Modelling methane production of dairy cows: A hierarchical Bayesian stochastic approach

Monitoring methane production from individual cows is required for evaluating the success of greenhouse gas reduction strategies. However, converting non-continuous measurements of methane production into daily methane production rates (MPR) remains challenging due to the general non-linearity of the methane production curve. In this paper, we propose a Bayesian hierarchical stochastic kinetic equation approach to this challenge. Modelling was used to fit a non-linear curve on climate respiration chamber (CRC) data of twenty-eight individual dairy cows before computing an area under the curve, thereby providing an estimate of MPR from individual cows. The shape parameters of this model were pooled across cows (population-level), while the scale parameter varied between individuals. This allowed for the characterization of variation in MPR within as well as between cows. Model fit was thoroughly investigated through posterior predictive checking, which showed that the model could reproduce the CRC data of twenty-eight cows well. Concordance between the values observed in the CRC and the ones predicted by the HMR model was assessed with R² (0.995), r (0.997), root mean square error (10.0 g/d), and Lin's concordance correlation coefficient (0.961). Overall, the predictions made by the HMR model appeared to reflect individual MPR levels and variation between cows as well as the standard approach taken by scientists with CRC data.