the number of boats is also coupled with the enhancement of fishing capacity in terms of size, engine horsepower, electronic equipment and other devices. Between the periods 1984–85 and 1998–99, the total number of mechanized boats more than doubled from 3049 from 6318. During the same period the number of non-mechanized fishing units also increased from 11,847 to 19,292. Karnataka has 29 fish landing centers with three major fishing harbors. Around 35% of the state’s total landing and 50% of the total fishing effort are concentrated in Mangalore fishing harbor alone.

3. The fishery model of multi-species, multi-fleet harvesting As mentioned before, most ports of Karnataka have multi-species, multi-fleet fisheries. The single-species, static model that is common in classical fishery literature is not adequate to incorporate the dynamic nature of fishery and multi-gear technical interactions into a management plan that is biologically and economically sustainable. A mathematical programming model is therefore used to represent the inter-temporal dynamics of stocks of various species and efforts exerted by multiple fishing gears. The model characterizes optimal combinations of fleet effort levels and landings over time. Such optimal combinations of landings would result in the maximum net revenue from the given

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fishery subject to usual stock sustainability and other socio-economic conditions. The model developed in the study could incorporate multiple objectives of a fishery management agency. Ideally, a public agency would like to optimize the combination of welfare accrued to fishery consumers (consumer surplus), producers (producer surplus) and laborers (wages). However, the main focus of this study is bioeconomic sustainability of fishery production. Also, productions from individual fishery management units, for instance, fishing grounds associated with ports, are not large enough to influence market supply and prices. Therefore, we do not consider the demand side of the market in the model. Thus included in the model is only the production side of a representative fishery. The model explicitly considers alternative methods of fishing. The costs of operation and productivity vary between fishing methods. Fish stocks are assumed to be distributed uniformly across a given fishing area. Similarly, fishing effort by each gear type is assumed to be evenly applied across the fishing ground. Vessels of each technology type target certain species, although there may be species overlap between two different harvesting technologies. For each period, the model keeps track of the effort applied by all vessel types toward each model species in terms of standardized fishing effort. Given this fishing effort, the total catch is determined using the catch-effort-stock relationship. For lack of better information and analytical simplicity, spatial (e.g., migration) and age structure aspects of the fishery are ignored. The model captures the dynamic nature of the fishery through an inter-temporal stock growth equation. This equation balances the stock in each period to the previous period’s stock plus net growth minus harvest. This gives the model the ability to track the impacts that the current fishing effort (technology and capital) has on future sustainability of fish stock. We can also impose a separate sustainability constraint that requires that each year’s stock be more than or equal to the last year’s stock. Through such a constraint, one can analyse the trade-off between biological sustainability and social welfare impacts on fishing community. 3.1. The conceptual model Suppose that a policy maker attempts to maximize the following objective function: ! X X X Max Z ¼ pi C it
cv E vt , (1) t

i

v

where Z is the total market value of all fish catches during the entire planning period, say 10 years (the first term on the right-hand side), minus the total costs of

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harvesting (the second term on the right-hand side). pi is the market price of species i (Rs./tonne), C it is the quantity of species i caught (tonnes) in year t; C v is the average cost of fishing by vessel type v (Rs./actual fishing hour), E vt is the level of effort spent by vessel type v (actual fishing hour) in year t: Time discounting of future returns and costs is not considered since it does not change the general results of our study. Following Onal et al. [14], the objective function in (1) is maximized subject to the following technological and biological constraints (2)–(8). The total annual harvesting effort for each vessel type v and year t is constrained by available fishing capacity, E max vt : This capacity could vary over time: E vt pE max for all v and t. vt

(2)

It seems unlikely that a fishery management agency would wish to eliminate the entire fleet of an existing vessel technology at once. Therefore, a set of constraints that requires that the fishery industry employs a minimum level of effort (E min vt ) for each vessel class is included: for all v and t. E vt XE min vt

(3)

