무지개송어(Oncorhynchus mykiss)의 사료 공급률에 따른 성장 속도 예측 모델 연구

Abstract

The growth of fish is a critical economic factor in aquaculture. However, conventional growth models often assume satiation feeding, limiting their ability to accurately predict real-world growth. This study proposes a variable growth model that incorporates feeding rate and feed conversion ratio (FC), based on the correlation between specific growth rate (SGR) and FC models, to enhance growth prediction accuracy. An 8-week experiment was conducted using rainbow trout Oncorhynchus mykiss in a recirculating aquaculture system under four feeding rate conditions (100%, 80%, 60%, and 40% of satiation). The variable SGR model adjusts the growth rate in response to feed input, providing more realistic predictions under feed-restricted conditions. As feeding rates decreased, the predicted values from the variable model declined relative to those from the conventional model, with the largest difference ovserved at a 40% feeding rate. The variable model also exhibited fluctuations in growth patterns following biweekly feed adjustments and showed the highest predicted weights when using regression-based feeding rates. This model can be applied in aquaculture by incorporating actual feeding data and FC, offering potential as a feed optimization tool. It also supports the development of smart aquaculture systems using artificial intelligence and internet of things technologies to improve growth prediction and economic efficiency.