Growth modelling including size heterogeneity: Application to the intensive culture of white shrimp (P. vannamei) in freshwater

Araneda, Marcelo; Hernández, Juan M.; Gasca‐Leyva, Eucario; Vela, Miguel A.

doi:10.1016/j.aquaeng.2013.03.003

Cited by 29 publications

(37 citation statements)

References 43 publications

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“…The white shrimp tolerates salinities from 15 to 40, and some experimental evidence suggests the possibility for farming shrimp in freshwater areas with special acclimation (Araneda et al 2008(Araneda et al , 2013Miranda et al 2010 ;Ortega-Salas and Rendón 2013 ). Even, some commercial farms in Mexico, particularly in the Northeastern region, operate under very low salinity to freshwater conditions.…”

Section: Aquaculture Of Shrimp In Mexico: Technological and Biologicamentioning

confidence: 97%

“…To this end, shrimp post larvae are previously adapted to freshwater in tanks before they are transferred to the grow-out ponds (Araneda et al 2008(Araneda et al , 2013Miranda et al 2010 ;Ortega-Salas and Rendón 2013 ). Some of these types of farms that culture shrimp in freshwater conditions, are located in the Mexican states of Baja California, Colima, Sinaloa, Sonora, and Tamaulipas which operate at commercial scale, using freshwater from artesian wells (2-10 ‰) (CONAPESCA 2011 ; Ortega-Salas and Rendón 2013 ).…”

Section: Fig 511mentioning

confidence: 99%

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Interaction of Fisheries and Aquaculture in the Production of Marine Resources: Advances and Perspectives in Mexico

Pérez‐Castañeda

Sánchez‐Martínez

Aguirre‐Guzmán

et al. 2014

Environmental Management and Governance

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Marine capture fi sheries in Mexico are dominated by sardine, shrimp, and tuna, representing as a whole 60 % of the total catch. However, shrimp and tuna are the most important fi shery resources in terms of economic value. Capture shrimp fi shery in Mexico has exhibited stagnating catches (around 65 thousand tonnes) since the last two decades, and shrimp stocks have been clearly depleted in some parts of the country. Conversely, shrimp ( Litopenaeus vannamei ) aquaculture has shown an exponential growth in the number of farms and shrimp production since mid-1980s. As a result, currently, shrimp aquaculture production has almost doubled the total production of the capture shrimp fi shery. On the other hand, total catch of tuna by Mexican tuna fl eet has fl uctuated around 130 thousand tonnes during the last 5 years (2007)(2008)(2009)(2010)(2011). Contribution of farmed tuna ( Thunnus orientalis ) has been negligible in terms of volume; however, the price of 1 tonne of farmed tuna is about 7-13 times that of tuna caught by the fi shing fl eet, making it an attractive alternative as source of employment and income. The case studies presented here are indicative of the potential value of aquaculture as a complementary productive activity to meet the growing human demand for food from the sea. This is especially relevant in terms of global fi sheries production because the maximum fi sheries catch potential from the oceans around the world has apparently been reached. However, there are still concerns associated with aquaculture impacts on the environment that must be addressed.

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Section: Aquaculture Of Shrimp In Mexico: Technological and Biologicamentioning

confidence: 97%

Section: Fig 511mentioning

confidence: 99%

Interaction of Fisheries and Aquaculture in the Production of Marine Resources: Advances and Perspectives in Mexico

Pérez‐Castañeda

Sánchez‐Martínez

Aguirre‐Guzmán

et al. 2014

Environmental Management and Governance

View full text Add to dashboard Cite

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“…Aspects related to feeding management are very important, but are usually studied in terms of nutrient levels, ingredients and feeding rates, among other factors. Nevertheless, fish stock aspects such as seasonal growth, density, optimum harvest size and weight dispersion, among others, are largely unknown in Mediterranean marine species, although some studies have been carried out (Araneda, Hernández & Gasca‐Leyva, ; Araneda, Hernández, Gasca‐Leyva & Vela, ; Dominguez‐May, Hernández & Gasca‐Leyva, ; Gasca‐Leyva, Hernández & Veliov, ; Sánchez‐Zazueta, Hernández & Martínez‐Cordero, ).…”

