IMPACT OF COMMERCIAL STRIPED CATFISH (PANGASIANODON HYPOPHTHALMUS) FARMING ON FARMERS’ LIVELIHOOD AND ENVIRONMENT IN BANGLADESH

 

Khaleda Nasrin^1* and Md. Yeamin Hossain^{2, *}

 

¹ Institute of Bangladesh Studies, University of Rajshahi, Rajshahi 6205, Bangladesh

² Department of Fisheries, University of Rajshahi, Rajshahi 6205, Bangladesh

 

*Corresponding author email: yeamin.fish@ru.ac.bd

ABSTRACT:

Striped catfish, Pangasianodon hypothalamus (Sauvage, 1878), has gained popularity among the Bangladeshi commercial fish farmers because of its rapid growth and high tolerance of adverse ecological conditions. The study aims to examine the impact of farmers’ socio-economic and farming features on their livelihood outcomes of Pangas (LOP), considering the sustainable livelihood approach (SLA) and the negative impacts of commercial farming activities on the surrounding environment using a mixed-method approach. A quantitative survey was conducted using a pre-tested structured questionnaire, and qualitative data were explored through focus group discussions (FGDs), in-depth interviews (IDIs), and key informant interviews (KIIs) to assess the impact. Data were computed using descriptive statistics, rank order, correlation coefficient, and regression analysis. In terms of LOP, the highest progress was observed in human capital (87.5%), followed by physical capital (75.7%), financial capital (70%), social capital (55.7%), and natural capital (38%). The farmers’ education, experience, knowledge of fish farming, and adoption of modern farming technology correlated positively, whereas age correlated negatively with the LOP and significantly impacted the achievement of the outcomes. Farming intervention was encouraged to establish various upward and backward linkage industries and create employment opportunities, which contributed to economic progress. On the other side, rapid expansion of pangas farming impacted the surrounding environment by converting two or more croplands (37%) into a closed pond and discharging polluted water (52%) and bottom sludge (49%) into natural water bodies. Raising dikes of fish ponds caused waterlogging in the crop field during cultivation, which also hampered the navigation of aquatic animals during the breeding season. Moreover, harmful chemical discharges were blamed for the decline of aquatic animals. Therefore, it is necessary to implement sustainable aquaculture practices along with proper monitoring and evaluation processes to ensure safe aqua-food production in an environmentally friendly manner, thereby achieving sustainable development goals.

1.        INTRODUCTION

        Aquaculture production around the world plays a significant role in meeting people’s dietary protein demands. Over the last two decades, there has been an increasing recognition of the fisheries and aquaculture sectors for their essential contribution to global food security and nutrition (FAO, 2022). Viet Nam is the world’s leading exporter of striped catfish that implements good aquaculture practices (GAP) and enhances its revenue earnings by exporting high-quality fish (FAO, 2023). Ninety percent of aquaculture production takes place in developing countries, serving as a primary source of animal protein in diets and enhancing food security through domestic consumption and economic growth from exports (Anderson et al., 2017). Bangladesh ranked third in inland fisheries behind China and India and fifth in aquaculture production in 2021–22 (FAO, 2022, DoF, 2023). Many countries introduce faster-growing exotic species from different regions to advance aquaculture production (Gu et al., 2022; Paul et al., 2022). Although Bangladesh introduced striped catfish, locally called pangas, in 1993 (Rahman, 2005, DoF, 2022), the production boom started after introducing the modern variant at the beginning of the recent century (Khan et al., 2018). Now, farming this species has become more cost-effective (Haider et al., 2023) and beneficial to farmers’ profit margins (Ali et al., 2011).

        Low initial investment, a simple culture technique, higher consumer demand, and effective feed conversion ratio have made it a popular fish for the aquaculturists in Bangladesh. Young educated farmers are showing interest in this species (Haider et al., 2023). Thus, commercial pangas farming has opened new employment opportunities for unemployed youth in Bangladesh. Pangas has a significant contribution to food security as a fish for the poor and a delicious protein source for the children because of its low cost and low bone characteristics, as fish is the major contributor of animal protein supply (67%) for consumption in Bangladesh (DoF, 2022). The fisheries sector contributed 2.4% to the national GDP, while 22.14% to the agricultural GDP, with striped catfish accounting for 8.54% (9406185 metric tons (MT)) of total fish production in 2022 (DoF, 2022). Along with this, Bangladesh has a competitive advantage in exporting of this fish after domestic consumption (Dhar et al., 2021). Due to its soft, delicate, and white flesh, this fish has significant export expansion potential into different countries from Bangladesh (Dhar et al., 2021).

        However, higher lease values and better returns on investment led landowners to convert cropland into ponds (van Asseldonk, 2013), which was accused of loss of agricultural land in the pangas producing area (Ali and Haque, 2011; Anka et al., 2013). Due to the traditional feeding method, uneaten feed accumulated at the pond bottom, facilitating heavy organic loads in the water, which consequently caused water quality deterioration (Anka et al., 2013). Poor management of polluted water and bottom waste was very common in the aquaculture system in Bangladesh (Anka et al., 2013). The rising disease burden was tackled with antibiotics, disinfectants, hormones, fertilizers and other chemicals (Heal et al. 2021). Moreover, farmers emphasized more on production return rather than biosecurity practices and environmental sustainability issues. Moreover, lack of strict monitoring of aqua waste disposal practices intensified the pollution issues. Industrial wastage increased heavy metals discharge (Hussain, 2023) in the open water system, which was deposited in soil sediment (Qaseem et al., 2023) and fish gills (Hoseini and Sulivany, 2024). Several risk factors, like overuse of antibiotics,incomplete treatment courses, and improper use contributed to the emergence of antibiotic resistance issues and bacterial sensitivity (Abduljabar and Naqid, 2022; Borek et al., 2023; Issa, 2024). So, the rapid expansion of commercial farms (Gurung et al., 2017; Hoque et al., 2021) was urging the implementation of a safe waste disposal system in this country (Haider et al., 2023).

 

Table1:  Available literatures on pangas farming in Bangladesh and other Asian countries.

 

BD = Bangladesh

Moreover, the unregulated growth of aquaculture in the country has ignited a contentious debate regarding the industry’s capacity to enhance rural communities and alleviate poverty (Filipski & Belton, 2018), alongside environmental concerns that have tarnished the reputation of aquaculture (Menezes et al., 2017; Khan et al., 2021b). Therefore, sustainable methods of pangas farming, complying with the requirements of the world’s aquaculture certification standard, should be implemented to expand the export market (Woźniacka, 2025), keep nature inact, and ensure domestic protein access.

        The existing literature has primarily focused on the socio-economic status of farmers, production efficiency, value chains, farming techniques, and fish diseases (Table 1). A few studies have conducted, focusing on the environmental issues of aquaculture without sufficient depth. There is a literature gap on how commercial pangas farming impacts farmers’ livelihood outcomes and the surrounding environment. Moreover, this study addresses these issues together, which might be helpful to promote a more sustainable approach to aquaculture.

        Therefore, the study’s goal is to gain a comprehensive understanding of the livelihood outcomes of farmers and the environmental effects of pangas farming. The first objective of this study is to determine the socioeconomic characteristics of farmers and other farming-related factors. The second one has focused on farmers’ livelihood outcomes of pangas (LOP), and the third objective has aimed at identifying the factors associated with LOP after the adoption of pangas farming. In contrast, the fourth objective is to understand the negative impacts of the unplanned expansion and harmful activities of commercial pangas farming on the natural environment. Finally, the fifth one is to find out the role of commercial pangas farming on attaining sustainable development goals (SDGs) in Bangladesh.

 

 

 

2.METHODS

Study Area:

        The multistage random sampling method was used to select study areas, and respondents were selected by the simple random sampling (SRS) method. The researchers identified six highly pangas-producing areas (Mymensingh, Cumilla, Bogura, Jashore, Barishal, and Naogaon) in Bangladesh based on the available literature and the Department of Fisheries (DoF, 2022) (Table 2). From these areas, the researchers selected five sub-districts across three districts. This study included Adamdighi along with Bogura and Muktagacha and Trishal under Mymensingh and Laksam and Nangalkot under the Cumilla district (Figure 1). Only pangas-producing sub-districts were considered in area selection, and the farmers who had at least one year of experience and were continuously involved in pangas farming were included in the study.

