globalchange  > 影响、适应和脆弱性
DOI: 10.1111/gcb.14094
Scopus记录号: 2-s2.0-85043383290
论文题名:
Prediction of enteric methane production, yield, and intensity in dairy cattle using an intercontinental database
作者: Niu M.; Kebreab E.; Hristov A.N.; Oh J.; Arndt C.; Bannink A.; Bayat A.R.; Brito A.F.; Boland T.; Casper D.; Crompton L.A.; Dijkstra J.; Eugène M.A.; Garnsworthy P.C.; Haque M.N.; Hellwing A.L.F.; Huhtanen P.; Kreuzer M.; Kuhla B.; Lund P.; Madsen J.; Martin C.; McClelland S.C.; McGee M.; Moate P.J.; Muetzel S.; Muñoz C.; O'Kiely P.; Peiren N.; Reynolds C.K.; Schwarm A.; Shingfield K.J.; Storlien T.M.; Weisbjerg M.R.; Yáñez-Ruiz D.R.; Yu Z.
刊名: Global Change Biology
ISSN: 13541013
出版年: 2018
卷: 24, 期:8
起始页码: 3368
结束页码: 3389
语种: 英语
英文关键词: dairy cows ; dry matter intake ; enteric methane emissions ; methane intensity ; methane yield ; prediction models
Scopus关键词: carbon emission ; cattle ; database ; dry matter ; greenhouse gas ; methane ; modeling ; prediction ; Australia ; Europe ; United States ; Animalia ; Bos
英文摘要: 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. © 2018 John Wiley & Sons Ltd
Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/110302
Appears in Collections:影响、适应和脆弱性
气候变化事实与影响

Files in This Item:

There are no files associated with this item.


作者单位: Department of Animal Science, University of California, Davis, CA, United States; Department of Animal Science, The Pennsylvania State University, University Park, PA, United States; Environmental Defense Fund, San Francisco, CA, United States; Wageningen Livestock Research, Wageningen University & Research, Wageningen, Netherlands; Milk Production Solutions, Green Technology, Natural Resources Institute Finland (Luke), Jokioinen, Finland; Department of Agriculture, Nutrition and Food Systems, University of New Hampshire, Durham, NH, United States; School of Agriculture and Food Science, University College Dublin, Belfield, Dublin 4, Ireland; Furst McNess Company, Freeport, IL, United States; School of Agriculture, Policy and Development, University of Reading, Reading, United Kingdom; Animal Nutrition Group, Wageningen University & Research, Wageningen, Netherlands; UMR Herbivores, INRA, VetAgro Sup, Université Clermont Auvergne, Saint-Genès-Champanelle, France; School of Biosciences, University of Nottingham, Loughborough, United Kingdom; Department of Large Animal Sciences, University of Copenhagen, Copenhagen, Denmark; Department of Animal Science, Aarhus University, Tjele, Denmark; Department of Agricultural Science for Northern Sweden, Swedish University of Agricultural Sciences, Umeå, Sweden; ETH Zurich, Institute of Agricultural Sciences, Zurich, Switzerland; Institute of Nutritional Physiology, Leibniz Institute for Farm Animal Biology, Dummerstorf, Mecklenburg-Vorpommern, Germany; Department of Soil and Crop Sciences, Colorado State University, Fort Collins, CO, United States; Teagasc, Agriculture and Food Development Authority, Carlow, Ireland; Agriculture Research Division, Department of Economic Development, Jobs, Transport and Resources, Melbourne, VIC, Australia; Ag Research, Palmerston North, New Zealand; Instituto de Investigaciones Agropecuarias, INIA Remehue, Osorno, Chile; Animal Sciences Department, Flanders Research Institute for Agriculture, Fisheries and Food, Melle, Belgium; Institute of Biological, Environmental and Rural Sciences, Aberystwyth University, Aberystwyth, United Kingdom; Department of Animal and Aquacultural Sciences, Norwegian University of Life Sciences, Ås, Norway; Estación Experimental del Zaidin (CSIC), Granada, Spain; Department of Animal Sciences, The Ohio State University, Columbus, OH, United States

Recommended Citation:
Niu M.,Kebreab E.,Hristov A.N.,et al. Prediction of enteric methane production, yield, and intensity in dairy cattle using an intercontinental database[J]. Global Change Biology,2018-01-01,24(8)
Service
Recommend this item
Sava as my favorate item
Show this item's statistics
Export Endnote File
Google Scholar
Similar articles in Google Scholar
[Niu M.]'s Articles
[Kebreab E.]'s Articles
[Hristov A.N.]'s Articles
百度学术
Similar articles in Baidu Scholar
[Niu M.]'s Articles
[Kebreab E.]'s Articles
[Hristov A.N.]'s Articles
CSDL cross search
Similar articles in CSDL Cross Search
[Niu M.]‘s Articles
[Kebreab E.]‘s Articles
[Hristov A.N.]‘s Articles
Related Copyright Policies
Null
收藏/分享
所有评论 (0)
暂无评论
 

Items in IR are protected by copyright, with all rights reserved, unless otherwise indicated.