





































Ecology, Economy, and Society–the INSEE Journal 7(1): 75-103, January 2024 

RESEARCH PAPER 

Sustainable Harvests under Different Bio-economic 
Scenarios of Chilika Wetland’s Fisheries 

Aditi Samant Singhar and Haripriya Gundimeda 

Abstract: The primary goal of fisheries management is to control overfishing and 
unregulated fisheries to protect stocks and boost the value of fish resources. In this 
study, we compare harvesting and fishing efforts in the maximum economic yield 
(MEY), maximum sustainable yield (MSY), and open access (OA) scenarios using 
Gordon-Schafer’s bioeconomic model to examine the economic status of Chilika’s 
fisheries. This paper also measures the effectiveness of Chilika Lake’s restoration 
measures. An independent sample t-test with bootstrap confidence intervals 
indicates the results’ robustness and concludes that the fisheries’ output has 
increased in the post-restoration period (2003–04 to 2020–21) in a statistically 
significant way. The estimated measures serve as the focal points for designing 
sustainable and optimal fisheries management strategies. They add to the ongoing 
research on stock evaluation, which helps determine harvesting effectiveness and 
strengthens the fishing stock to avoid exhaustion. Therefore, the expectation is that 
the outputs in the forms of optimal extraction and an enhanced management tool 
will improve livelihood opportunities and enhance other socioeconomic 
components of the fisheries sector. As a result, the findings will aid policymakers 
and other interested parties in creating a suitable harvesting strategy to attain 
economic optimality. 

Keywords: Bioeconomic, fisheries, sustainable yield, Ramsar wetland 

 

1. INTRODUCTION 

Wetlands provide various ecosystem products and services that benefit 
humankind (Gardner and Finlayson 2018). They help maintain a diverse 

 
 PhD Student, Dept. of Economics, Indian Institute of Technology-Bombay, India. 
aditisinghar@iitb.ac.in  

 Institute Chair Professor in Economics, Dept. of Economics, Indian Institute of 
Technology-Bombay, India-400076. Haripriya.gundimeda@iitb.ac.in    
Copyright © Singhar and Gundimeda 2024. Released under Creative Commons Attribution 

© NonCommercial 4.0 International licence (CC BY-NC 4.0) by the author.  

Published by Indian Society for Ecological Economics (INSEE), c/o Institute of Economic 

Growth, University Enclave, North Campus, Delhi 110007.  

ISSN: 2581–6152 (print); 2581–6101 (web).  

DOI: https://doi.org/10.37773/ees.v7i1.1022  

 

mailto:aditisinghar@iitb.ac.in
mailto:Haripriya.gundimeda@iitb.ac.in
https://doi.org/10.37773/ees.v7i1.1022


 Ecology, Economy and Society–the INSEE Journal [76] 

 

ecosystem and provide essential habitats for feeding and breeding for 
aquatic species, migratory birds, and other fauna. In addition to providing a 
means of subsistence and livelihoods for millions of fishers, wetlands play a 
significant role in water purification, hydrological regulation, carbon 
sequestration, climate regulation, and biodiversity conservation in addition 
to providing cultural and aesthetic services (Sukhdev et al. 2010). However, 
unsustainable harvesting, habitat degradation, loss of fishery resources, 
spawning, and the construction of nursery grounds worldwide has resulted 
in declining fishery yields. The prime intent behind designating a biodiverse 
wetland as a Ramsar site is to conserve the existing ecosystem from further 
damage and bring about overall sustainable development. The Ramsar 
Convention signed in 1971, officially referred to as the International 
Convention on Wetlands, primarily aims to promote the responsible 
utilization of wetlands while actively contributing to their preservation and 
maintenance (Ramsar 2023) However, studies such as Samraoui and 
Samraoui (2013), Geijzendorffer et al. (2019), Gaget et al. (2020), Gosh and 
Das (2020), and Mao et al. (2021) indicate how, in recent years, even these 
sites are being threatened despite significant protection efforts. This reveals 
how inefficiencies in the Ramsar Convention undermine the development 
of a strong conservation network. According to estimates from the United 
Nations Food and Agriculture Organization (FAO), the catch fisheries and 
aquaculture industry directly employs around 58.5 million people; indirectly, 
around 600 million people rely on the fisheries and aquaculture sector for a 
living (FAO 2020). Hence, sustainability is a significant concern raised in 
international agreements and guidelines pertaining to fisheries management. 
The sustainable use and conservation of sea and marine resources is the 
focus of Sustainable Development Goal (SDG) 14 of the United Nations, 
titled “Life Below Water” (Molony et al. 2022). The FAO’s Code of 
Conduct for Responsible Fisheries (1995) too necessitates incorporating 
planning measures for fisheries’ conservation and sustainable use (FAO 
2020). 

Fisheries, however, present the classic trade-off between profits and 
ecological deterioration. In the pursuit of economic goals—such as profit 
generation—social objectives such as employment, safe working conditions, 
and gender equality are undermined in fisheries, which also inadequately 
address their role in ecological deterioration (Kittinger et al. 2017; Farmery 
et al. 2019). In developing nations, overexploitation is driven by the 
commoditization of fish and the pursuit of economic development through 
industrialization and market expansion. According to a study by Sethi et al. 
(2010), a significant connection exists between demand and stock levels, 
whereby high prices due to high demand, and low extraction costs due to 



[77] Singhar and Gundimeda 

the availability of cheap labour in developing nations, results in over-
extraction, and subsequently, lower fishery stock. Thus, prioritizing 
economic benefits necessitates larger and more frequent harvests, which 
eventually threaten ecological sustainability. 