 

Figure 1:  Map of the study areas.

 

Table 2:  The top six pangas producing districts in Bangladesh

(Source: DoF, 2022).

 

Data Collection Method:

        This study proceeded through a mixed-methods approach. For quantitative data, a comprehensive survey with a structured questionnaire was conducted with 300 respondents from July 2023 to March 2024 from ten unions, with two unions from each sub-district, of Bangladesh. As there was no available list of pangas farmers in Bangladesh, a temporary list of farmers with farm size was made with the help of local people and the fisheries office of the respective sub-district. Approximately equal numbersof farmers were taken through the SRS method from the farmers’ list of the respective unions for the quantitative survey. For qualitative data, participatory rural appraisal tools (Chambers, 1992, 2015) were used, and data collection was continued until the repetition of data from the respondents. Due to the similarity in fish culture techniques, geography, and socio-demographic features of pangas farmers, only three focus group discussions (FGDs), 12 in-depth interviews (IDIs), and twelve key informant interviews (KIIs) were conducted for data triangulation. For each FGD, 8-10 members were selected randomly from different strata of farm size from the randomly selected areas, IDIs were selected through the SRS method and the purposive method was considered for KII selection.

Data Analysis And Presentation:

        Quantitative data were computed using IBM SPSS software (version 25) and MS Office 2016, and qualitative data were analyzed using key data themes. Both descriptive and inferential statistics were used for the quantitative data.

Model and Variable Description:

        A three-point rating scale was used to determine the extent of livelihood outcomes through pangas farming in Bangladesh. Farmers’ socioeconomic and other farming-related characteristics were represented by descriptive statistics: percentage, mean, and standard deviation. Secondly, a livelihood outcome index (LOI) was developed to evaluate the livelihood outcomes of commercial pangas farming (eq. (2)). The LOI helps to measure livelihood progress in different indicators of sustainable livelihood approach (SLA) regarding fish farming in Bangladesh (Udayakumara and Shrestha, 2011; Rahman, 2012; Amin et al., 2016; Uddin et al., 2021). Thirty-two fish farming-related indicators were considered under five dimensions of SLA. Eight indicators were measured under human capital, seven under social capital, five under natural capital, seven under physical capital, and five under financial capital. Each indicator was measured with three possible responses: worsen, unchanged, and improved, and assigned scores were -1, 0, and 1, respectively. Here, the score ranges from -32 to 32, where 32 indicates the highest positive outcomes, -32 indicates the highest negative outcomes, and 0 indicates no outcomes from pangas farming. The extent of the score of outcomes was classified into three categories: poor, medium, and high.

Livelihood Outcomes of Pangas

 (LOP) =   ………… (1)

 

 ................. (2)

 

Where, LOI = Livelihood Outcomes Index, W = number of respondents with worsening livelihood assets, U = number of respondents with unchanged livelihood assets, I = number of respondents with improved livelihood assets, and N = total number of responses.

        The Pearson’s correlation coefficient was computed to understand the extent of the relationship between dependable, LOP (eq. (1)), and independent variables farmers’ socioeconomic & other farming-related characteristics (eq. (3)).

 

 ............. (3)

 

Where,  = Pearson’s product-moment correlation coefficient, and

A multiple linear regression (MLR) model was used to measure the effect of explanatory variables (socioeconomic and other farming-related characteristics) on focus variables (LOP) (eq. (4), and normality of residuals was checked according to Kolmogorov-Smirnov (p = 0.002) and Shapiro-Wilk’s (0.001) test at the significance level (p < 0.05). For scale reliability tests, Cronbach's alpha (> 0.7) was considered.

Regression model:

​= ​ + ​ + ​ + ​​  ​+ ​  ​+​ ​+​ ​+ ​  ​+ ​  ​+   ​+ ​​ + ϵ ....................(4)

 

Where, ​ = Livelihood outcomes of pangas (LOP), β₀ = Constant, ​, ​, .......... ​ = Independent variables (Age, Education, Experience in pangas farming, starting year homestead area, starting year cropland, Starting year farm size, Annual income before, Extension exposure, Knowledge of fish farming, Adoption of modern farming technology), β = co-efficient, € = error term of the model. All relevant statistical tests have been done at significant levels (P < 0.05). In this model, unstandardized coefficient (B) was considered instead of standardized coefficient (β).

3.RESULTS

        The results were represented according to the objectives of the study. The study focused on the positive and negative dimensions of pangas farming in Bangladesh. The positive dimension included objectives 1, 2, and 3, and the negative dimension included objective 4, and the fifth one was on the linkage of commercial pangas farming with the Sustainable Development Goals (SDGs).

Livehood Impact of Pangas Farming:

Farmer’s Socio-Demographic Features and Farming Characteristics:

        Table 3 describes the socio-demographic and farming features of pangas farmers in Bangladesh. Middle-aged farmers (49.3%) were more involved in pangas farming than young (28.3%) and old-aged (20.3%). Many of the respondents (43.3%) had secondary education and a high level of experience in fish farming (64%). The mean experience (13.27 years) and the mean schooling (10.96 years) was accompanied with the youth involvement. Most of the farmers (84.0%) had small homestead area, and 15.7% had no cropland. FGDs and IDIs reported that farmers converted their agricultural land and homestead-raised land into ponds for better profit margins from pangas rather than crop production. Moreover, cropland owners leased their lands to the medium-scale and large-scale fish farmers for pond construction, getting higher returns.

        According to farm size, a larger portion (93.3%) of the farmers started their business on a small scale rather than a medium (4%) or large scale (2.7%) in the first year of pangas adoption. Before adopting pangas, their mean yearly earnings were $196, and the largest proportion of them fell under low income (82.3%). The majority of them acquired a high level of knowledge about fish farming (65.7%) and adopted moderately modern farming technology (40%). This indicated a gradual shift in people’s occupation to fish farming, with the smallest number of farms and minimal investment initially contributing to low income. Therefore, increased experience, knowledge, and modern farming technology played a significant role in promoting their business and achieving livelihood outcomes (FGDs, KIIs).

 

Table 3:  Socio-economic and farming characteristics of pangas farmers in Bangladesh (n = 300).

Note: $ 1 = Tk 109.58 (January 2024)

Farmer’s Livelihood Outcomes Through Pangas Farming:

        The extent of livelihood outcomes is presented in figure 2(a). The study revealed that 65.3% of the respondents achieved high livelihood outcomes, followed by moderate (27.7%) and low (7%) outcomes. Among the five dimensions of SLA, farmers achieved the highest score on human capital (87.5%), followed by physical capital (75.7%), financial capital (70%), social capital (55.7%), and natural capital (38%) (Figure 2 (b)). Findings from FGDs and KIIs support that Pangas farming increased farmers’ income, leading to improved access to education for their children. Moreover, they were taking a nutritional diet with sufficient protein and health services. They further mentioned that farmers shared their ideas and difficulties within the community, feed dealers, company agents, and fisheries officers. Thus, they solved their farming-related problems. Horticulture on the dykes ensured economic use of land but their reluctance to conserve natural resources made low scored in natural capital. Overall, income promotion improved their physical and financial assets in higher extents rather than social and natural capital.

        Table 4 computed and represented the LOI and rank order of thirty-two variables of five dimensions of SLA. The findings showed that knowledge of modern farming techniques and access to farming-related information, with a score of 0.98, ranked first. The second highest score (0.97) was for income. The ability to practice modern farming techniques (0.91) ranked third, and sharing ideas with others (0.90) ranked fourth. Ability to take a nutritional and balanced diet (0.89) ranked fifth. Most farmers took informal training from various available sources, viz. successful farmers, neighbors, friends, relatives, and local representatives of input suppliers, feed dealers, and medicine sellers. Young educated farmers were prone to learning from online sources, especially from YouTube (FGDs, IDIs).

