| Grant number: | 13/06259-6 |
| Support Opportunities: | Scholarships abroad - Research Internship - Doctorate |
| Start date: | October 01, 2013 |
| End date: | July 31, 2014 |
| Field of knowledge: | Agronomical Sciences - Animal Husbandry - Animal Nutrition and Feeding |
| Principal Investigator: | Izabelle Auxiliadora Molina de Almeida Teixeira |
| Grantee: | Rafael Fernandes Leite |
| Supervisor: | Pekka Juhani Huhtanen |
| Host Institution: | Faculdade de Ciências Agrárias e Veterinárias (FCAV). Universidade Estadual Paulista (UNESP). Campus de Jaboticabal. Jaboticabal , SP, Brazil |
| Institution abroad: | Swedish University of Agricultural Sciences, Uppsala (SLU), Sweden |
| Associated to the scholarship: | 10/20087-5 - Passage rate in weaned goat kids subjected to different feeding levels, BP.DR |
Abstract Advances in animal nutrition allow the identification of large gap on passage rate, which often are not yet fully understood the factors that affect it. The use of mathematical models to estimate the passage rate in ruminants has been applied to cattle and sheep, but the development of models to estimate the passage rate in goats is still incipient, particularly when it is considered that extrapolation would be wrong, because these animals show naturally lower retention time and hence greater rate of passage. Therefore, the objective of this project is to develop mathematical models to estimate the passage rate in goats in the growing phase. To achieve this goal, it will be used variables that can be measured and calculated routinely and presenting no collinearity. The database to develop prediction models of passage rate in goats is based on the students doctoral thesis (FAPESP - 2010/20087-5), which includes goats in the growing phase with a body weight ranging from 15 to 30 kg. The models will be developed using the mixed model analysis. The models will be selected based on the criteria of convergence. The sensitivity analysis will be determined using the Monte Carlo technique. The models will be evaluated with an independent database and the precison of the model will be evaluated by the coefficient of determination (R2) of the linear regression between the predicted and observed values, the concordance correlation coefficient (CCC) and the prediction mean square error (msep) and its decomposition in mean bias, linear bias and error. (AU) | |
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