Prediction of enteric methane production, yield and intensity of beef cattle using an intercontinental database

Detalhes bibliográficos
Autor(a) principal: van Lingen, Henk J.
Data de Publicação: 2019
Outros Autores: Niu, Mutian, Kebreab, Ermias, Valadares Filho, Sebastião C., Rooke, John A., Duthie, Carol-Anne, Schwarm, Angela, Kreuzer, Michael, Hynd, Phil I., Caetano, Mariana, Eugène, Maguy, Martin, Cécile, McGee, Mark, O'Kiely, Padraig, Hünerberg, Martin, McAllister, Tim A., Berchielli, Telma T. [UNESP], Messana, Juliana D. [UNESP], Peiren, Nico, Chaves, Alex V., Charmley, Ed, Cole, N. Andy, Hales, Kristin E., Lee, Sang-Suk, Berndt, Alexandre, Reynolds, Christopher K., Crompton, Les A., Bayat, Ali-Reza, Yáñez-Ruiz, David R., Yu, Zhongtang, Bannink, André, Dijkstra, Jan, Casper, David P., Hristov, Alexander N.
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Institucional da UNESP
Texto Completo: http://dx.doi.org/10.1016/j.agee.2019.106575
http://hdl.handle.net/11449/190396
Resumo: Enteric methane (CH4) production attributable to beef cattle contributes to global greenhouse gas emissions. Reliably estimating this contribution requires extensive CH4 emission data from beef cattle under different management conditions worldwide. The objectives were to: 1) predict CH4 production (g d−1 animal−1), yield [g (kg dry matter intake; DMI)−1] and intensity [g (kg average daily gain)−1] using an intercontinental database (data from Europe, North America, Brazil, Australia and South Korea); 2) assess the impact of geographic region, and of higher- and lower-forage diets. Linear models were developed by incrementally adding covariates. A K-fold cross-validation indicated that a CH4 production equation using only DMI that was fitted to all available data had a root mean square prediction error (RMSPE; % of observed mean) of 31.2%. Subsets containing data with ≥25% and ≤18% dietary forage contents had an RMSPE of 30.8 and 34.2%, with the all-data CH4 production equation, whereas these errors decreased to 29.3 and 28.4%, respectively, when using CH4 prediction equations fitted to these subsets. The RMSPE of the ≥25% forage subset further decreased to 24.7% when using multiple regression. Europe- and North America-specific subsets predicted by the best performing ≥25% forage multiple regression equation had RMSPE of 24.5 and 20.4%, whereas these errors were 24.5 and 20.0% with region-specific equations, respectively. The developed equations had less RMSPE than extant equations evaluated for all data (22.5 vs. 23.2%), for higher-forage (21.2 vs. 23.1%), but not for the lower-forage subsets (28.4 vs. 27.9%). Splitting the dataset by forage content did not improve CH4 yield or intensity predictions. Predicting beef cattle CH4 production using energy conversion factors, as applied by the Intergovernmental Panel on Climate Change, indicated that adequate forage content-based and region-specific energy conversion factors improve prediction accuracy and are preferred in national or global inventories.
