Symposium review

Uncertainties in enteric methane inventories, measurement techniques, and prediction models

Alexander Nikolov Hristov, E. Kebreab, M. Niu, J. Oh, A. Bannink, A. R. Bayat, T. M. Boland, A. F. Brito, D. P. Casper, L. A. Crompton, J. Dijkstra, M. Eugène, P. C. Garnsworthy, N. Haque, A. L.F. Hellwing, P. Huhtanen, M. Kreuzer, B. Kuhla, P. Lund, J. Madsen & 13 others C. Martin, P. J. Moate, S. Muetzel, C. Muñoz, N. Peiren, J. M. Powell, C. K. Reynolds, A. Schwarm, K. J. Shingfield, T. M. Storlien, M. R. Weisbjerg, D. R. Yáñez-Ruiz, Z. Yu

Research output: Contribution to journalArticle

15 Citations (Scopus)

Abstract

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.

Original languageEnglish (US)
Pages (from-to)6655-6674
Number of pages20
JournalJournal of dairy science
Volume101
Issue number7
DOIs
StatePublished - Jul 1 2018

Fingerprint

Methane
methane
Uncertainty
uncertainty
Equipment and Supplies
prediction
Livestock
dry matter intake
Sulfur Hexafluoride
Diet
sulfur hexafluoride
Ruminants
Statistical Models
methodology
Respiration
dairy cattle
Head
production technology
livestock
emissions factor

All Science Journal Classification (ASJC) codes

  • Food Science
  • Animal Science and Zoology
  • Genetics

Cite this

Hristov, Alexander Nikolov ; Kebreab, E. ; Niu, M. ; Oh, J. ; Bannink, A. ; Bayat, A. R. ; Boland, T. M. ; Brito, A. F. ; Casper, D. P. ; Crompton, L. A. ; Dijkstra, J. ; Eugène, M. ; Garnsworthy, P. C. ; Haque, N. ; Hellwing, A. L.F. ; Huhtanen, P. ; Kreuzer, M. ; Kuhla, B. ; Lund, P. ; Madsen, J. ; Martin, C. ; Moate, P. J. ; Muetzel, S. ; Muñoz, C. ; Peiren, N. ; Powell, J. M. ; Reynolds, C. K. ; Schwarm, A. ; Shingfield, K. J. ; Storlien, T. M. ; Weisbjerg, M. R. ; Yáñez-Ruiz, D. R. ; Yu, Z. / Symposium review : Uncertainties in enteric methane inventories, measurement techniques, and prediction models. In: Journal of dairy science. 2018 ; Vol. 101, No. 7. pp. 6655-6674.
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abstract = "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.",
author = "Hristov, {Alexander Nikolov} and E. Kebreab and M. Niu and J. Oh and A. Bannink and Bayat, {A. R.} and Boland, {T. M.} and Brito, {A. F.} and Casper, {D. P.} and Crompton, {L. A.} and J. Dijkstra and M. Eug{\`e}ne and Garnsworthy, {P. C.} and N. Haque and Hellwing, {A. L.F.} and P. Huhtanen and M. Kreuzer and B. Kuhla and P. Lund and J. Madsen and C. Martin and Moate, {P. J.} and S. Muetzel and C. Mu{\~n}oz and N. Peiren and Powell, {J. M.} and Reynolds, {C. K.} and A. Schwarm and Shingfield, {K. J.} and Storlien, {T. M.} and Weisbjerg, {M. R.} and Y{\'a}{\~n}ez-Ruiz, {D. R.} and Z. Yu",
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Hristov, AN, Kebreab, E, Niu, M, Oh, J, Bannink, A, Bayat, AR, Boland, TM, Brito, AF, Casper, DP, Crompton, LA, Dijkstra, J, Eugène, M, Garnsworthy, PC, Haque, N, Hellwing, ALF, Huhtanen, P, Kreuzer, M, Kuhla, B, Lund, P, Madsen, J, Martin, C, Moate, PJ, Muetzel, S, Muñoz, C, Peiren, N, Powell, JM, Reynolds, CK, Schwarm, A, Shingfield, KJ, Storlien, TM, Weisbjerg, MR, Yáñez-Ruiz, DR & Yu, Z 2018, 'Symposium review: Uncertainties in enteric methane inventories, measurement techniques, and prediction models', Journal of dairy science, vol. 101, no. 7, pp. 6655-6674. https://doi.org/10.3168/jds.2017-13536

Symposium review : Uncertainties in enteric methane inventories, measurement techniques, and prediction models. / Hristov, Alexander Nikolov; Kebreab, E.; Niu, M.; Oh, J.; Bannink, A.; Bayat, A. R.; Boland, T. M.; Brito, A. F.; Casper, D. P.; Crompton, L. A.; Dijkstra, J.; Eugène, M.; Garnsworthy, P. C.; Haque, N.; Hellwing, A. L.F.; Huhtanen, P.; Kreuzer, M.; Kuhla, B.; Lund, P.; Madsen, J.; Martin, C.; Moate, P. J.; Muetzel, S.; Muñoz, C.; Peiren, N.; Powell, J. M.; Reynolds, C. K.; Schwarm, A.; Shingfield, K. J.; Storlien, T. M.; Weisbjerg, M. R.; Yáñez-Ruiz, D. R.; Yu, Z.

In: Journal of dairy science, Vol. 101, No. 7, 01.07.2018, p. 6655-6674.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Symposium review

T2 - Uncertainties in enteric methane inventories, measurement techniques, and prediction models

AU - Hristov, Alexander Nikolov

AU - Kebreab, E.

AU - Niu, M.

AU - Oh, J.

AU - Bannink, A.

AU - Bayat, A. R.

AU - Boland, T. M.

AU - Brito, A. F.

AU - Casper, D. P.

AU - Crompton, L. A.

AU - Dijkstra, J.

AU - Eugène, M.

AU - Garnsworthy, P. C.

AU - Haque, N.

AU - Hellwing, A. L.F.

AU - Huhtanen, P.

AU - Kreuzer, M.

AU - Kuhla, B.

AU - Lund, P.

AU - Madsen, J.

AU - Martin, C.

AU - Moate, P. J.

AU - Muetzel, S.

AU - Muñoz, C.

AU - Peiren, N.

AU - Powell, J. M.

AU - Reynolds, C. K.

AU - Schwarm, A.

AU - Shingfield, K. J.

AU - Storlien, T. M.

AU - Weisbjerg, M. R.

AU - Yáñez-Ruiz, D. R.

AU - Yu, Z.

PY - 2018/7/1

Y1 - 2018/7/1

N2 - 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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