Mathematical modeling applied to the growth of tilapia in net cages in the sub middle of the São Francisco river

Júnior, José de Alencar de Sousa; Garrido, Marlon da Silva; Carvalho, Paulo Gustavo Serafim de; Rocha, Luciano Gomes da; Campeche, D. F. B.

doi:10.1590/s0100-69162014000500019

Cited by 6 publications

(3 citation statements)

References 25 publications

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“…This algorithm is described as more robust than others offered in statistical software (Elzhov et al, 2013;Lugert et al, 2017). This is especially important, as non-convergence situations of models for aquaculture data are described by several authors (Costa et al, 2009;Mansano et al, 2012;Allaman et al, 2013;Sousa Júnior et al, 2014).…”

Section: Discussionmentioning

confidence: 99%

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

Janampa-Sarmiento

Takata

Freitas

et al. 2020

Revista Brasileira De Zootecnia

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Aquaculture Full-length research article Modeling the weight gain of freshwater-reared rainbow trout (Oncorhynchus mykiss) during the grow-out phase ABSTRACT-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.

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Section: Discussionmentioning

confidence: 99%

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

Janampa-Sarmiento

Takata

Freitas

et al. 2020

Revista Brasileira De Zootecnia

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“…Mediante el R 2 se determinó que el modelo con mejor capacidad de predicción es el modelo de Richards, seguidos por los modelos de Gompertz y Logístico. En tanto que la medida de compensación relativa R de Bates & Watts, (1981) indica que se logró convergencia en todo los casos dado que dichos valores son inferiores a 10 -5 .Es decir, la suma cuadrado de los residuales es mínima y tiende a cero(De Sousa Júnior, Garrido, Carvalho, Rocha, & Campeche, 2014).…”

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CARACTERIZACIÓN DEL CRECIMIENTO DE TRUCHAS ARCOIRIS (Oncorhynchus mykiss) EN EL LAGO TITICACA MEDIANTE MODELOS NO LINEALES

Choque

2017

SE-FIE

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Esta investigación se realizó con el objetivo de caracterizar el crecimiento de truchas arcoíris (Oncorhynchus mykiss) en el lago Titicaca mediante modelos no lineales, a partir de los cuales se identificaron puntos de interés biológico y económico, así como predicciones de requerimiento de alimentos para mejorar el desempeño productivo y económico. La información utilizada corresponde a la empresa Perú Pacífico ubicada en el distrito de Capachica, provincia de Puno. Los modelos analizaron fueron Logistic, Gompertz y Richards que se estimaron mediante NLIN de SAS ® y el algoritmo de Marquardt. El modelo de Richards mostró un coeficiente de determinación (R2) más alto. Es decir, describe y predice adecuadamente el crecimiento de truchas en el lago Titicaca, bajo condiciones específicas de manejo. Los modelos Logistic y Gompertz mostraron menor capacidad de predicción al sobrestimar y subestimar el peso en la fase inicial de crecimiento, en cambio en la fase final, subestiman y sobrestiman, respectivamente. El perfil de crecimiento muestra incrementos de peso lento en la fase de adaptación al ambiente, acelerado hasta el punto de inflexión, tiende hacia un valor asintótico a medida que se aproxima a la edad adulta, y tiene forma sigmoidea, típico en las truchas. Finalmente, se ha determinado el periodo de adaptación al ambiente, el punto de inflexión, el tiempo en el que alcanzarían peso comercial (200, 250, 330 y 500 gr), y el requerimiento de alimentos en determinados momento de un ciclo productivo de 269 días.

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“…Mediante el R 2 se determinó que el modelo con mejor capacidad de predicción es el modelo de Richards, seguidos por los modelos de Gompertz y Logístico. En tanto que la medida de compensación relativa R deBates & Watts, (1981) indica que se logró convergencia en todo los casos dado que dichos valores son inferiores a 10 -5 .Es decir, la suma cuadrado de los residuales es mínima y tiende a cero(De Sousa Júnior, Garrido, Carvalho, Rocha, & Campeche, 2014).…”

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Caracterización del crecimiento de truchas arcoiris (oncorhynchus mykiss) en el Lago Titicaca mediante modelos no lineales

Choque

2017

Semest. econ.

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This research was carried out with the objective of characterizing the growth of rainbow trout (Oncorhynchus mykiss) in Lake Titicaca using nonlinear models, from which points of biological and economic interest were identified, as well as predictions of food requirements to improve the productive and economic performance. The information used corresponds to the company Perú Pacífico located in the district of Capachica, province of Puno. The models analyzed were Logistic, Gompertz and Richards that were estimated using SAS ® NLIN and the Marquardt algorithm. The Richards model showed a higher coefficient of determination (R2). That is, it adequately describes and predicts trout growth in Lake Titicaca, under specific management conditions. The Logistic and Gompertz models showed lower predictive capacity when overestimating and underestimating the weight in the initial phase of growth, while in the final phase, they underestimate and overestimate, respectively. The growth profile shows slow weight increases in the adaptation phase to the environment, accelerated to the point of inflection, tends toward an asymptotic value as it approaches adulthood, and has a sigmoid shape, typical in trout. Finally, it has been determined the period of adaptation to the environment, the point of inflection, the time in which they would reach commercial weight (200, 250, 330 and 500 gr), and the requirement of food at certain times of a productive cycle of 269 days.

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Mathematical modeling applied to the growth of tilapia in net cages in the sub middle of the São Francisco river

Cited by 6 publications

References 25 publications

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

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

CARACTERIZACIÓN DEL CRECIMIENTO DE TRUCHAS ARCOIRIS (Oncorhynchus mykiss) EN EL LAGO TITICACA MEDIANTE MODELOS NO LINEALES

Caracterización del crecimiento de truchas arcoiris (oncorhynchus mykiss) en el Lago Titicaca mediante modelos no lineales

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