A review on fish growth calculation: multiple functions in fish production and their specific application
Abstract Modern aquaculture recirculation systems ( RAS s) are a necessary tool to provide sustainable and continuous aquaculture production with low environmental impact. But, productivity and efficiency of such RAS still have to be optimized to ensure economic viability, putting growth performance...
| Published in: | Reviews in Aquaculture |
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| Main Authors: | , , , , |
| Other Authors: | |
| Format: | Article in Journal/Newspaper |
| Language: | English |
| Published: |
Wiley
2014
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| Subjects: | |
| Online Access: | http://dx.doi.org/10.1111/raq.12071 https://api.wiley.com/onlinelibrary/tdm/v1/articles/10.1111%2Fraq.12071 https://onlinelibrary.wiley.com/doi/pdf/10.1111/raq.12071 |
| Summary: | Abstract Modern aquaculture recirculation systems ( RAS s) are a necessary tool to provide sustainable and continuous aquaculture production with low environmental impact. But, productivity and efficiency of such RAS still have to be optimized to ensure economic viability, putting growth performance into the focus. Growth is often reported as absolute (gain per day), relative (percentage increase in size) or specific growth rate (percentage increase in size per day), based on stocking and harvesting data. These functions describe growth very simplified and are inaccurate because intermediate growth data are not considered. In contrast, nonlinear growth models attempt to provide information of growth across different life stages. On the basis of an empirical RAS data set of 150 all‐female turbot reared in an RAS during a period of 340 days of outgrowth, this paper reviews the most commonly used growth rates (relative, absolute, specific), the thermal‐unit growth coefficient and five nonlinear growth functions (logistic, Gompertz, von Bertalanffy, Kanis and Schnute). Goodness of fit is expressed by R 2 and as mean percentage deviation. Nonlinear growth models are also compared by their residual standard error ( RSE ) and the Akaike information criterion. All processed functions are modelled to illustrate the shape of the generated curve and the possibility of the function to realistically predict growth. Further, the biological meaning of their regression parameters is discussed. This way we can point out differences in nonlinear growth models in contrast to purely descriptive growth rates and the specific advantages, disadvantages and possible applications of each function we review. |
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