The total catch of each given species is a result of effort expended by multiple vessel types. In order to find the total effort directed toward a given species, we follow a two-stage computational process. In the first stage, we compute the constant proportion (d vi ) of total annual effort of vessel type v directed toward a given species i: This constant proportion can be derived by computing the ratio of the individual species catch of a given vessel type v to its total catch. Here we assume that the species distribution of effort of a given vessel is proportional to the species distribution of catch. The product (d vi E vt ) is the effort of each vessel-type directed toward a given species. We must note that the efforts of different vessel types are technologically different. Therefore, in the second stage, before we aggregate them in order to estimate the total effort toward a given species (T it ), we need to convert the species-specific, individual vessel efforts to standardized efforts. A standardization parameter sv is constructed by taking the ratio of the catch-per-unit effort (CPUE) of each vessel to that of the vessel class that has the highest CPUE. Thus, sv for the vessel with highest CPUE is one and, for the rest of the vessel type, is less than one. Note that this standardization parameter is nothing but the relative fishing power of a given vessel type, as defined by Beverton and Holt [15]. Based on the above computations, the following set of constraints that sums up standardized efforts exercised by all vessel types directed toward a species is added: X sv d vi E vt
T it ¼ 0 for all i and t. (4) v

The next set of constraints represents the standard non-linear catch–effort–stock relationship associated with every species and time period: C it ¼ mi T it Sit mi 40 for all i and t,

(5)

where S it is the natural stock of fish species i during period t; and mi is the constant parameter of the function. Specifically, mi is the catchability coefficient, the proportion of stock caught by a unit fishing effort. The above formulation is based on Schaefer’s [16] assumptions that the catch per unit effort or CPUE is a constant proportion mi of the stock, and that this proportionality does not change irrespective of the levels of stock and effort. However, as noted by Clark [17, p. 235], this second assumption of stock- and effortinvariant proportionality is based on certain additional assumptions: (a) that the fish stock is uniformly distributed, (b) that the fishing gear is not saturated, and (c) that the fishing gears are not congested. If the above assumptions were invalid, probably a different production function with constant or decreasing return to scale of the type C it ¼ mi T dit S
it would be more appropriate. In this general form, d and
represent the relative contributions of factors T it and S it ; respectively, to the total production C it ; also called the input elasticity of production. The values of d and
are generally less than one. The sum of the two constants is equal to one for a production function with constant return to scale and is less than one for a function with decreasing return to scale. However, in the case of a standard and simple production function of type Eq. (5), both d and
are assumed to be equal to one (i.e., a unitary elasticity of production), provided assumptions (a)–(c) above hold true. With the exception of (b), the above assumptions seemed realistic in our study since the model is designed for a uniform, single fishing ground. Only in the case of the baseline simulation (to be explained later), which represented a more open-access harvesting regime with high vessel concentration, assumption (b) may be a bit unrealistic. For this regime, the above formulation might slightly over-estimate the catch. Under the optimal or the various policy scenarios, the effort variables are unlikely to reach saturation levels. Therefore, we believe that the weaker assumption (b) does not affect the overall results of our analysis. The next constraint set balances the fish stock in the next period to the current period stock plus current period net recruitment less current period catch. Factors such as age structure, size, spatial location, and migration are ignored in this study for lack of better information and model simplicity: S itþ1 ¼ ð1 þ ðji
Zi Sit ÞÞS it
C it

(6)

for all species i and year t except the initial period. The expression ðji
Zi Sit Þ represents the density-dependent,

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annual rate of net growth of stock. ji and Zi are constant parameters. The initial year stock is exogenously set at user-defined levels (Si0 ) for all species as S it ¼ S i0 .

(7)

Finally, the model must comply with usual nonnegativity constraints: C it ; E vt ; T it ; S it X0.

(8)

3.2. Data collection and simulation The multi-species optimization model is simulated for the Mangalore port. Mangalore port is the largest fishing port in Karnataka, constituting 35% of the state’s total fish landing in 1998. Seventeen of the most important species, which constituted more than 85% of the total fish catch at Mangalore in 1998, are included in the model (Table 1). Each of these species contributed at least 1.5% of the total annual landing during 1994–1998. The top five species harvested in the year 1998, in the order of their weight, were breams, Indian mackerel, stolephorus, sardine and cephalopods. For the same period, more than 10 different types of fishing vessel technologies were reported to have been employed. For the purpose of this study, these vessel classes are regrouped into five homogenous vessel classes: multi-day trawl nets (MTN), purse seines (PS), day-trawl nets (TN), out-board motor fishing units (OBM), and non-motorized boats (NMB). The data for the study came mainly from two sources. The landings and effort data for the period from 1994 to 1998 were drawn from the Central Marine Fisheries Research Institute (CMFRI). The CMFRI collects the statistics on marine fish production in major and minor landing centers in India. It uses the multi-stage stratified random sampling design for the estimation of marine fish production and fishing effort. From the CMFRI data, the parameters d vi that convert a unit effort of each vessel type v to effort directed toward model species i were computed by taking the ratio of the 5-year total of individual species catch (1994–98) to the total catch reported for the same period for that vessel class. Also computed were effort standardization parameters S v that convert effort by each vessel class v to a standard unit of effort. These standardization parameters were derived by computing the ratio of the CPUE of each technology class to that of purse seine vessel, which had the highest CPUE. The catchability coefficients mi for most model species were estimated using data from a much larger area than the Mangalore port. CMFRI data on multi-species catch and multi-gear effort was available for six ports in Karnataka for ten years. This data was used to estimate a species-wise Fox production model [18]. The model’s parameters were employed in computing a time-invar-

