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

Enteric methane (CH (4)) production from cattle contributes to global greenhouse gas emissions. Measurement of enteric CH (4) is complex, expensive, and impractical at large scales; therefore, models are commonly used to predict CH (4) production. However, building robust prediction models requires...

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Autores principales: Niu, Mutian, 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
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6055644/
https://www.ncbi.nlm.nih.gov/pubmed/29450980
http://dx.doi.org/10.1111/gcb.14094
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author Niu, Mutian
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
author_facet Niu, Mutian
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
author_sort Niu, Mutian
collection PubMed
description Enteric methane (CH (4)) production from cattle contributes to global greenhouse gas emissions. Measurement of enteric CH (4) is complex, expensive, and impractical at large scales; therefore, models are commonly used to predict CH (4) 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 CH (4) production from individual lactating dairy cattle; (2) determine the availability of key variables for predicting enteric CH (4) 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 CH (4) 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 CH (4) 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 CH (4) emission conversion factors for specific regions are required to improve CH (4) 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 CH (4) yield and intensity prediction, information on milk yield and composition is required for better estimation.
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spelling pubmed-60556442018-07-23 Prediction of enteric methane production, yield, and intensity in dairy cattle using an intercontinental database Niu, Mutian 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 Glob Chang Biol Primary Research Articles Enteric methane (CH (4)) production from cattle contributes to global greenhouse gas emissions. Measurement of enteric CH (4) is complex, expensive, and impractical at large scales; therefore, models are commonly used to predict CH (4) 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 CH (4) production from individual lactating dairy cattle; (2) determine the availability of key variables for predicting enteric CH (4) 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 CH (4) 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 CH (4) 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 CH (4) emission conversion factors for specific regions are required to improve CH (4) 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 CH (4) yield and intensity prediction, information on milk yield and composition is required for better estimation. John Wiley and Sons Inc. 2018-03-08 2018-08 /pmc/articles/PMC6055644/ /pubmed/29450980 http://dx.doi.org/10.1111/gcb.14094 Text en © 2018 John Wiley & Sons Ltd This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
spellingShingle Primary Research Articles
Niu, Mutian
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
Prediction of enteric methane production, yield, and intensity in dairy cattle using an intercontinental database
title Prediction of enteric methane production, yield, and intensity in dairy cattle using an intercontinental database
title_full Prediction of enteric methane production, yield, and intensity in dairy cattle using an intercontinental database
title_fullStr Prediction of enteric methane production, yield, and intensity in dairy cattle using an intercontinental database
title_full_unstemmed Prediction of enteric methane production, yield, and intensity in dairy cattle using an intercontinental database
title_short Prediction of enteric methane production, yield, and intensity in dairy cattle using an intercontinental database
title_sort prediction of enteric methane production, yield, and intensity in dairy cattle using an intercontinental database
topic Primary Research Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6055644/
https://www.ncbi.nlm.nih.gov/pubmed/29450980
http://dx.doi.org/10.1111/gcb.14094
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