Uncontrolled access to the commons leads to a typical “tragedy of the 
commons” situation, wherein individual fisherfolk make decisions in their 
best interests, eventually reducing the stock of the resource and thus overall 
profits. Solutions to restrict access to the commons have been suggested, 
such as private or single ownership, but these have not been successful in 
preventing overfishing. In some situations, a single owner may find it 
economical to drive a fish stock to extinction (Clark 1973; Oosterveer 2008; 
Pauly et al. 2002; Costello et al. 2016). In some countries, fisheries 
management based on central government planning has failed to halt the 
degradation of fishery stocks. The most common approach is for the state 
to regulate resource access. It usually does this in three ways: first, control 
effort through boat registration, fishing permits, licensing fees, and taxes; 
second, regulate fishing gear type; third, impose seasonal limits, close access 
to the resource periodically, and protect a portion of the area for 
biodiversity conservation (Nagothu 2004). Thus, it is inevitable that 
fisheries must be managed such that there is a balance between the 
economy, ecology, and livelihoods, which implies that realising sustainable 
biological, social, and economic advantages from the abundant renewable 
aquatic resources available is the overarching purpose of fisheries 
management. 

This study examines the economic principles central to fishery resource 
management to suggest a regulatory regime that can assure economic 
sustainability while utilizing fishery resources. Further, a thorough 
knowledge of bioeconomic resources and their management is required to 
encourage the sustainable development of fishery resources. Bioeconomic 
theory combines a fish species’ biological and economic dimensions to 
explain stock, catch, and effort dynamics under various regimes and offers 
suggestions for the best stock management. The Gordon and Schaefer 
(1954) biomass approach to stock assessment and Beverton and Holt 
(1957) cohort analysis form the basis for various other optimization models 
in fisheries economics. However, the Gordon-Schaefer method has 
dominated fishery economics as it allows for empirical simplicity in 
comparison to a more in-depth, age-structured categorization of harvestable 
populations in situations where there is insufficient data and one is 
compelled to use models with a minimal number of parameters 
(Zimmermann, Steinshamn, and Heino 2011; Habib, Ullah, and Duy 2014). 
Standard approaches to fisheries’ sustainable management include the MSY 



 Ecology, Economy and Society–the INSEE Journal [78] 

 

(maximum sustainable yield), MEY (maximum economic yield), and open 
access (OA) scenarios. These parameters predict over-exploitation 
situations and require information on the fishery’s stock, catch, and fishing 
effort. Depending on the availability of empirical data, a suitable 
bioeconomic model that investigates the complexity of ecological 
mechanisms can be used to understand static and dynamic behaviours. This 
analysis can help in assessing the long-term sustainability of measures to 
increase fishers’ profitability as per various social, economic, and ecological 
objectives. 

The objectives of this article are as follows: 1) examine the evolution of 
Chilika’s fishery resources and offer relevant policy recommendations to 
optimise for sustainable harvesting in the wetland; and 2) use Gordon-
Schafer’s bioeconomic model to examine the economic prospect of 
Chilikas’ fisheries by comparing harvesting and fishing efforts in the MEY, 
MSY, and OA regimes. These biological measurements support ongoing 
research on stock evaluation, which aim at increasing harvesting 
effectiveness and strengthening the fishing stock to avoid the point of 
exhaustion. Chilika, declared as India’s first “Ramsar site” in 1982, was 
chosen as the study area due to its significant biodiversity and ecological 
importance. The National Wetlands, Mangroves, and Coral Reefs 
Committee of the Ministry of Environment Forests (MoEFCC), 
Government of India, has also identified the lagoon as a priority site for 
conservation and management. Nalaban Island within the lagoon is notified 
as a bird sanctuary under the Wildlife (Protection) Act, 1972. Chilika was 
considered a dying lake in the late 1990s, and because of the alteration in its 
biological nature, the Ramsar Convention included it in the Montreux 
Record (threatened list) in 1993. However, following practical restoration 
work carried out by the CDA and the state government, the lake was the 
first in Asia to be removed from the Montreux Record in 2002 (CDA 
2017). Chilika also presents a case where remunerative fishing and state 
intervention attract outsiders to the area, causing damage and making local 
community management challenging. 

The paper is structured as follows: Section 2 provides a detailed description 
of the study area along with the evolution of fishery resources. Section 3 
discusses the chosen bioeconomic model, the results of which are presented 
in Section 4. Section 5 concludes the study with discussions and policy 
implications. 

 

 

 



[79] Singhar and Gundimeda 

2. DESCRIPTION OF THE STUDY AREA 

Figure 1: Map of Chilika Lake 

Source: CDA website (https://www.chilika.com/how-to-reach.php) 

Chilika, one of the largest brackish water lagoons, features a rare 
combination of fresh water, brackish water, and marine environments and 
is in the state of Odisha along the Bay of Bengal around latitudes 19°’28’ 
and 19°’54’ N and 85°’5’ and 85°’38’E (Figure 1). This unique combination 
of factors have given it a rich biodiversity and a highly productive ecology, 
with fishery resources offering attractive advantages. Irregular water 
channels and several tiny, sandy, and transitory islands connect this pear-
shaped lagoon to the sea. The lagoon is a significant migratory waterfowl 
wintering area renowned for having a diverse range of species, including 
Irrawaddy dolphins and several rare, vulnerable, and endangered species on 
the IUCN Red List of Threatened Species. The lagoon is one of Odisha’s 
primary sources of capture fisheries (Figure 2), and over 0.15 million 
commercial and subsistence fisherfolk who live in and around Chilika 
lagoon depend on it for their nutritional and economic security (Figure 3) 
(CDA 2017, 2022). The annual fisheries output of Chilika majorly 
comprises fish, prawn/shrimp, and crab. Due to its distinctive hydrology, 
which combines brackish and riverine features, its products are in great 
demand. In 1978–79, the total output was 6,454 million tons (MT), valued 

at ₹89.1 million. Of this, shrimp accounted for 1,869 MT, worth ₹51.2 



 Ecology, Economy and Society–the INSEE Journal [80] 

 

million. However, later periods (1988–2001) witnessed a steady decline, 
mainly due to the lake’s health deterioration. The site was included under 
the Montreux Record from 1993–2002, and major restoration works were 
undertaken to rehabilitate the lake to its original state. Thus, from Figure 2, 
it is clear that this period witnessed the least amount of total fisheries yield. 
After the restoration period, output steadily increased relative to the earlier 
phase, but there have been fluctuations in yield—studies such as Sahu, Pati, 
and Panigrahy (2014), Kankara and Panda (2020), and Finlayson et al. (2020) 
indicate that hydrological changes due to natural and anthropogenic factors 
impact the productivity of the lake due to varying sediments and salinity 
levels. 

