FISH SIMULATION CULTURE MODEL (FIS-C) - A BIOENERGETICS BASED MODEL FOR AQUACULTURAL WASTELOAD APPLICATION

Citation
Me. Mcdonald et al., FISH SIMULATION CULTURE MODEL (FIS-C) - A BIOENERGETICS BASED MODEL FOR AQUACULTURAL WASTELOAD APPLICATION, Aquacultural engineering, 15(4), 1996, pp. 243-259
Citations number
55
Categorie Soggetti
Engineering,Fisheries
Journal title
ISSN journal
01448609
Volume
15
Issue
4
Year of publication
1996
Pages
243 - 259
Database
ISI
SICI code
0144-8609(1996)15:4<243:FSCM(->2.0.ZU;2-9
Abstract
A generic bioenergetics model for chinook salmon was modified to estim ate solid wastes from a commercial net-pen aquaculture operation in a Minnesota mine-pit lake. The model was calibrated using data from the operation on growth, ration, and temperature. Multiple simulations wer e run to form three-dimensional response surfaces for consumption, ege stion, excretion and respiration as a function of fish size and water temperature. These formed the basis for the Fish Simulation Culture (F IS-C) Model. Predictions for food consumption and solids load were com pared with actual ration levels and sedimentation within the mine-pit lake from 1989 to 1992, and compared well with the general trends of t he observed data. However, the actual predictive power of FIS-C was ve ry sensitive to our initial model assumption that aquaculture operatio ns are predicated on maximizing the growth of their stock. FIS-C curre ntly does not account for management decisions electing sub-optimal st ock growth, but under these conditions does estimate a worst case load ing scenario for the system. The annual phosphorus load to the system predicted by FIS-C was not significantly different from that of the me an of 17 values of annual P-load estimated empirically from the litera ture. However, FIS-C's estimate of P-loading shows a pronounced season al pattern to the annual loading. FIS-C offers substantial benefits to users by estimating seasonal and shorter term food wastage and wastel oads to receiving waters under particular operating conditions. Then, other operational scenarios can be created to examine the effects of c hanging fish inventory, feeding schedule, food composition, etc., in o rder to examine the impacts on production, environmental and/or regula tory requirements, prior to costly implementation.