Evaluation of the performance of existing mathematical models predicting enteric methane emissions from ruminants: Animal categories and dietary mitigation strategies

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  • Mohammed Benaouda, UMR1213 Herbivores, Université Clermont Auvergne
  • ,
  • Cécile Martin, UMR1213 Herbivores, Université Clermont Auvergne
  • ,
  • Xinran Li, UMR1213 Herbivores, Université Clermont Auvergne
  • ,
  • Ermias Kebreab, UC Davis
  • ,
  • Alexander N. Hristov, The Pennsylvania State University
  • ,
  • Zhongtang Yu, The Ohio State University
  • ,
  • David R. Yáñez-Ruiz, CSIC - Estación Experimental del Zaidín (EEZ)
  • ,
  • Christopher K. Reynolds, Reading University
  • ,
  • L. A. Crompton, Reading University
  • ,
  • Jan Dijkstra, Wageningen University and Research Centre
  • ,
  • André Bannink, Wageningen University and Research Centre
  • ,
  • Angela Schwarm, ETH Zürich
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  • Michael Kreuzer, ETH Zürich
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  • Mark McGee, Animal and Grassland Research and Innovation Centre, Teagasc Moorepark, Fermoy, Co. Cork, Ireland
  • ,
  • P. Lund
  • Anne L.F. Hellwing
  • Martin R. Weisbjerg
  • Peter J. Moate, Agriculture Victoria
  • ,
  • A. R. Bayat, Natural Resources Institute Finland (Luke)
  • ,
  • Kevin J. Shingfield, Natural Resources Institute Finland (Luke)
  • ,
  • Nico Peiren, Flanders Research Institute for Agriculture
  • ,
  • M. Eugène, UMR1213 Herbivores, Université Clermont Auvergne

The objective of this study was to evaluate the performance of existing models predicting enteric methane (CH4) emissions, using a large database (3183 individual data from 103 in vivo studies on dairy and beef cattle, sheep and goats fed diets from different countries). The impacts of dietary strategies to reduce CH4 emissions, and of diet quality (described by organic matter digestibility (dOM) and neutral-detergent fiber digestibility (dNDF)) on model performance were assessed by animal category. The models were first assessed based on the root mean square prediction error (RMSPE) to standard deviation of observed values ratio (RSR) to account for differences in data between models and then on the RMSPE. For dairy cattle, the CH4 (g/d) predicting model based on feeding level (dry matter intake (DMI)/body weight (BW)), energy digestibility (dGE) and ether extract (EE) had the smallest RSR (0.66) for all diets, as well as for the high-EE diets (RSR = 0.73). For mitigation strategies based on lowering NDF or improving dOM, the same model (RSR = 0.48 to 0.60) and the model using DMI and neutral- and acid-detergent fiber intakes (RSR = 0.53) had the smallest RSR, respectively. For diets with high starch (STA), the model based on nitrogen, ADF and STA intake presented the smallest RSR (0.84). For beef cattle, all evaluated models performed moderately compared with the models of dairy cattle. The smallest RSR (0.83) was obtained using variables of energy intake, BW, forage content and dietary fat, and also for the high-EE and the low-NDF diets (RSR = 0.84 to 0.86). The IPCC Tier 2 models performed better when dietary STA, dOM or dNDF were high. For sheep and goats, the smallest RSR was observed from a model for sheep based on dGE intake (RSR = 0.61). Both IPCC models had low predictive ability when dietary EE, NDF, dOM and dNDF varied (RSR = 0.57 to 1.31 in dairy, and 0.65 to 1.24 in beef cattle). The performance of models depends mostly on explanatory variables and not on the type of data (individual vs. treatment means) used in their development or evaluation. Some empirical models give satisfactory prediction error compared with the error associated with measurement methods. For better prediction, models should include feed intake, digestibility and additional information on dietary concentrations of EE and structural and nonstructural carbohydrates to account for different dietary mitigating strategies.

Original languageEnglish
Article number114207
JournalAnimal Feed Science and Technology
Volume255
ISSN0377-8401
DOIs
Publication statusPublished - Aug 2019

    Research areas

  • Dietary strategy, Methane emission, Model evaluation, Ruminant

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