According to the CDA’s report, compared to yearly landings in 2016, 
average fish landings in 2017–18 totalled 16,657.3 tonnes, an increase of 

18.4%. The average landing value for 2017–2018 was ₹2,424.8 million, a 
27.5% rise over the catch value for the previous year. The predicted average 

per capita income of active fisherfolk for 2017–2018 was ₹59,141, a 5.5% 
increase over the previous year. Fish, prawn, and crab made up 69.7%, 
28.4%, and 1.9% of the average annual landing during these years. 

Figure 2: Annual Fisheries Output (1978–2019) 

 

Source: Fisheries Department, Government of Odisha  

 

 

 



[81] Singhar and Gundimeda 

Figure 3: Number of Active Fishers (2001–14) 

 

Source: CDA 

The lake is surrounded by about 132 fishing villages of varying sizes, and 
30% of the fishing village population is actively involved in fishing (Sekhar 
2004). Figures 3 and 4 indicate that the number of people involved in 
fishing and the number of boats has also increased. In 2016, the average per 

capita income of active fishers in the region was estimated to be ₹56,035 
(CDA 2017). 

Figure 4. Number of Fishing Boats Used for Regular Fishing (2001–18) 

 



 Ecology, Economy and Society–the INSEE Journal [82] 

 

Source: CDA 

Table 1 summarizes the evolving nature of fishing undertaken in Chilika. 
Sekhar (2004) and Iwasaki and Shaw (2009) note that the lake was part of 
zamindari holdings until the 1880s, and they leased out access rights to the 
surrounding fishing families in exchange for small token payments. The 
British maintained this arrangement in India, and the state government took 
over ownership post-1950. Leases were allocated through an auction system 
and were restricted to local fisherfolk. The local fishing community had 
access rights to fisheries till the late 1980s; these communities respected 
seasonal limits for fishing. The fishing communities collectively enforced 
boundary rules to restrict access to the lake and formed guiding principles 
for harvesting—such as restrictions on where to fish, size limitations, and 
seasonal closures to allow juvenile stocks to grow, thereby avoiding stock 
extinction. However, as several stakeholders laid claim to the resource, 
conflicts among various stakeholders took place. 

Chilika Lake presents a case of how a divergence of interests leads to the 
disintegration of prevalent social and economic structures and damages to 
the natural resource base. In his study, Sekhar (2004) reports that traditional 
fisherfolk fished sustainably as they used localized gear made of local 
materials, and the gear type depended on the species and area fished. 
However, external parties (various state departments), in the name of 
development, have introduced more mechanized fishing boats and gear, 
which has led to the overexploitation of the fishery resource and increased 
resource conflicts due to a lack of respect for resource boundaries and 
access norms and disturbance of breeding areas for commercial advantage. 
Further, studies such as Samal (2002), Adduci (2009), Nayak and Berkes 
(2011), Nayak (2012), and Das (2014) note that fishing in Chilika is 
predominantly small scale in nature; as a means to provide the fisher 
community with an alternative source of income, the state government 
promoted shrimp aquaculture through initiatives like the “Economic 
Rehabilitation of the Poor” programme. However, this had the opposite 
effect of what was intended. Not only did it have a detrimental effect on the 
local fisher community, but it also led to significant environmental damage. 
The timeframe shown in Table 1 also demonstrates how the 
rapid expansion of aquaculture in the region led to the socioeconomic 
decline of the traditional fishing population that depended on the lake. 
Kadekodi and Gulati (1999), Kadekodi and Nayampalli (2003), Mohapatra 
et al. (2007), and Mishra et al. (2019) indicate that the growth of shrimp 
farming intensified lake degradation by altering its hydrological balance. It 
hinders the free water and tidal migration of species—including juveniles,  



[83] Singhar and Gundimeda 

Table 1. Timeline of Changes in Fishing Policies with Respect to Chilika and Their 
Structural Implications   

Timelin
e 

Fishing Rights 
Situation 

Structural Implications 

Colonial 
rule 
(before 
1952) 

Proprietors included 
rajas, zamindars, local 
landowners, and the 
government. 

• Strict norms based on caste-defined fishing 
rights and entitlements. 

Anchal 
Adhikari 
(1953–
1959) 

Open auction 
mechanism, primarily 
employing local fishers. 

• The lease system and the strict caste-based 
fishing rights granted exclusive rights and 
preserved the interests of traditional 
fisherfolk.  

Revenue 
collector
s (1959) 

Leases are mainly given  
primarily to fisherfolk 
cooperative groups. 

• In addition to enabling higher economic 
gains to be distributed fairly among the 
fisherfolk, community-based structures 
also reduced disputes, preserved sound 
ecological health, and provided a strong 
resource base. 

The late 
1980s to 
1993 

Promotion of  
aquaculture farms, 
especially shrimp 
farming. However, the 
plan was 
withdrawn owing to 
protests by fisherfolk. 

Exclusion of traditional fishers as historical 
fishing grounds shrank due to changes in 
land-use patterns and resource reallocation 
towards shrimp farming and other uses. 

Fishing 
lease 
guidelin
es (1991) 

Fishing rights were 
officially granted to both 
non-fisherfolk and 
fisherfolk. 

