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Prediction of enteric methane production, yield and intensity in dairy cattle using an intercontinental database

Niu, Mutian; Kebreab, Ermias; Hristov, Alexander N.; Oh, Joonpyo; Arndt, Claudia; Bannink, André; Bayat, Ali R.; Brito, André F.; Boland, Tommy; Casper, David; Crompton, Les A.; Dijkstra, Jan; Eugène, Maguy A.; Garnsworthy, P.C.; Haque, Md Najmul; Hellwing, Anne L. F.; Huhtanen, Pekka; Kreuzer, Michael; Kuhla, Bjoern; Lund, Peter; Madsen, Jørgen; Martin, Cécile; McClelland, Shelby C.; McGee, Mark; Moate, Peter J.; Muetzel, Stefan; Muñoz, Camila; O’Kiely, Padraig; Peiren, Nico; Reynolds, Christopher K.; Schwarm, Angela; Shingfield, Kevin J.; Storlien, Tonje M.; Weisbjerg, Martin R.; Yáñez-Ruiz, David R.; Yu, Zhongtang

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Authors

Mutian Niu

Ermias Kebreab

Alexander N. Hristov

Joonpyo Oh

Claudia Arndt

André Bannink

Ali R. Bayat

André F. Brito

Tommy Boland

David Casper

Les A. Crompton

Jan Dijkstra

Maguy A. Eugène

P.C. Garnsworthy

Md Najmul Haque

Anne L. F. Hellwing

Pekka Huhtanen

Michael Kreuzer

Bjoern Kuhla

Peter Lund

Jørgen Madsen

Cécile Martin

Shelby C. McClelland

Mark McGee

Peter J. Moate

Stefan Muetzel

Camila Muñoz

Padraig O’Kiely

Nico Peiren

Christopher K. Reynolds

Angela Schwarm

Kevin J. Shingfield

Tonje M. Storlien

Martin R. Weisbjerg

David R. Yáñez-Ruiz

Zhongtang Yu



Abstract

Enteric methane (CH₄) production from cattle contributes to global greenhouse gas emissions. Measurement of enteric CH₄ is complex, expensive and impractical at large scales; therefore, models are commonly used to predict CH₄ production. However, building robust prediction models requires extensive data from animals under different management systems worldwide. The objectives of this study were to (1) collate a global database of enteric CH₄ production from individual lactating dairy cattle; (2) determine the availability of key variables for predicting enteric CH₄ production (g/d per cow), yield [g/kg dry matter intake (DMI)], and intensity (g/kg energy corrected milk) and their respective relationships; (3) develop intercontinental and regional models and cross-validate their performance; and (4) assess the trade-off between availability of on-farm inputs and CH₄ prediction accuracy. The intercontinental database covered Europe (EU), the US (US), Chile (CL), Australia (AU), and New Zealand (NZ). A sequential approach was taken by incrementally adding key variables to develop models with increasing complexity. Methane emissions were predicted by fitting linear mixed models. Within model categories, an intercontinental model with the most available independent variables performed best with root mean square prediction error (RMSPE) as a percentage of mean observed value of 16.6, 14.4, and 19.8% for intercontinental, EU, and US regions, respectively. Less complex models requiring only DMI had predictive ability comparable to complex models. Enteric CH₄ production, yield, and intensity prediction models developed on an intercontinental basis had similar performance across regions, however, intercepts and slopes were different with implications for prediction. Revised CH₄ emission conversion factors for specific regions are required to improve CH₄ production estimates in national inventories. In conclusion, information on DMI is required for good prediction, and other factors such as dietary NDF concentration, improve the prediction. For enteric CH₄ yield and intensity prediction, information on milk yield and composition is required for better estimation.

Citation

Niu, M., Kebreab, E., Hristov, A. N., Oh, J., Arndt, C., Bannink, A., …Yu, Z. (2018). Prediction of enteric methane production, yield and intensity in dairy cattle using an intercontinental database. Global Change Biology, 24(8), https://doi.org/10.1111/gcb.14094

Journal Article Type Article
Acceptance Date Jan 29, 2018
Online Publication Date Mar 8, 2018
Publication Date Aug 30, 2018
Deposit Date Feb 15, 2018
Publicly Available Date Mar 29, 2024
Journal Global Change Biology
Print ISSN 1354-1013
Electronic ISSN 1365-2486
Publisher Wiley
Peer Reviewed Peer Reviewed
Volume 24
Issue 8
DOI https://doi.org/10.1111/gcb.14094
Keywords Dairy cows; Enteric methane emissions; Prediction models; Dry matter intake; Methane yield; Methane intensity
Public URL https://nottingham-repository.worktribe.com/output/949184
Publisher URL http://onlinelibrary.wiley.com/doi/10.1111/gcb.14094/full

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