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

item HRISTOV, A - Pennsylvania State University
item KEBREAB, E - University Of California
item NIU, M - University Of California
item OH, J - Pennsylvania State University
item BANNINK, A - Wageningen University And Research Center
item BAYAT, A - Natural Resources Institute Finland (LUKE)
item BOLAND, T - University College Dublin
item BRITO, A - University Of New Hampshire
item CASPER, D - Furst-Mcness Company
item CROMPTON, L - University Of Reading
item DIJKSTRA, J - Wageningen University And Research Center
item EUGENE, M - Clermont Universite, Universite D'Auvergne, Unite De Nutrition Humaine
item GARNSWORTHY, P - University Of Nottingham
item HAQUE, N - University Of Copenhagen
item HELLWING, A.L. - Aarhus University
item HUHTANEN, P - Swedish University Of Agricultural Sciences
item KREUZER, M - Institute Of Agricultural Sciences
item KUHLA, B - Leibniz Institute
item LUND, P - Aarhus University
item MADSEN, J - University Of Copenhagen
item MARTIN, C - Clermont Universite, Universite D'Auvergne, Unite De Nutrition Humaine
item MOATE, P - Agriculture Victoria
item MUETZEL, S - Ag Research Limited
item MUNOZ, C - Inia Remehue - Osorno
item PEIREN, N - Institute For Agricultural And Fisheries Research (ILVO)
item Powell, Joseph
item REYNOLDS, C - University Of Reading
item SCHWARM, A - Institute Of Agricultural Sciences
item SHINGFIELD, K - Aberystwyth University
item STORLIEN, T - Norwegian University Of Life Sciences
item WEISBJERG, M - Aarhus University
item YANEZ-RUIZ, D - Estaciòn Experimental Aula Dei- Csic
item YU, Z - The Ohio State University

Submitted to: Journal of Dairy Science
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 3/25/2018
Publication Date: 4/18/2018
Citation: Hristov, A.N., Kebreab, E., Niu, M., Oh, J., Bannink, A., Bayat, A.R., Boland, T.M., Brito, A.F., Casper, D., Crompton, L.A., Dijkstra, J., Eugene, M., Garnsworthy, P.C., Haque, N., Hellwing, A.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. 2018. Symposium review: Uncertainties in enteric methane inventories, measurement techniques, and prediction models. Journal of Dairy Science. 101:6655-6674.

Interpretive Summary:

Technical Abstract: Ruminant production systems are important contributors to anthropogenic methane emissions. Globally, there is a large body of enteric methane emission data. The Global Network was established to collate and analyze methane emission and mitigation data for ruminants. Two separate databases have been developed: mitigation database and prediction database. The objective of the mitigation database is to summarize and recommend science-based enteric methane mitigation options to stakeholders. This database consists of 1,800 experimental treatment means from 410 publications. The goal of the prediction database, which consists of individual animal data, is to develop robust enteric methane emission prediction models for various ruminant species (dairy and beef cattle, sheep) and nutritional, animal, and farm management scenarios. The dairy cattle prediction database currently contains 5,899 individual animal observations from 159 studies from North and South America, Europe, and Oceania. Development of enteric methane prediction models was conducted using a sequential approach; available information was incrementally added to develop models with increasing complexity. In total, 11 models were developed. Methane emission (g/d, per dry matter intake [DMI], or per milk/energy-corrected milk yields) was predicted by fitting linear mixed models including random effect of study nested within the random effect of continent. As expected, a global methane emission (g/d) model with a greater number of independent variables fitted the data best [Root mean square prediction error as a percentage of mean observed value (RMSPE) = 13.4%]. Inputs were DMI, dietary concentrations of ether extract (EE) and neutral detergent fiber (NDF), milk fat and protein content, and cow BW. The predictive ability of fitted models was evaluated through cross-validation. Less complex models requiring only DMI, or DMI plus NDF or EE concentrations had predictive ability similar to more complex models (RMSPE = 14.0 to 14.3%). These prediction models, along with recommendations from the mitigation database analysis, provide robust enteric methane inventory and mitigation options for ruminant farming systems.