Dissolved oxygen (DO) is a critical factor for animal welfare and production performance in Atlantic salmon sea cage farming, but is challenging to monitor and manage in the complex farm environment due to complex interactions between the environmental variation, large fish populations, farm structures, and production activities. Understanding this intra-farm variation in DO is therefore key to improving management and production outcomes. In this study, we combine in-situ environmental measurements and production data with a mathematical model that simulates DO variation at the cage-level. The approach is applied to three commercial sea cage farms to understand production limitations not captured by sensors and to identify key drivers of DO variation using a statistical model. The results show substantial intra-farm variation in DO. The model output indicates more severe conditions than suggested by the in-situ measurements, with estimated production impairment up to 65 percentage points greater at the modelled cage-level. From July to October, model outputs indicate that all sites experienced continuous DO levels below maximum feed intake, for 62-80% of the time, with periods of heightened welfare risks. Environmental factors were identified as the main drivers of variation, while 13-15% was attributed to farm-controlled factors, including cage position, biomass and feed ration. These results demonstrate that integrating in-situ measurements with modelling can reveal intra-farm dynamics not captured by standard monitoring methods. By augmenting local sensors with mathematical and statistical models, a method for aligning operations to environmental restrictions and a greater precision in farm management is offered, without the need for dense sensor networks. Adjusting the number, placement and quality of the sensors, according to where the models have the highest benefit, will further improve the potential of future farm management.
Berntsson et al. (2026) studied this question.