ENBIS-17 in Naples

9 – 14 September 2017; Naples (Italy) Abstract submission: 21 November 2016 – 10 May 2017

Modeling Shrimp Growth in Freshwater

11 September 2017, 17:30 – 17:50


Submitted by
Susana Vegas
Susana Vegas (Universidad de Piura), Valeria Quevedo (Universidad de Piura/ Virginia Tech), Geoff Vining (Virginia Tech)
For a shrimp farm with 250 ponds, we want to analyze which factors help explain the variation of the shrimp weight. We notice that the shrimp growth has an asymptotic behavior independent of the pond. We use a two-stage non-linear model approach.
In the first stage, we use a conceptual Gompertz growth model
logW = θ_1-θ_2 e^(-θ_3 time)
where θ_1 is the asymptotic average log-weight of adult shrimp, θ_3 is the growth rate constant, and θ_1-θ_2 is the average initial log-weight to predict the shrimp log-weight based on time. Using data from five harvesting campaigns, our analysis show that the best estimates for and can be computed using data from previous campaigns, and for is based on the average log-weight from week 1 from the current campaign.
To explain the variability left unexplained from the first stage, we fit a multiple linear regression model with the non-linear model residuals as the response, and average food per shrimp, aeration, and water parameters as predictors. To analyze the model efficiency, we estimate the shrimp weight by going backwards to the original scale. We show that the two-stage non-linear model satisfies the model assumptions better than a one-stage MLR model.
The growth model from the first stage can be used to monitor the process for new campaigns by using a control chart using data from previous campaigns for the control limits. The second-stage regression analysis can be used to suggest corrections during the campaigns.
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