Evaluating the Rearing condition of Rainbow Trout (Oncorhynchus Mykiss) Using Fuzzy Inference System |
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| Institution: | 1. Department of Biosystems Engineering, Faculty of Agriculture, Ferdowsi University of Mashhad, Mashhad, Iran;2. Department of Fisheries, Faculty of Natural Resources and Environment, Ferdowsi University of Mashhad, Mashhad, Iran;1. Dept. of Civil and Environmental Engineering, University of Alberta, Edmonton, AB, T6G 2R3, Canada;2. Engineering College, Ocean University of China, Qingdao, 266100, China;3. Shandong Province Key Laboratory of Ocean Engineering, Ocean University of China, Qingdao, 266100, China;1. Department of Biotechnology Engineering, ORT Braude College of Engineering, Karmiel 2161002, Israel;2. Algae-Smart Ltd., Algae-Fish Lab, Meir Shfeya, 3080600, Israel;1. Korea Institute of Ocean Science and Technology, Busan, Republic of Korea;2. Sekwang Engineering Consultants CO., LTD, Seoul, Republic of Korea;1. Universidad de Sonora, Departamento de Investigaciones Científicas y Tecnológicas, Av. Luis D. Colosio s/n, Hermosillo, Sonora, 83000, Mexico;2. Centro de Investigaciones Biológicas del Noroeste (CIBNOR), Unidad Sonora, Apdo. Postal 349, Guaymas, Sonora, 85454, Mexico;1. Federal Institute of Education, Science and Technology of Mato Grosso do Sul, Mato Grosso do Sul, Campo Grande, Brazil;2. Federal Institute of Education, Science and Technology of Mato Grosso do Sul, Mato Grosso do Sul, Aquidauana, Brazil;3. National University of Brasilia, Via L3, Brasilia-DF 70910-900, Brazil;4. University of Nebraska – Lincoln, 1101 T St. Schorr Center 223, Lincoln, NE 68588-0038, United States;5. Dom Bosco Catholic University, Av. Tamandaré, 6000, Campo Grande, MS 79117-010, Brazil;6. Federal University of Mato Grosso do Sul, Av. Costa e Silva, s/no, Campo Grande, MS 79070-900, Brazil;1. Caunesp – São Paulo State University, Aquaculture Center, Jaboticabal, SP, 14884–900, Brazil;2. Embrapa – Brazilian Agricultural Research Corporation, Mid-North, Parnaíba, PI, 64200-970, Brazil |
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| Abstract: | Rainbow trout (Oncorhynchus mykiss) is one of the most popular aquacultured species in the world. Sustainable production of this fish at commercial scale is very important but requires maintaining good water quality throughout the total rearing period. The present study aimed to develop a rainbow trout production index in order to raise awareness about the conditions of the rearing environment, enhance production, and reduce losses. For this purpose, an intensive rainbow trout production system was selected as the study system. In this system, there were seven stations including (a) 3000 5-g fish, (b) 3000 25-g fish, (c) 3000 50-g fish, (d) 3000 100-g fish, (e) 3000 220-g fish, (f) 2000 350-g fish, and (g) 2000 830-g fish. The fuzzy inference system was used to develop the target rearing index. Water quality parameters involved in the variation in the rainbow trout rearing conditions were classified into three groups including un-ionized ammonia, nitrite, and nitrate, Alkalinity and phosphate, along with dissolved oxygen and linear velocity. For each group and condition of rearing, a separate fuzzy inference system was defined and the output of each fuzzy system was named I1, I2, I3. Finally, I1, I2, and I3 were considered as the inputs to a fuzzy system in order to evaluate their effects on the index of general rearing conditions (I). The results indicated that un-ionized ammonia, nitrite, nitrate, and phosphate had negative effects while dissolved oxygen, linear velocity, and alkalinity positively affected water quality and rearing index. Most of the decline in the rainbow trout rearing index was related to the effect of un-ionized ammonia, nitrite, and nitrate due to food decomposition. Therefore, intelligence feeding based on fish appetite through reducing food conversion rate and water pollution can improve rainbow trout production in this system. The index of rainbow trout production conditions reflects the type, amount, and effect of water quality pollutants on rearing conditions. Producers can use this information to reduce the negative environmental effects and improve the product quality. |
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| Keywords: | Fuzzy inference Rearing index Rainbow Trout |
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