Prediction of enteric methane production, yield, and intensity in dairy cattle using an intercontinental database
Enteric methane (CH) production from cattle contributes to global greenhouse gas emissions. Measurement of enteric CH is complex, expensive, and impractical at large scales; therefore, models are commonly used to predict CH production. However, building robust prediction models requires extensive da...
| Autores: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2018 |
| País: | España |
| Institución: | Consejo Superior de Investigaciones Científicas (CSIC) |
| Repositorio: | DIGITAL.CSIC. Repositorio Institucional del CSIC |
| OAI Identifier: | oai:digital.csic.es:10261/176365 |
| Acceso en línea: | http://hdl.handle.net/10261/176365 |
| Access Level: | acceso abierto |
| Palabra clave: | Methane intensity prediction models Methane yield Dairy cows Dry matter in take Enteric methane emissions |
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| dc.title.none.fl_str_mv |
Prediction of enteric methane production, yield, and intensity in dairy cattle using an intercontinental database |
| title |
Prediction of enteric methane production, yield, and intensity in dairy cattle using an intercontinental database |
| spellingShingle |
Prediction of enteric methane production, yield, and intensity in dairy cattle using an intercontinental database Niu, M. Methane intensity prediction models Methane yield Dairy cows Dry matter in take Enteric methane emissions |
| title_short |
Prediction of enteric methane production, yield, and intensity in dairy cattle using an intercontinental database |
| title_full |
Prediction of enteric methane production, yield, and intensity in dairy cattle using an intercontinental database |
| title_fullStr |
Prediction of enteric methane production, yield, and intensity in dairy cattle using an intercontinental database |
| title_full_unstemmed |
Prediction of enteric methane production, yield, and intensity in dairy cattle using an intercontinental database |
| title_sort |
Prediction of enteric methane production, yield, and intensity in dairy cattle using an intercontinental database |
| dc.creator.none.fl_str_mv |
Niu, M. Kebreab, E. Hristov, A. N. Oh, J. Arndt, C. Bannink, A. Bayat, A.R. Brito, A. F. Boland, T. Casper, D. P. Crompton, L. A. Dijkstra, J. Eugène, M. A. Garnsworthy, P. C. Haque, M. N. Hellwing, A. L. F. Huhtanen, P. Kreuzer, M. Kuhla, B. Lund, P. Madsen, J. Martín, C. McClelland, S. C. McGee, M. Moate, P.J. Muetzel, S. Muñoz, C. O'Kiely, P. Peiren, N. Reynolds, C. K. Schwarm, A. Shingfield, K. J. Storlien, T.M. Weisbjerg, M.R. Yáñez Ruiz, David R. Yu, Z. |
| author |
Niu, M. |
| author_facet |
Niu, M. Kebreab, E. Hristov, A. N. Oh, J. Arndt, C. Bannink, A. Bayat, A.R. Brito, A. F. Boland, T. Casper, D. P. Crompton, L. A. Dijkstra, J. Eugène, M. A. Garnsworthy, P. C. Haque, M. N. Hellwing, A. L. F. Huhtanen, P. Kreuzer, M. Kuhla, B. Lund, P. Madsen, J. Martín, C. McClelland, S. C. McGee, M. Moate, P.J. Muetzel, S. Muñoz, C. O'Kiely, P. Peiren, N. Reynolds, C. K. Schwarm, A. Shingfield, K. J. Storlien, T.M. Weisbjerg, M.R. Yáñez Ruiz, David R. Yu, Z. |
| author_role |
author |
| author2 |
Kebreab, E. Hristov, A. N. Oh, J. Arndt, C. Bannink, A. Bayat, A.R. Brito, A. F. Boland, T. Casper, D. P. Crompton, L. A. Dijkstra, J. Eugène, M. A. Garnsworthy, P. C. Haque, M. N. Hellwing, A. L. F. Huhtanen, P. Kreuzer, M. Kuhla, B. Lund, P. Madsen, J. Martín, C. McClelland, S. C. McGee, M. Moate, P.J. Muetzel, S. Muñoz, C. O'Kiely, P. Peiren, N. Reynolds, C. K. Schwarm, A. Shingfield, K. J. Storlien, T.M. Weisbjerg, M.R. Yáñez Ruiz, David R. Yu, Z. |
| 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 author author |
| dc.contributor.none.fl_str_mv |
European Commission University of California Fondo Nacional de Desarrollo Científico y Tecnológico (Chile) CSIC - Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA) Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72] |
| dc.subject.none.fl_str_mv |
Methane intensity prediction models Methane yield Dairy cows Dry matter in take Enteric methane emissions |
| topic |
Methane intensity prediction models Methane yield Dairy cows Dry matter in take Enteric methane emissions |
| description |
