Rainbow trout (Oncorhynchus mykiss) production has developed rapidly, particularly in the highlands of central Mexico. The objective of this study was to characterize rainbow trout production systems based on factor and cluster analysis. Data were collected for 21 variables from 71 trout production units (TPU) through semi-structured interviews. Four groups were obtained. Group 1: rural entrepreneurs, and Group 3: organized family TPU, are business-oriented operations developed with government support schemes with incorporated internal procedures enabling higher production and sales either to wholesalers or final consumers as fresh trout. These groups have the potential to scale up their operations. Group 2: small-scale family TPU, and Group 4: small-scale artisan TPU, are less organized, based on family labor, have lower yields, and sell their products at the farm gate or to small restaurants. These groups represent the role of aquaculture for social development in rural areas. Public policies, government support schemes, growth of markets, and high demand for trout have favored all four groups. Based on the results, development strategies and applied research must be directed differentially to each group of rainbow trout producers.

Inefficient management of dissolved oxygen (DO) in intensive aquaculture systems limits fish welfare and productivity by creating oxygen-deficient zones and promoting hydrodynamic conditions that hinder their dispersion. Because water movement directly influences how oxygen is transported and mixed within the culture unit, inadequate flow management can allow localized hypoxia to persist even when total oxygen input appears sufficient. To address this issue, this study proposes an integrated methodology that combines in situ respirometry measurements with Computational Fluid Dynamics (CFD) simulations to evaluate the spatial distribution of DO and diagnose the operational performance of aquaculture systems. The methodology quantifies oxygen consumption using intermittent-flow respirometry, applies a three-dimensional two-phase CFD model (water–oxygen) incorporating experimental oxygen consumption rates as boundary conditions, and validates the model under real operating conditions, focusing on active metabolism as the most demanding physiological state. The model generates a spatial distribution of DO patterns that are significantly modified by pond geometry, water flow characteristics, the metabolism of the fish and fish positioning. The differences between experimental and simulated values ranged from 7.8% to 10.7%, confirming the accuracy of the proposed method. The integration of in situ metabolic measurements with CFD modeling provides a realistic representation of DO dynamics, enabling system optimization and promoting more efficient and sustainable aquaculture.