Predicting Egg Weight from Egg Quality Traits of the Lohmann Brown Chicken Breed Using Stepwise Regression
Predicting Egg Weight from Egg Quality Traits of the Lohmann Brown Chicken Breed Using Stepwise Regression
Thobela Louis Tyasi*, Lebo Ngorima, Victoria Rankotsane Hlokoe
ABSTRACT
The best-fitted regression model determined using R2, RMSE, AIC and BIC can result in the egg quality traits that can best predict egg weight in chickens. The study aimed to develop a model that can be employed to estimate egg weight from egg quality traits with stepwise regression in the Lohmann brown chicken breed. The study was conducted at Kitamu farm in Ntsima village in Limpopo Province, South Africa. The Pearson’s correlation findings displayed that egg weight (EW) had a highly positive remarkable association (P < 0.01) with egg length (EL), yolk weight (YW), egg width (EWD), albumen weight (AW), shell surface area (SSA), albumen ratio (AR) and egg volume (EV), and a highly negative remarkable relationship (P < 0.01) with Y/A. Stepwise results revealed that the model, including EV, AW, SSA, EWD, EL, YW and AR, is the best-fitted regression model (R2 = 0.99, RMSE = 0.03, AIC = 14.17, BIC = 37.26) for estimation of egg weight in the Lohmann brown chicken breed. The study concludes that the improvement of EWD, YW, SSA, AW, AR, EV and EL might enhance the egg weight of the Lohmann chicken breed. The EV, AW, SSA, EWD, EL, YW and AR may be chosen when breeding for enhancement of egg weight in the Lohmann brown chicken. The study outcomes may assist farmers in the egg quality traits to consider during breeding to improve the egg weight of the Lohmann Brown chicken breed.
Keywords | Best-fitted model, Correlation, Egg length, Egg width, Shell weight
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March 2024
Vol. 12, Iss. 3, pp. 392-585
