Title of article :
Evaluate the Metabolism Responses of Cultured Paddlefish, Polyodon Spathula (Walbaum, 1792), Towards Some Ecological Stressors in the Volga-Caspian Basin using Fuzzy Modeling Control
Author/Authors :
Elnakeeb, Mahmoud A. Biotechnology Zoology and Aquaculture Department - Faculty of Biology - Astrakhan State University, Astrakhan, Russia , Vasilyeva, Lydia M. Biotechnology Zoology and Aquaculture Department - Faculty of Biology - Astrakhan State University, Astrakhan, Russia , Sudakova, Natalia V. Biotechnology Zoology and Aquaculture Department - Faculty of Biology - Astrakhan State University, Astrakhan, Russia , Anokhina, Adelya Z. Biotechnology Zoology and Aquaculture Department - Faculty of Biology - Astrakhan State University, Astrakhan, Russia , Gewida, Ahmed G. A. Department of Animal Production - Faculty of Agriculture - Al-Azhar University, Cairo, Egypt , Alagawany, Mahmoud Department of Poultry - Faculty of Agriculture - Zagazig University, Zagazig, Egypt , Naiel, Mohammed A. E. Department of Animal Production - Faculty of Agriculture - Zagazig University, Zagazig, Egypt
Abstract :
The American paddlefish (Polyodon spathula) is one of the most valuable native fish in the united states,
china, and several Eastern European countries. Sustainable paddlefish production at a commercial scale is essential for
caviar and meat production but requires maintaining high water quality throughout the rearing period. The present
study analyzes the paddlefish’ biological and physical Caspian environmental data using fuzzy logic control methodology from non-traditional analysis to deliver detailed insight into critical factors that affect the aquatic ecosystems. For
this hypothesis, there was one governate station (the Caspian Fisheries Research Institute), including twelve earthen
ponds with 850 kg fish per hectare in this study. The fuzzy inference system was performed to predictable solutions for
output data (metabolic rate), influencing the selected factors (temperature, dissolved oxygen, pH and oxygen uptake)
involved in the system. Through prediction and simulation, each input factor was determined during the breeding
season (seven months). Water quality parameters involved in the variation in the paddlefish rearing conditions were
classified into four groups, including temperature, pH and dissolved oxygen, along with oxygen consumption. For each
rearing condition, a separate fuzzy inference system was defined and the output of each fuzzy system was named F1,
F2, F3, F4 and F5. Finally, F1 and F2 were identified as the inputs to a fuzzy model to evaluate the relation between
the metabolic rate index’s and environmental factors. The results indicated that the decline in the paddlefish metabolic
rate rearing index was correlated with the water temperature and dissolved oxygen percentage during the breeding
season. In conclusion, improving oxygen flowing and optimum water temperature could reduce environmental stress
and improve paddlefish production in this system during the breeding season.
Keywords :
Aquatic environment , Fuzzy logic control , Paddlefish , Prediction , Simulation
Journal title :
Advances in Animal and Veterinary Sciences