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Gestation is an important phase in the life of an animal, as the transformations that take place affect not only the reproductive system, but also the whole organism of the pregnant animal. Inadequate nutrition of the female in this phase can impair the growth of the offspring. There is a lack of information in current feeding systems regarding the nutritional demands of pregnant goats. The aim of this study is to develop representative dynamic empirical models to predict the protein and energy requirements of pregnant goats. The database to be used to develop the models is from data generated in Project FAPESP 2009/10125-0, 2007/58239-8 and 2006/60480-2, which involve development of the fetus and pregnancy products, as well as nutritional requirements of pregnant goats. The sandwich internship will be undertaken at the Centre for Nutrition Modelling at University of Guelph, Canada under the supervision of Professor James France. Professor France is internationally recognized for its expertise in Biomathematics in Animal Nutrition. Thus the candidate for the internship will have the opportunity to learn to develop mathematical models relating to ruminant nutrition and will bring new knowledge on pregnant dairy goats to share with his research group. (AU)


The minerals are essential for all of animals and influence the animal productivity mainly in the growth fase. The deficeince or the excess of mineral can lead to metabolic disorders resulting to low production and low reproductive performace. Therefore the objectivo of this study is to evalue the effect od feed restrition on calcium (Ca), phosphorus (P) and magnesium (Mg) metabolism of male and female kids goats. Will be used 54 Saanen kids goats (18 intact males, 18 castrate and 18 female) with initial body weight of 15 kg subjected to feed restriction ( ad libitum, 25% and 50% of feed restriction). Blood samples will be collected each 15 days until the group ad libitum arrive to 30 kg of body weight. Analysis of Ca, P, Mg and Alkaline phosphoatase will be performed with especific comercial kit.The data will be analyse as a completed randomized blocks, with repeated measures over time. The statistical analises will be performed usin the MIXED procedure of SAS statistical progam. Will be choosed the covariate matrix that bette fit the data according to Bayesian information criterion ( BIC ). (AU)

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