The estimation of nutrient limit for predicting eutrophication using quantile regression model (case study: aquaculture pond at IBAT Punten, Batu)

Abstract

Aquaculture can be defined as the cultivation of aquatic organisms, and its systems are usually done in pond. Feeding management in aquaculture pond is very important since it will determine the yield of aquaculture production. Primary food source for most aquatic organism is phytoplankton, which its abundance in aquatic ecosystem is affected by nutrient such as nitrate and phosphate, light availability, temperature, and etc. The nutrient content must be maintained at the level that supports a beneficial phytoplankton abundance and a healthy fishery. Because when the nutrients increasing exceeds the desirable rate, the pond begins the eutrophication process. Eutrophic ponds are often undergo large algal bloom that will causing mass fish mortality and other environmental problem. The relationship between nutrients and phytoplankton abundance conventionally using simple linear regression. However, this method is less informative because it cannot provide the estimation limit of nutrient content to prevent eutrophication. This research proposes quantile regression to fix the flaw. The proposed method is applied to nutrient content and phytoplankton abundance of aquaculture pond at IBAT Punten, Batu. The result shows that the nitrate and phosphate limit are estimated at 0.731 mg/L and 0.022 mg/L respectively.