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Correlation between main morphologicaltraitsand body weight of mandarin fish Difference Analysis of male and female morphological traits

Dongjian shi

Abstract


 To learn about mandarin fish(Siniperca Chuatsi)Correlation between main morphological traits and body weight, and the difference between male and female morphological traits.2837Mandarin Fish7.Length, body height, head length, eye diameter, tail handle length and tail handle height and weight. Correlation analysis, Path Analysis and multiple linear regression analysis were used to analyze the relationship between morphological traits and body weight.150Multiple regression equation was established and randomly selected120Male and female discrimination verification. The results showed that the full length and body height had the largest correlation coefficient with body weight among all morphological traits. Through the establishment of regression equations and partial regression coefficient test, it is found that the correlation between head length and tail handle height and body weight is not significant.2.After that, we made a further analysis. Path analysis showed that total length and body height had the largest direct effect on body weight, which was greater than indirect effect, full length and body height had the highest independent weight determination, and the synergistic effect was the largest;4.Sum of the determining coefficient of individual traits on body weight(SigmaD)Da0.896To illustrate this4.There is a great correlation between individual traits and body weight, and the fitting multiple regression equation is of great reference significance. The discriminant regression equation of male and female fish was established and verified.200gThe accuracy rate of sex discrimination was higher. This study can provide a reference for the selection and evaluation index of growth traits of Siniperca Chuatsi, and help to improve the efficiency of breeding.


Keywords


Mandarin fish; morphological traits; body weight; correlation; multiple regression; Path Analysis

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DOI: http://dx.doi.org/10.18686/fsa.v1i1.1204

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