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Development of models for predicting intake and performance of beef cattle on pasture

Grant number: 15/11921-5
Support type:Research Grants - Visiting Researcher Grant - International
Duration: July 06, 2015 - July 26, 2015
Field of knowledge:Agronomical Sciences - Animal Husbandry - Animal Production
Principal Investigator:Ricardo Andrade Reis
Grantee:Ricardo Andrade Reis
Visiting researcher: Dennis Paul Poppi
Visiting researcher institution: University of Queensland, Gatton (UQ), Australia
Home Institution: Faculdade de Ciências Agrárias e Veterinárias (FCAV). Universidade Estadual Paulista (UNESP). Campus de Jaboticabal. Jaboticabal , SP, Brazil
Associated research grant:11/00060-8 - GHG full account and mitigation strategies in Brachiaria pastures submitted to different management, AP.TEM


The knowledge of the variables on intake and animal performance of bulls grazing pasture is essential for optimal animal production. Considering its importance, several studies were conducted in order to determine the animal performance on national conditions. Despite this effort, it is still possible to verify the need for a comprehensive study, especially considering the influence of factors such as plant characteristic, gender, physiologic stage and level of animal performance. Another factor to be considered for estimating nutrient requirements is the prediction of dry matter intake (DMI), because this has been done with low accuracy and precision, based on data collected in different weather conditions or even extrapolated from other species. Thus the aim of this study is to develop models to estimate dry matter intake and the animal performance for bulls grazing pasture. For this, it will be used database from individual data of 540 animals from studies conducted between 2009 and 2014 in Brazil. It will be performed a lifting of the variables that are related with the estimation of DMI for developing the models. The performance will be estimated by linear and non-linear allometric model, and their bias will be evaluated. For the development of linear equations will be used PROC MIXED and for nonlinear equations the PROC NLINMIX of SAS. The sensibility of the models will be analyzed using the MCMC technique. Moreover, the models are evaluated using criteria such as R2, coefficient of concordance correlation, the prediction mean square error and its decomposition into mean bias and systematic bias. (AU)