        A young farmer (IDI-2) stated, “It is very easy to get a company representative whenever I face any problem in my fishpond, and they come with their water testing kits and prescribe me to take actions to correct the condition.” Most of the farmers improved their farm size and yearly income through pangas farming (Figure 3(a, b)), which made them able to access a more nutritional and balanced diet than before. Most of the indicators scored high (>0.7), but the practice of conserving the natural gene bank of fish (-0.01) and preserving ecosystems and biodiversity (-0.04) yielded minimum and negative scores. A lower score (0.19) was also observed for the management of aquaculture pollutants, which indicated farmers negative concerns about eco-sustainability issues. FGDs and KIIs indicated that socioeconomic advancement was prevalent among pangas farmers, significantly improving their human capital and physical assets but not their natural capital. Furthermore, the lack of enforcement from regulatory agents meant that farmers were not compelled to follow environmental regulations regarding land conversion and waste disposal. Consequently, commercial farmers largely ignored environmental concerns in favor of economic gains.

 

Figure 2:  Livelihood outcomes of pangas farmers in Bangladesh (n = 300); (a) Extents of livelihood outcomes, (b) Resource-wise livelihood outcomes of pangas.

 

Table 4:  Livelihood Outcomes Index (LOI) and the rank order of assessed fish farming-related indicators across five dimensions of Sustainable Livelihoods Approach (SLA) (n = 300).

Note: H = human capital, F = financial capital, S = social capital, N = natural capital, P = physical capital, improved = 1, unchanged = 0 and worsen = -1 [possible scores against each statement]; LOI = livelihood outcomes index.

Correlation Between Farmer’s Characteristics and LOP of Pangas Farmers:

        Table 5 represents a summary of the relationship between farmers’ characteristics (explanatory variables) and livelihood outcomes (focus variables). The results showed a significant relationship between all the explanatory variables and the focus variable. A farmer's experience, knowledge of fish farming, and adoption of modern farming technology were strongly correlated (r > 6) to LOP. On the other hand, a panga farmer’s age, education, starting year homestead area, starting year cropland, starting year farm size, annual income before, and exposure to extension services exhibited moderate correlation (r > 2).

 

Table 5:  Correlation of farmer’s characteristics and livelihood outcomes of pangas (LOP) (n = 300)

**Correlation is significant at the 0.01 level (two-tailed), n = 300, df = 298.

 

Influencing Factors of Farmer’s Livelihood Outcomes of Pangas (Lop):

        We conducted a multiple linear regression analysis to identify the key factors influencing livelihood outcome performance (LOP) and assess their statistical significance. The results (Table 6) showed that five of the ten explanatory variables were significant (p < 0.05), and the adjusted R² value was 0.645. This meant that the explanatory variables explained about 64.5% of the LOP. The data did not have autocorrelation, as shown by the Durbin-Watson value of 1.837. Also, the variance inflation factor (VIF) was less than 6, which meant that the independent variables did not have multicollinearity. The analysis showed that a farmer’s age (B = -0.052, t = -2.134, p < 0.05) had a negative impact on LOP. The farmer’s education (B = 0.130, t = 2.046, p < 0.05), experience (B = 0.431, t = 9.082, p < 0.01), knowledge of fish farming (B = 2.858, t = 7.410, p < 0.01), and adoption of modern farming technology (B = 0.637, t = 5.205, p < 0.01) have a positive effect on attaining livelihood outcomes. The remaining five explanatory variables were statistically insignificant. Among these, starting annual income might have a negative impact (B = -3.244, t = -1.653, p < 0.10) on the livelihood outcomes of pangas.

        However, the initial homestead area, farm size, and extension exposure all had negative-insignificant coefficients had a negative impact on LOP. According to IDIs and FGDs, most of the farmers converted their raised land to lowland fishponds for better returns, resulting in an increase in LOP with a decrease in their starting-year homestead area and croplands. The qualitative study also stated that some of the farmers got their advice from feed dealers who weren’t experts in fisheries and had difficulties communicating with the government extension agents.

Stepwise Multiple Regression:

        We computed stepwise multiple regression to assess each explanatory variable’s individual contribution to the livelihood outcomes of pangas farming (Table 7). The model was made up of four explanatory variables: the farmer’s experience, knowledge, use of adaptive farming technology, and age. These four independent variables (R² = 64.7) explained about 65% of the livelihood outcomes of pangas. Experience in pangas farming as the first variable entered the model, able to explain the maximum variability of the model (44.7). The second variable, knowledge of fish farming, explained only 15.9%; the third one, adoption of modern farming technology, described 27%; and finally, the fourth variable, age, entered the model, explaining only 9% of the variation of the focus variable, LO

Table 6:  Multiple linear regression to understand the relationship between farmers’ characteristics and livelihood outcomes (n = 300).

Significant, if p < 0.05, confidence level = 95%, F = 55.245, df (degrees of freedom) = 299; *Regression is significant at the 0.05 level and **Regression is significant at the 0.01 level.

 

Table 7:  Summary of the Stepwise Multiple Regression (n = 300).

Raising Economic Opportunities of Pangas Farming:

        Overtime, both farm size and income of farmers have increased, leading to better livelihood outcomes (Figure 3(a, b)). Fish farming provided opportunities to be involved in various primary and secondary occupations alongside farming activities (Figure         3(c)). Only one-third (33%) engaged solely in fish farming, followed by 23% in crop production, 29% in fish-related business, 10% in service, and 4% in other businesses (Figure 3(c)). In addition to pangas farming, most farmers expanded their income-earning scopes by engaging in diversified employment opportunities..

 

Figure 3:  Impact of pangas adoption on the economic well-being of fish farmers in Bangladesh (n = 300); a) Betterment in farm size, b) Betterment in income, c) Scope of primary and secondary occupations.

 

        A local political agent (KII-3) stated, “The economy of Trishal heavily relies on pangas farming, and this has significantly increased job security for the local youth.” He also mentioned, “Pangas farming contributed to the expansion of the hatchery business in Adamdighi.” A pioneer pangas introducer in Adamdighi (KII-7) asserted, “We sell fry and fingerlings in different corners of Bangladesh as well as neighbouring provinces of India. Due to huge demand, this industry attracted people to switch their previous occupations and establish their own fish business, promoting their economy.” KIIs claimed that pangas farming has become popular because of simple farming techniques, higher consumer demand, higher growth of fish, and an effective and quick return on investment.

 

 

Figure 4:  Diagram of employment linkages of pangas farming in Bangladesh (Field study, 2022-2023).

 

        Moreover, favourable soil texture, temperature, availability of improved variants, and information made this business more popular among the entrepreneurs.

        The commercial pangas farming process has developed and facilitated both upward and backward linkage industries, as represented in Figure 4. The backward linkage industries, such as fish seed, feed, equipment, medicines, chemicals, and fertilizer, promoted employment opportunities. Furthermore, fish harvesting, transporting, marketing, processing, and exporting have expanded the upward linkages to foster income-generating activities through fish farming in Bangladesh

 

 

Environmental Impact of Farming Interventions:

Land Structure Modification Due To Commercial Scale of Fish Farming:

        There was a negative impact on the surrounding environment due to the farming activities in the study areas. Land structure modifications, particularly the shifting of cropland into closed waterbodies, decreased the area under crop production. Moreover, raising dikes in ponds resulted in the formation of waterlogged areas during the rainy season, which negatively impacted the cultivation process. The accumulation of organic matter and the intrusion of aqua chemicals residue in the open water bodies from fish farms negatively impacted the ecosystem, underscoring the need for sustainable aquaculture practices.

