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

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

© 2018 John Wiley & Sons Ltd.

Bibliographische Detailangaben
Veröffentlicht in:Global change biology. - 1999. - 24(2018), 8 vom: 16. Aug., Seite 3368-3389
1. Verfasser: Niu, Mutian (VerfasserIn)
Weitere Verfasser: 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, Phil 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
Format: Online-Aufsatz
Sprache:English
Veröffentlicht: 2018
Zugriff auf das übergeordnete Werk:Global change biology
Schlagworte:Journal Article Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, Non-P.H.S. dairy cows dry matter intake enteric methane emissions methane intensity methane yield prediction models Methane OP0UW79H66
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500 |a Date Revised 09.01.2021 
500 |a published: Print-Electronic 
500 |a Citation Status MEDLINE 
520 |a © 2018 John Wiley & Sons Ltd. 
520 |a 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 
650 4 |a Journal Article 
650 4 |a Research Support, Non-U.S. Gov't 
650 4 |a Research Support, U.S. Gov't, Non-P.H.S. 
650 4 |a dairy cows 
650 4 |a dry matter intake 
650 4 |a enteric methane emissions 
650 4 |a methane intensity 
650 4 |a methane yield 
650 4 |a prediction models 
650 7 |a Methane  |2 NLM 
650 7 |a OP0UW79H66  |2 NLM 
700 1 |a Kebreab, Ermias  |e verfasserin  |4 aut 
700 1 |a Hristov, Alexander N  |e verfasserin  |4 aut 
700 1 |a Oh, Joonpyo  |e verfasserin  |4 aut 
700 1 |a Arndt, Claudia  |e verfasserin  |4 aut 
700 1 |a Bannink, André  |e verfasserin  |4 aut 
700 1 |a Bayat, Ali R  |e verfasserin  |4 aut 
700 1 |a Brito, André F  |e verfasserin  |4 aut 
700 1 |a Boland, Tommy  |e verfasserin  |4 aut 
700 1 |a Casper, David  |e verfasserin  |4 aut 
700 1 |a Crompton, Les A  |e verfasserin  |4 aut 
700 1 |a Dijkstra, Jan  |e verfasserin  |4 aut 
700 1 |a Eugène, Maguy A  |e verfasserin  |4 aut 
700 1 |a Garnsworthy, Phil C  |e verfasserin  |4 aut 
700 1 |a Haque, Md Najmul  |e verfasserin  |4 aut 
700 1 |a Hellwing, Anne L F  |e verfasserin  |4 aut 
700 1 |a Huhtanen, Pekka  |e verfasserin  |4 aut 
700 1 |a Kreuzer, Michael  |e verfasserin  |4 aut 
700 1 |a Kuhla, Bjoern  |e verfasserin  |4 aut 
700 1 |a Lund, Peter  |e verfasserin  |4 aut 
700 1 |a Madsen, Jørgen  |e verfasserin  |4 aut 
700 1 |a Martin, Cécile  |e verfasserin  |4 aut 
700 1 |a McClelland, Shelby C  |e verfasserin  |4 aut 
700 1 |a McGee, Mark  |e verfasserin  |4 aut 
700 1 |a Moate, Peter J  |e verfasserin  |4 aut 
700 1 |a Muetzel, Stefan  |e verfasserin  |4 aut 
700 1 |a Muñoz, Camila  |e verfasserin  |4 aut 
700 1 |a O'Kiely, Padraig  |e verfasserin  |4 aut 
700 1 |a Peiren, Nico  |e verfasserin  |4 aut 
700 1 |a Reynolds, Christopher K  |e verfasserin  |4 aut 
700 1 |a Schwarm, Angela  |e verfasserin  |4 aut 
700 1 |a Shingfield, Kevin J  |e verfasserin  |4 aut 
700 1 |a Storlien, Tonje M  |e verfasserin  |4 aut 
700 1 |a Weisbjerg, Martin R  |e verfasserin  |4 aut 
700 1 |a Yáñez-Ruiz, David R  |e verfasserin  |4 aut 
700 1 |a Yu, Zhongtang  |e verfasserin  |4 aut 
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