Development of a Strategy of an Optimal Economic Management for Semi-intensive Shrimp Farming by Means of FIR

Introduction

This project represents one of the earliest practical applications of FIR methodology. In the North of Mexico, in the provinces of Sonora and Sinaloa, shrimp is grown in semi-intensive farming. The animals should grow as rapidly as possible, so that the farmers can maximize their profit. However, growth of the shrimp depends on a number of variables, including the frequency, with which the animals are being fed, the salinity of the water in the ponds, the transparency of the water, the water temperature, as well as the population density.

Although it is well known that these relations exist, no theory has been brought forward that would be capable of quantifying them. For this reason, shrimp farming represents an excellent application of a qualitative inductive modeling technique, such as FIR.

Our first task consisted of identifying a FIR model that would qualitatively capture the relations between the growth of shrimp and the influencing variables mentioned above. The aim of the model was to make the growth of shrimp somewhat predictable.

Subsequently, an optimization problem had to be solved that should determine, when and how often the animals ought to be fed, and when and how often the water ought to be changed, such that the profit of the farmers is maximized.

The competition between shrimp farmers is fierce, and therefore, the profit is relatively modest. Yet, by applying FIR methodology, it was possible to more than double the profit of the farmers.


Most Important Publications

  1. Carvajal, R. and A. Nebot (1998), "Growth Model for White Shrimp in Semi-intensive Farming Using Inductive Reasoning Methodology", Computers and Electronics in Agriculture, 19, pp.187-210.

  2. Nebot, A., F.E. Cellier, and R. Carvajal, "Fuzzy Inductive Reasoning for Variable Selection, Analysis, and Modeling of Biological Systems", Intl. J. General Systems, 27p., accepted for publication.

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Last modified: July 14, 2005 -- © François Cellier