Development and Application of a Mechanistic Nutrient-Based Model for Precision Fish Farming

• Published in: Journal of Marine Science and Engineering
• Main Authors: Filipe M. R. C. Soares, Ana M. D. Nobre, Andreia I. G. Raposo, Rodrigo C. P. Mendes, Sofia A. D. Engrola, Paulo J. A. P. Rema, Luís E. C. Conceição, Tomé S. Silva
• Format: Text
• Language: English
• Published: Multidisciplinary Digital Publishing Institute 2023
• Subjects: aquaculture; fish nutrition; precision fish farming; mechanistic nutrient-based model; numerical model; decision-support tool; Atlantic salmon; Salmo salar
• Online Access: https://doi.org/10.3390/jmse11030472

This manuscript describes and evaluates the FEEDNETICS model, a detailed mechanistic nutrient-based model that has been developed to be used as a data interpretation and decision-support tool by fish farmers, aquafeed producers, aquaculture consultants and researchers. The modelling framework comprises two main components: (i) fish model, that simulates at the individual level the fish growth, composition, and nutrient utilization, following basic physical principles and prior information on the organization and control of biochemical/metabolic processes; and (ii) farm model, that upscales all information to the population level. The model was calibrated and validated for five commercially relevant farmed fish species, i.e., gilthead seabream (Sparus aurata), European seabass (Dicentrarchus labrax), Atlantic salmon (Salmo salar), rainbow trout (Oncorhynchus mykiss), and Nile tilapia (Oreochromis niloticus), using data sets covering a wide range of rearing and feeding conditions. The results of the validation of the model for fish growth are consistent between species, presenting a mean absolute percentage error (MAPE) between 11.7 and 13.8%. Several uses cases are presented, illustrating how this tool can be used to complement experimental trial design and interpretation, and to evaluate nutritional and environmental effects at the farm level. FEEDNETICS provides a means of transforming data into useful information, thus contributing to more efficient fish farming.