Section: Introductionmentioning

confidence: 99%

“…() in gilthead sea bream with quantile regression growth models, Hurtado‐Herrera, Dominguez‐May and Gasca‐Leyva (), in tilapia and Araneda et al. () in white shrimp using characteristic growth curves for different fish sizes.…”

Section: Introductionmentioning

confidence: 99%

“…A large number of papers in recent years (Baer, Schultz, Traulsen & Krieter, 2011;Dumas & France, 2008;Dumas, France & Bureau, 2007, 2010Libralato & Solidoro, 2008;Mayer, Estruch, Blasco & Jover, 2008;Mayer, Estruch, Mart ı & Jover, 2009;Moses et al, 2008;Seginer & Halachmi, 2008) aim to describe and predict the growth of fish with different objectives. Almost all of them have in common that only the dynamic of the average value of the time-dependent weight is described by means of simple or multiple regression models, but weight dispersion is considered by few authors; for example, Mayer et al (2009) in gilthead sea bream with quantile regression growth models, Hurtado-Herrera, Dominguez-May and , in tilapia and Araneda et al (2013) in white shrimp using characteristic growth curves for different fish sizes.…”

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confidence: 99%

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Developing a new tool based on a quantile regression mixed-TGC model for optimizing gilthead sea bream (Sparus aurataL) farm management

et al. 2017

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In this work, a seasonal quantile regression growth model for the gilthead sea bream (Sparus aurata L) based on an aggregation of the quantile TGC models with exponent 1/3 and 2/3, named the “Quantile TGC‐Mixed Model”, is presented. This model generalizes the proposal of Mayer, Estruch and Jover (Aquaculture, 358‐359, 2012, 6) in the sense that the new model is able to describe the evolution of weight distribution throughout an entire production cycle, which could be a powerful tool for fish farm management. The information provided by the model simulations enables us to estimate total fish production and final fish size distribution and helps to design and simulate production and sales plan strategies considering the market price of different fish sizes, in order to increase economic profits. The most interesting alternative in the studied case results in sending all production when 0.25 quantile fish reach 600 g, although on each fish farm it would be necessary to evaluate optimum strategy depending on its own quantile regression model, the production cost and the market price.

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Optimization model to start harvesting in stochastic aquaculture system

Yoshioka

Yaegashi

2017

Appl Stoch Models Bus & Ind

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Establishment of cost-effective management strategy of aquaculture is one of the most important issues in fishery science, which can be addressed with bio-economic mathematical modeling. This paper deals with the aforementioned issue using a stochastic process model for aquacultured non-renewable fishery resources from the viewpoint of an optimal stopping (timing) problem. The goal of operating the model is to find the optimal criteria to start harvesting the resources under stochastic environment, which turns out to be determined from the Bellman equation (BE). The BE has a separation of variables type structure and can be simplified to a reduced BE with a fewer degrees of freedom. Dependence of solutions to the original and reduced BEs on parameters and independent variables is analyzed from both analytical and numerical standpoints. Implications of the analysis results to management of aquaculture systems are presented as well. Numerical simulation focusing on aquacultured Plecoglossus altivelis in Japan validates the mathematical analysis results. Bus. Ind. 2017, 33 476-493 The quantity W * is related to Ψ that solves the reduced BE (12), and therefore, Ψ and W * have to be simultaneously solved. The curve C is therefore a free boundary. Assuming the unique existence of such C, the one-dimensional domain 0 < w < +∞ for each t < T can be separated into the two regions: the waiting region S 1 (t) = {w; Ψ (t, w) > w} and the Appl. Stochastic Models Bus. Ind. 2017, 33 476-493 Appl. Stochastic ModelsProof of Proposition 3.3 J ,t with p = ( p) i (i = 1, 2) is denoted as J ,t,i . For each ∈ t,T , the inequalitydirectly follows from (5). This leads to

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Growth modelling including size heterogeneity: Application to the intensive culture of white shrimp (P. vannamei) in freshwater

Cited by 29 publications

References 43 publications

Interaction of Fisheries and Aquaculture in the Production of Marine Resources: Advances and Perspectives in Mexico

Interaction of Fisheries and Aquaculture in the Production of Marine Resources: Advances and Perspectives in Mexico

Developing a new tool based on a quantile regression mixed-TGC model for optimizing gilthead sea bream (Sparus aurataL) farm management

Optimization model to start harvesting in stochastic aquaculture system

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