Optimizing small-scale aquaculture systems in the Lake Victoria Basin,Uganda: Insights into profitability drivers

Optimizing small-scale aquaculture systems in the Lake Victoria Basin,Uganda: Insights into profitability drivers | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Optimizing small-scale aquaculture systems in the Lake Victoria Basin,Uganda: Insights into profitability drivers Syliver Byabasaija, Moses Limuwa, Ronald Semyalo This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5541053/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 8 You are reading this latest preprint version Abstract Small-scale aquaculture offers potential for improving livelihoods, food security, and economic growth. However, African fish farmers struggle to achieve profitability. This study investigated financial performance of three small-scale aquaculture systems (ponds, cages, and aquaponics) in Uganda's Lake Victoria Basin, to identify variables influencing profitability. Using stratified random sampling, 169 fish farmers from Mpigi, Wakiso, and Buikwe districts were interviewed. Econometric analysis of small-scale aquaculture systems was performed using LIMDEP 9.0 software. Profitability was measured using key financial criteria such as gross margin, net farm income, and net return on investment. Furthermore, a linear regression model was used to analyse attributes influencing profitability. The analysis considered variables such as farm size, stocking density, access to extension services, and other crucial determinants. The results revealed that cage culture achieved a positive gross margin, while pond and aquaponics systems faced financial challenges due to high fixed and variable expenses, including feed, labor, and infrastructure. Farm size, management practices, and fish prices have impact on profitability. Nevertheless, high fish feed costs, predator problems, and poor technical support hinders financial performance. To improve the financial sustainability of aquaculture systems, the study recommends cost-effective feed management practices, offering financial assistance in form of low-interest loans, extending training, and market linkages. Small-scale aquaculture profitability drivers aquaculture systems Cost-profit analysis financial performance. Gross margin Figures Figure 1 1. Introduction Globally, the aquaculture sector has experienced significant growth due to increasing demand for fish, as a primary source of protein and the need to reduce pressure on wild fish stocks [ 22 ]. Aquaculture contributes to local food security and economic activities [ 43 , 44 ]. However, the aquaculture sector faces persistent challenges, including the depletion of key inputs such as seed and feed due to escalating demand, as well as environmental concerns linked to intensive production practices [ 18 ]. These challenges necessitate sustainable solutions, such as improved hatchery technologies, efficient feed formulations, and ecosystem-based management approaches, to ensure long-term viability and productivity [ 40 , 22 ]. Aquaculture has emerged as a viable alternative of catch fisheries, especially in regions where small-scale and artisanal fisheries dominate fish production but face significant resource and operational limitations [ 2 , 7 , and 39 ]. In Uganda, aquaculture was introduced in the 1950s, but remains primarily subsistence-oriented due to socio-economic constraints [ 25 ]. Traditional practices involving small ponds with minimal inputs and reliance on family labor are common [ 38 ]. However, rising fish prices and the need for sustainable production methods, have led the Ugandan government, international organizations, and study institutions to promote small-scale aquaculture systems such as ponds, cages, and aquaponics. These efforts have resulted in increased investments in small-scale aquaculture technologies [ 33 , 37 , and 24 ]. Despite these developments, challenges persist, particularly regarding the economic and operational sustainability of small-scale aquaculture systems in Lake Victoria basin. Studies provided insights into the growth and challenges of aquaculture globally and regionally, relating to fish health and conflicts for resources [ 23 ]. However, systematic study on the financial performance and profitability drivers of small-scale aquaculture systems in Uganda, particularly in the Lake Victoria Basin is lacking [ 37 ]. Furthermore, there is a scarcity of knowledge on the specific costs and economic profits associated with systems such as ponds, cages, and aquaponics, as well as the constraints faced by local fish farmers [ 61 , 36 ]. Our study explores small-scale aquaculture systems in Ugandan communities, along Lake Victoria. Specifically, we focus on 1) analyzing the costs and profitability drivers associated with various small- scale aquaculture systems (pond, cage, and aquaponics), and 2) identifying the challenges encountered by local fish farmers in adopting and maintaining these aquaculture systems. The study informs sustainable aquaculture development strategies in Uganda and similar socio-economic contexts. It contributes to Uganda's National Development Plan III and Vision 2040, which prioritize sustainable agricultural practices and innovation-driven economic growth [ 37 ]. Additionally, the study supports policy development to facilitate the scaling up of small-scale aquaculture, ensuring it becomes a reliable and sustainable source of food and income for local communities. 2. Material and methods 2.1 The study area The study was carried out in selected districts surrounding Uganda's Lake Victoria Basin, specifically Buikwe (0.761029 N, 33.040723 E), Wakiso (0.054163 N, 32.518079 E) and Mpigi (0.305544 S, 32.037822 E). These districts (Fig. 1 ) were purposively selected, because of their established small-scale aquaculture systems and the prominence of fish farming activities in the area [ 37 ]. The Lake Victoria Basin is an important ecological and economic zone, supporting not only small-scale aquaculture and capture fisheries but also a wide range of other economic activities such as agriculture, water transportation, and ecotourism. These selected districts, which are at a moderate altitude and receive significant rainfall throughout the year, are ideal for agricultural practices [ 30 ]. For example, Mpigi and Wakiso are well-known for coffee cultivation [ 37 ], whereas Buikwe is known for sugarcane cultivation. Farmers in these districts also grow a variety of crops, including coffee, bananas, cassava, potatoes, maize, beans, and pineapples, as well as horticultural produce like tomatoes and vegetables. Livestock farming is also common in the area, with dairy and beef cattle, goats, and poultry being the most popular [ 37 ]. 2.2 Data Collection Data collection was conducted in November 2023 targeting 169 fish farmers across three purposively selected districts (Fig. 1 ). To ensure a comprehensive and representative sample, we employed a stratified random sampling technique. We divided the population of fish farmers into distinct subgroups based on the types of aquaculture systems they use (pond, cage, and aquaponics). The rationale for using stratified sampling, was to ensure that each production system is adequately represented within the sample, allowing for more analysis of the different aquaculture farming systems, and their associated challenges. The final sample population was 49 farmers from Mpigi, 55 from Wakiso, and 65 from Buikwe. The allocation is proportional to the number of fish farmers in each district. Within each district, fish farmers were categorized based on their primary aquaculture practice, ensuring that the stratification accurately reflected the diverse production systems in use. Before the main survey, we pre-tested the survey tool on eight fish farms, two in Wakiso, three in Buikwe, and three in Mpigi, to assess its reliability and clarity. Data collection focused on gathering information on farmers' aquaculture experience, systems type, fish species farmed, aquaculture products, income, demographics, production cycles, credit access, group affiliations, record-keeping practices, access to extension services, etc. 2.3 Data Analysis 2.3.1 Cost and profit analysis We coded and analyzed data using LIMDEP 9.0 econometric software [ 29 ]. To determine the profitability and cost structures. The financial performance of small-scale aquaculture systems was analyzed using enterprise budget data derived from survey responses, covering the three aquaculture systems: pond culture, cage culture, and aquaponics. This analysis provided detailed estimates of inputs and outflows, enabling the calculation of key financial metrics to evaluate the profits systems considering a fixed investment and depreciated over an estimated lifespan of five years, ensuring a realistic assessment of their impact on profitability. The key financial metrics calculated include: Gross Margin (GM) = Total Revenue (TR) − Total Variable Cost (TVC) ….. (1) Net Farm Income (NFI)) Net Farm Income = Gross Margin − Total Fixed Cost …………….. (ii) Net Return on Investment Net Return on Investment = Total Cost over Net Farm Income ………… (iii) We employed a multiple linear regression model to examine the factors influencing gross margin (GM), our measure of profitability (Table 1 ). This method is widely used in economic studies to assess the relationship between profitability and explanatory variables in aquaculture. For example, Bosma et al. (2017) [ 13 ] utilized linear regression to analyze economic performance and key drivers of profitability in aquaculture systems. Our model included key variables relevant to fish farming practices and their potential impact on GM. To evaluate the significance of each predictor, we calculated the t-value by dividing its estimated coefficient by its standard error, with the corresponding p-value indicating statistical significance. Additionally, to better understand the strength and direction of associations between individual variables and GM, we computed Pearson’s correlation coefficient (r) for all explanatory variables. Our estimated linear equation took the following form: $$\:\text{G}\text{M}=\:{{\beta\:}}_{0}{{\beta\:}}_{1}{\text{x}}_{1}{{\beta\:}}_{2}{\text{x}}_{2}\dots\:{{\beta\:}}_{\text{n}}{\text{x}}_{\text{n}}\in\:\:$$ Where, GM represents the gross margin, our measure of profit profits, \(\:{{\beta\:}}_{0}\) Is the intercept \(\:{{\beta\:}}_{1},\:{{\beta\:}}_{2},\dots\:.,{{\beta\:}}_{\text{n}}\) The coefficients for each explanatory variable \(\:{\text{x}}_{1},\:{\text{x}}_{2},\dots\:,\:{\text{x}}_{\text{n}}\) Denote the determinants hypothesized to affect profits, and \(\:\in\:\) Is the error term, capturing unexplained variability. Table 1 Factors influencing profitability in small-scale aquaculture included in the model Variable Effect on Gross Margin Gross margin (US $ ) Dependent Variable System size (m²) + Fingerlings stocked (number) + Contracted manager (1 = yes, 0 = no) ± Production cycle (months) ± Fish farmer group membership (1 = yes, 0 = no) + Extension services access (1 = yes, 0 = no) + Keeping records (1 = yes, 0 = no) + Experience in fish farming (years) + Fish price per unit (US $ /kg) + Fish harvested (kg) + Feed cost (US $ ) − Predators (1 major constraint, 0 otherwise) − Participation in training programs (1 = yes, 0 = no) + Species farmed ± Access to credit (1 = yes, 0 = no) + 3. Results 3.1 Cost and profit performance of small-scale pond aquaculture system Thevariable costs comprised the majority of the total expenses, accounting for 93% of the total costs (Table 2 ). Among these, the largest contributors were feed costs (35%), labor during production (25%), and fingerlings (25%), which together accounted for 85% of the total variable costs. Other variable costs included labor at harvesting (7%), net purchases (7%), and transportation costs (0.3%).Fixed costs, comprising only 7% of the total costs, were significantly smaller compared to variable costs. These fixed costs included pond construction (5.8%) and pond rent (1.4%). The total costs, including both variable and fixed costs, amounted to US $ 1,036 (Table 2 ). However, the total revenue generated from pond aquaculture system was only US $ 530, resulting in a gross margin of -US $ 431. This indicates that variable costs exceeded revenue. The net farm income was -US $ 506, and the net return on investment of -49%. Table 2 Cost distribution and profit performance of small-scale pond aquaculture system Cost category Average Cost (US $ ) Total Cost (%) Variable Costs Fingerlings 308 25 Cost of feeds 256 35 Labor at production 308 25 Labor at harvesting 7 7 Net purchase 72 7 Transportation costs 3.6 0.3 Total Variable Costs (TVC) 961 93 Fixed Costs Pond construction 60 5.8 Pond rent 15 1.4 Total Fixed Costs (TFC) 75 7 Total Costs (TVC + TFC) 1,036 100 Total Revenue (TR) 530 Gross Margin (TR - TVC) -431 Net Farm Income (NFI) -506 Net Return on Investment (NRI) -49% 3.2 Cost and profit