 

Figure 5:  Conversion of different types of lands into closed water pangas farms in Bangladesh (n = 300

The area of closed inland water increased tremendously in the last two decades (DoF, 2022). In this study, the closed pond originated from various land types (Figure 5). In the study areas, 37% constructed their ponds on two or more cropped agricultural lands, 36% reconstructed their derelict ponds, 13% converted single-cropped agricultural lands to aquaculture, and 10% used culturable ponds for aquaculture of pangas. A small percentage of respondents (2%) stated that they built their ponds on arid land, while 2% converted open water bodies into closed ponds. FGDs claimed that constructed embankments in the floodplains sometimes caused waterlogging in the croplands. Furthermore, this hampered aquatic animals’ navigation processes during the breeding season.

Intrusion of Pollutants in Natural Water:

        Farmers’ pollution management strategies in Bangladesh depended on their local resources, including natural water bodies, agricultural practices, and personal reservoirs, as shown in Figure 6. About half of the respondents (52%) discharged their chemically mixed polluted water in open water bodies, 27% reused it for irrigation purposes, and 21% discharged it in their own unit (Figure 6(a)). To remove the organic load, 49% of farmers disposed of bottom sludge in nearby rivers and canals; 35% reused it as organic fertilizer; and 15% utilized it in landfills for developing different infrastructure (Figure 6(b)). Figure 6(c) showed how often polluted water was released: 6 to 10 times (39%), then 1 to 5 times (24%), 11 to 15 times (22%), 15 to 20 times (7%), more than 20 times (6%), and no release (3%). Farmers used to dry their ponds at various intervals to remove bottom sludge. Many farmers (47%) removed bottom sludge annually, followed by three yearly (35%), two yearly (17%), and no discharge (1%), as shown in figure 6 (d).

        Farmers frequently faced problems such as gas bubble formation, excessive organic load in the pond bottom resulting in fish suffocation, and eutrophication. To improve their pond environment, they had to exchange heavily organic-loaded water. Most of the farmers did not have their own discharge units, so they relied on natural water bodies to release the pollutants. Consequently, the growth of pangas farms in the study areas increased the amount of heavy organic matter in the open water canals and riverbeds. This made it harder to find fish in open water, which in turn made it difficult for the poor to eat enough protein (FGDs)

 

Figure 6:  Pollutant management practices of pangas farmers in Bangladesh (n = 300); a) Discharge units of polluted water, b) Discharge units of bottom sludge, c) Frequencies of polluted water discharge, d) Frequencies of bottom sludge discharge.

 

        A farmer (IDI-7) from Trishal, Mymensingh, expressed his concern about the environmental deterioration stemming from waste discharge in the nearby 'Sutia' river:

        Pangas requires a substantial quantity of additional feed. Most of the farmers in our area use home-formulated low-cost pelleted feed crushed from nearby feed mills, rather than commercial floating feed. Sometimes, a big amount of the feed sinks to the bottom of the pond, which causes harmful gases and eutrophication issues. This exacerbates changes in the water parameters, leading to fish floating on the surface. This situation necessitates the application of various aquatic chemicals to correct water parameters. For quick recovery, we exchange some water and discharge the polluted water into this river. Consequently, the rivers have lost their fish and other animals.

KIIs also said that pangas farms were characterized by having a high density of fish, feeding them with low-quality food, using traditional feeding methods, and not managing the farms well. They also added that this lost bottom-settling feed portion and fish excreta were the main causes of excessive gas formation. Water quality deterioration and huge biodiversity loss in ‘Roktodoho Beel’ from farm discharges were claimed by IDIs from Adamdighi, Bogura.

Accumulation of Aqua Chemicals in Nature:

        Farmers used diverse types of insecticides to kill the unwanted insects and microorganisms in the water before stocking. They collected the medicines from the local feed dealer and veterinary/aquatic medicine shops. Most of them consulted with the company agent in cases of chemical or medicine selection. Medicine sellers encouraged farmers to experiment with various medicines and chemicals to improve their pond conditions, even though they struggled to find the right solution to correct the environment. Farmers used fenitrothion (Sumithion) as a killer of mini-food competitors, especially insects, in pond ecosystems; carbofuran for nematode killer; ivermectin for Arugulus sp. killer; and deltamethrin for insect killer. Furthermore, farmers used various veterinary antibiotics, such as tetracycline, fluoroquinolones, sulfonamides, phenicol, gentamicin, amoxicillin, and aminoglycoside-like agents to

        combat fish diseases. Farmers discharged this medicine-mixed water into nearby rivers or canals. Therefore, this polluted water contributed significantly to the depletion of open-water biodiversity. A 65-year-old farmer (IDI-4) stated:

        We have seen the canals, ditches and rivers in abundance with a vast number of fishes, snails, snakes, turtles, crabs, shrimps, and aquatic weeds. The poor villagers used to catch fish and sell them in the local market. These fishes not only contributed to their income but also met up their family’s protein demand. Aquaculture waste has left these waterways polluted and fishless; consequently, the marginalized people are facing challenges to access protein due to the scarcity of fish in these water bodies

SUSTAINIBLE AQUACULTURE FOR SUSTAINABLE LIVELIHOOD OUTCOMES

        Sustainable aquaculture focuses on economic, social, and environmental sustainability practices. In this study, economic sustainability was assessed by farmers’ progress in physical (75.7%), financial (70%), and human (87.5%) capitals; notable progress was observed in these areas. In the case of social capital (55.7%), moderate improvements occurred, explaining social sustainability. However, the poorest progress in natural capital indicated the farmer’s lowest concern about environmental

sustainability issues (Figure. 7). Among five indicators, biodiversity conservation practices scored the lowest value (-0.04), which ranked 24 among 32 indicators of LOP. The protection of the natural gene bank received the second lowest score (-0.01), ranking 23, indicating a retrogression in this indicator. Farm pollutant management showed negligible progress, with a score of just 0.19. These three indicators in natural capital raised a question regarding environmental sustainability practices through pangas farming adoption in Bangladesh (Table 4, Figure 7).

 

Table 8:  Role of pangas farming in attaining Sustainable Development Goals (SDGs).

4.DISCUSSION

        We conducted this study on 300 pangas farmers from a wide range of geographical locations in Bangladesh to investigate their livelihood changes and the environmental impact of commercial farming. The primary objectives of the study were to find out the impact factors of farmers’ livelihood outcomes and address the adverse effects of farming activities on the surrounding environment. A significant portion of farmers gained high (65.3%) to moderate (27.7%) outcomes, indicating the positive impact of shifting to pangas farming. Van Asseldonk (2013) referred to pangas as an internal boom crop and found rapid agrarian shifts to pangas farming leading to better livelihood outcomes in Bangladesh. Some findings on the socioeconomic status of fish farmers also suggested livelihood improvement (Sarwar et al., 2016; Adhikary et al., 2018; Sheheli et al., 2021; Khanom et al., 2022; Al Mahadi et al., 2022). This study found a strong correlation between explanatory variables, such as the farmer's experience, knowledge, and adoption of modern farming techniques, and the focus variable, LOP, suggesting that these factors significantly contributed to the positive change in the livelihood of pangas producers. Sheheli et al. (2021) identified that the farmer’s education, knowledge, farm size, and annual income were significantly correlated with the livelihood status of fishermen. According to a study by Tasnoova et al. (2017), pangasiid catfish aquaculture had greatly improved the social and economic situations of many people involved, such as hatchery owners, nursery pond managers, fingerling sellers, pangasiid growers, and fish vendors. Existing literature has discussed only the livelihood status of fish farmers without conducting an impact assessment. This study also suggested that new farmers with minimal resources gradually improved their livelihood over time. In this study, the farmer’s age significantly negatively impacted LOP, whereas the farmer’s education, experience, knowledge, and adoption of modern farming techniques positively influenced LOP. Alternative results, claimed by Adams et al. (2021), that the age of the farmers and their access to extension services positively impacted the adoption of technology. In this study, most of the farmers were middle-aged (49.3%) and young (28.3%), with an average age of 42.57 years, indicating that young people were more interested in pangas farming and doing better than the old. Another study on fish farmers by Rajan et al. (2013) reported that the majority were young. Most of the farmers achieved the secondary level of education, indicating that it was enough to face the technical challenges of fish farming and gain better livelihood outcomes. Uddin et al. (2021a) explained that farmers’ education increased as farm constraints decreased. Another study by Rajan et al. (2013) found that higher education showed a positive association with farmers knowledge. Young and educated farmers, according to FGDs, managed their farms more effectively and gathered information from both online and offline sources. KIIs also claimed that at least secondary education was needed to run the farm effectively. Therefore, livelihood outcomes decreased as farmers ages increased and education impacted vice versa.

