Characterization and Economic Feasibility of Fish Farming in the Metropolitan Region of Manaus, Brazil

Introduction: Fish farming in the Amazonas state has high productive potential due to the abundance of water resources and the cultivation of native species, especially tambaqui (Colossoma macropomum). However, the activity still presents low competitiveness, limited by technological deficiencies, high production costs, and insufficient public policies. This study aimed to characterize the fish farming profile in the Metropolitan Region of Manaus (MRM), Amazonas State, Brazil, and evaluate its economic feasibility. Methods: A total of 93 fish farmers from the MRM were interviewed using structured questionnaires containing socioeconomic, productive, and technical information. Economic analyses were performed using profitability indicators, Internal Rate of Return (IRR), Net Present Value (NPV), and Payback Period (PP). Simulations were also conducted considering different levels of technological intensification and government subsidies. Results: Semi-intensive systems in earthen ponds predominated, with tambaqui production occurring mainly in small-scale farms. Fish farming was characterized as a complementary income activity, with low technological adoption and limited financial management. The main difficulties reported were high feed costs, lack of rural credit, and insufficient technical assistance. Low-intensity production systems showed poor economic feasibility, with an IRR of 5.83% and negative NPV. In contrast, more intensive systems, with higher stocking densities and aeration use, showed better financial performance, reaching an IRR of 41.52% and capital recovery within 2.3 years. Subsidies related to technical assistance and reduced input costs positively influenced profitability. Conclusions: Fish farming in the MRM has significant expansion potential; however, its development depends on strengthening technical assistance, adopting production technologies, and expanding public policies aimed at reducing operational costs and improving productive efficiency.