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iant catchability coefficient for each species. See Appendix A for computational details. For certain model species, state-level estimates were not available. The catchability coefficients of most closely related species were used for such species. The annual capacities of fishing vessels in Actual Fishing Hours (AFH) are assumed at the 1998 levels. The second set of data on costs and earnings, market price, labor share and income, and other economic parameters were obtained by conducting a sample survey of the fishery vessels in coastal Karnataka and from published government documents. The survey data were collected for the two coastal districts, namely Dakshina Kannada and Udupi districts, for the fishing season of 1999–2000. The costs of fishing trips, capital and fixed costs of crafts and gear were compiled from 15 to 20 fishing units in each vessel class to estimate the economic efficiency of selected vessel types. In most cases frequent visits to the fishing ports to observe the species landed, prices and cost of each fishing trip yielded much better data. Since fish harvesting takes place on a day-to-day basis unlike crop production, such frequent observations were more useful in estimating accurate data. The costs included are fixed cost (interest, insurance, maintenance, and depreciation), operational costs (cost of fuel, ice, auction marketing, labor and port charges) and crew share. Based on this computation, the unit fishing costs are estimated at (in Rs./AFH) 410, 919, 460, 70, and 100 for technology classes MTN, PS, TN, OBM and NMB, respectively. However, these costs represent the efforts that landed all the reported species in the port. For certain individual vessel classes, particularly for OBM and NMB, the species included in the model represent a small portion of the vessel’s total effort. The initial year exogenous stock levels are computed using the Fox model (Eq. (A.1)) [18]. To use the Fox model, the 1998 levels of standardized effort were first computed for each model species of Mangalore port. These standardized effort values along with the estimates of catchability co-efficient and the observed quantities of species catch were substituted into the Fox model equation to estimate the initial year stocks. However, when we ran the baseline simulation model, to be discussed later, our goal was to make sure that the model-generated catch values came as close to matching the observed catch values for the year 1998. Of the four variables above, catchability coefficients were estimated using the state-level data of 10 years, which we assumed were more reliable than the initial year stock estimates for Mangalore. The 1998 observed catch and effort values were certainly more accurate than the unobserved stock estimates. Therefore, for the purpose of this analysis, we calibrated the initial exogenous stock values so that the baseline model generated species catch

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Table 1 Fish catch by model species and by vessel class in Mangalore Port, 1998 Species

Oil-sardine (Sardinella longiceps) Other sardines (Sardinella sp.) Stolephorus sp. Thryssa sp. Lizard-fishes (Saurida sp.) Rock-cods (Epinephelus sp.) Breams (Polynemus sp.) Other-perches (Nemipterus sp.) Ribbon fish (Trichiurus sp.) Scads (Selar sp.) Other-carangids (Caranax sp.) Black pomfret (Pampus sp.) Indian mackerel (Rastrelliger kanagurta) Soles (Cyanoglossus sp.) Prawns (Penaeus sp.) Stomatopods (Squilla sp.) Cephalopods (Loligo sp.) Port total Effort (actual fishing hours) CPUE (kg/AFH) Effort standardization parametera a

Multi day trawl net

Purse seine

Single day trawl net

6

1735

116

1

4125

21

66 71 31

3459 400

1011 294 1728

67

Out board fishing units

Nonmechanized fishing units

In tonnes 22

Total catch

Percent of species to port total

1879

3.77

4

4151

8.32

1 77

4537 842 1759

9.09 1.69 3.52

1235

2.47

7445 2391

14.92 4.79

1168

117 84

1

7328 2299

5

37

1

529

10

577

1.16

31 148

1706 1494

372 1259

2 30

2111 2931

4.23 5.87

6

41

114

4

165

0.33

84

4239

488

143

4954

9.93

689 597 1339

56 33

133 714 207

6 233

885 1577 1546

1.77 3.16 3.10

3871

7.76

108

2

1

3763

3923

19,377

25,129

1467

5

49,901

85.88

133,932

113,866

836,371

204,990

1908

1,291,067

85.88

29

170

30

7

3

0.1721

1.000

0.1766

0.0420

0.0154

Also known as the relative fishing power of a given vessel type [15].

