PREDICTION OF METHANE PRODUCTION FROM DAIRY-COWS USING EXISTING MECHANISTIC MODELS AND REGRESSION EQUATIONS

Ruminants may contribute to global warming through the release of meth ane gas by enteric fermentation. Until now, methane emissions from rum inants 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 f rom 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 challeng ed With 32 experimental diets selected from 13 publications. The predi ctive capacity-of mechanistic models and regression equations was eval uated by comparing predicted and observed methane production using reg ression analysis. Results of regression showed better prediction of me thane production with mechanistic models than with regression equation s. The modified model of Dijkstra et al. predicted methane production with the higher R-2 (.71) and the smaller error of prediction (19.87% of the observed mean). The model of Baldwin et al. predicted methane p roduction with a similar R-2 (.70) but a higher error of prediction (3 6.93%). However, a large proportion of this error can be eliminated by a correction factor. Predictions using the equations of Moe and Tyrre ll and Blaxter and Clapperton were poor (R-2 = .42 and .57; error of p rediction = 33.72% and 22.93%, respectively). This study demonstrated that from a large variation in diet composition, mechanistic models al low the prediction of methane production more accurately than simple r egression equations.