performance of small-scale cage aquaculture system The Total variable Costs accounted for 59% of the total expenses in the small-scale cage aquaculture system (Table 3 ). The largest contributors to variable costs were feed (22%), fingerlings (15%), and labor during production (10%), which together represented 47% of the total variable costs. Otherminor variable costs included labor at harvesting (5%), transportation (4%), and maintenance (3%). Fixed costs made up 30% of the total expenses, with cage setup being the only fixed cost. The total expenses, combining both variable and fixed costs, amounted to US $ 2,225 (Table 3 ). The total revenue generated was US $ 2,500, resulting in a gross margin of US $ 1,025. The net farm income was US $ 275, and the net return on investment was 12%. Table 3 Cost and profit performance of small-scale cage aquaculture system Cost category Average Cost (US $ ) Total Cost (%) Variable Costs Fingerlings (fish seed) 375 15 Fish feed 550 22 Labor at production 250 10 Labor at harvesting 125 5 Transportation costs 100 4 Maintenance 75 3 Total Variable Costs (TVC) 1,475 59 Fixed Costs Cage setup 750 30 Total Fixed Costs (TFC) 750 30 Total Costs (TVC + TFC) 2,225 Total Revenue (TR) 2,500 Gross Margin (TR - TVC) 1,025 Net Farm Income (NFI) 275 Net Return on Investment (NRI) 12% 3.3 Cost and profit performance of small-scale aquaponics system The analysis of the small-scale aquaponic system identified several challenges, predominantly arising from high infrastructure and operational costs, which outweighed revenue generation. Initial setup costs constituted 30% of total expenses, placing a financial strain on fish farmers due to substantial upfront investments in essential infrastructure, including tanks, pipes, and filters (Table 4 ). Variable costs, accounting for 54% of the overall expenses, were primarily driven by factors such as nutrients, growth media, and labor. Notably, feed and nutrients represented over 60% of the total variable costs. The system’s financial performance was further underscored by negative gross margins and a net farm income of - $ 1,133 and - $ 2,745, respectively. The poor profitability of the system was further emphasized by a Net Return on Investment of -77%, indicating a negative return on the capital invested. Table 4 Cost and profit performance of small-scale aquaponics system Category Average Cost (US $ ) Total Cost (%) Variable Costs Fingerlings costs 212 6.0 Fish feed 158 4.4 Labor at production 151 4.2 Labor at harvesting 106 3.0 Vegetable seeds 107 3.0 Nutrients & growth medium 214 60 Total Variable Costs (TVC) 1,933 54 Fixed Costs System setup cost 1,074 30 Pumps and aerators 214 6.1 Water & utilities cost 53 1.5 Electricity cost 104 3.0 Maintenance cost 161 4.50 Total Fixed Costs (TFC) 1,611 45.0 Total Revenue (TR) 800 - Gross Margin (TR - TVC) -1,133 - Net Farm Income (NFI) -2,745 - Net Return on Investment (NRI) --77% - 3.4 Variables affecting the gross margin in small-scale fish farming with pond system The analysis of variables affecting gross margin in small-scale aquaculture with pond systems revealed significant relationships (Table 5 ). Pond size (r = 0.4, p < 0.01), stocking density (r = 0.3, p < 0.05), experience in fish farming (r = 0.4, p < 0.01), fish price per unit (r = 0.5, p < 0.001), and fish harvested (r = 0.4, p < 0.01) all showed significant positive correlations with gross margin. Additionally, having a contracted manager (p < 0.05), access to extension services (p < 0.05), and keeping records (p < 0.01) were also significant factors influencing gross margin. The production cycle (p = 0.68) and fish farmer group membership (p = 0.07) did not show significant effects. Table 5 Variables affecting Gross Margin in small-scale aquaculture with pond system Variable Mean SD Variable vs Gross Margin Gross Margin (US $ ) -430 - - Pond size (m²) 2,500 750 r = 0.4, p < 0.01 Stocking density (fish/m²) 1.2 0.4 r = 0.3, p < 0.05 Contracted manager 0.4 - p < 0.05 Production cycle (months) 7.5 2 p = 0.68 Fish farmer group membership 0.5 - p = 0.07 Extension services access 0.6 - p < 0.05 Keeping records 0.7 - p < 0.01 Experience in fish farming (years) 5.5 3 r = 0.4, p < 0.01 Fish price per unit (US $ /kg) 4.2 1.5 r = 0.5, p < 0.001 Fish harvested (kg) 2,000 800 r = 0.4, p < 0.01 3.5 Variables affecting the gross margin in small-scale fish cage system Variables affecting gross margin in small-scale aquaculture with cage systems (Table 6 ), identified several key factors influencing financial performance. Cage size (r = 0.2, p < 0.03) and stocking density (r = 0.20, p < 0.04) both exhibited positive correlations with gross margin, suggesting that larger cages and higher stocking densities may improve profitability. Additionally, experience in fish farming (r = 0.3, p < 0.02), fish price per unit (r = 0.3, p < 0.01), and the quantity of fish harvested (r = 0.3, p < 0.02) were positively associated with gross margin, emphasizing their importance in financial outcomes. Management practices, such as having a management system in place (p < 0.05), access to extension services (p < 0.04), and keeping records (p < 0.03), were also found to positively impact gross margin, contributing to better financial results. While contracted management (p = 0.06) did not reach statistical significance, it suggested a potential trend toward increased profitability. Production cycle duration (r = -0.05, p = 0.60) and group membership (p = 0.09) showed no significant effect on gross margin, indicating that these variables have limited influence on profitability. Feed costs (r = -0.2, p < 0.04) had a negative relationship with gross margin, suggesting that higher feed costs may reduce profitability. Finally, the presence of predators (p < 0.05) significantly affected gross margin, highlighting the negative impact of predator-related losses on the financial viability of the system. Table 6 Variables affecting Gross Margin in small-scale aquaculture with cage system Variable Mean SD Variable vs Gross Margin p-value Gross Margin (US $ ) 1,500 - - - Cage size (m²) 100 25 r = 0.2 p < 0.03 Stocking density (fish/m²) 4.5 1 r = 0.20 p < 0.04 Management 0.8 - - p < 0.05 Contracted manager 0.6 - - p = 0.06 Production cycle (months) 6 1.5 r = -0.05 p = 0.60 Group membership 0.7 - - p = 0.09 Extension services access 0.6 - - p < 0.04 Keeping records 0.75 - - p < 0.03 Experience (years) 3 1.2 r = 0.3 p < 0.02 Fish Price per Unit (US $ /kg) 3.5 1.2 r = 0.3 p < 0.01 Fish harvested (kg) 450 120 r = 0.3 p < 0.02 Feed cost (US $ ) 400 150 r = -0.2 p < 0.04 Predators 0.1 - - p < 0.05 3.6 Variables affecting the gross margin in small-scale aquaponic system The analysis of variables affecting gross margin in small-scale aquaponic systems (Table 7 ), revealed several factors with significant influence on financial performance. System size (r = 0.2, p < 0.03) and stocking density (r = 0.2, p < 0.04) exhibited positive correlations with gross margin, suggesting that larger system sizes and higher stocking densities may improve profitability. Moreover, management practices (p < 0.05), access to extension services (p < 0.04), and keeping records (p < 0.03) all showed significant positive associations with gross margin, indicating the beneficial impact of these practices on financial outcomes. Experience in fish farming (r = 0.3, p < 0.02), fish price per unit (r = 0.3, p < 0.01), and the amount of fish harvested (r = 0.3, p < 0.02) also positively correlated with gross margin, highlighting their importance in achieving higher profitability. The length of the production cycle (r = -0.0, p = 0.60) was not significantly associated with gross margin, suggesting that the production cycle duration does not substantially affect profitability. While group membership (p = 0.09) and contracted management (p = 0.06) were not statistically significant, they suggest potential trends toward improving profitability. Feed costs (r = -0.2, p < 0.04) were negatively correlated with gross margin, indicating that higher feed costs reduce profitability. Lastly, the presence of predators (p < 0.05) had a significant impact on gross margin, underlining the negative financial effect of predator-related losses. Table 7 Variables affecting Gross Margin in small-scale aquaponic system Variable Mean SD Variable vs Gross Margin p-value Gross margin (US $ ) -1,133 - - - System size (m²) 24 6 r = 0.2 p < 0.03 Stocking density (fish/m²) 0.9 0.2 r = 0.2 p < 0.04 Management practices 0.3 - - p < 0.05 Contracted manager 0.2 - - p = 0.06 Production cycle (months) 4 1.2 r = -0.0 p = 0.60 Group membership 0.4 - - p = 0.09 Extension services access 0.35 - - p < 0.04 Keeping records 0.5 - - p < 0.03 Experience (years) 2 1 r = 0.3 p < 0.02 Fish price per unit (US $ /kg) 2.5 0.8 r = 0.3 p < 0.01 Fish harvested (kg) 150 45 r = 0.3 p < 0.02 Feed cost (US $ ) 120 40 r = -0.2 p < 0.04 Predators 0.15 - - p < 0.05 3.7 Farmer perspectives on financial strain and support needs in aquaculture systems In pond aquaculture, farmers emphasized the financial strain caused by high input costs, particularly for feed, labor, and fingerlings. One farmer remarked, "Feed is the biggest cost. We spend so much on it, and sometimes it feels like it eats up all our profits," while another highlighted the burden of labor costs, stating, "Labor costs are high, and we don't have machines to help us. It’s a lot of manual work, and that adds to our costs." In cage farming, similar financial pressures were observed, with farmers in Wakiso and Mpigi pointing to the high costs of cage setup materials such as frames and nets. One farmer from Wakiso noted, "The cost of materials like frames, nets, and anchors is too high; we often need loans to cover these expenses, which adds financial risk." Additionally, rising feed costs were a common concern, with a farmer in Mpigi commenting, "The expense of feed keeps increasing, and it’s directly affecting our profits. We can’t keep up with these prices." Farmers across both pond and cage systems also highlighted the need for external support to sustain their operations, with one farmer in Buikwe suggesting, "We need subsidies for cage setup and maintenance to reinvest and sustain cage fish farming in the long run," and another in Wakiso noting, "The extension services have been very helpful. They give us advice on feeding and water quality, which has helped us improve our yields." Similarly, in aquaponic systems, farmers in Wakiso and Mpigi expressed concerns about the significant infrastructure costs, especially for setting up tanks and filtration systems, with one farmer stating, "The system setup, especially tanks and filtration, takes up a large portion of our money, but it’s essential for the system to function." These insights reflect the financial challenges faced by small-scale farmers across different aquaculture systems, underscoring the need for financial support and technical assistance. 4. Discussion 4.1 Comparison of the cost of the three aquaculture systems In small-scale pond aquaculture systems, the main cost drivers are feed, labor, and fingerlings. Feed is one of the major observed expenses, aligning with the findings of Opiyo et al. (2015) [ 51 ], who highlighted feed as a primary cost in tilapia farming. Labor costs also contribute to about 30% of total expenses, exacerbating the financial vulnerability of small-scale systems, as noted by Omondi et al. [ 50 ]. Furthermore, the financial strain is worsened by the fact that a portion of farm output is consumed by the household, thus reducing potential revenue from market sales. Moreover, negative net farm income and low return on investment suggest that the financial sustainability of small-scale pond aquaculture is severely limited due to these high operational costs. This is in agreement with the findings of Okechi et al. [ 49 ] and Love et al. (2015) [ 35 ], who also reported high production costs as major challenges for small-scale aquaculture. For small-scale cage aquaculture, the setup costs, which constitute 30% of fixed costs, are a major financial consideration. The initial investment in durable materials like frames, nets, and anchors is essential for long-term system sustainability, as emphasized by Cruz & Ridha [ 16 , 17 ] and Beveridge [ 12 ]. Feed costs, which account for 22% of total expenses, also remain a concern. While the financial performance of cage aquaculture is positive with a net farm income of US $ 275 and a return on investment of 12%, it remains vulnerable to fluctuations in variable costs, particularly feed and labor. High labor costs, at 15% of total expenses, reflect the labor-intensive nature of cage farming. Although these systems can be profitable, managing these costs effectively is key to maintaining profitability. This is also is supported by Narayanakumar [ 45 ], who found that labor and feed costs play a crucial role in determining profitability in aquaculture systems. Aquaponic systems face financial barriers, primarily due to high infrastructure and operational costs. Setup costs represent 30% of total expenses, while variable costs, especially feed and nutrients, make up over 60%. The reliance on imported materials for nutrients further exacerbates these financial pressures, which is consistent with Bosma et al. (2017) [ 13 ] [ 1 ] [ 52 ], Labor costs, which are influenced by local conditions, also contribute to high operational expenses, particularly for tasks like harvesting, as pointed out by Asmah et al. [ 5 ]. Despite the challenges posed by high input costs, improving market access, sourcing locally produced inputs, and exploring alternative feeds could alleviate some of the financial strain and improve profitability in aquaponic systems. This is supported by the findings of Benjamin et al. (2020) [ 11 ], who highlighted the importance of improving input sourcing and market access. 