The effects of different levels of sugar beet molasses on performance, egg quality and blood parameters of laying hens

This experiment was conducted to evaluate the effects of different levels of sugar beet molasses on egg production, egg traits and blood metabolites of laying hens. In this experiment 192 Hy- line W36 laying hens were used from 32-43 weeks of age in 4 treatments, 4 replicates and 12 hens in each replicate in a completely randomized design. Experimental groups included: 1) control group, 2) 4% of sugar beet molasses, 3) 6% of sugar beet molasses, 4) 8% of sugar beet molasses. The results showed that using sugar beet molasses has significant effects on performance, egg quality and blood biochemical parameters and blood cells of laying hens (P<0.05). The highest amounts of egg weight and egg mass, the highest egg production percentage, the best feed conversion, and the lowest price of feed intake for production per kilogram of egg were resulted with 6% of sugar beet molasses. Using 8% of sugar beet molasses caused the performance significantly decreased. Using 4% of sugar beet molasses improved the eggshell percentage and Haugh unit. The lowest values of blood cholesterol were obtained with 4% of sugar beet molasses. The overall results showed that using sugar beet molasses in comparison with control group using sugar beet molasses has beneficial effects in laying hens. So that laying hens had the best performance and blood cells with 6% of sugar beet molasses, whereas the best egg traits and blood triglyceride and cholesterol were obtained with 4% of sugar beet molasses.
that the problem of multicollinearity within the data was associated to body weight of Yazdi camel breed and related independent variables could be fixed by the principal components analysis. Among the independent variables, body height and abdominal circumference had the highest and lowest coefficient in estimating the body weight of Yazdi camel, respectively. It suggested that the principal components analysis be used when there is the problem of collinearity in multivariable linearly regression analysis because of more precise estimation than least squares method. Also this method can help to the breeders to select the best animals by predicting the precise value of some important traits and selecting the best independent variables for predicting the traits.