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Journal Articles Journal of Cleaner Production Year : 2023

Prediction of enteric methane emissions by sheep using an intercontinental database

Alejandro Belanche
Alexander Hristov
  • Function : Author
Henk van Lingen
  • Function : Author
Stuart Denman
  • Function : Author
Ermias Kebreab
Angela Schwarm
Michael Kreuzer
  • Function : Author
Mutian Niu
Mark Mcgee
  • Function : Author
Christopher Reynolds
Les Crompton
  • Function : Author
Ali Reza Bayat
Zhongtang Yu
  • Function : Author
André Bannink
  • Function : Author
Jan Dijkstra
Alex Chaves
Harry Clark
  • Function : Author
Stefan Muetzel
  • Function : Author
Vibeke Lind
Jon Moorby
John Rooke
  • Function : Author
Aurélie Aubry
  • Function : Author
Walter Antezana
  • Function : Author
Min Wang
  • Function : Author
Roger Hegarty
  • Function : Author
V. Hutton Oddy
  • Function : Author
Julian Hill
  • Function : Author
Philip Vercoe
Jean Víctor Savian
Adibe Luiz Abdalla
Yosra Soltan
Alda Lúcia Gomes Monteiro
Juan Carlos Ku-Vera
Gustavo Jaurena
Carlos Gómez-Bravo
  • Function : Author
Olga Mayorga
  • Function : Author
Guilhermo F.S. Congio
  • Function : Author
David Yáñez-Ruiz
  • Function : Author


Enteric methane (CH4) emissions from sheep contribute to global greenhouse gas emissions from livestock. However, as already available for dairy and beef cattle, empirical models are needed to predict CH4 emissions from sheep for accounting purposes. The objectives of this study were to: 1) collate an intercontinental database of enteric CH4 emissions from individual sheep; 2) identify the key variables for predicting enteric sheep CH4 absolute production (g/d per animal) and yield [g/kg dry matter intake (DMI)] and their respective relationships; and 3) develop and cross-validate global equations as well as the potential need for age-, diet-, or climatic regionspecific equations. The refined intercontinental database included 2,135 individual animal data from 13 countries. Linear CH4 prediction models were developed by incrementally adding variables. A universal CH4 production equation using only DMI led to a root mean square prediction error (RMSPE, % of observed mean) of 25.4% and an RMSPE-standard deviation ratio (RSR) of 0.69. Universal equations that, in addition to DMI, also included body weight (DMI + BW), and organic matter digestibility (DMI + OMD + BW) improved the prediction performance further (RSR, 0.62 and 0.60), whereas diet composition variables had negligible effects. These universal equations had lower prediction error than the extant IPCC 2019 equations. Developing age-specific models for adult sheep (>1-year-old) including DMI alone (RSR = 0.66) or in combination with rumen propionate molar proportion (for research of more refined purposes) substantially improved prediction performance (RSR = 0.57) on a smaller dataset. On the contrary, for young sheep (<1-year-old), the universal models could be applied, instead of age-specific models, if DMI and BW were included. Universal models showed similar prediction performances to the diet- and region-specific models. However, optimal prediction equations led to different regression coefficients (i.e. intercepts and slopes) for universal, age-specific, diet-specific, and regionspecific models with predictive implications. Equations for CH4 yield led to low prediction performances, with DMI being negatively and BW and OMD positively correlated with CH4 yield. In conclusion, predicting sheep CH4 production requires information on DMI and prediction accuracy will improve national and global inventories if separate equations for young and adult sheep are used with the additional variables BW, OMD and rumen propionate proportion. Appropriate universal equations can be used to predict CH4 production from sheep across different diets and climatic conditions.
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Dates and versions

hal-04009386 , version 1 (01-03-2023)




Alejandro Belanche, Alexander Hristov, Henk van Lingen, Stuart Denman, Ermias Kebreab, et al.. Prediction of enteric methane emissions by sheep using an intercontinental database. Journal of Cleaner Production, 2023, 384, pp.135523. ⟨10.1016/j.jclepro.2022.135523⟩. ⟨hal-04009386⟩
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