4.2 Integrated analyses of the factors affecting Gross Margin In pond aquaculture, key factors such as pond size, stocking density, and management practices impact GM [ 21 ]. Larger ponds enable economies of scale, increasing profits, but only when effective management is implemented to avoid inefficiencies. Similarly, higher stocking densities can increase yields, but improper management may lead to overcrowding, raising the risks of disease and stress, which can negatively affect GM [ 15 , 56 , 46 ]. Additionally, good farm management practices such as maintaining optimal stocking densities, controlling feed costs, and using efficient resource management techniques have been shown to enhance financial outcomes, as also noted by Okechi et al. [ 49 ]. Contracting a farm manager for operations can help improve management efficiency, although this adds additional costs, which may not always be feasible for small-scale farms [ 27 , 41 ]. Access to extension services and maintaining proper records are positively correlated with GM, as they enable better resource management, decision-making, and financial tracking, supported by Ekesa et al. [ 20 ]. For cage aquaculture, cage sizes and higher stocking densities are both positively correlated with GM, supporting the idea that more space leads to better yields. However, as with pond aquaculture, overcrowding without proper management can result in negative impacts on GM due to higher disease rates and stress. Best management practices in cage farming also play a crucial role in ensuring profitability, with positive correlations observed between effective management and improved financial outcomes [ 48 , 55 , and 60 ]. Contracted managers, however, had only a marginal positive effect, indicating that their benefits might not justify the costs for smaller operations, as seen in the study by Daungsawasdi et al. [ 19 ]. Market dynamics, such as higher fish prices and larger harvests, had a positive impact on GM, further emphasizing the importance of external market conditions. The negative impact of feed costs on GM highlights the necessity for efficient feed management to ensure that feed expenditures do not undermine fish growth and overall profitability [ 3 , 53 ]. This concern has been discussed by Clark et al. (2016) [ 14 ], who stressed the importance of feed management in maximizing profit margins. Aquaponic systems exhibit correlations between system size and stocking density with GM, suggesting that systems with optimal stocking densities benefit from economies of scale and higher productivity [ 4 ]. As in both systems, good management practices are essential for improving financial performance. Contracted managers, although beneficial in terms of efficiency, might not justify the cost for small-scale systems, consistent with the findings of Azazy et al. [ 8 ]. The lack of a correlation between production cycle length and profitability indicates that factors like fish health and quality are more crucial for profitability than cycle duration, as highlighted by Omondi et al. [ 50 ], [ 6 ] [ 32 ]. Similar to the other systems, access to extension services and record-keeping was positively correlated with GM, emphasizing the value of technical support and organized financial management [ 31 , 34 , 59 , and 57 ]. Additionally, higher fish prices and greater experience were positively correlated with better financial outcomes, underscoring the role of market conditions and expertise in enhancing profitability, as discussed by Opiyo et al. [ 51 ] and Veverica et al. [ 57 ]. The negative impact of feed costs and the challenges posed by predator issues also affect GM in aquaponic systems [ 9 , 10 , 26 , 58 ], highlighting the need for effective feed management and predator control strategies to safeguard profits. These findings align with those of Munguti et al. [ 42 ] and Ogello et al. [ 47 ]. 4.3 Limitations of the study Four main weaknesses are highlighted. First, the study utilized cross-sectional data to assess the association between various factors affecting aquaculture profitability. While this design allows for understanding correlations at a specific point in time, it does not establish causal relationships. Future study should employ longitudinal designs to explore the causal-effect relationships between key variables and the long-term impacts of different aquaculture practices. Second, a stratified random sampling technique was used to select participants from different aquaculture systems. While this method ensured a diverse sample, the relatively small sample sizes in certain subgroups particularly for aquaponics and cage systems limit the generalizability of the results. Furthermore, the sampling approach could lead to unequal representation of certain groups, such as vulnerable populations or specific fish farming communities. Future studies could consider larger, more representative sample sizes, or apply alternative sampling methods, such as paired actors, where a respondent identifies relevant social connections to better capture network dynamics and ensure broader representation. Third, while this study primarily focused on quantitative data, qualitative insights from focus group discussions were also collected to inform the development of the questionnaire. Although these qualitative insights provided valuable context, they were not extensively analyzed. Future studies could integrate qualitative data analysis more systematically. Fourth, the study mainly relied on data from fish farmers and did not incorporate the perspectives of other key stakeholders, such as policymakers, extension services, or industry experts. Including these additional stakeholders would provide a more holistic view of the aquaculture sector and enhance the understanding of the broader challenges and opportunities facing the industry. Future research should strive for a more inclusive approach, collecting data from a variety of stakeholders to capture a comprehensive range of insights. Conclusions The cost analysis of small-scale aquaculture systems across pond, cage, and aquaponic reveals financial challenges. Pond aquaculture, characterized by high variable costs for feed, labor, and fingerlings, results in negative gross margins and unprofitable financial outcomes, as total costs exceed revenues. This highlights the financial strain on fish farmers due to high input and labor costs, emphasizing the need for strategies such as improved feed efficiency and better pond management practices. Cage aquaculture systems show better financial performance, despite high setup and operational costs, with positive net farm income and return on investment. Economies of scale, efficient feed management, and enhanced management practices contribute to profitability, although it remains sensitive to fluctuations in feed and labor costs. Qualitative insights from farmers in Uganda point to financial pressures and the need for financial support to sustain these systems. Aquaponic systems face financial bottlenecks due to high infrastructure and operational costs, resulting in negative gross margins. However, improvements in system size, stocking density, and management practices could enhance financial outcomes. Correlation analyses indicate that system sizes and optimal stocking densities can improve profitability through economies of scale, while access to extension services and experienced management also correlate positively with profit performance. Overall, the findings suggest that interventions, including optimizing input costs, improving and market linkages, are necessary to ensure the long-term profitability of small-scale aquaculture systems in the Lake Victoria basin, Uganda Declarations Ethics Statement This study received ethical approval from the relevant authorities in Uganda, including the Chief Administrative Officers of the respective districts where the study was conducted. Additionally, the study methodology was approved by the Lilongwe University of Agriculture and Natural Resources, Malawi. All necessary permissions were obtained to ensure the study complied with local regulations and ethical standards. Informed consent was obtained from all participants, and their confidentiality was maintained throughout the study. Clinical trial number: not applicable. Consent for Publication: Authors have approved the content of this manuscript, and it has been submitted for publication in the Journal of Discover sustainability. Conflict of interest The authors declare no conflicts of interest. Funding: The study was supported by a grant from the Rufford Foundation under the small grants program (Project Ref: 38553-2). Author Contribution S.B. collected and analyzed the data and wrote the manuscript. M.L. reviewed the manuscript and provided supervisory guidance. S.R. also reviewed the manuscript and provided additional supervisory support. All authors reviewed the final version of the manuscript. Acknowledgements We extend our sincere appreciation and gratitude to the Rufford Foundation and DAAD for their generous support and funding throughout the duration of this study. Special thanks are given to all survey respondents for their valuable time and effort in fully engaging with this study. Your contributions have been essential to the success of this study. Data Availability The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request References Adeleke BA. (2020). Bio economic feasibility of aquaponics in South Africa: Leapfrogging for sustainable development of freshwater aquaculture. Doctoral dissertation. Adeoye OB, Oladosu AS, Olawoye J. Socio-economic determinants of aquaculture productivity among fish farmers in Ogun State, Nigeria. J Fish Aquat Sci. 2020;15(2):66–75. Ahmed S, Khan N, Hossain M. Impact of feed costs on profitability in aquaculture systems: A case study from Bangladesh. J Aquaculture Econ. 2017;25(1):48–59. https://doi.org/10.1080/0974280802.2020.1752951 . Akinyele BJ, Adedeji AO, Ogunji JO, Awodiji AO. The effect of stocking density on the growth and survival of fish in aquaculture systems. J Environ Sci Sustain. 2003;12(2):23–32. Asmah R, Abban EK, Awity L, Engle CR. Economic feasibility of cage aquaculture in Volta Lake. Ghana Aquaculture Econ Manage. 2014;18(3):220–34. https://doi.org/10.1080/13657305.2014.926467 . Aswathy N, Joseph N. Profitability analysis of seabass aquaculture in Kerala. Int J Fisheries Aquat Stud. 2018;6(4):87–93. Aura CM, Roegner A, Owiti H, Birungi D, Fiorella KJ, Corman J, Kayanda R, Mbullo P, Nyamweya CS, Mchau G, Daniels M, Abila RO. Mind the gaps for the best practices: Enhancing the management of Lake Victoria fisheries resources. Volume 27. Lakes & Reservoirs: Science, Policy and Management for Sustainable Use; 2022. 3e12411. Azazy A, Hussien AH, Nasr Allah AM. Estimated costs and returns for commercial cage production of fingerlings and table-size mullet (Mugil cephalus) in Dakhlia Governorate, Egypt. Egyptian Journal of Aquaculture; 2012. Baker A, Charles A. Preliminary development and evaluation of an aquaponic system for the American Insular Pacific. December: University of Hawaii at Manoa; 2010. Bell J, Hempel M, Vandeperre F. Cost-profit analysis of predator control in fish farms. Mar Biology Aquaculture Rev. 2017;39(2):102–18. Benjamin P, et al. Impact of experience on fish farming profitability: A case study of smallholder aquaculture in Kenya. Aquaculture Econ Manage. 2020;24(3):250–63. Beveridge MCM. Cage Aquaculture. 2nd ed. Oxford: Fishing News; 1996. Bosma RH, Lacombe S, Baudron F. The financial feasibility of producing fish and vegetables through aquaponics. Aquac Eng. 2017;78(B):146–54. https://doi.org/10.1016/j.aquaeng.2017.07.002 . Clark P, Williams J, Taylor S. Cost-effective feeding strategies in aquaculture. Agricultural Econ Rural Dev J. 2016;20(4):217–29. Costa-Pierce B. Maximizing fish production through optimal stocking densities in pond aquaculture. Aquaculture Study. 