Enteric methane (CH) production from cattle contributes to global greenhouse gas emissions. Measurement of enteric CH is complex, expensive, and impractical at large scales; therefore, models are commonly used to predict CH production. However, building robust prediction models requires extensive data from animals under different management systems worldwide. The objectives of this study were to (1) collate a global database of enteric CH production from individual lactating dairy cattle; (2) determine the availability of key variables for predicting enteric CH production (g/day per cow), yield [g/kg dry matter intake (DMI)], and intensity (g/kg energy corrected milk) and their respective relationships; (3) develop intercontinental and regional models and cross-validate their performance; and (4) assess the trade-off between availability of on-farm inputs and CH prediction accuracy. The intercontinental database covered Europe (EU), the United States (US), and Australia (AU). A sequential approach was taken by incrementally adding key variables to develop models with increasing complexity. Methane emissions were predicted by fitting linear mixed models. Within model categories, an intercontinental model with the most available independent variables performed best with root mean square prediction error (RMSPE) as a percentage of mean observed value of 16.6%, 14.7%, and 19.8% for intercontinental, EU, and United States regions, respectively. Less complex models requiring only DMI had predictive ability comparable to complex models. Enteric CH production, yield, and intensity prediction models developed on an intercontinental basis had similar performance across regions, however, intercepts and slopes were different with implications for prediction. Revised CH emission conversion factors for specific regions are required to improve CH production estimates in national inventories. In conclusion, 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 yield and intensity prediction, information on milk yield and composition is required for better estimation. |
| publishDate |
2018 |
| dc.date.none.fl_str_mv |
2018 2019 2019 2019 |
| dc.type.none.fl_str_mv |
info:eu-repo/semantics/article http://purl.org/coar/resource_type/c_6501 Publisher's version info:eu-repo/semantics/publishedVersion |
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article |
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publishedVersion |
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http://hdl.handle.net/10261/176365 |
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http://hdl.handle.net/10261/176365 |
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Inglés |
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Inglés |
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Sí |
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info:eu-repo/semantics/openAccess |
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openAccess |
| dc.publisher.none.fl_str_mv |
Blackwell Publishing |
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Blackwell Publishing |
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reponame:DIGITAL.CSIC. Repositorio Institucional del CSIC instname:Consejo Superior de Investigaciones Científicas (CSIC) |
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Consejo Superior de Investigaciones Científicas (CSIC) |
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DIGITAL.CSIC. Repositorio Institucional del CSIC |
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DIGITAL.CSIC. Repositorio Institucional del CSIC |
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| _version_ |
1869417562001899520 |
| spelling |
Prediction of enteric methane production, yield, and intensity in dairy cattle using an intercontinental databaseNiu, M.Kebreab, E.Hristov, A. N.Oh, J.Arndt, C.Bannink, A.Bayat, A.R.Brito, A. F.Boland, T.Casper, D. P.Crompton, L. A.Dijkstra, J.Eugène, M. A.Garnsworthy, P. C.Haque, M. N.Hellwing, A. L. F.Huhtanen, P.Kreuzer, M.Kuhla, B.Lund, P.Madsen, J.Martín, C.McClelland, S. C.McGee, M.Moate, P.J.Muetzel, S.Muñoz, C.O'Kiely, P.Peiren, N.Reynolds, C. K.Schwarm, A.Shingfield, K. J.Storlien, T.M.Weisbjerg, M.R.Yáñez Ruiz, David R.Yu, Z.Methane intensityprediction modelsMethane yieldDairy cowsDry matter in takeEnteric methane emissionsEnteric methane (CH) production from cattle contributes to global greenhouse gas emissions. Measurement of enteric CH is complex, expensive, and impractical at large scales; therefore, models are commonly used to predict CH production. However, building robust prediction models requires extensive data from animals under different management systems worldwide. The objectives of this study were to (1) collate a global database of enteric CH production from individual lactating dairy cattle; (2) determine the availability of key variables for predicting enteric CH production (g/day per cow), yield [g/kg dry matter intake (DMI)], and intensity (g/kg energy corrected milk) and their respective relationships; (3) develop intercontinental and regional models and cross-validate their performance; and (4) assess the trade-off between availability