reference : Symposium review: Uncertainties in enteric methane inventories, measurement techniques, and prediction models

JSON YAML text HTML Turtle N-Triples JSON Triples RDF+XML RDF+JSON Graphviz SVG

The referenced publication is not connected.

Bibliographic fields
reftype Journal Article
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.
Accession Number 29680642
Author Hristov, A. N.; 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.; Eugene, 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.; Munoz, C.; Peiren, N.; Powell, J. M.; Reynolds, C. K.; Schwarm, A.; Shingfield, K. J.; Storlien, T. M.; Weisbjerg, M. R.; Yanez-Ruiz, D. R.; Yu, Z.
Author Address Department of Animal Science, The Pennsylvania State University, University Park 16802. Electronic address: Department of Animal Science, University of California, Davis 91616. Department of Animal Science, The Pennsylvania State University, University Park 16802. Wageningen Livestock Research, Wageningen University and Research, 6700 AH Wageningen, the Netherlands. Milk Production Solutions, Green Technology, Natural Resources Institute Finland, 31600 Jokioinen, Finland. School of Agriculture and Food Science, University College Dublin, Belfield, Dublin 4, Ireland. Department of Nutrition, Agriculture and Food Systems, University of New Hampshire, Durham 03824. Furst McNess Company, Freeport, IL 61032. School of Agriculture, Policy and Development, University of Reading, Earley Gate, RG6 6AR, United Kingdom. Animal Nutrition Group, Wageningen University and Research, 6700 AH Wageningen, the Netherlands. UMR Herbivores, INRA, VetAgro Sup, Universite Clermont Auvergne, 63122 Saint-Genes-Champanelle, France. School of Biosciences, University of Nottingham, Loughborough, LE12 5RD, United Kingdom. Department of Large Animal Sciences, University of Copenhagen, 1870 Frederiksberg, Denmark. Department of Animal Science, Aarhus University, Foulum, 8830 Tjele, Denmark. Department of Agricultural Science for Northern Sweden, Swedish University of Agricultural Sciences, SE-901 87 Umea, Sweden. ETH Zurich, Institute of Agricultural Sciences, 8092 Zurich, Switzerland. Institute of Nutritional Physiology, Leibniz Institute for Farm Animal Biology, 18196 Dummerstorf, Germany. Agriculture Victoria, Ellinbank, Victoria 3821, Australia. Ag Research, Palmerston North 4442, New Zealand. Instituto de Investigaciones Agropecuarias, INIA Remehue, Osorno, Region de Los Lagos 5290000, Chile. Animal Sciences Unit, Flanders Research Institute for Agriculture, Fisheries and Food, 9090 Melle, Belgium. USDA-ARS US Dairy Forage Research Center, Madison, WI 53706. Institute of Biological, Environmental and Rural Sciences, Aberystwyth University, Aberystwyth SY23 3EB, United Kingdom. Department of Animal and Aquacultural Sciences, Norwegian University of Life Sciences, AS 1432, Norway. Estacion Experimental del Zaidin, CSIC, 1, 18008 Granada, Spain. Department of Animal Sciences, The Ohio State University, Columbus 43210.
DOI 10.3168/jds.2017-13536
Date Jul
ISSN 1525-3198 (Electronic) 0022-0302 (Linking)
Issue 7
Journal Journal of Dairy Science
Keywords enteric methane; livestock; prediction model; uncertainty
Notes Hristov, A N 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 Eugene, 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 Munoz, C Peiren, N Powell, J M Reynolds, C K Schwarm, A Shingfield, K J Storlien, T M Weisbjerg, M R Yanez-Ruiz, D R Yu, Z eng 2018/04/24 06:00 J Dairy Sci. 2018 Jul;101(7):6655-6674. doi: 10.3168/jds.2017-13536. Epub 2018 Apr 19.
Pages 6655-6674
Title Symposium review: Uncertainties in enteric methane inventories, measurement techniques, and prediction models
Volume 101
Year 2018
Bibliographic identifiers
_record_number 1085
_uuid 78e7c559-67c6-4a4a-8c77-883de6891bc0