• Serious concerns involving access and 
entitlements were brought on by the loss 
of the institutional base, illegal 
encroachment, and unfavourable leasing 
policies.  

Orissa 
High 
Court’s 
decision 
(1993) 

30% of fishing rights 
were given to non-
fisherfolk. 

Institutional changes and worldwide 
increasing demand and pricing of shrimp 
have resulted in increased resource 
degradation, biodiversity loss, and 
fishing productivity. 

Suprem
e 
Court’s 
decision 
(1996) 

Aquaculture is to be 
prohibited within 1,000 
metres of the Chilika 
lagoon. 

 

Orissa 
legislati
ve 
assembl
y’s 

Orissa Fishing in Chilika 
Lake Bill failed to pass 
due to opposition from 
fisherfolk. 

 



 Ecology, Economy and Society–the INSEE Journal [84] 

 

decision 
(2002) 

Source: Revised from Iwasaki and Shaw (2009) 

whose sustenance is important for individual species to not become 
extinct—and causes increased silting, leading to reduced lake area. 

The declaration of the lake as a Ramsar site positively impacted fishery 
resource augmentation and a dramatic rise in fish landings has been 
observed. According to Ghosh, Pattnaik, and Ballatore (2006), the costs 
incurred in managing Chilika are taken care of by both the state and central 
government via funding allocated for specific projects. When it comes to 
financial aid from the latter, it is primarily in the form of support rendered 
by MoEFCC’s wetland-specific National Plan for Conservation of Aquatic 
Ecosystems. As part of their efforts to restore the lagoon, the CDA was 
awarded a “special problem grant” from the Ministry of Finance, totalling 

₹270 million under the 10th Finance Commission (1996–2000). 

Furthermore, they were awarded an additional amount worth ₹300 million 
through the 11th Finance Commission (Ghosh et al. 2006). Moreover, it is 
worth noting that the Orissa Water Resource Consolidation Project 
(OWRCP), backed by the World Bank, has acknowledged the significance 
of Chilika Lake by including it as a constituent of the project. This move 
has facilitated the allocation of funds for conducting hydro-biological 
surveillance of the lagoon. A significant portion of the funds was directed 
towards maintaining hydrological regimes essential for maintaining 
ecosystem connectivity with the Bay of Bengal and ensuring that the right 
salinity and water composition levels are maintained. This was done by 
dredging lead channels and opening a new mouth to the lake; apart from 
that, the remaining portions went into wetland monitoring/evaluation as 
well as fisheries development or livelihood improvement, respectively 
(Finlayson et al. 2020). Ghosh, Pattnaik, and Ballatore (2006) and Mohanty 
et al. (2008) also note that when the mouth of the channel was opened, 
there were changes in tidal movement that resulted in significant sediment 
flushing. This deepened the waterway and allowed for quicker freshwater 
discharge through the new opening. The restoration of salinity levels 
through this process improved auto-recruitment from the ocean and 
allowed for free-breeding migration. However, on the negative side, 
substantial evidence suggests that inland and small-scale coastal fisheries 
have declined due to the modification, overfishing, and other human 
activities (Kadekodi and Gulati 1999; Adduci 2009). During the post-
intervention period, the average annual fish landings were 12,070.54 metric 
tonnes, which almost matched the MSY level predicted by the Central 
Inland Fisheries Research Institute (CDA 2013). According to CDA’s 



[85] Singhar and Gundimeda 

Chilika Health Report (CDA, 2017), the total annual fish landing (fish, 
shrimps, and crabs) from the lake in 2016 amounted to 14,067.50 MT, 

equivalent to ₹1,901.96 million (see Figure 2 for the annual fisheries output 
from the lake). 

3. MODEL DESCRIPTION 

The literature on the economic performance of fishery management 
strategies suggests that the surplus production methodology of the 
bioeconomic model of fishery resources helps in determining sustainable 
harvest levels for a given effort and is regarded as a helpful tool for making 
an initial approximation even in conditions of time or data constraints 
(Dowling 2016; Honey et al. 2010; Habib et al. 2014). The biological linkages 
between fish stock growth and catch effort serve as the foundation for the 
surplus production model. It is based on the simplified stock–growth 
relationship that produces a positive rate of growth or surplus output 
(Coppola and Pascoe 1998). According to this theory, if the stock of fish 
population is not harvested, the uncaught population will gradually tend 
toward the maximum carrying capacity given a particular environment and a 
finite food source. As the population grows, surplus production is 
subsequently generated, which increases at a decreasing rate. However, this 
surplus output decreases at the point of maximum stock (corresponding to 
MSY as in Figure 5), because after this point, recruitment and growth of the 
population would be counterbalanced by natural mortality. Hence, beyond 
the MSY, the rate of increase of the fishery stock and the excess output 
becomes negative. The bioeconomic model thus offers a comprehensive 
method for assessing efficient fisheries management plans. 

Figure 5. Depiction of the Gordon-Schafer’s Bioeconomic Model 

 



 Ecology, Economy and Society–the INSEE Journal [86] 

 

Source: Adapted from Gordon (1954) and Schaefer (1957) 

The terms MEY and MSY refer to two distinct fisheries objectives that 
serve as the foundation for determining effective management strategies. 
OA denotes a situation where a shared pool resource and a lack of property 
rights result in no restrictions on harvested quantity. This is found to be 
socially inefficient as it results in suboptimal levels of outcome despite 
higher efforts compared to other scenarios. It thus results in lower catch 
levels with higher costs when compared with MSY and MEY. 