distribution as close to the 1998 observed catch distribution as possible. The data on the intercept and slope parameters of the annual net growth function (ji and Zi ) are not easily available. Therefore, for lack of better information, these parameter values represent our best educated guess. The mathematical programming model was solved using the Generalised Algebraic Modelling System (GAMS) software [19]. This software has a routine for solving non-linear programming models. The model was run for a period of 10 years with annual increment. The baseline model has a total of 663 equations (belonging to 11 separate blocks of equations), 563 variables, and

2362 non-zero elements. The model could be run easily on a personal computer. Various sensitivity analyses also were carried out to evaluate various management and policy scenarios.

4. Results and discussion 4.1. Baseline model and validation Before a model can be used for any meaningful policy and management analyses, the model results must first be validated with reference to some historical or observed outcomes that the model is trying to predict.

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In an optimization model like the current one, validation cannot be done by simply comparing the modelestimated values with some historical values [14]. That is because the model optimizes effort and catch while the observed effort and catch may not be optimal to begin with. Instead, we run a baseline simulation for 10 years in which we force the model effort levels for all the study years to be equal to the observed effort level of 1998. The baseline run will then optimally compute the corresponding catch and stock for the simulation period. In this case, therefore, the minimum and maximum effort constraints (Eqs. (2) and (3)) will not be applicable. Thus, we create a benchmark wherein the future effort levels are fixated at the initial year’s effort level. This effort restriction on the model also allows us to understand how the continuation of the current harvesting effort would impact the future sustainability of the fishery stock and catch. Now we can compare the observed and model-generated 1998 catch and stock to evaluate the model’s validity. Comparison of the 1998 observed and the modelestimated baseline catches is made in Table 2. For most species, the differences between the model and observed levels of 1998 catch are within 11%. For ribbonfish and stomatopods, the difference is around 18% of the observed level. For pomfret, there is a wide relative gap between the two values. There was a wide year-toyear fluctuation in the pomfret catch during 1994–1998. Also, this is one of the least significant model species in recent years. There may be errors in the specification of the unobservable biological parameters, and errors in collecting the third party data on catch and effort. Given these factors, we consider the model’s overall performance quite satisfactory. In the baseline simulation, the stocks of 9 out of 17 model species decline over time. These are Indian mackerel, rock cod, bream, ribbonfish, other carangids, black pomfret, prawns, stomatopods, and cephalopods. Stocks of six species, namely, sardines, stolephorus, thryssa, other perch, and sole, increase. Stocks of two other species, namely other sardines and scads, remain stable. The most interesting result to point out is mackerel. This species would become least sustainable in terms of both stock and catch in 10 years if the exploitation continues at the current effort intensity. Mackerel, which is only second to breams in catch with 5246 tonne in 1998, experiences more than 40% reduction in catch. Similarly, other species that suffer drastic decline in stock and catch are prawns, stomatopods, and cephalopods. The baseline harvesting results in a total market revenue over 10-year period of little over Rs. 4929 million, whereas the total cost of harvesting is as high as Rs. 4844 million. That leaves a net fishery rent of only Rs. 84 million or 1.7% of the total market revenue. Relative to the total market revenue, the rent margin

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under current harvesting scenario is insignificant. The crewmembers’ salary is estimated at Rs. 1391 million or 28.23%. Since the declining species are mostly caught by trawlers, which include both single- and multi-day trawlers, the trawler industry would more likely be affected by this decline. Concurrently, the employees of this industry would be adversely affected too.

