REVIEW OF AQUACULTURAL PRODUCTION SYSTEM MODELS

Abstract

The success and profitability of aquaculture production is highly dependent on the proper management employed during the culture period. This management is synonymous with water management since water of suitable quality and quantity is a pre-requisite for any successful aquacultural production. The knowledge of the environment (water) enhances better management. However, the complexity of an aquaculture system which result from multiple interactions makes it difficult to predict how the aquatic community will respond to changes with simple methods of analysis, especially if the methods address a single stressor at a time. These necessitated the development of numerous aquatic ecosystem models such as the Fish Simulation Culture Model (FIS-C), Tilapia Farming Support Tool (TFST), Farm Aquaculture Resource Management (FARM), Pond-Water Availability Period (PWAP) model, AquaFarm and Raceway design and simulation system (RDSS)),which have been used for years as tools to interpret, predict and better understand water quality changes. This paper reviewed existing simulation models of aquacultural production systems with the aim of adopting a suitable one for predicting the environment, performance of African cultured indigenous fish species under different management scenarioes. The reviewed models were found to have addressed specific problem that pertain to some foreign species, production systems and locality. There was none that could be used to model the effects of different management scenario and their effects on African Catfish (Clarials gariepinus) cultured in intensive static aquacultural system. Hence, the problem of predicting the environmental condition, so as to determine point of diminishing returns and optimize yield in an economical way still remains elusive for most fish farms in Nigeria. There is therefore a serious need to develop models that can predict the effects of environment on the performance of indigenous fish species. This will aid stake holders in predicting different management scenario so as to achieve a better crop (fish) yield.

http://dx.doi.org/10.4314/njt.v35i2.29