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

Henk van Lingen*, Mutian Niu, Ermias Kebreab, Sebastião Valadares Filho, JA Rooke, C-A Duthie, Angela Schwarm, Michael Kreuzer, Phil Hynd, Mariana Caetano, Maguy Eugène, Cécile Martin, Mark McGee, Padraig O'Kiely, Martin Hünerberg, Tim McAllister, Telma Berchielli, Juliana Messana, Nico Peiren, Alex ChavesEd Charmley, Andy Cole, Kristin Hales, Sang-Suk Lee, Alexandre Berndt, Christopher Reynolds, Les Crompton, Ali-Reza Bayat, David Yáñez-Ruiz, Zhongtang Yu, André Bannink, Jan Dijkstra, David Casper, Alexander Hristov

*Corresponding author for this work

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Enteric methane (CH4) production attributable to beef cattle contributes to global greenhouse gas emissions. Reliably estimating this contribution requires extensive CH4 emission data from beef cattle under different management conditions worldwide. The objectives were to: 1) predict CH4 production (g d¬-1 animal-1), yield [g (kg dry matter intake; DMI)-1] and intensity [g (kg average daily gain)-1] using an intercontinental database (data from Europe, North America, Brazil, Australia and South Korea); 2) assess the impact of geographic region, and of higher- and lower-forage diets. Linear models were developed by incrementally adding covariates. A K-fold cross-validation indicated that a CH4 production equation using only DMI that was fitted to all available data had a root mean square prediction error (RMSPE; % of observed mean) of 31.2%. Subsets containing data with ≥ 25% and ≤ 18% dietary forage contents had an RMSPE of 30.8 and 34.2%, with the all-data CH4 production equation, whereas these errors decreased to 29.3 and 28.4%, respectively, when using CH4 prediction equations fitted to these subsets. The RMSPE of the ≥ 25% forage subset further decreased to 24.7% when using multiple regression. Europe- and North America-specific subsets predicted by the best performing ≥ 25% forage multiple regression equation had RMSPE of 24.5 and 20.4%, whereas these errors were 24.5 and 20.0% with region-specific equations, respectively. The developed equations had less RMSPE than extant equations evaluated for all data (22.5 vs. 23.2%), for higher-forage (21.2 vs. 23.1%), but not for the lower-forage subsets (28.4 vs. 27.9%). Splitting the dataset by forage content did not improve CH4 yield or intensity predictions. Predicting beef cattle CH4 production using energy conversion factors, as applied by the Intergovernmental Panel on Climate Change, indicated that adequate forage content-based and region-specific energy conversion factors improve prediction accuracy and are preferred in national or global inventories.
Original languageEnglish
Article number106575
Number of pages19
JournalAgriculture, Ecosystems and Environment
Early online date17 Jun 2019
Publication statusPrint publication - 1 Nov 2019


  • Empirical modeling
  • Geographical region
  • Forage content
  • Dietary variables
  • Methane emission


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