Location: Location not imported yet.Title: Discovery and application of energetic principles to feeding systems for beef cattle: Use of dynamic models) Author
Submitted to: Journal of Animal Science Supplement
Publication Type: Abstract only
Publication Acceptance Date: 4/14/2008
Publication Date: 7/23/2008
Citation: Oltjen, J.W., Ferrell, C.L. 2008. Discovery and application of energetic principles to feeding systems for beef cattle: Use of dynamic models [abstract]. Journal of Animal Science. 86 (E-Supplement 2):611. Interpretive Summary:
Technical Abstract: Static feeding systems are being replaced by dynamic simulation models that attempt to capture the underlying biology which is sensitive to a wide range of genetic and environmental conditions. In 1986, the Davis Growth Model used cell number and size mechanisms of growth to predict growth and body composition as affected by frame size, implant status, and energy intake. Fat gain was underpredicted for high-energy diets, because fat gain was computed after subtracting energy for maintenance and protein gain. France proposed an integrated model of growth, carbon and nitrogen metabolism. Synthesis and degradation were represented for each body pool based on animal factors and absorbed nutrient levels. Di Marco extended the Davis Growth Model to two pools of protein and included a digestion and metabolism element. Separation of the protein pools accounted for variable maintenance wherein a relatively smaller viscera was associated with decreased FHP. The metabolism submodel corrected errors in prediction of fat gain since efficiency of each nutrient's use was explicitly represented. Input complexity precludes general use of these larger models. A dynamic sheep model of the visceral and muscle protein and fat pools was developed, with an upper bound for muscle and viscera. Heat production for maintenance depends on viscera size, hence nutritional history. New additions refine predictions at levels of energy intake at, or below maintenance. The model provides the structure for predicting composition of growing cattle, but has yet to be completely parameterized and tested. Generally, with each new system, advances in prediction accuracy came about by adding new terms to conceptually validated models.