2016;47(1):112–21. Cruz EM, Ridha M. Production of tilapia, Oreochromis spilurus (Günther), stocked at different densities in sea cages. Volume 99. Aquaculture; 1991. pp. 95–105. Cruz EM, Ridha MT. Farming tilapia in cages: Production aspects. Asian Fisheries Sci. 1989;2(3):223–40. Das SK, Mandal A. (2022). Diversification in aquaculture resources and practices for smallholder farmer Springer, Cham. https://doi.org/10.1007/978-3-030-93258-9_14 Daungsawasdi R, Eknath A, Doyle RW. Labor requirements in aquaculture systems. Asian Fisheries Sci. 1986;3(1):51–60. Ekesa PA, Aura CM, Ogello EO. Small-scale aquaculture management practices in East Africa: Lessons and opportunities. J Sustainable Aquaculture Dev. 2023;15(2):123–35. Engle CR, Valderrama D. Effect of stocking density on production characteristics, costs, and risk of producing fingerling channel catfish. North Am J Aquaculture. 2001;63:201–7. FAO. (2020). State of world fisheries and aquaculture 2020: Sustainability in action. Rome. https://doi.org/10.4060/ca9229en FAO. State of World Fisheries and Aquaculture 2020. Food and Agriculture Organization of the United Nations; 2021. FAO. State of the World Fisheries and Aquaculture. Status of Fishery Resources. Food and Agriculture Organization of the United Nations; 2022. Gebre A, Tesfaye Y, Solomon D. Traditional aquaculture practices in resource-limited regions: A focus on pond fish farming. J Aquaculture Dev. 2019;45(3):256–69. Gentry R, Ball J, Fisher S. Financial burden of predator damage in aquaculture systems. J Aquaculture Fisheries Manage. 2014;45(3):89–97. Hempel M, Stone T, Schwartz M. The role of contracted managers in enhancing operational efficiency in large-scale fish farms. Aquaculture Bus Manage. 2015;22(4):315–28. Hernandez M, Thompson G, Goldstein J. The balance between feed cost reduction and maintaining fish health in aquaculture. Aquat Nutr J. 2019;30(2):201–13. Hilbe J. A review of LIMDEP 9.0 and NLOGIT 4.0. Am Stat. 2006;60(2):187–202. https://doi.org/10.1198/000313006x110492 . Kamstra JD, et al. Economic advantages of cage aquaculture: Leveraging economies of scale and natural resources. Aquaculture Econ Manage. 2018;22(3):213–27. Killan W, et al. Technological advancements in pond management and water quality control. Aquaculture Technol J. 1998;12(3):145–58. Kim JY, Chang KY. Production cycle length and its impact on fish growth and profitability in aquaculture. J Fisheries Mar Sci. 2014;8(1):43–50. Kwikiriza G. Prospects of cage aquaculture in south-western Uganda. Agric Forestry Fisheries. 2018;7(2):52. Lee KT, Park HJ, Yoon YS. Role of extension services in improving aquaculture profitability in rural areas. J Agricultural Ext Dev. 2017;25(3):120–30. Love DC, Uhl MS, Genello L. Commercial aquaponics production and profitability: Findings from an international survey. Aquaculture. 2015;435:67–74. https://doi.org/10.1016/j.aquaculture.2014.09.023 . LVFO. (2020). Lake Victoria Fisheries Organization Annual Report. LVFO Secretariat. MAAIF. Guidelines for Aquaculture Development in Uganda. Ministry of Agriculture, Animal Industry and Fisheries; 2021. Mbowa S, Musoke D, Mugisha J. The impact of family structure on small-scale farming outcomes: Evidence from rural Uganda. Agric Syst. 2017;157:148–58. Miller RJ, Thompson GJ, Pape M. Best management practices for sustainable aquaculture. Sustainable Farming Rev. 2018;12(3):75–85. Mugisha J, Turyahabwe N. Water resource management practices in Uganda: Evidence from the Lake Victoria region. J Water Resour Prot. 2017;9(12):1521–35. Muir J, Muir S. Cost-effectiveness of contracted managers in small-scale aquaculture operations. Aquaculture Fisheries Manage J. 2013;19(1):72–85. Munguti JM, Kim JD, Ogello EO. Feed costs and economic sustainability in African aquaculture systems: Insights and trends. Afr J Aquaculture Econ. 2021;12(5):78–95. Mutiso RM, Odhiambo JA, Mutua JK. The role of small-scale aquaculture in the Lake Victoria basin. Environ Sci Policy. 2019;45:204–13. NaFiRRi. Sustainable fishery on Lake Victoria: Exploitation, gears, fishing methods and management. National Fisheries Resources study Institute; 2020. Narayanakumar R. Economic analysis of cage culture of sea bass. Course manual: National training on cage culture of seabass. Kochi: CMFRI & NFDB; 2009. Nelson RE, Smith GH, Taylor L. Managing risk in aquaculture through experience and operational improvements. J Risk Manage Policy. 2019;22(4):50–60. Ogello EO, Aura CM, Ouma SO. Profitability and challenges of fish farming in the Lake Victoria Basin: Insights for sustainable aquaculture. Afr J Fisheries Aquaculture Study. 2020;14(1):1–12. Okechi JK. (2004). Profitability assessment: A case study of African catfish ( Clarias gariepinus ) farming in the Lake Victoria Basin, Kenya. United Nations University-Fisheries Training Program. Available at: http://www.unuftp.is/static/fellows/document/okechiprf04.pdf Okechi M, Nyamwange M, Wambua L. Effects of production cycle length on fish size and profitability in small-scale cage fish farming. Aquaculture Study. 2012;43(5):951–60. Omondi JG, Gichuri WM, Veverica K. A partial economic analysis for Nile tilapia (Oreochromis niloticus) and sharp toothed catfish (Clarias gariepinus) polyculture in central Kenya. Aquaculture Study. 2001;32(1):693–700. Opiyo MA, Munguti JM, Ogello EO, Charo-Karisa H. Growth response, survival and profitability of Nile Tilapia (Oreochromis niloticus) fed at different feeding frequencies in fertilized earthen ponds. Int J Sci Study. 2014;3(9):893–8. Rakocy JE, Hargreaves JA. (1993). Nutrient accumulation in a recirculating aquaculture system integrated with hydroponic vegetable production. Roberts RM, Liu H, Viveros J. Impact of premium fish prices and larger harvests on profitability in aquaculture. Economic Perspect Agric. 2005;10(2):82–91. Smith AB, Davis JW, Thompson L. The relationship between cage size and yield in fish farming. Int J Fisheries Manage. 2016;19(3):118–24. Tukker A, Wender S, Collins M. Management challenges in large-scale pond aquaculture systems. Environ Sustain Aquaculture. 2020;28(2):140–52. Vandeperre F, Taylor R, Browne J. Balancing stocking density and resource availability in fish farms. Sustainable Aquaculture J. 2019;11(1):33–44. Veverica KL, Ngugi C, Amadiva J, Bowman JR, Report. PD/A CRSP Office of International Study and Development, Oregon State University, OR, USA, pp. 121-1. Wang H, Zhang J, Chen Y. Predator management in aquaculture: Effective strategies for increasing profitability. Pest Control Farm Manage. 2017;24(2):200–10. Ward J, Hageman M. The role of farmer groups in improving financial performance in aquaculture. Aquaculture Netw Rev. 2016;16(2):66–80. Woods PS, Masser MP. Cage culture basics. Southern Regional Aquaculture Center Publication No. 160; 2004. WorldFish. (2019). improving livelihoods through aquaculture in East. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 08 Apr, 2025 Reviews received at journal 03 Apr, 2025 Reviews received at journal 02 Apr, 2025 Reviewers agreed at journal 27 Mar, 2025 Reviewers agreed at journal 27 Mar, 2025 Reviewers invited by journal 27 Mar, 2025 Submission checks completed at journal 25 Mar, 2025 First submitted to journal 18 Mar, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5541053","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":435073814,"identity":"fe5d3093-c893-4759-a953-9081fee5831b","order_by":0,"name":"Syliver Byabasaija","email":"data:image/png;base64,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","orcid":"","institution":"Lilongwe University of Agriculture and Natural Resources","correspondingAuthor":true,"prefix":"","firstName":"Syliver","middleName":"","lastName":"Byabasaija","suffix":""},{"id":435073817,"identity":"6fdf6acf-a9be-4c9e-90e0-25f5f1177680","order_by":1,"name":"Moses Limuwa","email":"","orcid":"","institution":"Lilongwe University of Agriculture and Natural Resources","correspondingAuthor":false,"prefix":"","firstName":"Moses","middleName":"","lastName":"Limuwa","suffix":""},{"id":435073818,"identity":"fa0e4fa1-fb7d-489b-9909-653fc36d7665","order_by":2,"name":"Ronald Semyalo","email":"","orcid":"","institution":"Makerere University","correspondingAuthor":false,"prefix":"","firstName":"Ronald","middleName":"","lastName":"Semyalo","suffix":""}],"badges":[],"createdAt":"2024-11-28 09:08:20","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5541053/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5541053/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":79412866,"identity":"bccea0d3-87e8-4e88-b041-386360ec2c81","added_by":"auto","created_at":"2025-03-28 06:38:14","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":332712,"visible":true,"origin":"","legend":"\u003cp\u003eStudy area\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-5541053/v1/380abac182cbe6a16048ee56.jpeg"},{"id":79413660,"identity":"11b581b1-54d3-4350-98ae-bb2a49f48ee0","added_by":"auto","created_at":"2025-03-28 06:46:15","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1551222,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5541053/v1/e18c8dcf-b29f-4528-ba37-37dec7d34ac2.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Optimizing small-scale aquaculture systems in the Lake Victoria Basin,Uganda: Insights into profitability drivers","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eGlobally, the aquaculture sector has experienced significant growth due to increasing demand for fish, as a primary source of protein and the need to reduce pressure on wild fish stocks [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Aquaculture contributes to local food security and economic activities [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. However, the aquaculture sector faces persistent challenges, including the depletion of key inputs such as seed and feed due to escalating demand, as well as environmental concerns linked to intensive production practices [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. These challenges necessitate sustainable solutions, such as improved hatchery technologies, efficient feed formulations, and ecosystem-based management approaches, to ensure long-term viability and productivity [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Aquaculture has emerged as a viable alternative of catch fisheries, especially in regions where small-scale and artisanal fisheries dominate fish production but face significant resource and operational limitations [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, and \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn Uganda, aquaculture was introduced in the 1950s, but remains primarily subsistence-oriented due to socio-economic constraints [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Traditional practices involving small ponds with minimal inputs and reliance on family labor are common [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. However, rising fish prices and the need for sustainable production methods, have led the Ugandan government, international organizations, and study institutions to promote small-scale aquaculture systems such as ponds, cages, and aquaponics. These efforts have resulted in increased investments in small-scale aquaculture technologies [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, and \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Despite these developments, challenges persist, particularly regarding the economic and operational sustainability of small-scale aquaculture systems in Lake Victoria basin. Studies provided insights into the growth and challenges of aquaculture globally and regionally, relating to fish health and conflicts for resources [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. However, systematic study on the financial performance and profitability drivers of small-scale aquaculture systems in Uganda, particularly in the Lake Victoria Basin is lacking [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Furthermore, there is a scarcity of knowledge on the specific costs and economic profits associated with systems such as ponds, cages, and aquaponics, as well as the constraints faced by local fish farmers [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Our study explores small-scale aquaculture systems in Ugandan communities, along Lake Victoria. Specifically, we focus on 1) analyzing the costs and profitability drivers associated with various small- scale aquaculture systems (pond, cage, and aquaponics), and 2) identifying the challenges encountered by local fish farmers in adopting and maintaining these aquaculture systems. The study informs sustainable aquaculture development strategies in Uganda and similar socio-economic contexts. It contributes to Uganda's National Development Plan III and Vision 2040, which prioritize sustainable agricultural practices and innovation-driven economic growth [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Additionally, the study supports policy development to facilitate the scaling up of small-scale aquaculture, ensuring it becomes a reliable and sustainable source of food and income for local communities.