Simulation of Aquaculture Production (SAP)

Simulation of Aquaculture Production

Simulation of Aquaculture Production is a series of three .Excel spreadsheets for simulating the production of sea bass (D. Labrax) in cages in the open sea. It provides insight into aquaculture production and business simulations.
3 example are currently  at ZOO-TECH,  SIM_REAL and  SAP (still under construction) documentation is being updated  and is currently at SAP Documentation. Download.
ZOO_technical An introduction to the zoological and technical considerations



SIMulation & Real Data

SIMulation & Real Data

Sim_real Simulation integrating real weekly production data with simulated results
Documentation is at Here
Zoo-Tech is an introductory spread sheet  applications applied to marine aquaculture production. It provides important, easy to understand calculations with user controlled information.
The simulation uses 5 sheets is and is based upon a site’s

  • Thermal Profile, (mean weekly temperature in C°, by week, by year.),
  • Daily Growth Model which  is a : general growth model for Sea Bass (D. labrax) weighing between 1 and 500 grams and a temperature between 14.5 and 26 °C.  The Model, is obtained from Leclercq & Tanguy, calculates the weekly weight gain but by changing parameters it is possible to simulate other species growth function.
  • Daily Feeding Table (the mean fish weight (row), the temperature (column) and the (manufacture’s), suggested daily percentage (intersection) of food based upon the Biomass (number of individuals times their weight). This calculates into Kilos of food to distribute which calculates cost s and sales considerations.
  • Plus other information and models for example which calculate food to nitrate production, ammonium and organic nitrogen production rate,  particulate matter production,  oxygen consumption and even the mean weekly current speed (minimum in meters/hour).

The user can vary most simulation parameters
SIM_REAL Combines REAL weekly production data, (i.e. number of fish moved (in/out), mortality, mean weight and kilos of food distributed) with user controlled initial data (i.e. starting date, number initial, etc.)  the user can quickly summarize production for short-term results based upon real and current trends.
In many simulations, each week’s calculated results are based upon the previous weeks result’s.  This can cause the simulated results to “drift” each week from the actual production data.   After several months the simulated results could vary greatly from real production data.