In vivo description of body growth and chemical components of egg-laying pullets

• Autor(a) principal: Junior Alves, Warley [UNESP]
• Data de Publicação: 2019
• Outros Autores: Malheiros, Euclides Braga [UNESP], Sakomura, Nilva Kazue [UNESP], Silva, Edney Pereira da [UNESP], Viana, Gabriel da Silva [UNESP], Reis, Matheus de Paula [UNESP], Goncalves, Camila Angelica [UNESP], Suzuki, Rafael Massami [UNESP]
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Institucional da UNESP
• DOI: 10.1016/j.livsci.2018.12.023
• Texto Completo: http://dx.doi.org/10.1016/j.livsci.2018.12.023; http://hdl.handle.net/11449/186650
• topic: Body monitoring; Densitometer; Gompertz; Growth curves

In order to describe the growth potential and body chemical composition using the Gompertz function, a total of 360 one-day-old commercial pullets of four different strains (Dekalb White and Brown, and Lohmann White and Brown) (90 pullets/strain) were allotted in stainless steel cages in environmentally controlled room from 1 to 18 weeks of age. Hens were provided water and a unique diet (mash form) throughout 18 weeks and in 15-D-intervals six pullets from each strain had the body composition measured until 18 weeks of age using DXA method. Concomitantly to the DXA measurements, a group of pullets were euthanized and had their body and feather chemical composition determined in order to devise equation to correlate DXA measurements with body chemical composition. Feather composition was determined using chemical analysis. Analyzing the same pullet throughout the aforementioned period using DXA method allowed reducing the error in the prediction of bird growth. The genetic strains assessed in the current research exhibited few differences regarding body and feather growth and their bodily chemical components. The growth parameters described herein are in accordance with those described in literature. With the accurate description of genetic growth rate potential, the amount of nutrient required for maintenance purposes and growth may be predicted though the association of growth curves with factorial models.