We currently use empirically derived equations and transfer coefficients for metabolism of amino acids in this application level model (Table 21.2) because of the limitations in predicting end-products of ruminal fermentation, absorbed carbohydrate and amino acids, and the large number of metabolic routes connecting the numerous tissue and metabolic compartments, the multiple nutrient interactions, and the sophisticated metabolic regulations which drive the partitioning of absorbed nutrients in various productive states. The equations used to predict ME from DE reflect the variation in methane produced across a wide range in diets. The equations used for lactat-ing dairy cows to predict NE for lactation from ME reflects the energetic efficiency associated with the typical mix of metabolites in the ME, based on respiration chamber data (Moe, 1981), and validated on independent data (Roseler, 1994). The equations used for growing cattle to predict NE for maintenance and NE for growth reflect the wide variation in metabolites used in growing cattle and dry cows, and validated with little bias across a wide range of ME contents (NRC, 2000). To predict the impact of diet on composition of tissue and milk, our metabolism submodel needs to be able to predict absorbed carbohydrates, volatile fatty acids, lipids and amino acids available for various physiological functions and their metabolism with changes in productive states. Pitt et al. (1996) described the prediction of ruminal fermentation end-products within the CNCPS structure as a first step; we are in the process of incorporating this dynamic approach into the CNCPS model.
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