        Moreover, farmers’ experience showed a positive, significant impact on gaining higher livelihood outcomes. Rajan et al. (2013) claimed that the experience of fish farmers had a positive association with knowledge levels. FGDs and KIIs stated that farmers with more experience had a higher risk orientation, facilitating higher livelihood outcomes. Additionally, LOP increased with the level of farmers’ knowledge. This suggested that farmers with greater knowledge were more adept at managing production risks and achieving higher returns from their farms. The adoption of modern farming technology also positively influenced the achievement of higher outcomes. Access to technology information aimed to improve farmers’ efficiency and increase their net income, thereby enhancing their livelihoods (Murshed-E-Jahan and Pemsl, 2011; Kumar et al., 2018). Farmers usually chose technologies that would be more cost-effective and easier to manage (Ngo et al., 2019; Kumar et al., 2018), and these technologies also bestowed benefits to the well-being of small-scale farmers (Adams et al., 2021). Embracing modern and appropriate technologies not only decreased prices but also led to poverty alleviation, enhanced nutritional standards, increased employment opportunities, and improved overall welfare for individuals (Mango and Kariuki, 2015). Additionally, FGDs and KIIs asserted that small-scale farmers became more skilled with their experience gathering, adopting modern farming technology, and sharing their knowledge with various stockholders, subsequently boosting their livelihood outcomes. Furthermore, initial farm size and starting income negatively influenced the ability to achieve a higher LOP, indicating small-scale farmers improved their livelihoods more than medium and large-scale farmers. Uddin et al. (2021) found that constraints of farming increased with the farm area increasing. Moreover, small-scale farmers were more concerned about reducing farm risk; thus, they raised income and food security (Murshed-E-Jahan and Pemsl, 2011). In this study, about 93% of the pangas farmers had small farms at the start; however, over time they increased their farm size and achieved higher outcomes. Pangas farming inflated business scope both for the farmers and the local people due to its diverse linkage with various businesses (van Asseldonk, 2013). Aquaculture was playing a crucial role in Bangladesh by creating job opportunities and boosting foreign exchange earnings (Shamsuzzaman et al., 2020). Farming this fish expanded both full-time and part-time on-farm as well as non-farm employment opportunities for local people (Faruque, 2007; Latwal and Srivastava, 2016). Fish farming created opportunities for primary and secondary income sources, thus alleviating poverty and improving livelihood (Wuyep and Rampedi, 2018). This sector generated more employment than other activities (Meenakshi and Srivastava, 2016). Moreover, KIIs and FGDs also claimed that pangas farming changed the local economy, creating jobs for the unemployed people in the study areas.

        On the other side, people converted huge amounts of land in the farming-intensified areas (Figure 5). Most of them converted their raised lands to lowland fishponds for better return (Zaman, 2017). Along with this issue, the lowest score of natural capital (1.99) implied that farmers were less concerned about environmental eco-sustainability issues (Table 4). Moreover, raised dikes created barriers to water passage and caused waterlogging, which reduced crop production in pangas-producing areas. Beside this, frequent water exchange allowed toxic organic and inorganic components to enter the open water ecosystem. Polluted water discharge enhanced the entrance of organophosphorus components, killing the fish embryos, larvae, and adults, which impacted negatively in fish organs like fewer eggs being fertilized, eggs being malformed, induced larval mortality, and morphological aberration (Rahman et al., 2020); changes to the gills, liver, kidneys, and testes (Benli and Özkul, 2010); and fewer hemoglobin and red blood cells (Hossain et al., 2015). The breeding performance of stinging catfish was also hampered by the residual effect of sumithion (Das et al., 2016).         Therefore, we can hypothesize that the discharge of water containing agrochemicals damages natural ecosystems both directly and indirectly. Heavy metal accumulation, eutrophication, food poisoning, contamination of infectious diseases, and loss of valuable organisms were also intensified by waste discharge from commercial aquaculture units (Ojewole et al., 2024). The quick growth of the aquaculture sector (Drizo, 2023) generated environmental dangers and created questions about water pollution originating from aquaculture output facilities and pollutant discharge. Aquaculture effluent consisted mostly of leftover feed, silt particles, metabolic waste from the farmed fish, and hazardous aqua chemicals (Ojewole et al., 2024). The poor management techniques, such as excessive feeding, poor feed selections, crowding, wrong species selection, antibiotic abuse, and improper wastewater discharge, badly contaminated watersheds and squandered precious resources (Li et al., 2024). Aquaculture activities could thus generate more antibiotic residues, antibiotic-resistant bacteria, persistent organic pollutants, and heavy metals in produced finfish and shellfish (Sapkota, 2008).

        This agrarian transition from agriculture to pangas farming in the study areas raised a key concern about the waste management systems, which might hamper reaching the benefits of farming to all stockholders. Farmers’ registration, farm monitoring system and adaptive farming technology should be established to ensure environmental sustainability and social integrity. Moreover, reusing and recycling techniques can minimize the farm waste. Otherwise, one-sided beneficiary groups may tarnish the free protein access from common sharing resources and affect the livelihood of fishermen. To get the maximum benefit with the least compensation of nature, policy have to be put in action along with proper training, field-based demonstration, formal inspection, and establishment of pangas farmers’ community discharge units for reuse and recycling of waste (FGDs, IDIs). Modern environmentally friendly farming techniques can help to lower unfavourable effects on the surroundings (Araujo et al., 2022). Several models—constructed wetland treatment model (CWT), ecological ditch treatment model (EDTA), integrated multi-trophic aquaculture model (IMTA), and recirculating aquaculture model (RAS)—are suggested for modern aquaculture to lower the pollution.

        Finally, this study suggests a significant improvement in the farmers’ living standards, along with creating scope for job opportunities in this multi-faceted agro-based industry in Bangladesh. Food and nutrition security and national macroeconomic stability depend on fisheries (Sunny et al., 2021), which are related to SDG 1: no poverty; SDG 2: zero hunger; and SDG 14: life below water. Furthermore, achieving SDG-14 requires responsible aquaculture (FAO, 2023). Bangladesh meets its own protein needs with pangas (DoF, 2023) as a major fish protein source. She can also contribute to the global food security (SDG-1, 2.3) by supplying high-quality fish and fisheries products on the international market, abiding by the global GAP (Good Aquaculture Practices) and ASC (Aquaculture Stewardship Council) certification standards (Haque, 2021). But minimal emphasis on aquaculture sustainability and product standards is hampering the attainment of the SDGs (Goals 12, 14, 15, and 17) in Bangladesh.

        This cross-sectional study has some limitations in assessing the time being effects of pangas farming on attaining LOP. A longitudinal study might be suggested to get better interpretations. Future research can be conducted to reveal the underlying causes of farmers’ constraints to adopting more sustainable aquaculture practices in Bangladesh.

CONCLUSION

        The study showed a favourable score on human capital, physical capital, social capital, and financial capital but also expressed worries about its environmental consequences, providing the lowest score on the natural capital indicators. Positive outcomes on the indicators of the four capitals indicated that the farmer had acquired skills, physical assets, social responsibility, and financial efficiency. Therefore, pangas farming has positively impacted four aspects of SLA, except for natural capitals. From the interviews, it was understood that the respondents were more concerned about profit maximization than the liabilities of natural ecosystems. The farmer’s education level, experience, knowledge, and adoption of modern farming technology significantly contributed to promote their better livelihood outcomes. Quite the reverse, the respondents converted agricultural lands and discharged pollutants into the environment, raising questions about the sustainability of aquaculture practices in pangas farms in Bangladesh. So, to get the maximum benefits of pangas production in Bangladesh, farmers need to adopt modern feeding methods to reduce feed loss and follow the biosecurity practices to reduce the hazards. Additionally, governmental initiatives are required for farmer registration and field demonstration with laboratory support to ensure sustainable pangas farming. This study only focuses on farmers’ perceptions and practices; experimental findings are still missing in the study. Future research may be conducted to assess the residual effect of aqua drugs on human health.

