ATLAS INTERNATIONAL CONFERENCE ON RESEARCH IN APPLIED SCIENCES, Barcelona, İspanya, 9 - 10 Temmuz 2022, ss.1-6
For many years,
numerous mathematical functions have been employed to model the growth of
poultry. The majority of these are asymptotic-mechanistic models. Depending on
the biological growth phenomenon of the animal, these models are functions that
assume the dependent variable has an approximated asymptotic value while the
independent variable is at infinity. The majority of specific growth models are
nonlinear regression equations with a sigmoidal structure. The studies
conducted on poultry using growth models may be categorized into three groups:
"finding of the most fit model", "comparison of various
scientific application results using growth models", and "genetic
structure of the growth curve". The aim of this study is to determine the
most suitable growth model for Japanese quail. In similar studies conducted to
determine the most suitable growth curve model for Japanese quails, the
frequentist approach was widely employed. However, there has been a recent
surge of interest in the use of the Bayesian methodology to modeling studies.
In this study, the Bayesian approach was applied as an alternative to the
frequentist method for analyzing growth curve data. Richards, Gompertz,
Negative Exponential, Broody, and Logistic functions, which are commonly used
to model growth in poultry, were used in this study. Normal distribution was
assigned as prior distribution for all growth curve parameters. Jeffrey's
non-informative prior was allocated to the prior distribution of the variance
of the residuals. Bayesian analyzes were performed using the MCMC procedure in
SAS 9.4 software. After evaluating the trace plots for each parameter in all
models, a chain length of 120000 was assumed. The initial 10,000 samples were
removed as the burn-in period. The thinning interval was set to 20, and 5,500
samples were utilized to determine the descriptive statistics of the marginal
posterior distributions. The models were compared using the deviance
information criterion (DIC), which is a typically used Bayesian approach
goodness-of-fit criterion. Based on this criterion, the Richards model with the
smallest DIC value was determined to be the most appropriate model for Japanese
quail growth samples. The posterior mean value of the parameter defined as the asymptotic weight parameter of
the Richards model was found to be 270.0. The other model parameters, , , and , were estimated to be 0.81,
0.046, and 19.30, respectively. On the basis of the fit criterion, the Richards
function was followed by the Gompertz and Broody growth models, respectively.
For many years,
numerous mathematical functions have been employed to model the growth of
poultry. The majority of these are asymptotic-mechanistic models. Depending on
the biological growth phenomenon of the animal, these models are functions that
assume the dependent variable has an approximated asymptotic value while the
independent variable is at infinity. The majority of specific growth models are
nonlinear regression equations with a sigmoidal structure. The studies
conducted on poultry using growth models may be categorized into three groups:
"finding of the most fit model", "comparison of various
scientific application results using growth models", and "genetic
structure of the growth curve". The aim of this study is to determine the
most suitable growth model for Japanese quail. In similar studies conducted to
determine the most suitable growth curve model for Japanese quails, the
frequentist approach was widely employed. However, there has been a recent
surge of interest in the use of the Bayesian methodology to modeling studies.
In this study, the Bayesian approach was applied as an alternative to the
frequentist method for analyzing growth curve data. Richards, Gompertz,
Negative Exponential, Broody, and Logistic functions, which are commonly used
to model growth in poultry, were used in this study. Normal distribution was
assigned as prior distribution for all growth curve parameters. Jeffrey's
non-informative prior was allocated to the prior distribution of the variance
of the residuals. Bayesian analyzes were performed using the MCMC procedure in
SAS 9.4 software. After evaluating the trace plots for each parameter in all
models, a chain length of 120000 was assumed. The initial 10,000 samples were
removed as the burn-in period. The thinning interval was set to 20, and 5,500
samples were utilized to determine the descriptive statistics of the marginal
posterior distributions. The models were compared using the deviance
information criterion (DIC), which is a typically used Bayesian approach
goodness-of-fit criterion. Based on this criterion, the Richards model with the
smallest DIC value was determined to be the most appropriate model for Japanese
quail growth samples. The posterior mean value of the parameter defined as the asymptotic weight parameter of
the Richards model was found to be 270.0. The other model parameters, , , and , were estimated to be 0.81,
0.046, and 19.30, respectively. On the basis of the fit criterion, the Richards
function was followed by the Gompertz and Broody growth models, respectively.