ASAS CENTENNIAL PAPER: Net Energy Systems for Beef Cattle – Cattles, Application, and Future Models

Authors

Calvin Ferrell, University of Nebraska-LincolnFollow
J. W. Oltjen, University of California, DavisFollow

Document Type

Article

Date of this Version

2008

Comments

Published in J. Anim. Sci. 2008. 86:2779–2794.

Abstract

Development of nutritional energetics can be traced to the 1400s. Lavoisier established relationships among O₂ use, CO₂ production and heat production in the late 1700s, and the laws of thermodynamics and law of Hess were discovered during the 1840s. Those discoveries established the fundamental bases for nutritional energetics and enabled the fundamental entity ME = retained energy + heat energy to be established. Objectives became: 1) to establish relationships between gas exchange and heat energy, 2) to devise bases for evaluation of foods that could be related to energy expenditures, and 3) to establish causes of energy expenditures. From these endeavors, the basic concepts of energy partitioning by animals were developed, ultimately resulting in the development of feeding systems based on NE concepts. The California Net Energy System, developed for finishing beef cattle, was the first to be based on retained energy as determined by comparative slaughter and the first to use 2 NE values (NE_m and NE_g) to describe feed and animal requirements. The system has been broadened conceptually to encompass life cycle energy requirements of beef cattle and modified by the inclusion of numerous adjustments to address factors known to affect energy requirements and value of feed to meet those needs. The current NE system remains useful but is empirical and static in nature and thus fails to capture the dynamics of energy utilization by diverse animals as they respond to changing environmental conditions. Consequently, efforts were initiated to develop dynamic simulation models that captured the underlying biology and thus were sensitive to variable genetic and environmental conditions. Development of a series of models has been described to show examples of the conceptual evolution of dynamic, mechanistic models and their applications. Generally with each new system, advances in prediction accuracy came about by adding new terms to conceptually validated models. However, complexity of input requirements often limits general use of these larger models. Expert systems may be utilized to provide many of the additional inputs needed for application of the more complex models. Additional information available from these systems is expected to result in an ever-increasing range of application. These systems are expected to have increased generality and the capability to be integrated with other models to allow economic evaluation. This will eventually allow users to compute solutions that allow development of optimal production strategies.