CONFLICT OF INTEREST:

Authors have no conflict of interest.

ACKNOWLEDGEMENTS:

        The authors wish to express their gratitude to all participants for providing valuable responses in this study. The authors also convey gratefulness to the authority of the University Grant Commission (UGC), Bangladesh for approving grants (ref. 37.01.0000.075.42.006.20.256, date: 31.08.2023) for data collection.

AUTHORS’ CONTRIBUTIONS:

        Khaleda Nasrin, as primary author, implemented data collection, conceptualized the study, analysed the data, and wrote the manuscript. The study presented here is part of the primary author’s PhD degree. As a supervisor, Md. Yeamin Hossain designed the study’s methodology, conceptualized the work, analysed the data, and reviewed and edited the writing. We further declare that all writers have contributed, checked over, and approved the manuscript.

Notes:

1. The Pearson’s product-moment correlation coefficient, shortly known as Pearson’s correlation coefficient (Puth et al., 2014), is used to measure the linear relationship between dependent and independent variables for interval or ratio data. In this study, the dependent variable was index data, and the independent variables were either numeric or Likert scale-like data. So, linear relationships were considered among the explanatory and focus variables. The extent of the relationship ranges from strong negative (-1) to strong positive (1).

2. Cronbach's alpha coefficient (Robert, 2005) is typically utilized to assess the reliability of multi-item scales. For the scale reliability test, the value of Cronbach’s alpha is computed by IBM SPSS software.

3. In this study, multiple linear regression analysis was conducted to identify the factors and their significant impact in predicting the dependent variable, livelihood outcomes of pangas (LOP). The model residuals normality test was checked according to the Kolmogorov-Smirnov and Shapiro-Wilk tests at a 5% significance level.

4. Autocorrelation among dependent variables was checked by the Durbin-Watson test. Data normality was also verified by the Variance Inflation Factor (VIF) value (the rule of thumb indicates that a VIF below 10 signifies the absence of multicollinearity and the acceptance range of the Durbin-Watson value is 1.5 to 2.5).

REFERENCES

Abduljabar, S. S., & Naqid, I. A. (2022). Staphylococcus aureus among athletes in Zakho city, Kurdistan region, Iraq: Nasal carriage rate, risk factors, and antibiotic sensitivity profile. Science Journal of University of Zakho, 10(3), 112-118. https://doi.org/10.25271/sjuoz.2022.10.3.929

Adams, A., Jumpah, E. T., & Caesar, L. D. (2021). The nexuses between technology adoption and socioeconomic changes among farmers in Ghana. Technological Forecasting and Social Change, 173, 121133. https://doi.org/10.1016/j.techfore.2021.121133

Adhikary, R. K., Kar, S., Faruk, A., Hossain, A., & Bhuiyan, M. N. M. (2018). Contribution of aquaculture on livelihood development of fish farmer at Noakhali, Bangladesh. Asian-Australasian Journal of Bioscience and Biotechnology, 3(2), 106-121. https://doi.org/10.3329/aajbb.v3i2.64789

Ahmed, N., Alam, M.F., and Hasan, M.R., 2010. The economics of sutchi catfish (Pangasianodon hypophthalmus) aquaculture under three different farming systems in rural Bangladesh. Aquaculture Research, 41(11): 1668–1682. https://doi.org/10.1111/j.1365-2109.2010.02549.x

Aktar, S. S., Khan, M. A., Prodhan, M. M. H., & Mukta, M. A. (2018). Farm size, productivity and efficiency nexus: the case of pangas fish farming in Bangladesh. Journal of Bangladesh Agricultural University, 16(3), 513-522. https://doi.org/10.3329/jbau.v16i3.39449

Al Mahadi, M. S., Rahman, M. S., Islam, M. S., Rahman, K. M. M., & Khanum, R. (2022). Fish farming and its effect on household income: micro-level study in Bangladesh. Letters in Spatial and Resource Sciences, 15(2), 255-277. Retrieved from https://link.springer.com/article/10.1007/s12076-022-00303-x

Ali, H. and Haque, M.M. 2011. Impacts of Pangas aquaculture on land use patterns in Mymensingh district of Bangladesh. Journal of the Bangladesh Agricultural University, 9(1): 169-178. https://doi.org/10.3329/JBAU.V9I1.8759

Amin, M. A., Bashar, M. A., Akhter, N., Afroj, M., Islam, M. Z., Rahman, M. M., & Baque, M. A. (2016). Constraints faced by the farmers in IPM practices in rice cultivation. Journal of Science Technology and Environment Informatics, 4, 245-50. Retrieved from https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=Constraints+faced+by+the+farmers+in+IPM+practices+in+rice+cultivation.+&btnG=

Anka, I. Z., Faruk, M. A. R., Hasan, M. M., & Azad, M. A. K. (2013). Environmental issues of emerging pangas (Pangasianodon hypophthalmus) farming in Bangladesh. Progressive Agriculture, 24(1-2), 159-170. https://doi.org/10.3329/pa.v24i1-2.19118

Araujo, G. S., Silva, J. W. A. D., Cotas, J., & Pereira, L. (2022). Fish farming techniques: Current situation and trends. Journal of Marine Science and Engineering, 10(11), 1598. https://doi.org/10.3390/jmse10111598

Belton, B., Karim, M., Thilsted, S., Murshed-E-Jahan, K., Collis, W., & Phillips, M. (2011). Review of aquaculture and fish consumption in Bangladesh. Studies and Re-views 2011-53. The World Fish Center. November 2011. Search in. Retrieved from https://digitalarchive.worldfishcenter.org/bitstream/handle/20. 500.12348/1162/WF_2970.pdf?sequence1=

Benli, A. Ç. K., & Özkul, A. (2010). Acute toxicity and histopathological effects of sublethal fenitrothion on Nile tilapia, Oreochromis niloticus. Pesticide Biochemistry and physiology, 97(1), 32-35. https://doi.org/10.1016/j.pestbp.2009.12.001

Borek, A. J., Edwards, G., Santillo, M., Wanat, M., Glogowska, M., Butler, C. C., ... & Tonkin-Crine, S. (2023). Re-examining advice to complete antibiotic courses: a qualitative study with clinicians and patients. BJGP open, 7(2). DOI:10.3399/BJGPO.2022.0170

Chambers, R. (1992). Rural appraisal: rapid, relaxed and participatory (Vol. 311). Brighton: Institute of Development Studies.

                Retrievedfrom https://scholar.google.com/scholar_lookup?title=Rural%20Appraisal%3A%20Rapid%2C%20Relaxed%20and%20Participatory.%20Discussion%20Paper%20311&publication_year=1992&author=R.%20

Chambers, R. (2015). PRA, PLA and pluralism: Practice and theory. The SAGE handbook of action research, 31-46.  https://doi.org/10.4135/9781848607934.n28

Chowdhury, M. A., Roy, N. C., & Chowdhury, A. (2020). Growth, yield and economic returns of striped catfish (Pangasianodon hypophthalmus) at different stocking densities under floodplain cage culture system. The Egyptian Journal of Aquatic Research, 46(1), 91-95. https://doi.org/10.1016/j.ejar.2019.11.010

Das, T., Rahman, M. M., Hossen, M. S., & Mollah, M. F. A. (2016). Toxicity effects of sumithion on the breeding performance and viability of eggs, embryos and subsequent growth indices of Heteropneustes fossilis larvae. Retrieved from https://baures.bau.edu.bd/wp-content/uploads/2017/06/15.-JBAU-785-16-Toxicity_v14j2.pdf

DoF (2022). Yearbook of Fisheries Statistics of Bangladesh, 2020-21 (vol. 38, pp. 1-138). Fisheries Resource Survey System (FRSS), Department of Fisheries, Ministry of Fisheries and Livestock, Bangladesh. Retrieved from https://fisheries.gov.bd/

DoF (2023). Yearbook of Fisheries Statistics of Bangladesh, 2021-22 (vol. 39, pp.1-139). Fisheries Resource Survey System (FRSS), Department of Fisheries, Department of Fisheries, Ministry of Fisheries and Livestock, Bangladesh. Retrieved from https://fisheries.gov.bd/

DoF (2024). National Fish Week 2024 Compendium (in Bengali). Department of Fisheries and Livestock, Bangladesh,160.