The Gordon-Schaefer (GS) model is often used to identify the underlying 
relationship between the three management scenarios (Gordon 1954; 
Schaefer 1957). The model’s ability to provide preliminary 
recommendations for both single-species and multi-species fisheries while 
only requiring limited data is a significant benefit, especially because the 
available data in the public domain captures only boat numbers and not 
changes in fishing intensity over the years. Further, due to complex 
biological and technological interactions, and the prevalence of 
predominantly small-scale fishing in the lake region, we believe 
that alternative fishing techniques or equipment will have similar results and 
that the same fishing effort would be utilized for harvesting a variety of 
species. Hence, the simplest GS, which assumes homogenous boats and a 
single stock, is utilized in this study to give a broader perspective on the 
fishing status in Chilika. Thus, in this study, we use the GS model to 
estimate the optimal levels of yield and effort for fisheries extraction in the 
Chilika wetland. The GS model is based on the following logistic equation 
describing a parabolic curve as a function of stock: 

                                                                             (1) 

 Where, 

F(X) is the surplus biomass growth per unit of time. 

X is the biomass of the given stock. 

K and r refer to the carrying capacity and rate of intrinsic growth of 
the stock. 

The harvest rate is given by the following Schaefer’s catch function:  

                                                                          (2) 

E denotes the fishing effort and q is the catchability coefficient. Sustainable 
yield is then when the surplus growth equals the harvest rate. 

                                                   (3) 



[87] Singhar and Gundimeda 

 Based on (1) and (2): 

                              (4) 

 and solving for X at equilibrium:  

                                                   (5)         

Hence, eq. (2) then becomes: 

                    (6) 

 Dividing both sides by (E) gives: 

                                 (7)  

 Eq. (8) depicts the total revenue function at the equilibrium:  

                                                    (8) 

 Where p is assumed to be a constant price per unit harvest: 

                                               (9) 

Eq. (9) gives the total cost of fishing effort with c denoting per unit cost of 
effort. Obtaining the equilibrium resource rent from eqs. (8) and (9): 

                                         (10)                          

Assuming average revenue at OA equilibrium to be AR = TR/E and AR 
equal to marginal cost (MC = ‘TC’ (E): 

                                                     (11) 

We may determine the fish stock’s OA equilibrium level by considering the 
unit cost of harvest and the resource rent per unit harvest. Thus using eqs. 
(2) and (9), the unit cost of harvest is as follows: 

                                           (12) 

This shows that as stock size increases, the unit cost of harvest reduces. 
Further, the resource rent per unit of harvest then becomes: 

                                                             (13)                             

 The long-term harvest function is denoted by: 

                                                         (14) 



 Ecology, Economy and Society–the INSEE Journal [88] 

 

 and CPUE is denoted as follows: 

           (15) 

where CPUE = H/E, a = qK, and b = −aq/r. Thus, the parameters a and b 
are obtained from the OLS regression of CPUE upon effort. 

Eq. (13) is then used to calculate effort at MSY by calculating the partial 
derivative of H with respect to E and bringing it equal to zero: 

            (16) 

and the output at MSY is: 

              (17) 

According to the OA, total fishing costs equal total revenue from the 
fishery (TR(E) = TC(E)). As a result, the effort at OA yield may be 
calculated using the Gordon-Schaefer model by equating MC = AR, hence:  

                      (18) 

            (19) 

The maximum economic return is reached for positive economic rent at a 
reduced total fishing effort, lower than E(OA). MEY is accomplished at a 
profit-maximizing level of effort: 

                  (20) 

The impact of restoration measures implemented during 1992–93 and 
2002–03 as part of Chilika Lake being classified under the Montreux 
Record has been captured by performing an independent t-test for these 
periods. The base period selection indicates the period during which severe 
measures to rehabilitate the lake to its original state were undertaken. As 
stated earlier, in 2002, the lake was declared successfully restored and 
subsequently removed from the Montreux Record. Further, economic 
considerations are found to be mostly ignored in most studies on surplus 
production models. Differing consumer demand and supply responses can 
notably impact the fishery stock’s exploitation rate. 

In the context of Chilika, demand for its fishery products is mainly driven 
by the distinctive quality and flavour that these species have due to the 
brackish features of the water. Studies such as Gudmundsson and Wessells 
(2000), Courchamp et al. (2006), Hall, Milner-Gulland, and Courchamp 



[89] Singhar and Gundimeda 

(2008), and Booth, Squires, and Milner-Gulland (2019) indicate how 
perceived rarity affects consumer behaviour, thereby influencing prices 
through changes in demand. The cost of extraction also plays a role in 
influencing the sustainability of the resource. Changes in costs are driven by 
expenses incurred towards the type of gear, boats and their maintenance, 
fuel costs, lease fees, costs incurred in setting up storage facilities, etc. 
Habitat degradation driven by climate change and anthropogenic factors 
can also increase the cost of harvesting through its impact on stock levels. 
The following five scenarios indicate how bioeconomic estimates and 
profits are impacted by changes in prices and how costs are modelled in this 
study. It is to be noted that, although choosing a 10% change may seem 
arbitrary, it offers the right amount of significance to affect the results 
without making it unfeasible or unrealistic. This approach facilitates a 
constructive appraisal of the model’s sensitivity by not introducing undue 
instability or volatility. The five scenarios are as follows: 

S0: Baseline, denoting the current situation 
S1: Cost increases by 10% + Price is the same 
S2: Cost reduces by 10% + Price is the same 
S3: Cost is the same + Price increases by 10% 
S4: Cost is the same + Price reduces by 10% 
 
4. DATA AND RESULTS 

The data used to obtain the sustainable catch level using the GS 
bioeconomic model was obtained from the CDA and Odisha Fisheries 
Department. The data for the bioeconomic model was for the period 2001–
18, and the data on the evaluation of restoration efforts was available for 
the period 1978–79 to 2000–21. Catch data was proxied by the total 
landings for the given period. The effort was calculated in terms of the 
number of boat days in a year and the number of boats deployed for 
fishing. The given data was normalized and brought to a units-per-day 
basis. Since there was a lack of secondary data on input costs, the cost of 
fishing effort was calculated based on the fuel price and quantity of fuel 
consumed on a fishing day (i.e., 3.7 litres of diesel per fishing trip and the 

price of fuel in 2018 was between ₹55–₹62) and the minimum daily wage 

rate earned per fishing day (₹200 as per MGNREGA). The unit cost of 
fishing effort and the unit price of the harvest in Chilika is thus estimated to 

be ₹52/kg (= (3.7*60) + 200 = ₹422/8.11) and ₹151.32/kg (= 

(2474.1*1000000) / (6000*336) = ₹1227.23/8.11), respectively, in 2018. 

