Symposium review: Uncertainties in enteric methane inventories, measurement techniques, and prediction models Article Swipe
A.N. Hristov
,
E. Kebreab
,
Mutian Niu
,
J. Oh
,
A. Bannink
,
A.R. Bayat
,
T.M. Boland
,
A.F. Brito
,
D. P. Casper
,
L.A. Crompton
,
J. Dijkstra
,
Maguy Eugène
,
P. C. Garnsworthy
,
Md Najmul Haque
,
Anne Louise Frydendahl Hellwing
,
Pekka Huhtanen
,
Michael Kreuzer
,
Björn Kuhla
,
Peter Lund
,
J. Madsen
,
Cécile Martin
,
Peter J. Moate
,
Stefan Muetzel
,
Camila Muñoz
,
Nico Peiren
,
J. M. Powell
,
C.K. Reynolds
,
Angela Schwarm
,
K.J. Shingfield
,
T. M. Storlien
,
Martin Riis Weisbjerg
,
David R. Yáñez-Ruíz
,
Zhongtang Yu
·
YOU?
·
· 2018
· Open Access
·
· DOI: https://doi.org/10.3168/jds.2017-13536
YOU?
·
· 2018
· Open Access
·
· DOI: https://doi.org/10.3168/jds.2017-13536
Ruminant production systems are important contributors to anthropogenic methane (CH4) emissions, but there are large uncertainties in national and global livestock CH4 inventories. Sources of uncertainty in enteric CH4 emissions include animal inventories, feed dry matter intake (DMI), ingredient and chemical composition of the diets, and CH4 emission factors. There is also significant uncertainty associated with enteric CH4 measurements. The most widely used techniques are respiration chambers, the sulfur hexafluoride (SF6) tracer technique, and the automated head-chamber system (GreenFeed; C-Lock Inc., Rapid City, SD). All 3 methods have been successfully used in a large number of experiments with dairy or beef cattle in various environmental conditions, although studies that compare techniques have reported inconsistent results. Although different types of models have been developed to predict enteric CH4 emissions, relatively simple empirical (statistical) models have been commonly used for inventory purposes because of their broad applicability and ease of use compared with more detailed empirical and process-based mechanistic models. However, extant empirical models used to predict enteric CH4 emissions suffer from narrow spatial focus, limited observations, and limitations of the statistical technique used. Therefore, prediction models must be developed from robust data sets that can only be generated through collaboration of scientists across the world. To achieve high prediction accuracy, these data sets should encompass a wide range of diets and production systems within regions and globally. Overall, enteric CH4 prediction models are based on various animal or feed characteristic inputs but are dominated by DMI in one form or another. As a result, accurate prediction of DMI is essential for accurate prediction of livestock CH4 emissions. Analysis of a large data set of individual dairy cattle data showed that simplified enteric CH4 prediction models based on DMI alone or DMI and limited feed- or animal-related inputs can predict average CH4 emission with a similar accuracy to more complex empirical models. These simplified models can be reliably used for emission inventory purposes.
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Metadata
- Type
- review
- Language
- en
- Landing Page
- https://doi.org/10.3168/jds.2017-13536
- http://www.journalofdairyscience.org/article/S0022030218303709/pdf
- OA Status
- bronze
- Cited By
- 159
- References
- 128
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2800886780
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W2800886780Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3168/jds.2017-13536Digital Object Identifier
- Title
-
Symposium review: Uncertainties in enteric methane inventories, measurement techniques, and prediction modelsWork title
- Type
-
reviewOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2018Year of publication
- Publication date
-
2018-04-19Full publication date if available
- Authors
-
A.N. Hristov, E. Kebreab, Mutian Niu, J. Oh, A. Bannink, A.R. Bayat, T.M. Boland, A.F. Brito, D. P. Casper, L.A. Crompton, J. Dijkstra, Maguy Eugène, P. C. Garnsworthy, Md Najmul Haque, Anne Louise Frydendahl Hellwing, Pekka Huhtanen, Michael Kreuzer, Björn Kuhla, Peter Lund, J. Madsen, Cécile Martin, Peter J. Moate, Stefan Muetzel, Camila Muñoz, Nico Peiren, J. M. Powell, C.K. Reynolds, Angela Schwarm, K.J. Shingfield, T. M. Storlien, Martin Riis Weisbjerg, David R. Yáñez-Ruíz, Zhongtang YuList of authors in order
- Landing page
-
https://doi.org/10.3168/jds.2017-13536Publisher landing page
- PDF URL
-
https://www.journalofdairyscience.org/article/S0022030218303709/pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
bronzeOpen access status per OpenAlex
- OA URL
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https://www.journalofdairyscience.org/article/S0022030218303709/pdfDirect OA link when available
- Concepts
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Methane emissions, Environmental science, Empirical modelling, Range (aeronautics), Ruminant, Methane, Extant taxon, Biochemical engineering, Computer science, Ecology, Engineering, Simulation, Biology, Evolutionary biology, Crop, Aerospace engineeringTop concepts (fields/topics) attached by OpenAlex
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159Total citation count in OpenAlex
- Citations by year (recent)
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2025: 18, 2024: 32, 2023: 26, 2022: 27, 2021: 18Per-year citation counts (last 5 years)
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128Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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