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

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, Phil C. Garnsworthy, Md Najmul Haque, Anne L.F. Hellwing, Pekka Huhtanen, Michael Kreuzer, Bjoern Kuhla, Peter LundJørgen 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

Research output: Contribution to journalArticlepeer-review

169 Scopus citations


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/day 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 United States (US), and Australia (AU). 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%, 14.7%, and 19.8% for intercontinental, EU, and United States 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 neutral detergent fiber (NDF) concentration, improve the prediction. For enteric CH4 yield and intensity prediction, information on milk yield and composition is required for better estimation.

Original languageEnglish (US)
Pages (from-to)3368-3389
Number of pages22
JournalGlobal Change Biology
Issue number8
StatePublished - Aug 2018

All Science Journal Classification (ASJC) codes

  • Global and Planetary Change
  • Environmental Chemistry
  • Ecology
  • General Environmental Science


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