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spelling Prediction of enteric methane production, yield and intensity of beef cattle using an intercontinental databaseDietary variablesEmpirical modelingForage contentGeographical regionMethane emissionEnteric methane (CH4) production attributable to beef cattle contributes to global greenhouse gas emissions. Reliably estimating this contribution requires extensive CH4 emission data from beef cattle under different management conditions worldwide. The objectives were to: 1) predict CH4 production (g d−1 animal−1), yield [g (kg dry matter intake; DMI)−1] and intensity [g (kg average daily gain)−1] using an intercontinental database (data from Europe, North America, Brazil, Australia and South Korea); 2) assess the impact of geographic region, and of higher- and lower-forage diets. Linear models were developed by incrementally adding covariates. A K-fold cross-validation indicated that a CH4 production equation using only DMI that was fitted to all available data had a root mean square prediction error (RMSPE; % of observed mean) of 31.2%. Subsets containing data with ≥25% and ≤18% dietary forage contents had an RMSPE of 30.8 and 34.2%, with the all-data CH4 production equation, whereas these errors decreased to 29.3 and 28.4%, respectively, when using CH4 prediction equations fitted to these subsets. The RMSPE of the ≥25% forage subset further decreased to 24.7% when using multiple regression. Europe- and North America-specific subsets predicted by the best performing ≥25% forage multiple regression equation had RMSPE of 24.5 and 20.4%, whereas these errors were 24.5 and 20.0% with region-specific equations, respectively. The developed equations had less RMSPE than extant equations evaluated for all data (22.5 vs. 23.2%), for higher-forage (21.2 vs. 23.1%), but not for the lower-forage subsets (28.4 vs. 27.9%). Splitting the dataset by forage content did not improve CH4 yield or intensity predictions. Predicting beef cattle CH4 production using energy conversion factors, as applied by the Intergovernmental Panel on Climate Change, indicated that adequate forage content-based and region-specific energy conversion factors improve prediction accuracy and are preferred in national or global inventories.Department of Animal Science University of CaliforniaFarmer's Business Network Inc.Animal Science Department Universidade Federal de ViçosaSRUC, West Mains RoadETH Zurich Institute of Agricultural SciencesDepartment of Animal and Veterinary Bioscience The University of Adelaide, Roseworthy CampusINRA UMR Herbivores VetAgro Sup Université Clermont AuvergneTeagasc Grange DunsanyDepartment of Agricultural Food and Nutritional Science University of AlbertaLethbridge Research and Development Centre Agriculture and Agri-Food CanadaAnimal Science Department São Paulo State University UNESPFlanders Research Institute for Agriculture Fisheries and Food Animal Sciences Unit, Scheldeweg 68The University of Sydney Faculty of Science School of Life and Environmental SciencesCSIRO Agriculture and Food, Private Mail Bag, POUSDA-ARSDepartment of Animal Science and Technology Sunchon National UniversityResearch and Development EMBRAPA Southeast Livestock, Rod Washington Luiz, km 234, PO Box 339School of Agriculture Policy and Development University of ReadingMilk Production Production Systems Natural Resources Institute Finland (Luke)Estación Experimental del Zaidin (CSIC)Department of Animal Sciences The Ohio State UniversityWageningen Livestock Research Wageningen University & ResearchAnimal Nutrition Group Wageningen University & ResearchFurst McNess CompanyDepartment of Animal Science The Pennsylvania State University, University ParkAnimal Science Department São Paulo State University UNESPUniversity of CaliforniaFarmer's Business Network Inc.Universidade Federal de Viçosa (UFV)SRUCInstitute of Agricultural SciencesThe University of AdelaideUniversité Clermont AuvergneDunsanyUniversity of AlbertaAgriculture and Agri-Food CanadaUniversidade Estadual Paulista (Unesp)Animal Sciences UnitSchool of Life and Environmental SciencesCSIRO Agriculture and FoodUSDA-ARSSunchon National UniversityEmpresa Brasileira de Pesquisa Agropecuária (EMBRAPA)University of ReadingNatural Resources Institute Finland (Luke)Estación Experimental del Zaidin (CSIC)The Ohio State UniversityWageningen University & ResearchFurst McNess CompanyThe Pennsylvania State Universityvan Lingen, Henk J.Niu, MutianKebreab, ErmiasValadares Filho, Sebastião C.Rooke, John A.Duthie, Carol-AnneSchwarm, AngelaKreuzer, MichaelHynd, Phil I.Caetano, MarianaEugène, MaguyMartin, CécileMcGee, MarkO'Kiely, PadraigHünerberg, MartinMcAllister, Tim A.Berchielli, Telma T. [UNESP]Messana, Juliana D. [UNESP]Peiren, NicoChaves, Alex V.Charmley, EdCole, N. AndyHales, Kristin E.Lee, Sang-SukBerndt, AlexandreReynolds, Christopher K.Crompton, Les A.Bayat, Ali-RezaYáñez-Ruiz, David R.Yu, ZhongtangBannink, AndréDijkstra, JanCasper, David P.Hristov, Alexander N.2019-10-06T17:11:48Z2019-10-06T17:11:48Z2019-11-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttp://dx.doi.org/10.1016/j.agee.2019.106575Agriculture, Ecosystems and Environment, v. 283.0167-8809http://hdl.handle.net/11449/19039610.1016/j.agee.2019.1065752-s2.0-85067265264Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengAgriculture, Ecosystems and Environmentinfo:eu-repo/semantics/openAccess2024-06-07T18:44:44Zoai:repositorio.unesp.br:11449/190396Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T23:20:42.315155Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false
dc.title.none.fl_str_mv Prediction of enteric methane production, yield and intensity of beef cattle using an intercontinental database
title Prediction of enteric methane production, yield and intensity of beef cattle using an intercontinental database
spellingShingle Prediction of enteric methane production, yield and intensity of beef cattle using an intercontinental database
van Lingen, Henk J.
