Abstract
We have developed a mathematical modelwhich estimates the growth performance of Atlantic salmon in aquaculture
production units. The model consists of sub-models estimating the behaviour and energetics of the fish,
the distribution of feed pellets, and the abiotic conditions in the water column. A field experiment where three
full-scale cages stocked with 120,000 salmon each (initial mean weight 72.1 ± SD 2.8 g) were monitored over
six months was used to validate the model. The model was set up to simulate fish growth for all the three
cages using the feeding regimes and observed environmental data as input, and simulation results were compared
with the experimental data. Experimental fish achieved end weights of 878, 849 and 739 g in the three
cages respectively. However, the fish contracted Pancreas Disease (PD) midway through the experiment, a factor
which is expected to impair growth and increase mortality rate. The model was found able to predict growth
rates for the initial period when the fish appeared to be healthy. Since the effects of PD on fish performance
are not modelled, growth rates were overestimated during the most severe disease period.
Thiswork illustrates how models can be powerful tools for predicting the performance of salmon in commercial
production, and also imply their potential for predicting differences between commercial scale and smaller experimental
scales. Furthermore, such models could be tools for early detection of disease outbreaks, as seen in
the deviations between model and observations caused by the PD outbreak. A model could potentially also
give indications on how the growth performance of the fish will suffer during such outbreaks.
Statement of relevance:We believe that our manuscript is relevant for the aquaculture industry as it examines the
growth performance of salmon in a fish farm in detail at a scale, both in terms of number of fish and in terms of
duration, that is higher than usual for such studies. In addition, the fish contracted a disease (PD) midway
through the experiment, thus resulting in a detailed dataset containing information on how PD affects salmon
growth, which can serve as a foundation to understanding disease effects better.
Furthermore, the manuscript describes an integrated mathematical model that is able to predict fish behaviour,
growth and energetics of salmon in response to commercial production conditions, including a dynamicmodel of
the distribution of feed pellets in the production volume. To our knowledge, there exist no models aspiring to estimate
such a broad spectre of the dynamics in commercial aquaculture production cages. We believe this model
could serve as a future tool to predict the dynamics in commercial aquaculture net pens, and that it could represent
a building block that can be utilised in a future development of knowledge-driven decision-support tools for
the salmon industry.
production units. The model consists of sub-models estimating the behaviour and energetics of the fish,
the distribution of feed pellets, and the abiotic conditions in the water column. A field experiment where three
full-scale cages stocked with 120,000 salmon each (initial mean weight 72.1 ± SD 2.8 g) were monitored over
six months was used to validate the model. The model was set up to simulate fish growth for all the three
cages using the feeding regimes and observed environmental data as input, and simulation results were compared
with the experimental data. Experimental fish achieved end weights of 878, 849 and 739 g in the three
cages respectively. However, the fish contracted Pancreas Disease (PD) midway through the experiment, a factor
which is expected to impair growth and increase mortality rate. The model was found able to predict growth
rates for the initial period when the fish appeared to be healthy. Since the effects of PD on fish performance
are not modelled, growth rates were overestimated during the most severe disease period.
Thiswork illustrates how models can be powerful tools for predicting the performance of salmon in commercial
production, and also imply their potential for predicting differences between commercial scale and smaller experimental
scales. Furthermore, such models could be tools for early detection of disease outbreaks, as seen in
the deviations between model and observations caused by the PD outbreak. A model could potentially also
give indications on how the growth performance of the fish will suffer during such outbreaks.
Statement of relevance:We believe that our manuscript is relevant for the aquaculture industry as it examines the
growth performance of salmon in a fish farm in detail at a scale, both in terms of number of fish and in terms of
duration, that is higher than usual for such studies. In addition, the fish contracted a disease (PD) midway
through the experiment, thus resulting in a detailed dataset containing information on how PD affects salmon
growth, which can serve as a foundation to understanding disease effects better.
Furthermore, the manuscript describes an integrated mathematical model that is able to predict fish behaviour,
growth and energetics of salmon in response to commercial production conditions, including a dynamicmodel of
the distribution of feed pellets in the production volume. To our knowledge, there exist no models aspiring to estimate
such a broad spectre of the dynamics in commercial aquaculture production cages. We believe this model
could serve as a future tool to predict the dynamics in commercial aquaculture net pens, and that it could represent
a building block that can be utilised in a future development of knowledge-driven decision-support tools for
the salmon industry.
