Prediction of enteric methane emissions by sheep using an intercontinental database

Alejandro Belanche, Alexander N. Hristov, Henk J. van Lingen, Stuart E. Denman, Ermias Kebreab, Angela Schwarm, Michael Kreuzer, Mutian Niu, Maguy Eugène, Vincent Niderkorn, Cécile Martin, Harry Archimède, Mark McGee, Christopher K. Reynolds, Les A. Crompton, Ali Reza Bayat, Zhongtang Yu, André Bannink, Jan Dijkstra, Alex V. ChavesHarry Clark, Stefan Muetzel, Vibeke Lind, Jon Moorby, John A. Rooke, Aurélie Aubry, Walter Antezana, Min Wang, Roger Hegarty, V. Hutton Oddy, Julian Hill, Philip E. Vercoe, Jean Víctor Savian, Adibe L. Abdalla Filho, Yosra A. Soltan, Alda Lúcia Gomes Monteiro, Juan Carlos Ku-Vera, Gustavo Jaurena, Carlos A. Gómez-Bravo, Olga L. Mayorga, Guilhermo de Souza, David R. Yáñez-Ruiz

Research output: Contribution to conferenceAbstract

Abstract

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 region-specific 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 (
Original languageEnglish
Pages61
Publication statusPublished - 05 Jun 2022
Event8th International Greenhouse Gas & Animal Agriculture Conference - University of Florida, Orlando, Florida, United States of America
Duration: 05 Jun 202209 Jun 2022
https://conference.ifas.ufl.edu/ggaa/index.html

Conference

Conference8th International Greenhouse Gas & Animal Agriculture Conference
Abbreviated titleGGAA 2022
Country/TerritoryUnited States of America
CityOrlando, Florida
Period05 Jun 202209 Jun 2022
Internet address

Keywords

  • Age
  • Diet composition
  • Climatic regions
  • Prediction models
  • Rumen fermentation

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  • Prediction of enteric methane emissions by sheep using an intercontinental database

    Belanche, A., Hristov, A. N., van Lingen, H. J., Denman, S. E., Kebreab, E., Schwarm, A., Kreuzer, M., Niu, M., Eugène, M., Niderkorn, V., Martin, C., Archimède, H., McGee, M., Reynolds, C. K., Crompton, L. A., Bayat, A. R., Yu, Z., Bannink, A., Dijkstra, J., Chaves, A. V., & 22 othersClark, H., Muetzel, S., Lind, V., Moorby, J. M., Rooke, J. A., Aubry, A., Antezana, W., Wang, M., Hegarty, R., Hutton Oddy, V., Hill, J., Vercoe, P. E., Savian, J. V., Abdalla, A. L., Soltan, Y. A., Gomes Monteiro, A. L., Ku-Vera, J. C., Jaurena, G., Gómez-Bravo, C. A., Mayorga, O. L., Congio, G. F. S. & Yáñez-Ruiz, D. R., 15 Jan 2023, In: Journal of Cleaner Production. 384, 17 p., 135523.

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