Data assimilation as a key step towards the implementation of an efficient management of dissolved oxygen in land-based aquaculture

Abstract

A data assimilation (DA) methodology, e.g. the continuous-discrete Kalman filter (CD-KF), was applied to the assimilation of dissolved oxygen data, in order to obtain a dynamic estimation of the oxygen demand in a land-based aquafarm. The CD-KF was implemented on a dynamic model, which included as state variables the concentration of dissolved oxygen (DO) and fish respiration rate: the latter was considered as a non-observable stochastic variable. The model was applied to a 1-month long set of observations collected at a raceway rainbow trout farm, including (1) hourly time series of water temperature and dissolved oxygen concentration in the raceway influent and effluent and (2) a daily time series of fish number and fish weight distribution. The results show that the assimilation of DO data led to a dynamic estimate of DO demand which showed changes in the daily mean and the daily pattern: these were related to changes in the feeding regime. Furthermore, the methodology provided accurate short-term predictions of the DO concentration also in the presence of short-term fluctuations, which would be very difficult to relate to external forcings in a mechanistic model. These findings indicate that DA could be effectively used to design and implement efficient and robust control systems for optimizing the oxygen supply, thus contributing to the implementation of Precision Fish Farming in land-based aquafarms.