Dietary variables
Empirical modeling
Forage content
Geographical region
Methane emission
title_short Prediction of enteric methane production, yield and intensity of beef cattle using an intercontinental database
title_full Prediction of enteric methane production, yield and intensity of beef cattle using an intercontinental database
title_fullStr Prediction of enteric methane production, yield and intensity of beef cattle using an intercontinental database
title_full_unstemmed Prediction of enteric methane production, yield and intensity of beef cattle using an intercontinental database
title_sort Prediction of enteric methane production, yield and intensity of beef cattle using an intercontinental database
author van Lingen, Henk J.
author_facet van Lingen, Henk J.
Niu, Mutian
Kebreab, Ermias
Valadares Filho, Sebastião C.
Rooke, John A.
Duthie, Carol-Anne
Schwarm, Angela
Kreuzer, Michael
Hynd, Phil I.
Caetano, Mariana
Eugène, Maguy
Martin, Cécile
McGee, Mark
O'Kiely, Padraig
Hünerberg, Martin
McAllister, Tim A.
Berchielli, Telma T. [UNESP]
Messana, Juliana D. [UNESP]
Peiren, Nico
Chaves, Alex V.
Charmley, Ed
Cole, N. Andy
Hales, Kristin E.
Lee, Sang-Suk
Berndt, Alexandre
Reynolds, Christopher K.
Crompton, Les A.
Bayat, Ali-Reza
Yáñez-Ruiz, David R.
Yu, Zhongtang
Bannink, André
Dijkstra, Jan
Casper, David P.
Hristov, Alexander N.
author_role author
author2 Niu, Mutian
Kebreab, Ermias
Valadares Filho, Sebastião C.
Rooke, John A.
Duthie, Carol-Anne
Schwarm, Angela
Kreuzer, Michael
Hynd, Phil I.
Caetano, Mariana
Eugène, Maguy
Martin, Cécile
McGee, Mark
O'Kiely, Padraig
Hünerberg, Martin
McAllister, Tim A.
Berchielli, Telma T. [UNESP]
Messana, Juliana D. [UNESP]
Peiren, Nico
Chaves, Alex V.
Charmley, Ed
Cole, N. Andy
Hales, Kristin E.
Lee, Sang-Suk
Berndt, Alexandre
Reynolds, Christopher K.
Crompton, Les A.
Bayat, Ali-Reza
Yáñez-Ruiz, David R.
Yu, Zhongtang
Bannink, André
Dijkstra, Jan
Casper, David P.
Hristov, Alexander N.
author2_role author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
dc.contributor.none.fl_str_mv University of California
Farmer's Business Network Inc.
Universidade Federal de Viçosa (UFV)
SRUC
Institute of Agricultural Sciences
The University of Adelaide
Université Clermont Auvergne
Dunsany
University of Alberta
Agriculture and Agri-Food Canada
Universidade Estadual Paulista (Unesp)
Animal Sciences Unit
School of Life and Environmental Sciences
CSIRO Agriculture and Food
USDA-ARS
Sunchon National University
Empresa Brasileira de Pesquisa Agropecuária (EMBRAPA)
University of Reading
Natural Resources Institute Finland (Luke)
Estación Experimental del Zaidin (CSIC)
The Ohio State University
Wageningen University & Research
Furst McNess Company
The Pennsylvania State University
dc.contributor.author.fl_str_mv van Lingen, Henk J.