4.2. Optimal fishery harvesting Our next goal is to run the model to characterize the optimal combination of vessel efforts. We ran the simulation again without forcing endogenous effort values in the model as in the baseline run. The only restrictions are the minimum and maximum constraints on the effort. The minimum effort constraint is included to reflect the political reality that no single vessel class could be completely eliminated from the fishery. The maximum constraint is necessary to avoid the model becoming unbounded. Also, there are real-world capital constraints on the limits to which fishery capital can expand in a given time period. The maximum fishing efforts are assumed to be 25% above the 1998 observed level for all vessel classes except NMB. For the latter, the effort restriction is placed at twice the current level. The minimum efforts are assumed at 50% of the current level. Thus, the optimal model is run with all the constraints in Eqs. (2)–(8). Further, this model will optimally decide values of all three major decision variables effort, catch and stock. Note that in the baseline model, the effort variable was pre-determined at the initial level effort. While there is still some degree of overfishing in some cases, the stocks of all but four species either increase or remain stable (Table 2). Indian mackerel, pomfret, stomatopods, and cephalopods still experience a decline in the stocks. However, the rates of this decline, particularly for stomatopods, and cephalopods, are much slower than that under the baseline scenario. Interestingly enough, the rates of stock and catch decline for Indian mackerel remain the same as the rates under the baseline situation. This is because of the fact that under the optimal scenario, the number of fishing hours by PS, the effort of which is mostly dedicated to mackerel, increases quite substantially. This increase in PS effort is offset by any reduction in the effort by other vessel classes, for instance, OBM. The production of shrimp, which is one of the highly targeted species of trawlers, becomes more stable under the optimal harvesting strategy. It is important to note that shrimp contributes more than 50% of the total marine exports. Because of its economic significance, a management (optimal) strategy that has potential for making shrimp more sustainable merits further attention.

2036 4523 4618 763 818 567 3553 1141 341 2258 2151 360 5808 406 850 915 1876

1664 3726 4398 872 1619 1132 7106 2255 681 2112 2760 337 5246 793 1574 1823 3745

1998 simulated catch

185,603 195,000 102,000 123,456 28,000 4979 40,000 32,000 2500 13,000 20,000 3194 25,356 5200 3800 3200 5900

185,603 195,000 102,000 123,456 28,000 4979 40,000 32,000 2500 13,000 20,000 3194 25,356 5200 3800 3200 5900

1998

188,180 194,500 109,732 131,534 30,420 5169 41,183 34,567 2512 12,646 20,161 3111 22,415 5998 3776 3037 5012

188,553 195,297 109,952 131,424 29,619 4605 37,630 33,453 2171 12,792 19,552 3133 22,978 5610 3052 2129 3143

1999

Model-projected stocks

190,575 194,044 117,312 139,035 32,793 5338 42,237 36,816 2524 12,322 20,308 3033 20,171 6884 3753 2883 4312

191,300 195,569 117,763 138,818 31,166 4312 35,699 34,685 1895 12,599 19,153 3076 21,118 6037 2501 1421 1780

2000

192,795 193,630 124,642 145,879 35,081 5484 43,168 38,722 2,535 12,023 20,444 2959 18,396 7859 3733 2737 3746

193,850 195,817 125,329 145,560 32,626 4075 34,100 35,710 1660 12,419 18,795 3022 19,620 6478 2080 950 1037

2001

194,849 193,253 131,635 152,022 37,251 5610 43,985 40,291 2546 11,748 20,568 2890 16,954 8918 3714 2600 3280

196,212 196,043 132,556 151,609 33,989 3881 32,755 36,548 1459 12,252 18,474 2970 18,387 6931 1749 636 614

2002

196,744 192,909 138,219 157,452 39,274 5718 44,697 41,549 2557 11,493 20,681 2824 15,756 10,054 3697 2470 2890

198,394 196,250 139,367 156,955 35,249 3718 31,611 37,226 1286 12,096 18,185 2921 17,355 7394 1484 426 366

2003

198,490 192,595 144,341 162,187 41,132 5809 45,313 42,538 2568 11,256 20,785 2762 14,746 11,255 3682 2347 2561

200,405 196,439 145,701 161,618 36,401 3581 30,629 37,766 1136 11,951 17,923 2874 16,477 7864 1267 285 220

2004

200,095 192,309 149,965 166,266 42,812 5886 45,844 43,302 2578 11,037 20880 2703 13,880 12,504 3668 2231 2279

202,256 196,611 151,521 165,635 37,445 3463 29,778 38,194 1006 11,815 17,686 2829 15,721 8338 1088 191 132

2005

201,569 192,049 155,075 169,742 44,311 5950 46,299 43,884 2588 10,832 20,967 2647 13,130 13,783 3655 2121 2036

203,955 196,768 156,805 169,060 38,383 3362 29,036 38,531 892 11,687 17,470 2787 15,065 8813 939 128 80

