The Use of Some Nonlinear Functions to Explain Growth in Japanese Quails with Multivariate Adaptive Regression Splines Algorithm
نویسندگان
چکیده
The study aimed was to determine the best nonlinear function describing growth stages of Japanese quail breed. To this aim, functions such as exponential, logistic, von Bertalanffy, Brody, and Gompertz were used is in description body weight-age relationship male female quails. Multivariate Adaptive Regression Splines (MARS) data mining algorithm applied individual parameters obtained from determined fit model, between sex with it has been revealed. dataset 1267 records collected hatching 6th week age 181 quails consisting 90 females 91 males. Each model separately for both males females. Model criteria coefficient determination (R2), adjusted (R2adj), Akaike's information criterion (AIC), Bayes (BIC) evaluate performances individually. All statistical analyses made by R package program. curve models ranked form Logistic > Bertalanffy Brody Exponential according goodness criteria. most suitable among non-linear terms performance logistic. When weight logistic explained MARS algorithm, showed that reliable performance. In addition, Pearson’s correlation real estimated found quite strong (r=0.935). results can be presented a good reference breeders establish breed standards selection strategies breeding purposes.
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ژورنال
عنوان ژورنال: Turkish Journal of Agriculture: Food Science and Technology
سال: 2022
ISSN: ['2148-127X']
DOI: https://doi.org/10.24925/turjaf.v10i10.1807-1813.5410