 

 



 Ecology, Economy and Society–the INSEE Journal [90] 

 

Table 2: Key Statistics Related to Chilika Lake 
 

2001/0
2 

2017/18 

Number of fish landing centres/sampling stations 17 34 

Number of fishing boats used for regular fishing during 
the year 

5,000 6,000 

Annual landing (fisheries output) in tonnes 11,988.8
8 

16,358.3
4 

Total catch value in million rupees (nominal) 571.60 2,474.10 

Average CPUE (Kg/boat-day) 6.950 8.11 

Total number of fishing days 345 336 

 

The surplus production assessment method uses catch per unit effort 
(CPUE) as a measure of stock abundance. In the case of Chilika, the fish 
CPUE trend had a significant drop that started in 2005–06, followed by a 
rise that is found to have persisted. However, the fluctuations are not 
drastic and are between the 6–8 CPUE range (Table 2). The effort at 
maximum sustainable yield (MSY) based on the regression coefficients is 
0.655 kg/boat/day, and that of maximum efficient yield (MEY) and open 
access (OA) are 0.64 and 1.29 kg/boat/day; profits estimated per unit at 

MSY is ₹1,207.63, at MEY is ₹1,199.67, and at OA is ₹73.82. Comparing 
the CPUE and effort shows that the number of boats has increased over 
the years, but the CPUE remains more or less the same. This could indicate 
the presence of unsustainable fishing methods where the effort burden is 
placed on smaller fish varieties, which does not significantly contribute to 
total yield or weight, thereby resulting in a reduced CPUE. This situation 
could further worsen if the continuous increase in fishing efforts persists 
without proper regulatory measures. Additional increases in fishing efforts 
would negatively impact the fish population, and neither of the reference 
points (MSY and MEY) will be in an equilibrium state.  
Regression results for the GS model are depicted in Figure 6 and through 
equation 21. Regression of CPUE on effort shows a downward sloping 
linear trend (negative slope coefficient b), indicating that a higher effort 
would result in a lower yield after a specific time as the stock size would be 
limited. The estimates are significant at a 10% significance level, and the 
model R2 value suggests that changes in effort levels explain a 25% 
variation in the catch. The GS curve for the Chilika fishery resource can be 
indicated as follows: 

                        



[91] Singhar and Gundimeda 

                                                             (21) 

                                             (0.006)                (0.039) 

The parameters a and b generate the predicted yields given the effort levels. 
These are then matched with Gordon-Schafaer’s production curve, which 
traces an inverted-U shape, indicating the relationship between regeneration 
capacity and the sustainable harvesting rate. 

The projected MSY was obtained using the regression coefficients in the 
GS model, and the corresponding effort levels were compared with the 
actual catch and effort data to determine if present fish harvesting 
techniques are ecologically sustainable (Figures 6 and 7). Figures 6 and 7 
show that the actual catch rates are mostly higher than sustainable levels. 
Upon analysing the actual catch vis-a-vis predicted catch values for the 
same given levels of effort, it can be seen that the harvesting trend has 
remained more or less the same as that of the sustainable levels, with only 
slight variations biologically. 

Figure 6: Regression of CPUE upon Effort and Actual Catch and Predicted yield 
with Respect to the Effort–Harvest Relationship 

 

Source: Author’s analysis 

An independent sample t-test is conducted to compare the impact of 
restoration for the periods before and after when the site was declared out 
of the Montreux Record (Table 3). In addition, for higher accuracy, a  BCa 
95% confidence interval is also obtained with 1,000 bootstrap samples 
using the bias-corrected accelerated (BCa) bootstrapping technique. There 
are significant differences (t(df = 40) = −9.71, p = 0.001) in the fisheries 



 Ecology, Economy and Society–the INSEE Journal [92] 

 

output with mean landings for the period before restoration (M = 5,311.61, 
SD = 2,832.46, BCa 95% confidence interval: 4,181.24 to 6,549.75) found 
to be lower than in the period after 2003 (M = 13,065.31, SD = 2,134.49, 
BCa 95% confidence interval: 12,018.84 to 14,163.17). The magnitude of 
the difference in the means (mean difference = −7,753.69) is significant. 
Thus, the model suggests that restoration efforts have positively impacted 
the level of fisheries output. However, Pauly et al. (2013) argue that fisheries 
output need not necessarily correlate with stock abundance and reflect 
better fisheries’ health. Various external factors beyond fish availability may 
also influence catch size—from shifting policies to revamped management 
strategies. They further add that, though this measure should be used with 
caution, it is also worth noting that for most species of fish, declining 
trends might go unnoticed without information on actual catch levels. 

Figure 7:  Actual Catch vs. Predicted Sustainable Yield Values 

 

Source: Author’s analysis 

The different scenarios with varying per unit harvest price and cost per unit 
effort were analysed. The effects of the cost and price variations on the 
predicted parameters of MSY, MEY, and OA were observed. It is to be 
noted that these observations denote the impact under the assumption that 
intrinsic growth and the carrying capacity of the lake are constant. Table 4 
summarizes the impact of changes in costs and prices upon harvest levels 
and per-unit profits. In S1, when costs increase by 10% and prices remain at 
the base level (S0), the harvest goes up by 0.69% for the given level of 
effort, whereas the average change in profits declines by 2.36% at the MEY 
level. In the case of OA, both harvests and profits decline when compared 



[93] Singhar and Gundimeda 

to the S0 scenario. In S2, when costs reduce by 10%, profits decline further 
by 1.79%, despite an increase in harvests by 0.7%. In scenario S4 (where 
costs do not change, but prices decline by 10%), there is a 12.77% decrease 
in profits compared to the base scenario, despite the harvest rate being the 
same. In general, the situation of OA for different scenarios was 
unsustainable from an economic point of view. S3, where the prices 
increased by 10% with costs remaining the same, was found to be 
favourable as the overall profits went up by 7.48% with a slight increase 
(0.24%) in harvest rates.  