Niu, Mutian
Kebreab, Ermias
Valadares Filho, Sebastião C.
Rooke, John A.
Duthie, Carol-Anne
Schwarm, Angela
Kreuzer, Michael
Hynd, Phil I.
Caetano, Mariana
Eugène, Maguy
Martin, Cécile
McGee, Mark
O'Kiely, Padraig
Hünerberg, Martin
McAllister, Tim A.
Berchielli, Telma T. [UNESP]
Messana, Juliana D. [UNESP]
Peiren, Nico
Chaves, Alex V.
Charmley, Ed
Cole, N. Andy
Hales, Kristin E.
Lee, Sang-Suk
Berndt, Alexandre
Reynolds, Christopher K.
Crompton, Les A.
Bayat, Ali-Reza
Yáñez-Ruiz, David R.
Yu, Zhongtang
Bannink, André
Dijkstra, Jan
Casper, David P.
Hristov, Alexander N.
dc.subject.por.fl_str_mv Dietary variables
Empirical modeling
Forage content
Geographical region
Methane emission
topic Dietary variables
Empirical modeling
Forage content
Geographical region
Methane emission
description Enteric methane (CH4) production attributable to beef cattle contributes to global greenhouse gas emissions. Reliably estimating this contribution requires extensive CH4 emission data from beef cattle under different management conditions worldwide. The objectives were to: 1) predict CH4 production (g d−1 animal−1), yield [g (kg dry matter intake; DMI)−1] and intensity [g (kg average daily gain)−1] using an intercontinental database (data from Europe, North America, Brazil, Australia and South Korea); 2) assess the impact of geographic region, and of higher- and lower-forage diets. Linear models were developed by incrementally adding covariates. A K-fold cross-validation indicated that a CH4 production equation using only DMI that was fitted to all available data had a root mean square prediction error (RMSPE; % of observed mean) of 31.2%. Subsets containing data with ≥25% and ≤18% dietary forage contents had an RMSPE of 30.8 and 34.2%, with the all-data CH4 production equation, whereas these errors decreased to 29.3 and 28.4%, respectively, when using CH4 prediction equations fitted to these subsets. The RMSPE of the ≥25% forage subset further decreased to 24.7% when using multiple regression. Europe- and North America-specific subsets predicted by the best performing ≥25% forage multiple regression equation had RMSPE of 24.5 and 20.4%, whereas these errors were 24.5 and 20.0% with region-specific equations, respectively. The developed equations had less RMSPE than extant equations evaluated for all data (22.5 vs. 23.2%), for higher-forage (21.2 vs. 23.1%), but not for the lower-forage subsets (28.4 vs. 27.9%). Splitting the dataset by forage content did not improve CH4 yield or intensity predictions. Predicting beef cattle CH4 production using energy conversion factors, as applied by the Intergovernmental Panel on Climate Change, indicated that adequate forage content-based and region-specific energy conversion factors improve prediction accuracy and are preferred in national or global inventories.
publishDate 2019
dc.date.none.fl_str_mv 2019-10-06T17:11:48Z
2019-10-06T17:11:48Z
2019-11-01
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://dx.doi.org/10.1016/j.agee.2019.106575
Agriculture, Ecosystems and Environment, v. 283.
0167-8809
http://hdl.handle.net/11449/190396
10.1016/j.agee.2019.106575
2-s2.0-85067265264
url http://dx.doi.org/10.1016/j.agee.2019.106575
http://hdl.handle.net/11449/190396
identifier_str_mv Agriculture, Ecosystems and Environment, v. 283.
0167-8809
10.1016/j.agee.2019.106575
2-s2.0-85067265264
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv Agriculture, Ecosystems and Environment
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.source.none.fl_str_mv Scopus
reponame:Repositório Institucional da UNESP
instname:Universidade Estadual Paulista (UNESP)
instacron:UNESP
instname_str Universidade Estadual Paulista (UNESP)
instacron_str UNESP
institution UNESP
reponame_str Repositório Institucional da UNESP
collection Repositório Institucional da UNESP
repository.name.fl_str_mv Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)
repository.mail.fl_str_mv
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