Statistical Models and Evaluation Criteria Used in Poultry Farming

Abstract

In this thesis study, the statistical models and evaluation criteria used in poultry breeding were examined comprehensively, taking into account live weight gain, egg weight and egg numbers. For this purpose, as an example of live weight increase; 9-week live weight gains of Ross-308 broiler line, as an example of egg weight; The average of 20-week-old egg weights of brown Lohmann laying hens and 9-week egg yields of Japanese quail were used as an example of the number of eggs. In modeling, for live weight gain; 10 different models for egg production; 11 different models and 8 different models for egg weight were considered. In evaluating the models; error mean squares, coefficient of determination, corrected coefficient of determination, Akaike information criterion, corrected Akaike information criterion, Bayesian information criterion, accuracy factor, deviation factor and Durbin-Watson autocorrelation values were taken into consideration. As a result of the study, in terms of live weight gain; The cubic piecewise regression model is the best model in terms of egg yields; It was determined that the modified Compartmental model and the logistic model gave better results than the others in terms of egg weight. The worst models are in live weight gains; Brody, egg yields; It was concluded that there was a quadratic linear and Von Bertalanffy model for egg weight.