\u003c/p\u003e"},{"header":"2. Material and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 The study area\u003c/h2\u003e \u003cp\u003eThe study was carried out in selected districts surrounding Uganda's Lake Victoria Basin, specifically Buikwe (0.761029 N, 33.040723 E), Wakiso (0.054163 N, 32.518079 E) and Mpigi (0.305544 S, 32.037822 E). These districts (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) were purposively selected, because of their established small-scale aquaculture systems and the prominence of fish farming activities in the area [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. The Lake Victoria Basin is an important ecological and economic zone, supporting not only small-scale aquaculture and capture fisheries but also a wide range of other economic activities such as agriculture, water transportation, and ecotourism. These selected districts, which are at a moderate altitude and receive significant rainfall throughout the year, are ideal for agricultural practices [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. For example, Mpigi and Wakiso are well-known for coffee cultivation [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e], whereas Buikwe is known for sugarcane cultivation. Farmers in these districts also grow a variety of crops, including coffee, bananas, cassava, potatoes, maize, beans, and pineapples, as well as horticultural produce like tomatoes and vegetables. Livestock farming is also common in the area, with dairy and beef cattle, goats, and poultry being the most popular [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Data Collection\u003c/h2\u003e \u003cp\u003eData collection was conducted in November 2023 targeting 169 fish farmers across three purposively selected districts (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). To ensure a comprehensive and representative sample, we employed a stratified random sampling technique. We divided the population of fish farmers into distinct subgroups based on the types of aquaculture systems they use (pond, cage, and aquaponics). The rationale for using stratified sampling, was to ensure that each production system is adequately represented within the sample, allowing for more analysis of the different aquaculture farming systems, and their associated challenges. The final sample population was 49 farmers from Mpigi, 55 from Wakiso, and 65 from Buikwe. The allocation is proportional to the number of fish farmers in each district. Within each district, fish farmers were categorized based on their primary aquaculture practice, ensuring that the stratification accurately reflected the diverse production systems in use. Before the main survey, we pre-tested the survey tool on eight fish farms, two in Wakiso, three in Buikwe, and three in Mpigi, to assess its reliability and clarity. Data collection focused on gathering information on farmers' aquaculture experience, systems type, fish species farmed, aquaculture products, income, demographics, production cycles, credit access, group affiliations, record-keeping practices, access to extension services, etc.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Data Analysis\u003c/h2\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003e2.3.1 Cost and profit analysis\u003c/h2\u003e \u003cp\u003eWe coded and analyzed data using LIMDEP 9.0 econometric software [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. To determine the profitability and cost structures. The financial performance of small-scale aquaculture systems was analyzed using enterprise budget data derived from survey responses, covering the three aquaculture systems: pond culture, cage culture, and aquaponics. This analysis provided detailed estimates of inputs and outflows, enabling the calculation of key financial metrics to evaluate the profits systems considering a fixed investment and depreciated over an estimated lifespan of five years, ensuring a realistic assessment of their impact on profitability. The key financial metrics calculated include:\u003c/p\u003e \u003cp\u003eGross Margin (GM)\u0026thinsp;=\u0026thinsp;Total Revenue (TR)\u0026thinsp;\u0026minus;\u0026thinsp;Total Variable Cost (TVC) \u0026hellip;.. (1)\u003c/p\u003e \u003cp\u003eNet Farm Income (NFI))\u003c/p\u003e \u003cp\u003eNet Farm Income\u0026thinsp;=\u0026thinsp;Gross Margin\u0026thinsp;\u0026minus;\u0026thinsp;Total Fixed Cost \u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.. (ii)\u003c/p\u003e \u003cp\u003eNet Return on Investment\u003c/p\u003e \u003cp\u003eNet Return on Investment\u0026thinsp;=\u0026thinsp;Total Cost over Net Farm Income \u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip; (iii)\u003c/p\u003e \u003cp\u003eWe employed a multiple linear regression model to examine the factors influencing gross margin (GM), our measure of profitability (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). This method is widely used in economic studies to assess the relationship between profitability and explanatory variables in aquaculture. For example, Bosma et al. (2017) [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] utilized linear regression to analyze economic performance and key drivers of profitability in aquaculture systems. Our model included key variables relevant to fish farming practices and their potential impact on GM. To evaluate the significance of each predictor, we calculated the t-value by dividing its estimated coefficient by its standard error, with the corresponding p-value indicating statistical significance. Additionally, to better understand the strength and direction of associations between individual variables and GM, we computed Pearson\u0026rsquo;s correlation coefficient (r) for all explanatory variables.\u003c/p\u003e \u003cp\u003eOur estimated linear equation took the following form:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:\\text{G}\\text{M}=\\:{{\\beta\\:}}_{0}{{\\beta\\:}}_{1}{\\text{x}}_{1}{{\\beta\\:}}_{2}{\\text{x}}_{2}\\dots\\:{{\\beta\\:}}_{\\text{n}}{\\text{x}}_{\\text{n}}\\in\\:\\:$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere, GM represents the gross margin, our measure of profit profits,\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:{{\\beta\\:}}_{0}\\)\u003c/span\u003e \u003c/span\u003e Is the intercept\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:{{\\beta\\:}}_{1},\\:{{\\beta\\:}}_{2},\\dots\\:.,{{\\beta\\:}}_{\\text{n}}\\)\u003c/span\u003e \u003c/span\u003e The coefficients for each explanatory variable\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{x}}_{1},\\:{\\text{x}}_{2},\\dots\\:,\\:{\\text{x}}_{\\text{n}}\\)\u003c/span\u003e \u003c/span\u003e Denote the determinants hypothesized to affect profits, and\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:\\in\\:\\)\u003c/span\u003e \u003c/span\u003e Is the error term, capturing unexplained variability.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eFactors influencing profitability in small-scale aquaculture included in the model\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEffect on Gross Margin\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGross margin (US\u003cspan\u003e$\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eDependent Variable\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eSystem size (m\u0026sup2;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eFingerlings stocked (number)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eContracted manager (1\u0026thinsp;=\u0026thinsp;yes, 0\u0026thinsp;=\u0026thinsp;no)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026plusmn;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eProduction cycle (months)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026plusmn;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eFish farmer group membership (1\u0026thinsp;=\u0026thinsp;yes, 0\u0026thinsp;=\u0026thinsp;no)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eExtension services access (1\u0026thinsp;=\u0026thinsp;yes, 0\u0026thinsp;=\u0026thinsp;no)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eKeeping records (1\u0026thinsp;=\u0026thinsp;yes, 0\u0026thinsp;=\u0026thinsp;no)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eExperience in fish farming (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eFish price per unit (US\u003cspan\u003e$\u003c/span\u003e/kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eFish harvested (kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eFeed cost (US\u003cspan\u003e$\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003ePredators (1 major constraint, 0 otherwise)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eParticipation in training programs (1\u0026thinsp;=\u0026thinsp;yes, 0\u0026thinsp;=\u0026thinsp;no)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eSpecies farmed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026plusmn;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eAccess to credit (1\u0026thinsp;=\u0026thinsp;yes, 0\u0026thinsp;=\u0026thinsp;no)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Cost and profit performance of small-scale pond aquaculture system\u003c/h2\u003e \u003cp\u003eThevariable costs comprised the majority of the total expenses, accounting for 93% of the total costs (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Among these, the largest contributors were feed costs (35%), labor during production (25%), and fingerlings (25%), which together accounted for 85% of the total variable costs. Other variable costs included labor at harvesting (7%), net purchases (7%), and transportation costs (0.3%).Fixed costs, comprising only 7% of the total costs, were significantly smaller compared to variable costs. These fixed costs included pond construction (5.8%) and pond rent (1.4%). The total costs, including both variable and fixed costs, amounted to US\u003cspan\u003e$\u003c/span\u003e1,036 (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). However, the total revenue generated from pond aquaculture system was only US\u003cspan\u003e$\u003c/span\u003e530, resulting in a gross margin of -US\u003cspan\u003e$\u003c/span\u003e431. This indicates that variable costs exceeded revenue. The net farm income was -US\u003cspan\u003e$\u003c/span\u003e506, and the net return on investment of -49%.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCost distribution and profit performance of small-scale pond aquaculture system\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCost category\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAverage Cost (US\u003cspan\u003e$\u003c/span\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTotal Cost (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eVariable Costs\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFingerlings\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e308\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCost of feeds\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e256\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLabor at production\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e308\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLabor at harvesting\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNet purchase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTransportation costs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal Variable Costs (TVC)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e961\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e93\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFixed Costs\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePond construction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePond rent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal Fixed Costs (TFC)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal Costs (TVC\u0026thinsp;+\u0026thinsp;TFC)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,036\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal Revenue (TR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e530\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGross Margin (TR - TVC)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-431\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNet Farm Income (NFI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-506\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNet Return on Investment (NRI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-49%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Cost and profit performance of small-scale cage aquaculture system\u003c/h2\u003e \u003cp\u003eThe Total variable Costs accounted for 59% of the total expenses in the small-scale cage aquaculture system (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The largest contributors to variable costs were feed (22%), fingerlings (15%), and labor during production (10%), which together represented 47% of the total variable costs. Otherminor variable costs included labor at harvesting (5%), transportation (4%), and maintenance (3%). Fixed costs made up 30% of the total expenses, with cage setup being the only fixed cost. The total expenses, combining both variable and fixed costs, amounted to US\u003cspan\u003e$\u003c/span\u003e2,225 (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The total revenue generated was US\u003cspan\u003e$\u003c/span\u003e2,500, resulting in a gross margin of US\u003cspan\u003e$\u003c/span\u003e1,025. The net farm income was US\u003cspan\u003e$\u003c/span\u003e275, and the net return on investment was 12%.