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Updated: Jun 30, 2026

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The Use of an Automated System (GreenFeed) to Monitor Enteric Methane and Carbon Dioxide Emissions from Ruminant Animals
Published on: September 7, 2015
ASAS centennial paper: net energy systems for beef cattle--concepts, application, and future models
1USDA, ARS, US Meat Animal Research Center, Clay Center, NE 68933-0166, USA.
Journal of Animal Science
|September 30, 2008
Summary
Nutritional energetics evolved from 1400s principles to dynamic models for animal energy. Modern systems integrate biology and environment for optimal production strategies.
Area of Science:
- Animal Nutrition
- Bioenergetics
- Thermodynamics
Background:
- Nutritional energetics traces roots to the 1400s, with foundational discoveries in the 1700s and 1840s.
- Key principles include relationships between gas exchange, heat production, and energy partitioning in animals.
- Early systems like the California Net Energy System for beef cattle emerged from these principles.
Purpose of the Study:
- To trace the historical development of nutritional energetics and feeding systems.
- To highlight the limitations of empirical, static models.
- To introduce the evolution and application of dynamic, mechanistic simulation models.
Main Methods:
- Historical review of key discoveries and system developments in nutritional energetics.
- Description of the conceptual evolution from static to dynamic simulation models.
- Discussion of the integration of expert systems for complex model applications.
Main Results:
- Development of fundamental concepts like ME = retained energy + heat energy.
- Creation of Net Energy (NE) systems, including NE(m) and NE(g) for beef cattle.
- Advancement towards dynamic, mechanistic models sensitive to genetic and environmental variations.
Conclusions:
- Empirical models are limited; dynamic models offer greater accuracy and adaptability.
- Complex models can be enhanced by expert systems for broader application.
- Future systems aim for increased generality, integration, and economic evaluation for optimal production strategies.
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