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

  • Author(s): Niu, Mutian
  • Kebreab, Ermias
  • Hristov, Alexander
  • Oh, Joonpyo
  • Arndt, Claudia
  • Bannink, André
  • Bayat, Ali
  • Brito, André
  • Boland, Tommy
  • Casper, David
  • Crompton, Les
  • Dijkstra, Jan
  • Eugène, Maguy
  • Garnsworthy, Phil
  • Haque, Md
  • Hellwing, Anne
  • Huhtanen, Pekka
  • Kreuzer, Michael
  • Kuhla, Bjoern
  • Lund, Peter
  • Madsen, Jørgen
  • Martin, Cécile
  • McClelland, Shelby
  • McGee, Mark
  • Moate, Peter
  • Muetzel, Stefan
  • Muñoz, Camila
  • OKiely, Padraig
  • Peiren, Nico
  • Reynolds, Christopher
  • Schwarm, Angela
  • Shingfield, Kevin
  • Storlien, Tonje
  • Weisbjerg, Martin
  • Yáñez-Ruiz, David
  • Yu, Zhongtang
  • et al.

Published Web Location

https://doi.org/10.1111/gcb.14094
Abstract

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.

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