A computer model to predict the effects of level of nutrition on composition of empty body gain in beef cattle: II. Evaluation of the model.
A computer model developed to predict composition of empty body gain of beef cattle fed at different levels of nutrition was evaluated with data from one unpublished and seven published experiments. These experiments used several breeds of beef cattle growing at rates that varied from negative to fast and various combinations of these growth rates. There was close agreement between observed and simulated absolute treatment means for fatness, except when animals were fed diets low in protein. In this case, experimental animals fed inadequate protein grew at slower rates and became fatter than contemporaries fed adequate protein, whereas the model predicted the opposite. Ability of the model to predict effects of nutrition not associated with changes in BW was evaluated using the proportion of residual variation in experimental fat percentage accounted for by the model, after fitting linear and quadratic terms for empty BW. The model accounted for 13.8 to 56.2% of the residual variation in observed fatness in four experiments in which significant differences in fatness were observed among nutritional treatments after accounting for differences in empty BW. Regression of observed fatness on model-simulated fatness resulted in regression coefficients that were positive and close to 1 in these four experiments. This suggests that the model can accurately predict some of the effects of nutrition on fatness that are not associated with changes in empty BW.