Comparative analysis of two dynamic mechanistic models of beef cattle growth

International audience; The INRA Growth Model (IGM) and the Davis Growth Model (DGM) are two dynamic mechanistic models developed to predict protein and fat deposition in growing cattle whatever the production system. Both models depend on animal genotype and age, metabolizable energy intake (MEI) and knowledge of previous growth. The aim of this paper was (i) to identify in which situations DGM and/or IGM provide reliable estimations of body protein and fat, (ii) to give insight on the improvements needed in each model and (iii) to discuss the usefulness of comparative analysis for improvement of mechanistic models. We performed a comparative analysis of DGM and IGM with three datasets from published experiments on Salers heifers, Angus-Hereford steers and Charolais bulls. Each model was fitted independently to each dataset. Both models gave accurate and precise predictions of body protein. They also performed well for body fat in Charolais bulls growing continuously. However, DGM tended to underestimate body fat deposition during feeding restriction periods with Salers heifers. This suggests that DGM overestimated heat production during periods of low MEI. IGM was not sensitive enough to MEI as it overestimates body fat at low MEI and it underestimates body fat at high MEI in Angus-Hereford steers. Presently, IGM does not take into account metabolizable energy concentration (MEC) of the diet and thus does not simulate different growth trajectories for same MEI but different MEC. These results suggest that model's structure and equations for protein accretion in DGM and IGM are valid. Future improvements will focus on prediction of heat production during feed restriction periods for DGM and on mathematical formulation of feed energy utilisation for fat synthesis in IGM in order to improve model sensitivity to MEI. Comparative analysis provides meaningful information on the models behaviour for further improvement of processes simulations.