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Bioeconomic prediction model for climatic and financial risk analysis of aquaculture enterprises with high-producing in a hydroelectric reservoir

Abstract

Aquaculture is an activity that stands out in providing protein to a growing population. In this scenario, the estimated by 2030, 62% of the fish consumed worldwide will come from aquaculture, especially the Nile tilapia (Oreochromis niloticus) produced in cages in lakes and reservoirs, which only in the state of São Paulo is a 90% of fish farmers tilapia production. However, the lack of knowledge of producers and investors regarding the economic, financial and climatic risks of this activity has caused economic and productive losses, causing many producers to give up the activity. In this context, the main objective this project is a develop the bioeconomic model prediction for assist the commercial enterprise of O. niloticus in cages in the reservoir of Chavantes Hydroelectric Power Plant, São Paulo, Brazil. A historical data series from 2015 to 2019 (n = 250) will be used with information on water quality, zootechnical performance and costs of fish farm. Likewise, 30 field lots will be monitored during a production cycle for collect the same information obtained in the historical series. We will apply Coeficient Thermal Growth Modeling (TGC) for determined the weight and time to commercialization and integrating to economic modeling for define the costs and profitability of the enterprise. Productive and financial variables will be used in the elaboration of the base algorithm of a new bioeconomic model (MMPB). The MMPB model will be statistically validated by comparing the results modeled in the MMPB against the observed field data. Thus, we will conduct interviews for collecting social information from fish farming. These will be used to apply indicators and form socioeconomic scenarios of the enterprise. Subsequently, risk simulations will be performed by the Monte Carlo model to demonstrate the different bioeconomic and socioeconomic scenarios. Finally, we will turn MMPB into a program code (python language), which will be installed on the Raspiberry pi hardware system coupled with climate monitoring plataform located in the fish farm. Using weather sensors for real-time information gathering and the support vector machine (SVM) technique, it will allow you to increase data frequency and make the MMPB model algorithm more robust and accurate. This tool will make it possible to optimize aquaculture processes, assist in analyzing the environmental, financial and economic risk of the activity for managers and investors in the sector. (AU)

Scientific publications
(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)
VALENTI, WAGNER C.; BARROS, HELENICE P.; MORAES-VALENTI, PATRICIA; BUENO, GUILHERME W.; CAVALLI, RONALDO O. Aquaculture in Brazil: past, present and future. AQUACULTURE REPORTS, v. 19, MAR 2021. Web of Science Citations: 0.

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