Evaluation of methane prediction equations for Australian feedlot cattle fed barley and wheat-based diets

A. K. Almeida; J. P. McMeniman; M. R. Van der Saag; F. C. Cowley

doi:10.1071/AN24212

RESEARCH ARTICLE (Open Access)

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Evaluation of methane prediction equations for Australian feedlot cattle fed barley and wheat-based diets

A. K. Almeida

^A ^* , J. P. McMeniman ^B , M. R. Van der Saag ^B and F. C. Cowley

^A

+ Author Affiliations

- Author Affiliations

^A School of Environmental and Rural Science, University of New England, Armidale, NSW 2351, Australia.

^B Meat and Livestock Australia Limited (MLA), 40 Mount Street, North Sydney, NSW 2060, Australia.

^* Correspondence to: a.almeida@massey.ac.nz

Handling Editor: James Dougherty

Animal Production Science 65, AN24212 https://doi.org/10.1071/AN24212

Submitted: 28 June 2024 Accepted: 19 February 2025 Published: 13 March 2025

© 2025 The Author(s) (or their employer(s)). Published by CSIRO Publishing. This is an open access article distributed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND)

Abstract

Context

Accurately predicting baseline methane (CH₄) emissions from beef cattle is of utmost importance for the beef industry and governments alike. It serves as a vital component for accounting as part of national GHG inventories and enables the development and implementation of greenhouse gas (GHG) mitigation strategies.

Aims

The aim of this study was to evaluate equations in the literature for predicting CH₄ emissions of beef cattle when fed barley and wheat-based diets typical of the Australian feedlot industry. Then, propose the best prediction equation to accurately reflect CH₄ emissions of feedlot cattle under Australian conditions.

Methods

As part of the project, a large database of methane measurements performed in respiratory calorimeters taken from beef cattle fed a range of feedlot diets was assembled. The dataset included a wide range of factors that are known to impact CH₄ production, such as dry matter intake (DMI), ether extract (EE), crude protein (CP), and cell wall components, amongst others. The database contained 713 individual measurements, from 175 animals and 12 studies.

Key results

The equation currently utilised by the Australian National Inventory Report had poor accuracy, with mean bias overprediction of 115 g/day (P < 0.01), along with significant linear bias (P < 0.01) and poor precision (r² = 0.05). The mean bias was 144% of average observed CH₄ production. All evaluated equations lacked accuracy and precision in predicting CH₄ emissions for the diets fed in this study. Roughage concentrations (DM basis) ranged from 5.54 to 43.0% with a mean of 20.5 ± 11.1%. Given these findings, two specific equations were developed, (1) a CH₄ yield equation based on DMI: CH₄ (g/day) = 9.89 ± 1.54 × DMI (n = 384; P < 0.01; root mean square error (RMSE) = 32.6 g/day; r² = 0.85); and (2) an equation based on DMI, neutral detergent fibre (NDF) and EE: CH₄ (g/day) = 5.11 ± 1.58 × DMI − 4.00 ± 0.821 × EE + 2.26 ± 0.125 × NDF (n = 384; P < 0.05; RMSE = 22.2 g/day; r² = 0.91). When validated, the second equation yielded a mean bias of 6.10 g overprediction, with no linear bias, and better fit than any of the literature equations.

Conclusions

Based on a thorough model evaluation, our findings support the need to revise current methods to predict CH₄ for barley and wheat-based diets.

Implications

This study contributes to developing accurate estimations of enteric CH₄ emissions for cattle fed barley and wheat-based diets.

Keywords: barley, enteric methane, greenhouse gas, inventory, model evaluation, prediction equations.

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