Evaluation of beef cattle growth and composition dynamics to develop an optimal economic slaughter endpoint model

Abstract

Brazil has the largest commercial herd in the world, which represents the largest sector of national gross agribusiness production value. Consequently, the improvement of productive efficiency of the beef sector may result in expressive economic and environmental benefits to the country. Economic benefits could be achieved by the development of monitoring systems used to evaluate the growth of individuals coupled with models for prediction of optimal slaughter endpoint. However, this technology is not fully developed. To achieve this, the first aspect is the need for a dynamic growth model, parameterized from experiments based on evaluation of genotypes adapted in the Brazilian conditions, particularly Zebu germplasm. A second aspect is that new systems should be developed to integrate: monitoring technologies to evaluate the growth of individual animals in real time; simulation models that are able to predict feed intake; dynamic filtering, and optimization methods to enable a cattle producer and a slaughterhouse take an optimal economic decision with reduced environmental impact. These both aspects are going to be contemplated in this innovative project, that has the general objective of evaluating the growth and energetic metabolism dynamics of finishing Nellore genotype, generating parameters to develop a dynamic model that optimize the economic slaughter endpoint of individuals. The current project expected deliverables are: i) the first dataset obtained from direct determination of rate and chemical composition of different organs and tissues during growing; ii) a dynamic growth model from parameterized from collected intake and tissue deposition (including fat deposits); and iii) a model to predict the optimal economic slaughter endpoint based on individuals. (AU)