Drizo, A., & Shaikh, M. O. (2023). An assessment of approaches and techniques for estimating water pollution releases from aquaculture production facilities. Marine Pollution Bulletin, 196, 115661. https://doi.org/10.1016/j.marpolbul.2023.115661

FAO (2013). Sustainability Assessment of Food and Agriculture System (SAFA), (version 3, pp 1-204), Rome, Food and Agriculture Organization of the United Nations (FAO), Retrieved from https://www.fao.org/fileadmin/templates/nr/sustainability_pathways/docs/SAFA_Guidelines_Final_122013.pdf

FAO (2020). The State of World Fisheries and Aquaculture 2020: Sustainability in action (pp. 4). Food and Agricultural Organization, Rome.  https://doi.org/10.4060/ca9229en

FAO (2022). The States of World Fisheries and Aquaculture 2022: Towards Blue Transformation. Rome (pp.1-223).  Food and Agricultural Organization, Rome. https://doi.org/10.4060/cc0461en

FAO (2023). Pangasius: Robust demand supported by increased harvests. Information and Analysis on Markets and Trade of Fisheries and Aquaculture Products, Globefish. Retrieved from https://www.fao.org/in-action/globefish/news-events/news/news-detail/Pangasius-Robust-demand-supported-by-increased-harvests/en

Faruk, M. A. R., Rahman, N., & Patwary, Z. P. (2017). Risk factors associated with tilapia and pangasius diseases. Journal of the Bangladesh Agricultural University, 15(2), 325-331. https://doi.org/10.3329/jbau.v15i2.35083

Faruque, G. (2007). Thesis, An exploration of impacts of aquaculture production and marketing on rural livelihoods in three regions in    Bangladesh: 1-359. https://doi.org/10.1111/j.1365-2109.2010.02549.x

Gu, D. E., Wang, J. W., Xu, M., Mu, X. D., Wei, H., Yu, F. D., ... & Hu, Y. C. (2022). Does aquaculture aggravate exotic fish invasions in the rivers of southern China?. Aquaculture, 547, 737492. https://doi.org/10.1016/j.aquaculture.2021.737492

Gurung, K., Bhandari, H., & Paris, T. (2016). Transformation from rice farming to commercial aquaculture in Bangladesh: Implications for gender, food security, and livelihood. Gender, Technology and Development, 20(1), 49-80. https://doi.org/10.1177/0971852415618747

Haider, S., Haque, S., Islam, S., Sayadat, N., & Salman, M. (2023). An economic analysis of Pangas (Pangasianodon hypophthalmus) farming considering socioeconomic and environmental impacts: A case study in Trishal Upazila, Bangladesh. International Journal of Agricultural Extension, 11(2), 129-138. https://doi.org/10.1111/j.1365-2109.2010.02549.x

Haque, M. M., Alam, M. M., Hoque, M. S., Hasan, N. A., Nielsen, M., Hossain, M. I., & Frederiksen, M. (2021). Can Bangladeshi pangasius farmers comply with the requirements of aquaculture certification? Aquaculture Reports, 21, 100811. https://doi.org/10.1016/j.aqrep.2021.100811

Heal, R. D., Hasan, N. A., & Haque, M. M. (2021). Increasing disease burden and use of drugs and chemicals in Bangladesh shrimp aquaculture: A potential menace to human health. Marine Pollution Bulletin, 172, 112796. https://doi.org/10.1016/j.marpolbul.2021.112796

Hoa, T. T. T., Boerlage, A. S., Duyen, T. T. M., Thy, D. T. M., Hang, N. T. T., Humphry, R. W., & Phuong, N. T. (2021). Nursing stages of striped catfish (Pangasianodon hypophthalmus) in Vietnam: Pathogens, diseases and husbandry practices. Aquaculture, 533, 736114. https://doi.org/10.1016/j.aquaculture.2020.736114

Hossain, S., Khatun, M. H., Rahman, M. K., & Shahjahan, M. (2015). Impacts of sumithion on blood glucose and some hematological parameters in common carp. Int. J. Environ, 5(1), 8-13. Retrieved from https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=Hossain+S%2C+Khatun+MH%2C+Rahman+MK%2C+Shahjahan+M+%282015%29+Impacts+of+sumithion+on+blood+glucose+and+some+hematological+parameters+in+common+carp.&btnG=

Hoseini, S. M., & Al Sulivany, B. S. (2024). Copper and microplastic exposure affects the gill gene expression of common carp during saltwater challenge. Science Journal of University of Zakho, 12(3), 382-387. https://doi.org/10.25271/sjuoz.2024.12.3.1335

Hoque, M. S., Haque, M. M., Nielsen, M., Rahman, M. T., Hossain, M. I., Mahmud, S., ... & Larsen, E. P. (2021). Prospects and challenges of yellow flesh pangasius in international markets: secondary and primary evidence from Bangladesh. Heliyon, 7(9). Retrieved from https://www.cell.com/heliyon/fulltext/S2405-8440(21)02163-0

Hussain, G. A., Umer, M. I., & Ahmed, M. R. (2023). A STUDY ON THE CONTAMINATION OF KHABUR RIVER WITH HEAVY METALS DUE TO SPATIAL AND SEASONAL DISCHARGED WASTEWATER IN THE IRAQI KURDISTAN REGION: Heavy Metals. Science Journal of University of Zakho, 11(2), 298-305. https://doi.org/10.25271/sjuoz.2023.11.2.1060

Issa, F. A. (2024). ANTIBIOTIC RESISTANCE PATTERNS OF COMMON UROPATHOGENS ISOLATED FROM FEMALES AT ZAKHO CITY, KURDISTAN REGION, IRAQ. Science Journal of University of Zakho, 12(4), 490-496. https://doi.org/10.25271/sjuoz.2024.12.4.1395

Khan A., Guttormsen A. and Roll K.H. (2018). Production risk of pangas (Pangasius hypophthalmus) fish farming. Aquaculture Economics and Management, 22(2): 192–208. http://dx.doi.org/10.1080/13657305.2017.1284941

Khan M. A., Begum R., Nielsen R., and Hoff A. (2021). Production risk, technical efficiency, and input use nexus: lessons from Bangladesh aquaculture. Journal of the World Aquaculture Society, 52(1), 57–72. https://doi.org/10.1111/jwas.12767

Khanum, R., Schneider, P., Al Mahadi, M. S., Mozumder, M. M. H., & Shamsuzzaman, M. M. (2022). Does fish farming improve household nutritional status? Evidence from Bangladesh. International Journal of Environmental Research and Public Health, 19(2), 967. https://doi.org/10.3390/ijerph19020967

Kumar, G., Engle, C., & Tucker, C. (2018). Factors driving aquaculture technology adoption. Journal of the world aquaculture society, 49(3), 447-476. https://doi.org/10.1111/jwas.12514

Latwal, M., & Srivastava, S. K. (2016). Contribution of fish farming in farm household income and employment: a study of Kumaun hills of Uttarakhand. Journal of Hill Agriculture, 7(1), 110-113. http://dx.doi.org/10.5958/2230-7338.2016.00019.7

Murshed-E-Jahan, K., & Pemsl, D. E. (2011). The impact of integrated aquaculture–agriculture on small-scale farm sustainability and farmers’ livelihoods: experience from Bangladesh. Agricultural Systems, 104(5), 392-402. https://doi.org/10.1016/j.agsy.2011.01.003