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCost and profit performance of small-scale cage aquaculture system\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCost category\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAverage Cost (US\u003cspan\u003e$\u003c/span\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTotal Cost (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eVariable Costs\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFingerlings (fish seed)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e375\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFish feed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e550\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLabor at production\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e250\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLabor at harvesting\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTransportation costs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMaintenance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal Variable Costs (TVC)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,475\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e59\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFixed Costs\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCage setup\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e750\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal Fixed Costs (TFC)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e750\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal Costs (TVC\u0026thinsp;+\u0026thinsp;TFC)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,225\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal Revenue (TR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGross Margin (TR - TVC)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNet Farm Income (NFI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e275\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNet Return on Investment (NRI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Cost and profit performance of small-scale aquaponics system\u003c/h2\u003e \u003cp\u003eThe analysis of the small-scale aquaponic system identified several challenges, predominantly arising from high infrastructure and operational costs, which outweighed revenue generation. Initial setup costs constituted 30% of total expenses, placing a financial strain on fish farmers due to substantial upfront investments in essential infrastructure, including tanks, pipes, and filters (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Variable costs, accounting for 54% of the overall expenses, were primarily driven by factors such as nutrients, growth media, and labor. Notably, feed and nutrients represented over 60% of the total variable costs. The system\u0026rsquo;s financial performance was further underscored by negative gross margins and a net farm income of -\u003cspan\u003e$\u003c/span\u003e1,133 and -\u003cspan\u003e$\u003c/span\u003e2,745, respectively. The poor profitability of the system was further emphasized by a Net Return on Investment of -77%, indicating a negative return on the capital invested.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCost and profit performance of small-scale aquaponics system\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCategory\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAverage Cost (US\u003cspan\u003e$\u003c/span\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTotal Cost (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eVariable Costs\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFingerlings costs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e212\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFish feed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e158\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLabor at production\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e151\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLabor at harvesting\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e106\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVegetable seeds\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e107\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNutrients \u0026amp; growth medium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e214\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal Variable Costs (TVC)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,933\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e54\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFixed Costs\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSystem setup cost\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,074\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePumps and aerators\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e214\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWater \u0026amp; utilities cost\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eElectricity cost\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e104\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMaintenance cost\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e161\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal Fixed Costs (TFC)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,611\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e45.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal Revenue (TR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e800\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGross Margin (TR - TVC)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1,133\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNet Farm Income (NFI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-2,745\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNet Return on Investment (NRI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e--77%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Variables affecting the gross margin in small-scale fish farming with pond system\u003c/h2\u003e \u003cp\u003eThe analysis of variables affecting gross margin in small-scale aquaculture with pond systems revealed significant relationships (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Pond size (r\u0026thinsp;=\u0026thinsp;0.4, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), stocking density (r\u0026thinsp;=\u0026thinsp;0.3, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), experience in fish farming (r\u0026thinsp;=\u0026thinsp;0.4, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), fish price per unit (r\u0026thinsp;=\u0026thinsp;0.5, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and fish harvested (r\u0026thinsp;=\u0026thinsp;0.4, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) all showed significant positive correlations with gross margin. Additionally, having a contracted manager (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), access to extension services (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), and keeping records (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) were also significant factors influencing gross margin. The production cycle (p\u0026thinsp;=\u0026thinsp;0.68) and fish farmer group membership (p\u0026thinsp;=\u0026thinsp;0.07) did not show significant effects.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eVariables affecting Gross Margin in small-scale aquaculture with pond system\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eVariable vs Gross Margin\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGross Margin (US\u003cspan\u003e$\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-430\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePond size (m\u0026sup2;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e750\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003er\u0026thinsp;=\u0026thinsp;0.4, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStocking density (fish/m\u0026sup2;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003er\u0026thinsp;=\u0026thinsp;0.3, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eContracted manager\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProduction cycle (months)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.68\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFish farmer group membership\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExtension services access\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKeeping records\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExperience in fish farming (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003er\u0026thinsp;=\u0026thinsp;0.4, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFish price per unit (US\u003cspan\u003e$\u003c/span\u003e/kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003er\u0026thinsp;=\u0026thinsp;0.5, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFish harvested (kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e800\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003er\u0026thinsp;=\u0026thinsp;0.4, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Variables affecting the gross margin in small-scale fish cage system\u003c/h2\u003e \u003cp\u003eVariables affecting gross margin in small-scale aquaculture with cage systems (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e), identified several key factors influencing financial performance. Cage size (r\u0026thinsp;=\u0026thinsp;0.2, p\u0026thinsp;\u0026lt;\u0026thinsp;0.03) and stocking density (r\u0026thinsp;=\u0026thinsp;0.20, p\u0026thinsp;\u0026lt;\u0026thinsp;0.04) both exhibited positive correlations with gross margin, suggesting that larger cages and higher stocking densities may improve profitability. Additionally, experience in fish farming (r\u0026thinsp;=\u0026thinsp;0.3, p\u0026thinsp;\u0026lt;\u0026thinsp;0.02), fish price per unit (r\u0026thinsp;=\u0026thinsp;0.3, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), and the quantity of fish harvested (r\u0026thinsp;=\u0026thinsp;0.3, p\u0026thinsp;\u0026lt;\u0026thinsp;0.02) were positively associated with gross margin, emphasizing their importance in financial outcomes. Management practices, such as having a management system in place (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), access to extension services (p\u0026thinsp;\u0026lt;\u0026thinsp;0.04), and keeping records (p\u0026thinsp;\u0026lt;\u0026thinsp;0.03), were also found to positively impact gross margin, contributing to better financial results. While contracted management (p\u0026thinsp;=\u0026thinsp;0.06) did not reach statistical significance, it suggested a potential trend toward increased profitability. Production cycle duration (r = -0.05, p\u0026thinsp;=\u0026thinsp;0.60) and group membership (p\u0026thinsp;=\u0026thinsp;0.09) showed no significant effect on gross margin, indicating that these variables have limited influence on profitability. Feed costs (r = -0.2, p\u0026thinsp;\u0026lt;\u0026thinsp;0.04) had a negative relationship with gross margin, suggesting that higher feed costs may reduce profitability. Finally, the presence of predators (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) significantly affected gross margin, highlighting the negative impact of predator-related losses on the financial viability of the system.