2006

202,911 191,758 159,637 172,659 45,632 6,003 46,689 44,320 2595 10,613 21,003 2589 12,314 15,068 3607 2017 1825

205,512 196,912 161,553 171,953 39,220 3274 28,385 38,793 793 11,568 17,274 2746 14,489 9286 813 86 48

2007

The baseline simulation is run by forcing the endogenous model effort values for the entire simulation period at the 1998 observed levels. See Section 4.1 for details. The optimal simulation is run with all the model constraints, Eqs. (2)–(8). See Section 4.2 for details.

b

a

Optimal scenariob Oil-sardine Other-sardines Stolephorus Thryssa Lizard-fishes Rock-cods Breams Other-perches Ribbon fish Scads Other-carangids Black pomfret Indian mackerel Soles Prawns Stomatopods Cephalopods

1879 4151 4537 842 1759 1235 7445 2391 577 2111 2931 165 4954 885 1577 1546 3871

1998 observed catch

Increasing Increasing Increasing Increasing Increasing Increasing Increasing Increasing Stable Almost stable Stable Decreasing Decreasing Increasing Stable Decreasing Decreasing

Increasing Stable Increasing Increasing Increasing Decreasing Decreasing Increasing Decreasing Almost stable Decreasing Decreasing Decreasing Increasing Decreasing Decreasing Decreasing

Change in stock over 10-year period

244

Baseline scenarioa Oil-sardine Other-sardines Stolephorus Thryssa Lizard-fishes Rock-cods Breams Other-perches Ribbon fish Scads Other-carangids Black pomfret Indian mackerel Soles Prawns Stomatopods Cephalopods

Species

Table 2 Comparison of model performances between actual, baseline and optimal scenarios for Mangalore Port (amounts in tonnes)

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Another interesting finding is that under this scenario there is almost no change in the total value of harvest (Rs. 4969 million) from that of the baseline simulation (Rs. 4929 million). However, there is a substantial reduction in the cost of fishing from the baseline level of Rs. 4845 million to the optimal level of Rs. 3155 million, resulting in a profit of Rs. 1814 million or 36.5% of the total market value (Table 3). This is due to both biological and economic reasons. Biologically, more number of species become sustainable over the 10-year period under the optimal scenario. This certainly increases the total catch of certain species. Economically, the model allocates effort more toward high value species and high-productivity (or low unit cost) vessel. This helps the industry realize higher rent from the fishery. Thus, the optimal effort distribution not only increases fishery rent but also increases the chance of several model species to become either sustainable or more sustainable. 4.3. Model application to alternative fishery policies The fishery management policies in India are broadly governed by the Indian Fisheries Act of 1897 and the marine fisheries (regulation) acts of the respective states, which were enacted in the 1980s. The Karnataka Marine Fisheries (Regulation) Act of 1986 provides for the regulation of fishing through seasonal closure of fishing operation by specified vessels, restriction of fishing in specified areas, and control of indiscriminate fishing of brood stock and juveniles through regulating mesh size. For example in Karnataka, mechanized vessels are prohibited from fishing during the monsoon season (June–August) for about 75 days. However, most of the Act has not been implemented due to the lack of information on the impacts of such policy measures on different stakeholders. In this section, we calibrate our model to simulate the impacts of two important policy scenarios such that the results of the simulation might help shed light on how the unsustainable harvesting, as seen in the baseline or optimal fishery simulations, can be corrected. The two policy scenarios simulated are: Policy scenario I. Under this scenario, we analyze the effects of placing restrictions on harvesting technology

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that reduces the fishing capacity of purse seines. Both under the baseline and optimal harvesting scenarios, this vessel type is found to promote an unsustainable harvesting of mackerel, one of the commercially important pelagic species. The technology restriction may be implemented by changing the mesh size of nets that are used on this vessel. This policy change is represented in the model by reducing the effort standardization parameter for purse seines from the baseline level of 1.0 to 0.7. Policy scenario II. For this scenario, a seasonal restriction of 2 months on all mechanized vessels such as purse seines, trawlers and motorized outboard fishing nets is considered. This policy allows stocks to rejuvenate during the spawning season and directly impacts the total annual effort expended by fishers. A moratorium on harvesting is most common under the Karnataka laws. Therefore, maximum effort constraints are reduced by 2/12 of the baseline levels for the above vessel types. Table 4 presents the estimated values of stocks of selected species, gross returns, total cost, net profits and wage payments under policy scenarios I and II. The initial stock (actual stock in 1998) presented in the table allows comparison of the effectiveness of each of the policy options in enhancing stock levels and also economic costs and returns. It is clear that the two policy options have different impacts on individual species. For most of the species both scenarios will result in much higher stock levels through the year 2007 than the optimal harvesting scenario discussed earlier, except in the case of species such as stomatopods and cephalopods. It is interesting to note that under the optimal harvesting plan, there is a decrease in the mackerel stock to all most half of the initial stock. However, each of the policy scenario discussed here shows an improvement of the stock. The stocks of oil sardines and stolephrous also improve under both the scenarios. Certain species such as shrimps, pomfrets and scads, which are unsustainable under optimal harvesting scenario (Table 2), become sustainable under the both policy scenarios. Particularly, the sustainability of shrimps, which is one of the most highly targeted

