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Ali R. Bayat
Researcher at Natural Resources Institute Finland
Publications - 49
Citations - 1435
Ali R. Bayat is an academic researcher from Natural Resources Institute Finland. The author has contributed to research in topics: Rumen & Silage. The author has an hindex of 16, co-authored 40 publications receiving 858 citations. Previous affiliations of Ali R. Bayat include Massachusetts Institute of Technology & Shiraz University.
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Journal ArticleDOI
A heritable subset of the core rumen microbiome dictates dairy cow productivity and emissions
R. John Wallace,Goor Sasson,Philip C. Garnsworthy,Ilma Tapio,Emma Gregson,Paolo Bani,Pekka Huhtanen,Ali R. Bayat,Francesco Strozzi,Filippo Biscarini,Timothy J. Snelling,N. Saunders,Sarah L. Potterton,Jim Craigon,Andrea Minuti,Erminio Trevisi,Maria Luisa Callegari,Fiorenzo Piccioli Cappelli,E.H. Cabezas-Garcia,Johanna Vilkki,C. S. Pinares-Patiño,K. Fliegerová,Jakub Mrázek,Hana Sechovcová,Jan Kopečný,Aurélie Bonin,Frédéric Boyer,Pierre Taberlet,Fotini Kokou,Eran Halperin,John L. Williams,Kevin J. Shingfield,Itzhak Mizrahi +32 more
TL;DR: The heritable core microbes, therefore, present primary targets for rumen manipulation toward sustainable and environmentally friendly agriculture.
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Prediction of enteric methane production, yield, and intensity in dairy cattle using an intercontinental database
M. Niu,Ermias Kebreab,Alexander N. Hristov,Joonpyo Oh,C. Arndt,André Bannink,Ali R. Bayat,Andre F. Brito,Tommy M. Boland,David P. Casper,Les A. Crompton,Jan Dijkstra,Maguy Eugène,Phil C. Garnsworthy,Najmul Haque,A.L.F. Hellwing,Pekka Huhtanen,Michael Kreuzer,B. Kuhla,Peter Lund,Jørgen Steen Madsen,Cécile Martin,Shelby C. McClelland,Mark McGee,Peter J. Moate,Stefan M. Muetzel,Camila Muñoz,Padraig O'Kiely,Nico Peiren,Christopher K. Reynolds,Angela Schwarm,K. J. Shingfield,T. M. Storlien,Martin Riis Weisbjerg,David R. Yáñez-Ruiz,Zhongtang Yu +35 more
TL;DR: Information on DMI is required for good prediction, and other factors such as dietary neutral detergent fiber (NDF) concentration, improve the prediction for enteric CH 4 yield and intensity prediction.
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Invited review: Nitrogen in ruminant nutrition: A review of measurement techniques.
Alexander N. Hristov,André Bannink,Les A. Crompton,Pekka Huhtanen,Michael Kreuzer,Mark McGee,Pierre Noziere,Christopher K. Reynolds,Ali R. Bayat,David R. Yáñez-Ruiz,Jan Dijkstra,Ermias Kebreab,Angela Schwarm,K. J. Shingfield,Zhongtang Yu +14 more
TL;DR: Methods to study ruminant N metabolism have been developed over 150 yr of animal nutrition research, but many of them are laborious and impractical for application on a large number of animals and results can be variable, especially the methods based on measurements of digesta or blood flow.
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Symposium review: Uncertainties in enteric methane inventories, measurement techniques, and prediction models
Alexander N. Hristov,Ermias Kebreab,M. Niu,Joonpyo Oh,A. Bannink,Ali R. Bayat,Tommy M. Boland,Andre F. Brito,David P. Casper,Les A. Crompton,Jan Dijkstra,Maguy Eugène,Philip C. Garnsworthy,Nazmul Haque,A.L.F. Hellwing,Pekka Huhtanen,Michael Kreuzer,B. Kuhla,Peter Lund,Jørgen Steen Madsen,Christine Martin,Peter J. Moate,Stefan M. Muetzel,C. Muñoz,Nico Peiren,J. M. Powell,Christopher K. Reynolds,Angela Schwarm,Kevin J. Shingfield,T. M. Storlien,Martin Riis Weisbjerg,David R. Yáñez-Ruiz,Zhongtang Yu +32 more
TL;DR: 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.
Journal ArticleDOI
Oral Samples as Non-Invasive Proxies for Assessing the Composition of the Rumen Microbial Community.
Ilma Tapio,Kevin J. Shingfield,Nest McKain,Aurélie Bonin,Daniel Fischer,Ali R. Bayat,Johanna Vilkki,Pierre Taberlet,Timothy J. Snelling,R. John Wallace +9 more
TL;DR: It can be concluded that either proxy sample type could be used as a predictor of the rumen microbial community, thereby enabling more convenient large-scale animal sampling for phenotyping and possible use in future animal breeding programs aimed at selecting cattle with a lower environmental footprint.