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eVariables affecting Gross Margin in small-scale aquaculture with cage system\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eVariable vs Gross Margin\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGross Margin (US\u003cspan\u003e$\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCage size (m\u0026sup2;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003er\u0026thinsp;=\u0026thinsp;0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStocking density (fish/m\u0026sup2;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003er\u0026thinsp;=\u0026thinsp;0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eManagement\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eContracted manager\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProduction cycle (months)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003er = -0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.60\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup membership\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExtension services access\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKeeping records\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExperience (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003er\u0026thinsp;=\u0026thinsp;0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFish Price per Unit (US\u003cspan\u003e$\u003c/span\u003e/kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003er\u0026thinsp;=\u0026thinsp;0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFish harvested (kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e450\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003er\u0026thinsp;=\u0026thinsp;0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFeed cost (US\u003cspan\u003e$\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003er = -0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePredators\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.6 Variables affecting the gross margin in small-scale aquaponic system\u003c/h2\u003e \u003cp\u003eThe analysis of variables affecting gross margin in small-scale aquaponic systems (Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e), revealed several factors with significant influence on financial performance. System size (r\u0026thinsp;=\u0026thinsp;0.2, p\u0026thinsp;\u0026lt;\u0026thinsp;0.03) and stocking density (r\u0026thinsp;=\u0026thinsp;0.2, p\u0026thinsp;\u0026lt;\u0026thinsp;0.04) exhibited positive correlations with gross margin, suggesting that larger system sizes and higher stocking densities may improve profitability. Moreover, management practices (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), access to extension services (p\u0026thinsp;\u0026lt;\u0026thinsp;0.04), and keeping records (p\u0026thinsp;\u0026lt;\u0026thinsp;0.03) all showed significant positive associations with gross margin, indicating the beneficial impact of these practices on financial outcomes. Experience in fish farming (r\u0026thinsp;=\u0026thinsp;0.3, p\u0026thinsp;\u0026lt;\u0026thinsp;0.02), fish price per unit (r\u0026thinsp;=\u0026thinsp;0.3, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), and the amount of fish harvested (r\u0026thinsp;=\u0026thinsp;0.3, p\u0026thinsp;\u0026lt;\u0026thinsp;0.02) also positively correlated with gross margin, highlighting their importance in achieving higher profitability. The length of the production cycle (r = -0.0, p\u0026thinsp;=\u0026thinsp;0.60) was not significantly associated with gross margin, suggesting that the production cycle duration does not substantially affect profitability. While group membership (p\u0026thinsp;=\u0026thinsp;0.09) and contracted management (p\u0026thinsp;=\u0026thinsp;0.06) were not statistically significant, they suggest potential trends toward improving profitability. Feed costs (r = -0.2, p\u0026thinsp;\u0026lt;\u0026thinsp;0.04) were negatively correlated with gross margin, indicating that higher feed costs reduce profitability. Lastly, the presence of predators (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) had a significant impact on gross margin, underlining the negative financial effect of predator-related losses.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eVariables affecting Gross Margin in small-scale aquaponic system\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eVariable vs Gross Margin\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGross margin (US\u003cspan\u003e$\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1,133\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSystem size (m\u0026sup2;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003er\u0026thinsp;=\u0026thinsp;0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStocking density (fish/m\u0026sup2;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003er\u0026thinsp;=\u0026thinsp;0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eManagement practices\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eContracted manager\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProduction cycle (months)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003er = -0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.60\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup membership\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExtension services access\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKeeping records\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExperience (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003er\u0026thinsp;=\u0026thinsp;0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFish price per unit (US\u003cspan\u003e$\u003c/span\u003e/kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003er\u0026thinsp;=\u0026thinsp;0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFish harvested (kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003er\u0026thinsp;=\u0026thinsp;0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFeed cost (US\u003cspan\u003e$\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003er = -0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePredators\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.7 Farmer perspectives on financial strain and support needs in aquaculture systems\u003c/h2\u003e \u003cp\u003eIn pond aquaculture, farmers emphasized the financial strain caused by high input costs, particularly for feed, labor, and fingerlings. One farmer remarked, \"Feed is the biggest cost. We spend so much on it, and sometimes it feels like it eats up all our profits,\" while another highlighted the burden of labor costs, stating, \"Labor costs are high, and we don't have machines to help us. It\u0026rsquo;s a lot of manual work, and that adds to our costs.\" In cage farming, similar financial pressures were observed, with farmers in Wakiso and Mpigi pointing to the high costs of cage setup materials such as frames and nets. One farmer from Wakiso noted, \"The cost of materials like frames, nets, and anchors is too high; we often need loans to cover these expenses, which adds financial risk.\" Additionally, rising feed costs were a common concern, with a farmer in Mpigi commenting, \"The expense of feed keeps increasing, and it\u0026rsquo;s directly affecting our profits. We can\u0026rsquo;t keep up with these prices.\" Farmers across both pond and cage systems also highlighted the need for external support to sustain their operations, with one farmer in Buikwe suggesting, \"We need subsidies for cage setup and maintenance to reinvest and sustain cage fish farming in the long run,\" and another in Wakiso noting, \"The extension services have been very helpful. They give us advice on feeding and water quality, which has helped us improve our yields.\" Similarly, in aquaponic systems, farmers in Wakiso and Mpigi expressed concerns about the significant infrastructure costs, especially for setting up tanks and filtration systems, with one farmer stating, \"The system setup, especially tanks and filtration, takes up a large portion of our money, but it\u0026rsquo;s essential for the system to function.\" These insights reflect the financial challenges faced by small-scale farmers across different aquaculture systems, underscoring the need for financial support and technical assistance.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Comparison of the cost of the three aquaculture systems\u003c/h2\u003e \u003cp\u003eIn small-scale pond aquaculture systems, the main cost drivers are feed, labor, and fingerlings. Feed is one of the major observed expenses, aligning with the findings of Opiyo et al. (2015) [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e], who highlighted feed as a primary cost in tilapia farming. Labor costs also contribute to about 30% of total expenses, exacerbating the financial vulnerability of small-scale systems, as noted by Omondi et al. [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. Furthermore, the financial strain is worsened by the fact that a portion of farm output is consumed by the household, thus reducing potential revenue from market sales. Moreover, negative net farm income and low return on investment suggest that the financial sustainability of small-scale pond aquaculture is severely limited due to these high operational costs. This is in agreement with the findings of Okechi et al. [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e] and Love et al. (2015) [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e], who also reported high production costs as major challenges for small-scale aquaculture.\u003c/p\u003e \u003cp\u003eFor small-scale cage aquaculture, the setup costs, which constitute 30% of fixed costs, are a major financial consideration. The initial investment in durable materials like frames, nets, and anchors is essential for long-term system sustainability, as emphasized by Cruz \u0026amp; Ridha [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] and Beveridge [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Feed costs, which account for 22% of total expenses, also remain a concern. While the financial performance of cage aquaculture is positive with a net farm income of US\u003cspan\u003e$\u003c/span\u003e275 and a return on investment of 12%, it remains vulnerable to fluctuations in variable costs, particularly feed and labor. High labor costs, at 15% of total expenses, reflect the labor-intensive nature of cage farming. Although these systems can be profitable, managing these costs effectively is key to maintaining profitability. This is also is supported by Narayanakumar [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e], who found that labor and feed costs play a crucial role in determining profitability in aquaculture systems.\u003c/p\u003e \u003cp\u003eAquaponic systems face financial barriers, primarily due to high infrastructure and operational costs. Setup costs represent 30% of total expenses, while variable costs, especially feed and nutrients, make up over 60%. The reliance on imported materials for nutrients further exacerbates these financial pressures, which is consistent with Bosma et al. (2017) [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e] [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e], Labor costs, which are influenced by local conditions, also contribute to high operational expenses, particularly for tasks like harvesting, as pointed out by Asmah et al. [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Despite the challenges posed by high input costs, improving market access, sourcing locally produced inputs, and exploring alternative feeds could alleviate some of the financial strain and improve profitability in aquaponic systems. This is supported by the findings of Benjamin et al. (2020) [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], who highlighted the importance of improving input sourcing and market access.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Integrated analyses of the factors affecting Gross Margin\u003c/h2\u003e \u003cp\u003eIn pond aquaculture, key factors such as pond size, stocking density, and management practices impact GM [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Larger ponds enable economies of scale, increasing profits, but only when effective management is implemented to avoid inefficiencies. Similarly, higher stocking densities can increase yields, but improper management may lead to overcrowding, raising the risks of disease and stress, which can negatively affect GM [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. Additionally, good farm management practices such as maintaining optimal stocking densities, controlling feed costs, and using efficient resource management techniques have been shown to enhance financial outcomes, as also noted by Okechi et al. [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. Contracting a farm manager for operations can help improve management efficiency, although this adds additional costs, which may not always be feasible for small-scale farms [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Access to extension services and maintaining proper records are positively correlated with GM, as they enable better resource management, decision-making, and financial tracking, supported by Ekesa et al. [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFor cage aquaculture, cage sizes and higher stocking densities are both positively correlated with GM, supporting the idea that more space leads to better yields. However, as with pond aquaculture, overcrowding without proper management can result in negative impacts on GM due to higher disease rates and stress. Best management practices in cage farming also play a crucial role in ensuring profitability, with positive correlations observed between effective management and improved financial outcomes [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e, and \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e]. Contracted managers, however, had only a marginal positive effect, indicating that their benefits might not justify the costs for smaller operations, as seen in the study by Daungsawasdi et al. [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Market dynamics, such as higher fish prices and larger harvests, had a positive impact on GM, further emphasizing the importance of external market conditions. The negative impact of feed costs on GM highlights the necessity for efficient feed management to ensure that feed expenditures do not undermine fish growth and overall profitability [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. This concern has been discussed by Clark et al. (2016) [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], who stressed the importance of feed management in maximizing profit margins.