Prediction of methane production from dairy cows using existing mechanistic models and regression equations

Abstract

Ruminants may contribute to global warming through the release of methane gas by enteric fermentation. Until now, methane emissions from ruminants were estimated using simple regression equations. The objective of this study was to compare the capacity of dynamic and mechanistic models to that of regression equations to predict methane production from dairy cows. The updated version of the model of Baldwin et al. and a modified version of the model of Dijkstra et al. and the regression equations of Blaxter and Clapperton and Moe and Tyrrell were challenged with 32 experimental diets selected from 13 publications. The predictive capacity of mechanistic models and regression equations was evaluated by comparing predicted and observed methane production using regression analysis. Results of regression showed better prediction of methane production with mechanistic models than with regression equations. The modified model of Dijkstra et al. predicted methane production with the higher R2 (.71) and the smaller error of prediction (19.87% of the observed mean). The model of Baldwin et al. predicted methane production with a similar R2 (.70) but a higher error of prediction (36.93%). However, a large proportion of this error can be eliminated by a correction factor. Predictions using the equations of Moe and Tyrrell and Blaxter and Clapperton were poor (R2 = .42 and .57; error of prediction = 33.72% and 22.93%, respectively). This study demonstrated that from a large variation in diet composition, mechanistic models allow the prediction of methane production more accurately than simple regression equations.