Table 3 Comparison of cost and return in the baseline and optimal models Baseline model

Gross revenue Total harvesting cost Net profit Crewmen salary

Optimal model

Amount (million rupees)

Percentage share of gross revenue

Amount (million rupees)

Percentage share of gross revenue

4929 4845 84 1392

100.0 98.3 1.7 28.2

4969 3155 1814 1426

100.0 63.5 36.5 28.7

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Table 4 Impacts of alternative policy scenarios on stocks Species

Oil-sardine Other-sardines Stolephorus Thryssa Lizard-fishes Rock-cods Threadfin breams Other-perches Ribbonfish Scads Other-carangids Pomfret Indian-mackerel Soles Prawns Stomatopods Cephalopods

Initial year (1998) actual stock (in tonnes)

185603 195000 102000 123456 28000 4979 40000 32000 2500 13000 20000 3194 25356 5200 3800 3200 5900

Fish stocks in the final year, 2007 (in tonnes) Optimal harvesting simulation

Policy scenario I

Policy scenario II

202911 191758 159637 172659 45632 6003 46689 44323 2595 10613 21003 2589 12314 15070 3607 2017 1825

207103 200263 170118 173728 45670 6008 46689 44353 2591 14799 23617 3283 18287 15149 3776 2027 1830

205256 196517 165474 173266 45653 6006 46689 44341 2595 12843 22465 2964 15566 15115 3720 2023 1828

species by the trawlers, improves. The stock level of scads, which is reduced under optimal harvesting strategy to 10,613 tonne from the initial stock of 13,000 tonne, increases to 14,799 tonne under scenario I and to 12,843 under scenario II. As expected, the net profits and wage payments are slightly lowered under the policy scenarios. The gross returns diminished from Rs. 4969 million under the optimal harvest level to Rs. 4212 and 4557 million under scenarios I and II, respectively. A similar trend can be observed with respect to labour payment. The total cost of harvesting also slightly decreases from Rs. 3169 million under optimal harvest levels to Rs. 2953 million in scenario II due in most part to reduced fishing effort. However, net profits and wage payments under either policy will be higher than that under the baseline scenario. Although a 2-month moratorium on fishing effort (scenario II) improved long-term stock levels of most species considerably, the major species of the Mangalore port such as Indian mackerel experiences a decrease in stocks by the end of the simulation period by as much as 25%. High-value species like cephalopods and stomatopods also show significant decrease in stock levels. It is thus clear that the current policy of seasonal restriction is not fully effective in stemming the unsustainability problem of at least some major species.

5. Concluding remarks In this paper, we develop a bioeconomic model of multi-species harvesting for a representative coastal fishery in India by explicitly integrating the dynamic

nature of the fishery and the technical interactions between multiple species and fleets. The model is cast under an optimization framework that allows the evaluation of economic performance of alternative management policies. The model is detailed enough to track the impacts of multi-fleet control measures on individual species and fleets. This multi-species, multigear analysis framework developed in the study is believed to be the first of its kind for a tropical marine fishery in a developing country like India. The model is implemented using a large amount of data collected by government agencies by species and fleets. The study thus represents the first analysis of the region’s fishery to show how such a detailed fishery production data could inform public policy evaluation and decision-making. The analytical results demonstrate the urgent need for regulation of fishery effort. The fishery effort in the study region is rapidly approaching the levels that would make fishery for the most valuable species unsustainable in the long-term. At the current harvesting intensity, the fishery rent is estimated to be negligible. This result supports the conventional wisdom that the rent from an open-access fishery dissipates. Also, zero or negligible rent shows that the harvesting intensity in the study region is almost at the open-access level of bionomic equilibrium [16]. Such a fishery can afford no further expansion in effort. If the fishery under study were managed more efficiently with the objective of rent maximization, the net profits would increase many fold. The optimal model simulation calls for a reallocation of fishery effort toward high-value species and high-productivity, or low unit cost, gears. Nevertheless, the economically