Table  3: Results of the Differences in Total Output Before and After Restoration 
of the Lake (Independent Sample T-test with Bootstrapping Confidence Intervals) 

Variabl
es 

N Mean SD Mean 
difference 

t p BCa 95% CI 

Before 2
4 

5,311.6
1 

2,832.
46 

−7,753.69 −9.
71 

0.00
1 

4,181.24 – 
6,549.75 

After 1
8 

13,065.
31 

2,134.
49 

12,018.84 – 
14,163.17 

# bootstrap results are based on 1,000 bootstrap samples; BCa: bias-corrected 
accelerated; 
Before the restoration period (1978–79 to 2002–03); after the restoration period 
(2003–04 to 2020–21) 
Source: Author’s analysis 

 

5. DISCUSSION AND CONCLUDING REMARKS 

Bioeconomic models play an essential role in fishery harvesting 
management by prioritizing three key scenarios: MSY, MEY, and OA (Seijo 
2001). These impactful strategies help enhance the sustainability and 
optimization of fisheries management practices and help raise awareness 
among stakeholders directly impacted by it. The unique perspectives 
presented by each scenario enable deeper insights that can promote more 
informed approaches towards managing Chilika Lake’s precious resources. 
MSY seeks ecological sustainability while prioritizing species conservation, 
whereas OA favours economic efficiency by establishing a necessary 
balance between costs and profits while determining harvest rates. Finally, 
MEY focuses on providing a middle ground where both ecological 
sustainability and economic efficiency are regarded equally. Policymakers 
and fishery managers should take into consideration all three scenarios to 
ensure the sustainability of Chilika Lake and a profitable future for fishing 
communities. In this study, we used CPUE and fishing efforts to analyse 
the stock abundance and sustainability of the present harvesting techniques 
employed in Chilika. The GS model ignores biological cycles, such as those  



 Ecology, Economy and Society–the INSEE Journal [94] 

 

 

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[95] Singhar and Gundimeda 

brought on by variations in recruitment (i.e., transitioning from very small 
and surviving to becoming old fish), and assumes that the resource 
responds instantly to changes in effort and total costs grow linearly as effort 
increases (Seijo 2001; Hilborn and Walters 2013). Therefore, this study 
takes into consideration the classical GS model which assumes linearity or 
direct proportionality in stock size, fishing effort, and catch values. For the 
specified research period, the study found that MSY and MEY for the GS 
model of the Chilika fishery had similar outcomes with only minor 
fluctuations. However, it is interesting to note that effort as a function of 
boat numbers grew while the annual number of fishing days remained 
constant. It is also to be noted that there has been little distinction between 
outcomes of OA under different scenarios over the years. MSY does not 
indicate effective harvesting from an economic perspective, as efficiency is 
defined as maximizing the net profit from the use of economic resources or 
maximizing resource rent (Holma et al. 2019; Grafton, Kompas, and 
Hilborn 2007). Therefore, MEY is usually regarded as an appropriate 
reference point for fisheries management. However, in the case of Chilika, 
it is found that both MSY and MEY are occurring at nearly the same point 
with significantly less difference. This indicates that the economic efficiency 
of the sustainable yield curve is the same as that of harvesting at the profit-
maximizing point. This could be concerning if fishing is characterized by 
smaller pelagic and demersal fishery, with the burden on smaller varieties. 

Further, there are no reports of fisherfolk discarding bycatch from Chilika, 
which can indicate biological overfishing. However, based on the results 
presented in the paper, it is not severe for the fishery resources. A fishery 
endeavour cannot be considered sustainable if the overall catch exceeds the 
MSY level. The MEY strategy is best described as one that considers the 
economic efficiency of the sustainable yield curve, and pursuing this 
objective—or at least analysing it for every specific fishery species—has 
several advantages. Given this backdrop, the results of the current model 
reveal that the Chilika fishery is at a critical stage as both the MSY and 
MEY have been reached, and resource depletion is quite likely if it is not 
managed going forward. Larkin et al. (2011) suggest that among the 
reference points, it is important to prioritise MEY, as this approach is 
responsive to variations in economic conditions and is more efficient as it 
minimizes harvesting costs. Further, the results obtained under OA 
conditions also indicate that unless managed efficiently, resources will 
probably be over-exploited. We believe that the shifting governance 
structure and the subsequent changes in fishing policies in Chilika—which 
are more oriented towards the privatization of the lake’s resources (e.g., 
growing demand for shrimp aquaculture)—have in fact disrupted the 



 Ecology, Economy and Society–the INSEE Journal [96] 

 

ecological balance even more. Further, the evolution of Chilika’s fisheries 
demonstrates that, despite its potential for discrimination, the earlier caste-
based fishing encouraged collective management of the lake’s resources, 
hence preventing undue pressure on resources (Nayak and Berkes 2011, 
2014; Sekhar 2007). Also, studies such as Ostrom (2009) and Folke et al. 
(2005) have established that the success or failure of the sustainable use of 
common areas is correlated with institutional and governance structures. In 
addition, we must consider factors such as emerging technologies, changing 
knowledge patterns, as well as shifts in power dynamics, all of which affect 
how the lake’s resources are utilized. Therefore, to avoid the rapid depletion 
of the lake’s resources, it is critical to emphasize collaborative practices 
while striving for collective governance when managing Chilika Lake. 
Hence, we stress on inclusive management strategies that consider local 
community support for restoration of the lake and concerted actions 
towards the efficient usage of its assets. 