of on-farm inputs and CH prediction accuracy. The intercontinental database covered Europe (EU), the United States (US), and Australia (AU). A sequential approach was taken by incrementally adding key variables to develop models with increasing complexity. Methane emissions were predicted by fitting linear mixed models. Within model categories, an intercontinental model with the most available independent variables performed best with root mean square prediction error (RMSPE) as a percentage of mean observed value of 16.6%, 14.7%, and 19.8% for intercontinental, EU, and United States regions, respectively. Less complex models requiring only DMI had predictive ability comparable to complex models. Enteric CH production, yield, and intensity prediction models developed on an intercontinental basis had similar performance across regions, however, intercepts and slopes were different with implications for prediction. Revised CH emission conversion factors for specific regions are required to improve CH production estimates in national inventories. In conclusion, 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 yield and intensity prediction, information on milk yield and composition is required for better estimation.This study is part of the Joint Programming Initiative on Agriculture, Food Security and Climate Change (FACCE‐JPI)'s “GLOBAL NETWORK” project and the “Feeding and Nutrition Network” (http://animalscience.psu.edu/fnn) of the Livestock Research Group within the Global Research Alliance for Agricultural Greenhouse Gases (www.globalresearchalliance.org). Authors gratefully acknowledge funding for this project from: USDA National Institute of Food and Agriculture Grant no. 2014‐67003‐21979) University of California, Davis Sesnon Endowed Chair Program, USDA, and Austin Eugene Lyons Fellowship (University of California, Davis); Funding from USDA National Institute of Food and Agriculture Federal Appropriations under Project PEN 04539 and Accession number 1000803, DSM Nutritional Products (Basel, Switzerland), Pennsylvania Soybean Board (Harrisburg, PA, USA), Northeast Sustainable Agriculture Research and Education (Burlington, VT, USA), and PMI Nutritional Additives (Shoreview, MN, USA); the Ministry of Economic Affairs (the Netherlands; project BO‐20‐007‐006; Global Research Alliance on Agricultural Greenhouse Gases), the Product Board Animal Feed (Zoetermeer, the Netherlands) and the Dutch Dairy Board (Zoetermeer, the Netherlands); USDA National Institute of Food and Agriculture (Hatch Multistate NC‐1042 Project Number NH00616‐R; Project Accession Number 1001855) and the New Hampshire Agricultural Experiment Station (Durham, NH); French National Research Agency through the FACCE‐JPI program (ANR‐13‐JFAC‐0003‐01), Agricultural GHG Research Initiative for Ireland (AGRI‐I), Academy of Finland (No. 281337), Helsinki, Finland; Swiss Federal Office of Agriculture, Berne, Switzerland; the Department for Environment, Food and Rural Affairs (Defra; UK); Defra, the Scottish Government, DARD, and the Welsh Government as part of the UK's Agricultural GHG Research Platform projects (www.ghgplatform.org.uk); INIA (Spain, project MIT01‐GLOBALNET‐EEZ); German Federal Ministry of Food and Agriculture (BMBL) through the Federal Office for Agriculture and Food (BLE); Swedish Infrastructure for Ecosystem Science (SITES) at Röbäcksdalen Research Station; Comisión Nacional de Investigación Científica y Tecnológica, Fondo Nacional de Desarrollo Científico y Tecnológico (Grant Nos. 11110410 and 1151355) and Fondo Regional de Tecnología Agropecuaria (FTG/RF‐1028‐RG); European Commission through SMEthane (FP7‐SME‐262270). The authors are thankful to all colleagues who contributed data to the GLOBAL NETWORK project and especially thank Luis Moraes, Ranga Appuhamy, Henk van Lingen, James Fadel, and Roberto Sainz for their support on data analysis. All authors read and approved the final manuscript. The authors declare that they have no competing interests.Peer ReviewedBlackwell PublishingEuropean CommissionUniversity of CaliforniaFondo Nacional de Desarrollo Científico y Tecnológico (Chile)CSIC - Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA)Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]2019201920182019info:eu-repo/semantics/articlehttp://purl.org/coar/resource_type/c_6501Publisher's versioninfo:eu-repo/semantics/publishedVersionhttp://hdl.handle.net/10261/176365reponame:DIGITAL.CSIC. Repositorio Institucional del CSICinstname:Consejo Superior de Investigaciones Científicas (CSIC)InglésSíinfo:eu-repo/semantics/openAccessoai:digital.csic.es:10261/1763652026-05-22T06:33:51Z |
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15.811543 |