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In the near future, the role of aquaculture in human diet is likely to increase due to the rising demand for fish proteins. With a long tradition of rainbow trout (Oncorhynchus mykiss) farming, Italy is one of the main producers of this species in the European Union (EU). The EU is allocating economic resources to foster the sustainable development of the aquaculture sector, aiming to produce more while using less resources. Precision fish farming (PFF) is a promising approach to achieve this goal and its implementation is being facilitated thanks to the reduction of costs of sensors. PFF will likely lead to a new generation of mathematical dynamic models based on sets of control variables and external forcing functions, coping with Big Data and machine learning techniques. In this work, we developed an individual-based dynamic model for the simulation of the fish size distribution and total biomass of a population of rainbow trout within a raceway. At its core, there is a bioenergetic individual model that can simulate weight changes taking into account water temperature and feeding regime. This model was tested against weight observations collected by a non–invasive monitoring system, that was deployed for the first time in a trout farm. The model allows one to estimate the optimal feeding ration based on fish weight and water temperature.  The results indicate that current methodologies, based on the estimation of the average weight, lead to slightly overestimate the feed ration: therefore, the model proposed here would allow one to save feed, thus reducing operational costs.

Effects of climatic changes in transitional ecosystems are often not linear, with some areas likely experiencing faster or more intense responses, which something important to consider in the perspective of climate forecasting. In this study of the Venice lagoon, time series of the past decade were used, and primary productivity was estimated from hourly oxygen data using a published model. Temporal and spatial patterns of water temperature, salinity and productivity time series were identified by applying clustering analysis. Phytoplankton and nutrient data from long-term surveys were correlated to primary productivity model outputs. pmax, the maximum oxygen production rate in a given day, was found to positively correlate with plankton variables measured in surveys. Clustering analysis showed the occurrence of summer heatwaves in 2008, 2013, 2015 and 2018 and three warm prolonged summers (2012, 2017, 2019) coincided with lower summer pmax values. Spatial effects in terms of temperature were found with segregation between confined and open areas, although the patterns varied from year to year. Production and respiration differences showed that the lagoon, despite seasonality, was overall heterotrophic, with internal water bodies having greater values of heterotrophy. Warm, dry years with high salinity had lower degrees of summer autotrophy.

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.