From the results of the different scenarios analysed, it can be concluded 
that given the biological parameters, ensuring that the fish stock does not 
reach the critical limit or get exhausted would entail policies that target 
harvest rates and prices. Strict measures should be in place to ensure a 
specific quantity of catch. However, being a common pool resource, this 
would be difficult to implement in the case of Chilika. Practical solutions to 
this could be limiting the number of boats, regulating the number of fishing 
licenses and fishing days, monitoring the market for the final products, and 
providing access to credit facilities for fishers and alternative employment 
opportunities. Also, international agreements like the Ramsar Convention 
may offer vital resources for nations stepping up their conservation efforts. 
Most of these international accords rely on non-binding measures for 
protection. Hence, many times, their goals get compromised either due to 
weak management strategies (Munguía and Heinen 2021) or due to the 
ineffectiveness of the government (Pomeranz et al. 2021). As the Ramsar 
Convention seeks to establish a global cooperation network, its 
implementation should serve as a role model. This is especially critical given 
that the network aims to sustain vital habitats for endemic and migratory 
species, directly enhance human livelihoods, and preserve wetlands as an 
integral part of local cultures. This is necessary to preserve the Ramsar 
Convention’s integrity and increase public perception. 

The analysis has implications for small-scale fishers as well. Small-scale 
fisheries are characterized by the following: little technological 
sophistication, lower capital–output ratio, labour-intensive production, the 
presence of manual labour and part-time fishing, a large number of 
traditional boats targeting a diverse range of species, poor economic 



[97] Singhar and Gundimeda 

returns, and a complex production process affected by the culture and 
customs of small-scale fishing communities (Tietze 2016). Consequently, 
fisheries of such scale are mostly undervalued and unregulated (Mills et al. 
2011). The general effects of fishing on ecosystems are made worse by the 
complacent perception that this sector has a more negligible impact than 
industrial fisheries, especially considering that small-scale fisheries 
frequently occur in sensitive regions like mangroves (Hutchison, Spalding, 
and Ermgassen 2014). Hence, ignoring its effects and interlinkages increases 
the vulnerability of fisheries to climatic and global changes. For instance, 
already poor fisherfolk may get loans they cannot pay to counter decreased 
yield by increasing their effort and catch. This might result in the 
overfishing of species that fetch higher market prices. The ecology thus 
suffers from this kind of endeavour, which has detrimental effects on local 
livelihoods (Salayo et al. 2008). Given this context, maintaining 
socioeconomic benefits and conserving fishery resources in an ecologically 
sensitive area like Chilika is crucial in global and local contexts. Failing to 
protect such small-scale fisheries would not only negatively impact the 
locals’ ability to support themselves, but it would also have a detrimental 
effect on the ecological characteristics of the lake. 

In conclusion, fisheries management is better understood by evaluating 
changes in fishery output. For instance, the slight alterations noted in this 
study’s analysis could represent the start of far more severe adverse effects 
on the species and the people who depend on fishing, such as fishers and all 
other users of the fish value chain. Furthermore, the lack of timely 
interventions and management of a common pool resource like Chilika may 
increase the strain on natural resources to meet the demands resulting from 
its products. Identifying the changes the fisheries sector goes through in 
ecologically sensitive areas also has social implications because stock 
depletion may result in food loss and revenue loss. This study was able to 
account for the economic contributions made by fisheries to the local 
economy; a further improvement to the analysis would be to capture the 
damage to stocks that fisheries cause or its net social impact on the local 
economy. 

Though this study does not measure stock levels in light of the regeneration 
rates of individual species, studies such as Coppola and Pascoe (1998), 
Holma et al. (2019), and Grafton, Kompas, and Hilborn (2007) indicate that 
the stock gets depleted in the long run if the harvested levels are higher 
than the regenerating capacity. This can be a severe cause of concern in the 
case of Chilika as it would not only impact ecological diversity but also 
negatively impact the dependent fisher community. Lastly, the results 
should be viewed as the upper bound in this case, as the model considered 



 Ecology, Economy and Society–the INSEE Journal [98] 

 

in this study does not account for ecological complexities and 
environmental uncertainties due to the paucity of information. This study 
employs the classical Gordon-Schaefer model in its simplest form as it is 
easily adaptable to suit different fisheries’ requirements owing to its broad 
application range. By revealing the standard tradeoffs between fishing effort 
management decisions versus supplying sustainably desired yields without 
considering any fish species or other peculiarities relevant to fisheries 
operations, this approach is beneficial during initial assessments, besides 
being an excellent benchmark for complex modelling techniques. Future 
studies can consider stock assessments that include non-linear relationships 
between variables and species specificness and which also encompass 
spatial dimensions for precision analysis. The study currently ignores the 
real economic opportunities that multiple stakeholders face and the 
dynamics of biological populations and parameters, which are subject to 
uncertainty. Hence, further improvement is needed in this area. Though the 
data requirements are high, the study needs to consider ecosystem-based 
management approaches that emphasize ecological, economic, and social 
objectives for the present and future generations. 

Acknowledgment: The authors would like to thank Chilika Development 
Authority (CDA)  for providing access to all the necessary resources and the 
academic support of the Department of Economics, Indian Institute of 
Technology (IIT) Bombay, in carrying out this research work. We would also like 
to thank in advance the respected editor and anonymous reviewers for their 
valuable suggestions in further improving the quality of the manuscript 

Ethics Statement: I hereby confirm that this study complies with requirements of 
ethical approvals from the institutional ethics committee for the conduct of this 
research.  

Data Availability statement: The data used to support this research is not 

provided in a repository as it can be downloaded from the original sources 

mentioned in the paper. 

Conflict of Interest Statement: No potential conflict of interest was reported by 

the author. 

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