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

Autor: 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., Martin, 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.
Přispěvatelé: Department of Animal Science, University of California, Pennsylvania State University (Penn State), Penn State System-Penn State System, Environmental Defense Fund (EDF), Wageningen Livestock Research, Wageningen University and Research [Wageningen] (WUR), Natural Resources Institute Finland (LUKE), University of New Hampshire (UNH), University College Dublin (UCD), Independent, School of Agriculture, Policy and Development, University of Reading (UOR), Animal Nutrition Group, Unité Mixte de Recherche sur les Herbivores - UMR 1213 (UMRH), Institut National de la Recherche Agronomique (INRA)-VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement, School of Biosciences [Cardiff], Cardiff University, University of Copenhagen = Københavns Universitet (KU), Aarhus University [Aarhus], Swedish University of Agricultural Sciences (SLU), Eidgenössische Technische Hochschule - Swiss Federal Institute of Technology [Zürich] (ETH Zürich), Leibniz Institute for Farm Animal Biology (FBN), Colorado State University [Fort Collins] (CSU), Teagasc Agriculture and Food Development Authority (Teagasc), Department of Economic Development, Jobs, Transport and Resources (DEDJTR), Agresearch Ltd, INIA Remehue, Partenaires INRAE, Research Institute for Agricultural, Fisheries and Food (ILVO), Aberystwyth University, Norwegian University of Life Sciences (NMBU), Estación Experimental del Zaidín (EEZ), Consejo Superior de Investigaciones Científicas [Madrid] (CSIC), Department of Animal Sciences, University of Illinois at Urbana-Champaign [Urbana], University of Illinois System-University of Illinois System, FONDECYT11110410 1151355, FontagroFTG/RF-1028-RG, Netherlands Ministry of Economic Affairs BO-20-007-006, Austin Eugene Lyons Fellowship, Academy of Finland, European Commission, Fondo Nacional de Desarrollo Científico y Tecnológico (Chile), CSIC - Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA), Institut National de la Recherche Agronomique (INRA)-VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)
Jazyk: angličtina
Rok vydání: 2018
Předmět:
Zdroj: Global Change Biology
Global Change Biology, Wiley, 2018, 24 (8), pp.3368-3389. ⟨10.1111/gcb.14094⟩
Global Change Biology, 24 (8)
Niu, M, Kebreab, E, Hristov, A N, Oh, J, Arndt, C, Bannink, A, Bayat, A R, Brito, A F, Boland, T, Casper, D, 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, Martin, 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, D R & Yu, Z 2018, ' Prediction of enteric methane production, yield, and intensity in dairy cattle using an intercontinental database ', Global Change Biology, vol. 24, no. 8, pp. 3368-3389 . https://doi.org/10.1111/gcb.14094
Global change biology, vol 24, iss 8
Global Change Biology, 24(8), 3368-3389
Niu, M, Kebreab, E, Hristov, A N, Oh, J, Arndt, C, Bannink, A, Bayat, A R, Brito, A F, Boland, T, Casper, D, 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, Martin, 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, D R & Yu, Z 2018, ' Prediction of enteric methane production, yield, and intensity in dairy cattle using an intercontinental database ', Global Change Biology, vol. 247, no. 8, pp. 3368-3389 . https://doi.org/10.1111/gcb.14094
Global Change Biology 24 (2018) 8
Digital.CSIC. Repositorio Institucional del CSIC
instname
ISSN: 1354-1013
1365-2486
Popis: 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.
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.
Databáze: OpenAIRE
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