Journal of Animal Science and Veterinary Medicine

Accurate prediction of egg and yolk weights using body and egg quality traits can enhance productivity, consistency, and selection efficiency in layer chickens. This study evaluated predictive models for body weight, egg, and yolk traits in two commercial strains. A total of 100-layer chickens (50 Nera Black and 50 Isa Brown) were housed in battery cages for 55 weeks. Each strain was further replicated five times with 10 birds per replicate. Weekly body weights and morphometric traits were recorded. Six eggs per replicate (60 eggs in total) were randomly sampled and analysed for external traits (egg weight, length, width, shell weight and shell thickness) and internal traits (albumen weight, albumen height, yolk weight, Haugh unit). Linear and multiple regression analyses were used to develop predictive models for body weight, internal and external egg traits. The multiple regression model provided greater accuracy than the simple linear model. For body weight prediction, combinations of body length (BL), wing length (WL), and body girth (BG) achieved the highest precision (R² = 1.000; SEE < 0.001). In egg and yolk weight prediction, significant (p < 0.001) relationships were observed with specific quality traits. For Isa Brown, egg length (EL) and shell thickness (ST) were the best external predictors, while albumen height (AH) and albumen ratio (AR) were key internal predictors. For Nera Black, shell thickness (ST) and albumen ratio (AR) were most reliable. Integrating morphometric and egg quality traits through multiple regression enhances prediction accuracy for body weight, egg weight, and yolk weight. These findings support improved husbandry, breeding, and egg marketing strategies in layer chicken production.