title: Equations of prediction for abdominal fat in brown egg-laying hens fed different diets.
abstract: The objective was to use noninvasive measurements to formulate equations for predicting the abdominal fat weight of laying hens in a noninvasive manner. Hens were fed with different diets; the external body measurements of birds were used as regressors. We used 288 Hy-Line Brown laying hens, distributed in a completely randomized design in a factorial arrangement, submitted for 16 wk to 2 metabolizable energy levels (2,550 and 2,800 kcal/kg) and 3 levels of crude protein in the diet (150, 160, and 170 g/kg), totaling 6 treatments, with 48 hens each. Sixteen hens per treatment of 92 wk age were utilized to evaluate body weight, bird length, tarsus and sternum, greater and lesser diameter of the tarsus, and abdominal fat weight, after slaughter. The equations were obtained by using measures evaluated with regressors through simple and multiple linear regression with the stepwise method of indirect elimination (backward), with P < 0.10 for all variables remaining in the model. The weight of abdominal fat as predicted by the equations and observed values for each bird were subjected to Pearson's correlation analysis. The equations generated by energy levels showed coefficients of determination of 0.50 and 0.74 for 2,800 and 2,550 kcal/kg of metabolizable energy, respectively, with correlation coefficients of 0.71 and 0.84, with a highly significant correlation between the calculated and observed values of abdominal fat. For protein levels of 150, 160, and 170 g/kg in the diet, it was possible to obtain coefficients of determination of 0.75, 0.57, and 0.61, with correlation coefficients of 0.86, 0.75, and 0.78, respectively. Regarding the general equation for predicting abdominal fat weight, the coefficient of determination was 0.62; the correlation coefficient was 0.79. The equations for predicting abdominal fat weight in laying hens, based on external measurements of the birds, showed positive coefficients of determination and correlation coefficients, thus allowing researchers to determine abdominal fat weight in vivo.
authors.forename: C
authors.initials: C
authors.lastname: Souza
authors.forename: J J B
authors.initials: JJB
authors.lastname: Jaimes
authors.forename: C E
authors.initials: CE
authors.lastname: Gewehr
mesh_terms: D050153:Abdominal Fat; D000821:Animal Feed / Q000032:analysis; D000824:Animal Nutritional Physiological Phenomena; D000818:Animals; D049628:Body Size; D002645:Chickens / Q000502:physiology*; D004032:Diet / Q000662:veterinary*; D004044:Dietary Proteins / Q000032:analysis; D002149:Energy Intake; D005260:Female; D016014:Linear Models
chemical_list: D004044:Dietary Proteins
keywords: measures; noninvasive method; regressors