Skip to main content
Open Access Publications from the University of California

Improving the prediction of methane production and representation of rumen fermentation for finishing beef cattle within a mechanistic model

  • Author(s): Ellis, JL
  • Dijkstra, J
  • Bannink, A
  • Kebreab, E
  • Archibeque, S
  • Benchaar, C
  • Beauchemin, KA
  • Nkrumah, JD
  • France, J
  • et al.

The purpose of this study was to evaluate prediction of methane emissions from finishing beef cattle using an extant mechanistic model with pH-independent or pH-dependent volatile fatty acid (VFA) stoichiometries, a recent stoichiometry adjustment for the use of monensin, and adaptation of the underlying model structure, to see if prediction improvements could be made for beef cattle. The database used for independent evaluation of methane predictions consisted of 74 animal means from six studies. For the "Bannink" stoichiometries, pH-dependence of stoichiometry improved the root mean square prediction error (RMSPE) statistic (38.8 to 36.6%) but the concordance correlation coefficient (CCC) statistic was reduced (0.509 to 0.469). Inclusion of monensin in the stoichiometry improved both pH-independent and pH-dependent predictions. For the "Murphy" stoichiometries, pH-dependence worsened the RMSPE (31.2 to 33.7%) as well as the CCC (0.611 to 0.465) statistic. Inclusion of monensin in the stoichiometry improved predictions with the pH-independent but not with the pH-dependent stoichiometry. Results indicate that although improvements have been made to the mechanistic model, further improvement in the representation of VFA stoichiometry, and likely the representation and prediction of pH and neutral detergent fiber digestibility, are required for more accurate prediction of methane emissions for finishing beef cattle. However, inclusion of an adjustment for monensin feeding generally lead to improved methane predictions.

Many UC-authored scholarly publications are freely available on this site because of the UC's open access policies. Let us know how this access is important for you.

Main Content
Current View