Modelling the egg components and internal cycle length of laying hens

A model that can estimate the changes that occur to the composition of egg components over time is an important tool for the nutritionists, since it can provide information about the nutrients required by a laying hen to achieve her potential egg output. In this context, the present study was aimed to model the potential egg production of laying hens during the egg-production period. One hundred and twenty Hy-Line W36 and ISA-Brown layers were used from 18 to 60 weeks of age, with each bird being an experimental unit. The birds were housed in individual cages during the experimental period. Egg production (%), egg weight (g) and the weight of egg components were recorded for each bird. The data were used to calculate the parameters of equations for predicting the weights of yolk, albumen and shell, and for predicting internal cycle length. The predicted results were evaluated by regressing residual (observed minus predicted) values of the predicted values centred of their average value. The equations for predicting mean yolk weight with age are for Hy-Line W36 (y1) and ISA-Brown (y2) respectively. Albumen and shell weights for Hy-Line W36 were described by the equations 15.07 × (yolk weight)0.37 and 0.70 × (yolk + albumen weight)0.50 respectively, and for ISA-Brown, 21.99 × (yolk weight)0.24 and 1.60 × (yolk + albumen weight)0.34 respectively. The average internal cycle length over time for Hy-Line W36 (ICL1) is described by the model 22.95 + 5.24 × (0.962t) + 0.02 × t and for ISA-Brown by 24.01 + 10.29 × (0.94t) + 0.004 × t, where t is the age at first egg (days). The assessment of the results indicated that the equations for predicting egg weight were more accurate for Hy-Line W36 but less precise for both strains, whereas the equation models for predicting the internal cycle lengths were more accurate and precise for ISA-Browns. The models could predict the potential weight of egg components and the rate of laying associated with the internal cycle lengths, and, on the basis of this information, it is possible to improve the nutrient requirement estimated.