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

被引：225

作者：

Niu, Mutian ^{[1
]}

Kebreab, Ermias ^{[1
]}

Hristov, Alexander N. ^{[2
]}

Oh, Joonpyo ^{[2
]}

Arndt, Claudia ^{[3
]}

Bannink, Andre ^{[4
]}

Bayat, Ali R. ^{[5
]}

Brito, Andre F. ^{[6
]}

Boland, Tommy ^{[7
]}

Casper, David ^{[8
]}

Crompton, Les A. ^{[9
]}

Dijkstra, Jan ^{[10
]}

Eugene, Maguy A. ^{[11
]}

Garnsworthy, Phil C. ^{[12
]}

Haque, Md Najmul ^{[13
]}

Hellwing, Anne L. F. ^{[14
]}

Huhtanen, Pekka ^{[15
]}

Kreuzer, Michael ^{[16
]}

Kuhla, Bjoern ^{[17
]}

Lund, Peter ^{[14
]}

Madsen, Jorgen ^{[13
]}

Martin, Cecile ^{[11
]}

McClelland, Shelby C. ^{[18
]}

McGee, Mark ^{[19
]}

Moate, Peter J. ^{[20
]}

Muetzel, Stefan ^{[21
]}

Munoz, Camila ^{[22
]}

O'Kiely, Padraig ^{[19
]}

Peiren, Nico ^{[23
]}

Reynolds, Christopher K. ^{[9
]}

Schwarm, Angela ^{[16
]}

Shingfield, Kevin J. ^{[24
]}

Storlien, Tonje M. ^{[25
]}

Weisbjerg, Martin R. ^{[14
]}

Yanez-Ruiz, David R. ^{[26
]}

Yu, Zhongtang ^{[27
]}

机构：

[1] Univ Calif Davis, Dept Anim Sci, Davis, CA 95616 USA

[2] Penn State Univ, Dept Anim Sci, University Pk, PA 16802 USA

[3] Environm Def Fund, San Francisco, CA USA

[4] Univ Wageningen & Res, Wageningen Livestock Res, NL-6700 HB Wageningen, Netherlands

[5] Nat Resoures Inst Finland Luke, Milk Prod Solut Green Technol, Jokioinen, Finland

[6] Univ New Hampshire, Dept Agr, Nutr & Food Syst, Durham, NH 03824 USA

[7] Univ Coll Dublin, Sch Agr & Food Sci, Dublin 2, Ireland

[8] Frust McNess Co, Freeport, IL USA

[9] Univ Reading, Sch Agr, Policy & Dev, Reading, Berks, England

[10] Univ Wageningen & Res, Anim Nutr Grp, Wageningen, Netherlands

[11] Univ Clermont Auvergne, INRA, VetAgro Sup, UMR Herbivores, St Genes Champanelle, France

[12] Univ Nottingham, Sch Biosci, Loughborough, Leics, England

[13] Univ Copenhagen, Dept Large Anim Sci, Copenhagen, Denmark

[14] Aarhus Univ, Dept Anim Sci, Tjele, Denmark

[15] Swedish Univ Agr Sci, Dept Agr Sci Northern Sweden, S-90183 Umea, Sweden

[16] Swiss Fed Inst Technol, Inst Agr Sci, Zurich, Switzerland

[17] Leibniz Inst Farm Ani Biol, Inst Nutr Phsysiol, Dummerstorf, Mecklenburg Vor, Germany

[18] Colorado State Univ, Dept Soil & Crop Sci, Ft Collins, CO 80523 USA

[19] Teagasc Agr & Food Dev Authority, Carlow, Ireland

[20] Dept Econ Dev Jobs Transport & Resources, Agr Res Div, Melbourne, Vic, Australia

[21] AgResearch, Palmerston North, New Zealand

[22] INIA Remehue, Inst Invest Agropecuarias, Osorno, Chile

[23] Flanders Res Inst Agr Fisheries & Food, Anim Sci Dept, Melle, Belgium

[24] Aberystwyth Univ, Inst Biol Environm & Rural Sci, Aberystwyth SY23 3FG, Dyfed, Wales

[25] Norwegian Univ Life Sci, Dept Anim & Aquacultural Sci, As, Norway

[26] CSIC, Estac Expt Zaidin, Granada, Spain

[27] Ohio State Univ, Dept Anim Sci, Columbus, OH 43210 USA

来源：

GLOBAL CHANGE BIOLOGY | 2018年 / 24卷 / 08期

基金：

芬兰科学院; 美国食品与农业研究所;

关键词：

dairy cows; dry matter intake; enteric methane emissions; methane intensity; methane yield; prediction models; RUMEN FERMENTATION; FEED-INTAKE; DRY-MATTER; EMISSIONS; COWS; RUMINANTS; DIGESTIBILITY; METAANALYSIS; MITIGATION; MANAGEMENT;

D O I：

10.1111/gcb.14094

中图分类号：

X176 [生物多样性保护];

学科分类号：

090705 ;

摘要：

Enteric methane (CH4) production from cattle contributes to global greenhouse gas emissions. Measurement of enteric CH4 is complex, expensive, and impractical at large scales; therefore, models are commonly used to predict CH4 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 CH4 production from individual lactating dairy cattle; (2) determine the availability of key variables for predicting enteric CH4 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 CH4 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 CH4 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 CH4 emission conversion factors for specific regions are required to improve CH4 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 CH4 yield and intensity prediction, information on milk yield and composition is required for better estimation.

引用

页码：3368 / 3389

页数：22

共 63 条

[1] Effects of diet on short-term regulation of feed intake by lactating dairy cattle [J].