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-weekold 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 ?). 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;
R2=0.620). In line ? a more accurate prognosis was attained by adding egg
breadth as an additional predictor to the weight in the model
(ChW=0.587EW+0.566? - 19.853; R2=0.692). The study demonstrated that
multiple linear regression models were more precise that single linear
models.