Regression models for estimating chick hatchling weight from some egg geometry traits

• Main Authors: Mincheva Nadya, Lalev Mitko, Oblakova Magdalena, Hristakieva Pavlina
• Format: Article
• Language: English
• Published: Institute for Animal Husbandry, Belgrade 2018-01-01
• Series: Biotechnology in Animal Husbandry
• Subjects: chick weight; egg weight; egg geometry parameters; regression model
• Online Access: https://scindeks-clanci.ceon.rs/data/pdf/1450-9156/2018/1450-91561803323M.pdf

The prediction of chicks' weight before hatching is an important element of selection, aimed at improving the uniformity rate and productivity of birds. With this regards, our goal was to develop and evaluate optimum models for similar prediction in two White Plymouth Rock chickens lines-line L and line K on the basis of the incubation egg weight and egg geometry characteristics-egg maximum breadth (B), egg length (L), geometric mean diameter (Dg), egg volume (V), egg surface area (S). A total of 280 eggs (140 from each line) laid by 40-week-old hens were randomly selected. Mean arithmetic values, standard deviations and coefficients of variation of studied parameters were determined for each line. Correlation coefficients between the weight of hatchlings and predictors were the highest for egg weight, geometric mean diameter, volume and surface area of eggs (r=0.731-0.779 for line L; r=0.802-0.819 for line К). Nine linear regression models were developed and their accuracy evaluated. The regression equations of hatchlings' weight vs egg length had the lowest coefficient of determination (0.175 for line K and 0.291 for line L), but when egg length and breadth entered the model together, its value increased significantly up to 0.541 and 0.665 for lines L and K, respectively. The weight of day-old chicks from line L could be predicted with higher accuracy with a model involving egg surface area apart egg weight (ChW=0.513EW+0.282S-10.345; R 2 =0.620). In line К a more accurate prognosis was attained by adding egg breadth as an additional predictor to the weight in the model (ChW=0.587EW+0.566В-19.853; R 2 =0.692). The study demonstrated that multiple linear regression models were more precise that single linear models.