\u003c/p\u003e \u003cp\u003eAquaponic systems exhibit correlations between system size and stocking density with GM, suggesting that systems with optimal stocking densities benefit from economies of scale and higher productivity [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. As in both systems, good management practices are essential for improving financial performance. Contracted managers, although beneficial in terms of efficiency, might not justify the cost for small-scale systems, consistent with the findings of Azazy et al. [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. The lack of a correlation between production cycle length and profitability indicates that factors like fish health and quality are more crucial for profitability than cycle duration, as highlighted by Omondi et al. [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e], [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e] [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Similar to the other systems, access to extension services and record-keeping was positively correlated with GM, emphasizing the value of technical support and organized financial management [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e, and \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]. Additionally, higher fish prices and greater experience were positively correlated with better financial outcomes, underscoring the role of market conditions and expertise in enhancing profitability, as discussed by Opiyo et al. [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e] and Veverica et al. [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]. The negative impact of feed costs and the challenges posed by predator issues also affect GM in aquaponic systems [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e], highlighting the need for effective feed management and predator control strategies to safeguard profits. These findings align with those of Munguti et al. [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e] and Ogello et al. [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Limitations of the study\u003c/h2\u003e \u003cp\u003eFour main weaknesses are highlighted. First, the study utilized cross-sectional data to assess the association between various factors affecting aquaculture profitability. While this design allows for understanding correlations at a specific point in time, it does not establish causal relationships. Future study should employ longitudinal designs to explore the causal-effect relationships between key variables and the long-term impacts of different aquaculture practices. Second, a stratified random sampling technique was used to select participants from different aquaculture systems. While this method ensured a diverse sample, the relatively small sample sizes in certain subgroups particularly for aquaponics and cage systems limit the generalizability of the results. Furthermore, the sampling approach could lead to unequal representation of certain groups, such as vulnerable populations or specific fish farming communities. Future studies could consider larger, more representative sample sizes, or apply alternative sampling methods, such as paired actors, where a respondent identifies relevant social connections to better capture network dynamics and ensure broader representation. Third, while this study primarily focused on quantitative data, qualitative insights from focus group discussions were also collected to inform the development of the questionnaire. Although these qualitative insights provided valuable context, they were not extensively analyzed. Future studies could integrate qualitative data analysis more systematically. Fourth, the study mainly relied on data from fish farmers and did not incorporate the perspectives of other key stakeholders, such as policymakers, extension services, or industry experts. Including these additional stakeholders would provide a more holistic view of the aquaculture sector and enhance the understanding of the broader challenges and opportunities facing the industry. Future research should strive for a more inclusive approach, collecting data from a variety of stakeholders to capture a comprehensive range of insights.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThe cost analysis of small-scale aquaculture systems across pond, cage, and aquaponic reveals financial challenges. Pond aquaculture, characterized by high variable costs for feed, labor, and fingerlings, results in negative gross margins and unprofitable financial outcomes, as total costs exceed revenues. This highlights the financial strain on fish farmers due to high input and labor costs, emphasizing the need for strategies such as improved feed efficiency and better pond management practices. Cage aquaculture systems show better financial performance, despite high setup and operational costs, with positive net farm income and return on investment. Economies of scale, efficient feed management, and enhanced management practices contribute to profitability, although it remains sensitive to fluctuations in feed and labor costs. Qualitative insights from farmers in Uganda point to financial pressures and the need for financial support to sustain these systems. Aquaponic systems face financial bottlenecks due to high infrastructure and operational costs, resulting in negative gross margins. However, improvements in system size, stocking density, and management practices could enhance financial outcomes. Correlation analyses indicate that system sizes and optimal stocking densities can improve profitability through economies of scale, while access to extension services and experienced management also correlate positively with profit performance. Overall, the findings suggest that interventions, including optimizing input costs, improving and market linkages, are necessary to ensure the long-term profitability of small-scale aquaculture systems in the Lake Victoria basin, Uganda\u003c/p\u003e"},{"header":"Declarations","content":" \u003cp\u003e \u003cb\u003eEthics Statement\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThis study received ethical approval from the relevant authorities in Uganda, including the Chief Administrative Officers of the respective districts where the study was conducted. Additionally, the study methodology was approved by the Lilongwe University of Agriculture and Natural Resources, Malawi. All necessary permissions were obtained to ensure the study complied with local regulations and ethical standards. Informed consent was obtained from all participants, and their confidentiality was maintained throughout the study. Clinical trial number: not applicable.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent for Publication:\u003c/strong\u003e \u003cp\u003eAuthors have approved the content of this manuscript, and it has been submitted for publication in the Journal of Discover sustainability.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConflict of interest\u003c/strong\u003e \u003cp\u003eThe authors declare no conflicts of interest.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding:\u003c/h2\u003e \u003cp\u003eThe study was supported by a grant from the Rufford Foundation under the small grants program (Project Ref: 38553-2).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eS.B. collected and analyzed the data and wrote the manuscript. M.L. reviewed the manuscript and provided supervisory guidance. S.R. also reviewed the manuscript and provided additional supervisory support. All authors reviewed the final version of the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e \u003cp\u003eWe extend our sincere appreciation and gratitude to the Rufford Foundation and DAAD for their generous support and funding throughout the duration of this study. Special thanks are given to all survey respondents for their valuable time and effort in fully engaging with this study. Your contributions have been essential to the success of this study.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAdeleke BA. (2020). Bio economic feasibility of aquaponics in South Africa: Leapfrogging for sustainable development of freshwater aquaculture. 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(2019). improving livelihoods through aquaculture in East.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"discover-sustainability","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"disu","sideBox":"Learn more about [Discover Sustainability](https://www.springer.com/43621)","snPcode":"","submissionUrl":"","title":"Discover Sustainability","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Small-scale aquaculture, profitability drivers, aquaculture systems, Cost-profit analysis, financial performance. Gross margin","lastPublishedDoi":"10.21203/rs.3.rs-5541053/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5541053/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eSmall-scale aquaculture offers potential for improving livelihoods, food security, and economic growth. However, African fish farmers struggle to achieve profitability. This study investigated financial performance of three small-scale aquaculture systems (ponds, cages, and aquaponics) in Uganda's Lake Victoria Basin, to identify variables influencing profitability. Using stratified random sampling, 169 fish farmers from Mpigi, Wakiso, and Buikwe districts were interviewed. Econometric analysis of small-scale aquaculture systems was performed using LIMDEP 9.0 software. Profitability was measured using key financial criteria such as gross margin, net farm income, and net return on investment. Furthermore, a linear regression model was used to analyse attributes influencing profitability. The analysis considered variables such as farm size, stocking density, access to extension services, and other crucial determinants. The results revealed that cage culture achieved a positive gross margin, while pond and aquaponics systems faced financial challenges due to high fixed and variable expenses, including feed, labor, and infrastructure. Farm size, management practices, and fish prices have impact on profitability. Nevertheless, high fish feed costs, predator problems, and poor technical support hinders financial performance. To improve the financial sustainability of aquaculture systems, the study recommends cost-effective feed management practices, offering financial assistance in form of low-interest loans, extending training, and market linkages.\u003c/p\u003e","manuscriptTitle":"Optimizing small-scale aquaculture systems in the Lake Victoria Basin,Uganda: Insights into profitability drivers","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-03-28 06:30:10","doi":"10.21203/rs.3.rs-5541053/v1","editorialEvents":[{"type":"communityComments","content":3},{"type":"decision","content":"Revision requested","date":"2025-04-08T08:39:35+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-03T17:35:06+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-02T06:18:40+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"225671945737843142454684569648414326177","date":"2025-03-28T01:26:35+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"21061176603698384230372713166762566283","date":"2025-03-27T17:19:30+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-03-27T16:20:48+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-03-25T08:12:08+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Sustainability","date":"2025-03-18T19:48:46+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-sustainability","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"disu","sideBox":"Learn more about [Discover Sustainability](https://www.springer.com/43621)","snPcode":"","submissionUrl":"","title":"Discover Sustainability","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"535ccc52-d10b-4610-825a-48aeb5e445e3","owner":[],"postedDate":"March 28th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-07-27T20:53:10+00:00","versionOfRecord":[],"versionCreatedAt":"2025-03-28 06:30:10","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5541053","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5541053","identity":"rs-5541053","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}