Advances in Animal and Veterinary Sciences

Research Article
Adv. Anim. Vet. Sci. 9(6): 773-786
Http://dx.doi.org/10.17582/journal.aavs/2021/9.6.773.786
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Mahmoud A. Elnakeeb1, Lydia M. Vasilyeva1, Natalia V. Sudakova1, Adelya Z. Anokhina1, Ahmed G. A. Gewida2, Mahmoud Alagawany3, Mohammed A. E. Naiel4 *

1Biotechnology, Zoology and Aquaculture Department, Faculty of Biology, Astrakhan State University, Astrakhan, Russia; 2Department of Animal Production, Faculty of Agriculture, Al-Azhar University, Cairo, Egypt; 3Department of Poultry, Faculty of Agriculture, Zagazig University, Zagazig, 44511, Egypt; 4Department of Animal Production, Faculty of Agriculture, Zagazig University, Zagazig 44511, 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.