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Turkish Journal of Fisheries and Aquatic Sciences 2017, Vol 17, Num, 5     (Pages: 1009-1016)

Evaluation of the Dietary Protein Requirement of a Selectively Bred (F-5 Generation) Strain of Olive Flounder, Paralichthys Olivaceus

Kang-Woong Kim 1 ,Kyoung-Duck Kim 1 ,Hyon-Sob Han 1 ,Seonghun Won 2 ,Mohammad Moniruzzaman 2-3 ,Jin-Hyeok Lee 2 ,Youn Hee Choi 2 ,Sungchul C. Bai 2

1 Aquafeed Research Center, National Institute of Fisheries Science (NIFS), Pohang 37517, Korea
2 Department of Marine Bio-materials and Aquaculture, Pukyong National University, Busan, 608-737, Korea
3 Bangladesh Fisheries Research Institute (BFRI), Mymensingh 2201, Bangladesh
DOI : 10.4194/1303-2712-v17_5_16 Viewed : 3050 - Downloaded : 2905 We determined the optimum dietary protein level required for a family selected (F-5 generation) strain of olive flounder Paralichthysolivaceus. Six isocaloric diets (average 20.7 kJ/g diet gross energy) were formulated to contain crude protein levels (CP) as 30 (CP30), 40 (CP40), 45 (CP45), 50 (CP50), 55 (CP55) and 60% (CP60). Triplicate groups of fish averaging 58.4 ± 0.28g (mean ± SD) were randomly distributed into the tanks as groups of 25 fish and fed one of the 6 diets at apparent satiation twice a day. At the end of 8-weeks feeding trial, weight gain (WG) and specific growth rate of fish fed 50 to 60% CP diets were significantly higher than those of fish fed 30 to 45% CP diets. Feed efficiency and protein efficiency ratio were inversely related to the dietary protein level. Broken-line model analysis indicated that the optimum dietary protein level was 50.1% for maximum WG in selected strain of olive flounder. The second-order polynomial regression analysis revealed that the maximum WG occurred at 59.2%. Based on the study, the optimum dietary protein level for maximum growth of family selected olive flounder could be greater than 50.1%, but less than 59.2% CP with 20.7 kJ/g gross energy. Keywords : Olive flounder, Family selection, Protein requirement, Broken-line model, Second-order polynomial regression