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Updated: Jul 20, 2026

The Use of an Automated System GreenFeed to Monitor Enteric Methane and Carbon Dioxide Emissions from Ruminant Animals

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Predicting Enteric Methane Emissions from Dairy and Beef Cattle Using Nutrient Composition and Intake Variables.

Yaodong Wang¹, Weitao Song¹, Qian Wang¹

¹College of Information Science and Technology, Gansu Agricultural University, Lanzhou 730070, China.

Animals : an Open Access Journal From MDPI

|December 17, 2024

Summary

This study developed statistical models to predict enteric methane emissions from cattle. The new models improve the accuracy of methane inventories for beef and dairy cattle.

Keywords:

beef cattle dairy cattle energy methane emission statistical model

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Published on: November 30, 2018

Area of Science:

Agricultural Science
Animal Science
Environmental Science

Background:

Enteric methane emissions from cattle contribute to greenhouse gas production.
Accurate prediction of methane emissions is crucial for environmental management and inventory preparation.
Existing models may overestimate methane production, impacting accuracy.

Purpose of the Study:

To develop and validate linear and nonlinear statistical models for predicting enteric methane emissions (EME) in beef and dairy cattle.
To utilize ration nutrient composition and digestibility as predictors for methane (CH4) production.
To improve the accuracy and precision of methane emission estimations in cattle.

Main Methods:

Development of three databases (beef, dairy, combined) from 34 published experiments.
Application of a mixed-model approach to create linear and nonlinear regression models.
Evaluation of model performance using Root Mean Square Percentage Error (RMSPE) and analysis of error sources.

Main Results:

Linear models for beef cattle, dairy cattle, and combined databases showed minimized RMSPE values of 21.99%, 15.99%, and 24.4%, respectively.
The DMI-based exponential nonlinear model showed superior performance among nonlinear models but did not surpass linear model predictability.
Error analysis indicated that random error constituted over 83% of the total error in the developed models.

Conclusions:

The developed statistical models provide improved predictions for enteric methane emissions from beef and dairy cattle.
These models enhance the preparation of methane inventories and the estimation of methane production.
The study highlights the importance of accurate modeling for environmental impact assessment in livestock agriculture.