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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...
Autores principales: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley and Sons Inc.
2018
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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. |
format | Online Article Text |
id | pubmed-6055644 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | John Wiley and Sons Inc. |
record_format | MEDLINE/PubMed |
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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