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optimal management strategy would not save some of the commercially important species like mackerel, pomfret, stomatopods, and cephalopods. Shrimp, the most sought-after species, showed declining production and stocks in the baseline simulation, but was stable in the optimal harvesting scenario. In addition, the traditional non-mechanized fishing fleet would fail to constitute an economically optimal effort mix unless equity considerations warrant its presence. The policy simulation results show that the traditional management approaches such as season and fishing technology restrictions are unlikely to tackle the overexploitation problem completely. A more rigorously enforced limited entry system, such as transferable quotas, fishing power restrictions, and effort limits, may be needed to manage the stock effectively. The optimal effort allocation obviously requires decommissioning of some of the existing vessels. Additional management and socio-economic issues warrant considerations prior to implementing such policy. It is not sufficient to restrict the number of vessels. The overall fishing capacity of each gear must be controlled [20]. Increased regulation and monitoring will be necessary lest vessel operators spend more fishing effort than is reasonable at sea and lest this effort exceeds optimal capacity utilization. Kirkley et al. found that even with effort reduction, the conflicts among gear types, resource users (small versus large fishers), and fishing areas (inshore versus offshore fishers) will prevail [12]. Mandatory spatial and seasonal restrictions on gear types might minimize such conflicts. More innovative policy tools such as Individual Transferable Quotas may be worth considering [21], but would require a thorough feasibility study. Further, in order to ease the employment hardship that might occur due to effort reduction, programs creating employment in the nonfishery sectors must be designed. Rapid industrial growth in coastal cities like Mangalore has attracted a cheap labor force from interior cities and states. Through proper job training programs in place, the excess fishery laborers might eventually find jobs in local industries. The study results also shed some light on the existing state’s fishery subsidy policy. The state government subsidizes fuel costs incurred by mechanized fishing fleets. The fuel costs account for 65–70% of their operating costs. The cost parameters in the model represent after-subsidy costs. The actual costs of fishing under all the model scenarios are more than the costs represented in the simulations and could exceed the total revenue reported for different model scenarios. In other words, the current level of fishing effort would not be economically viable without the government subsidy. As already stated, the current fishing effort is also found to become biologically unsustainable in the future. The fuel subsidy policy therefore could promote economically

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inefficient and biologically unsustainable fishing. The state government might consider reducing the subsidy and utilizing the same fund for compensating probable income losses caused by effort reduction. Revenue saved could also be used for funding increased enforcement activities and stock enhancement programs.

Acknowledgments This study was supported by a grant provided by the Indira Gandhi Institute of Development Research, Mumbai, India, under the World Bank assisted Environmental Economics Capacity Building, India Project. The authors wish to thank Kirit Parikh for his valuable suggestions.

Appendix A Computation of catchability coefficients For each species, the Fox model in (A.1) was estimated. The Fox model is a modified version of Schaefer’s model [16], in which a logarithmic relationship between catch per unit effort (CPUE) and fishing effort is formulated as lnðCPUEÞ ¼ f ðeffortÞ: More formally,
 Y ln ¼ a
bE, (A.1) E where Y is total annual catch, E the annual standardized fishing effort, and a and b are constant parameters. Once we have the estimates of a and b for each species, the values of annual catchability coefficients mit for species i and period t can be computed using the following formula:   zit U it1
m þ 1=b 1  , (A.2) mit ¼  1
m z ðm zit U it
1 þ 1=b it
1Þ where zit ¼
ða=b þ E^ it Þ and E^ it ¼ ðE it
E it
1 Þ=2, Uit is catch of species i per unit of standardized fishing effort in year t; and m is a constant parameter with a value of 1.0001 for the Fox model. The catchability coefficient describes the effectiveness of each unit of fishing effort. The constant catchability coefficient for each species implies there is no change in technology over a certain period of time. Even though the catchability coefficient may vary with time, its time variance is difficult to measure [22]. Therefore, a timeinvariant co-efficient as an average over n years can be

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computed as "  # n
1 X lnmit  . mi ¼ exp ðn
1Þ t¼1

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(A.3)

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