TY - JOUR

T1 - Prediction of enteric methane production, yield, and intensity in dairy cattle using an intercontinental database

AU - Niu, Mutian

AU - Kebreab, Ermias

AU - Hristov, Alexander N

AU - Oh, Joonpyo

AU - Arndt, Claudia

AU - Bannink, André

AU - Bayat, Ali R

AU - Brito, André F

AU - Boland, Tommy

AU - Casper, David

AU - Crompton, Les A

AU - Dijkstra, Jan

AU - Eugène, Maguy A

AU - Garnsworthy, Phil C

AU - Haque, Md Najmul

AU - Hellwing, Anne L F

AU - Huhtanen, Pekka

AU - Kreuzer, Michael

AU - Kuhla, Bjoern

AU - Lund, Peter

AU - Madsen, Jørgen

AU - Martin, Cécile

AU - McClelland, Shelby C

AU - McGee, Mark

AU - Moate, Peter J

AU - Muetzel, Stefan

AU - Muñoz, Camila

AU - O'Kiely, Padraig

AU - Peiren, Nico

AU - Reynolds, Christopher K

AU - Schwarm, Angela

AU - Shingfield, Kevin J

AU - Storlien, Tonje M

AU - Weisbjerg, Martin R

AU - Yáñez-Ruiz, David R

AU - Yu, Zhongtang

N1 - © 2018 John Wiley & Sons Ltd.

PY - 2018/8

Y1 - 2018/8

N2 - Enteric methane (CH4) production from cattle contributes to global greenhouse gas emissions. Measurement of enteric CH4is complex, expensive, and impractical at large scales; therefore, models are commonly used to predict CH4production. 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 CH4production from individual lactating dairy cattle; (2) determine the availability of key variables for predicting enteric CH4production (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 CH4prediction 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 CH4production, 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 CH4emission conversion factors for specific regions are required to improve CH4production 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 CH4yield and intensity prediction, information on milk yield and composition is required for better estimation.

AB - Enteric methane (CH4) production from cattle contributes to global greenhouse gas emissions. Measurement of enteric CH4is complex, expensive, and impractical at large scales; therefore, models are commonly used to predict CH4production. 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 CH4production from individual lactating dairy cattle; (2) determine the availability of key variables for predicting enteric CH4production (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 CH4prediction 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 CH4production, 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 CH4emission conversion factors for specific regions are required to improve CH4production 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 CH4yield and intensity prediction, information on milk yield and composition is required for better estimation.

U2 - 10.1111/gcb.14094

DO - 10.1111/gcb.14094

M3 - Journal article

C2 - 29450980

VL - 247

SP - 3368

EP - 3389

JO - Global Change Biology

JF - Global Change Biology

SN - 1354-1013

IS - 8

ER -