Ngo, T., Vu, H. V., Ho, H., Dao, T. T., & Nguyen, H. T. (2019). Performance of fish farms in Vietnam–does financial access help improve their cost efficiency? International Journal of Financial Studies, 7(3), 45. https://doi.org/10.3390/ijfs7030045

Nguyen, L. A., Pham, T. B., Bosma, R., Verreth, J., Leemans, R., De Silva, S., & Lansink, A. O. (2018). Impact of climate change on the technical efficiency of striped catfish, Pangasianodon hypophthalmus, farming in the Mekong Delta, Vietnam. Journal of the World Aquaculture Society, 49(3), 570-581. https://doi.org/10.1111/jwas.12488

Ojewole, A. E., Ndimele, P. E., Oladele, A. H., Saba, A. O., Oladipupo, I. O., Ojewole, C. O., ... & Kalejaye, O. S. (2024). Aquaculture Wastewater Management in Nigeria's Fisheries Industry for Sustainable Aquaculture Practices. Scientific African, e02283. https://doi.org/10.1016/j.sciaf.2024.e02283

Paul, T. T., Vandana, G. D., Pandit, A., Das, B. K., & Salim, S. S. (2022). Dilemma on the ban on exotics in Indian subcontinent: A       systematic review in a socio-economic perspective. Fisheries Research, 246, 106150. https://doi.org/10.1016/j.fishres.2021.106150

Puth, M. T., Neuhäuser, M., & Ruxton, G. D. (2014). Effective use of Pearson's product–moment correlation coefficient. Animal behaviour, 93, 183-189. https://doi.org/10.1016/j.anbehav.2014.05.003

Qaseem, N. M. A., Khalid, M., & Al-Saffawi, A. (2023). Qualitative Assessment of Soil and Sediment Pollution with Some Heavy Metals: A Case Study of Duhok Valley in The Kurdistan Region of Iraq. Science Journal of University of Zakho, 11(1), 65-72. https://doi.org/10.25271/sjuoz.2023.11.1.1048

Rahman, A. A. (2005). Freshwater fishes of Bangladesh (ed. 2^nd, p. 24). Zoological Society of Bangladesh, Dhaka, Bangladesh.

Rahman, M. M. (2012). Problems and suggestions for farmers' adoption of IPM practices in rice (Oryza sativa L) cultivation, Bangladesh J. Agril.Res., 37(1), 121-128. Retrieved from https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=PROBLEMS+AND+SUGGESTIONS+FOR+FARMERS%E2%80%99+ADOPTION+OF+IPM+PRACTICES+IN+RICE+%28Oryza+sativa+L%29+CULTIVA&btnG=

Rahman, M. S., Islam, S. M., Haque, A., & Shahjahan, M. (2020). Toxicity of the organophosphate insecticide sumithion to embryo and larvae of zebrafish. Toxicology reports, 7, 317-323. https://doi.org/10.1016/j.toxrep.2020.02.004

Rajan, P., Dubey, M. K., Singh, S. R. K., & Khan, M. A. (2013). Factors affecting knowledge of fish farmers regarding fish production technology. Fish farming, 9, 10-00. Retrieved from https://api.seea.org.in/uploads/pdf/2013-37-126-128.pdf

Ramsundar, B. (2011). Impact of Education on Fish Farming in West Bengal: A Study Report. Advances in Life Science and Technology, 1, 14-25. Retrieved from https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=Ramsundar%2C+B.+%282011%29.+Impact+of+Education+on+Fish+Farming+in+West+Bengal%3A+A+Study+Report.+Advances+in+Life+Science+and+Technology&btnG=

Robert F. De Vellis (2005). Encyclopedia of Social Measurement (pp. 317-322). Elsevier. https://doi.org/10.1016/B0-12-369398-5/00095-5

Sapkota, A., Sapkota, A. R., Kucharski, M., Burke, J., McKenzie, S., Walker, P., & Lawrence, R. (2008). Aquaculture practices and potential human health risks: current knowledge and future priorities. Environment international, 34(8), 1215-1226. https://doi.org/10.1016/j.envint.2008.04.009

Shamsuzzaman, M. M., Islam, M. M., Tania, N. J., Al-Mamun, M. A., Barman, P. P., & Xu, X. (2017). Fisheries resources of Bangladesh: Present status and future direction. Aquaculture and Fisheries, 2(4), 145-156. https://doi.org/10.1016/j.aaf.2017.03.006

Sheheli, S., Mithun, M. N. A. S., & Hoque, M. J. (2021). Exploring Impact of Fish Farming in Changing Livelihood Status of Fishermen in Bangladesh. Retrieved from https://www.researchgate.net/profile/Md-Nur-Alom-Mithun/publication/355204135_Exploring_Impact_of_Fish_Farming_in_Changing_Livelihood_Status_of_Fishermen_in_Bangladesh/links/616813b666e6b95f07c43dcb/Exploring-Impact-of-Fish-Farming-in-Changing-Livelihood-Status-of-Fishermen-in-Bangladesh.pdf

Sunny, A. R., Mithun, M. H., Prodhan, S. H., Ashrafuzzaman, M., Rahman, S. M. A., Billah, M. M., ... & Hossain, M. M. (2021). Fisheries in the context of attaining Sustainable Development Goals (SDGs) in Bangladesh: COVID-19 impacts and future prospects. Sustainability, 13(17), 9912. https://doi.org/10.3390/su13179912

Tasnoova, S., Iqbal, K. M., Iwamoto, I., & Haque, M. (2017). Livelihoods of the people involved in pangasiid catfish (Pangasius hypophthalmus) farming in Mymensingh, Bangladesh. In World Sustainable Development Outlook 2007 (pp. 241-248). Routledge. Retrieved from https://www.taylorfrancis.com/chapters/edit/10.4324/9781351280242-22/livelihoods-people-involved-pangasiid-catfish-pangasius-hypophthalmus-farming-mymensingh-bangladesh-shuraya-tasnoova-khan-iqbal-izumi-iwamoto-mahfuzul-haque

Udayakumara, E. P. N., & Shrestha, R. P. (2011). Assessing livelihood for improvement: Samanalawewa reservoir environs, Sri Lanka. International Journal of Sustainable Development & World Ecology, 18(4), 366-376. https://doi.org/10.1080/13504509.2011.562740

Uddin, M. N., Kabir, K. H., Roy, D., Hasan, M. T., Sarker, M. A., & Dunn, E. S. (2021). Understanding the constraints and its related factors in tilapia (Oreochromis sp.) fish culture at farm level: A case from Bangladesh. Aquaculture, 530, 735927. https://doi.org/10.1016/j.aquaculture.2020.735927

van Asseldonk, I. J. M (2013). Agrarian transition: Evolving livelihood pathways in the context of the pangasius boom crop in Bangladesh. Wageningen University – Wageningen, The Netherlands. MScThesis. 1-84. Retrieved from https://edepot.wur.nl/276404  

Woźniacka, K., Bickley, L. K., Heal, R. D., Maclean, I. M., Hasan, N. A., Haque, M. M., ... & Tyler, C. R. (2025). Seeking environmentally sustainable solutions for inland aquaculture in Bangladesh. Environmental Challenges, 18, 101062. https://doi.org/10.1016/j.envc.2024.101062

Wuyep, S. Z., & Rampedi, I. T. (2018). Urban fish farming in Jos, Nigeria: Contributions towards employment opportunities, income generation, and poverty alleviation for improved livelihoods. Agriculture, 8(7), 110. https://doi.org/10.3390/agriculture8070110

Zaman, M. F. U., Samad, M. A., Islam, M. A., Jaman, M. H. U., Khondoker, S., & Asif, A. A. (2017). Assessment of sustainability of Pangasius (Pangasius hypophthalmus) farming at Jhikargachha upazila in Jessore district, Bangladesh. International Journal of Fauna and Biological Studies, 4(5), 109-119. Retrieved from https://www.faunajournal.com/archives/2017/vol4issue5/PartB/4-5-10-784.pdf