Modeling the weight gain of freshwater-reared rainbow trout (Oncorhynchus mykiss) during the grow-out phase

• Autor(a) principal: Janampa-Sarmiento, Peter Charrie
• Data de Publicação: 2020
• Outros Autores: Takata, Rodrigo, Freitas, Thiago Mendes de, Freire, Licius de Sá, Pereira, Marcelo Menezes de Britto, Lugert, Vincent, Heluy, Guilherme Melgaço, Pereira, Marcelo Maia
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: LOCUS Repositório Institucional da UFV
• Texto Completo: https://locus.ufv.br//handle/123456789/30686; https://doi.org/10.37496/rbz4920190028
• topic: aquaculture; logistic model; non-linear equations; Oncorhynchus mykiss

We used five nonlinear models to calculate the weight gain of rainbow trout (122.11±15.6 g) during the final grow-out phase of 98 days under three different feed types (two commercials diets, A and B, and one experimental diet, C) in triplicate groups. We fitted the von Bertalanffy growth function with allometric and isometric scaling coefficient, Gompertz, Logistic, and Brody functions to weight (g) at age data of 900 fish, distributed in nine tanks. The equations were fitted to the data based on the least squares method using the Marquardt iterative algorithm. The accuracy of the fitted models was evaluated using a model performance metrics, combining mean squared residuals (MSR), mean absolute error (MAE), and Akaike’s Information Criterion corrected for small sample sizes (AICc). All models converged in all cases tested. The evaluation criteria for the Logistic model indicated the best overall fit (0.704) under all different feed types, followed by the Gompertz model (0.148), and the von Bertalanffy-I and von Bertalanffy-A with 0.074 each. The obtained asymptotic values are in agreement with the biological attributes of the species, except for the Brody model, whose values were massively exceeding the biologic traits of rainbow trout in 0.556 of tested cases. Additionally, ∆AICc results identify the Brody model as the only model not substantially supported by the data in any case. All other models are capable of reflecting the effects of various feed types; these results are directly applicable in farm management decisions