Abstract
The present study aimed to evaluate non-linear models (power and exponential) in predicting enteric methane (CH4) production from a data-set of respiration chamber studies (207 treatment means). The developed power model was CH4 (L/d) = 51.5(±4.5)×dry matter intake (DMI, kg/d)0.792(±0.034) (adj. root mean square error, RMSE = 25.5 L/d), whereas the developed exponential model (Mitscherlich) was CH4 (L/d) = 976(±95.3)×[(1 – e (−0.0407(±0.00510)×DMI (kg/d))], (adj. RMSE = 25.0 L/d). Adjusting the exponents for dietary concentration of ether extract, proportion of non-fibre carbohydrates of total carbohydrates and organic matter digestibility improved the models, and the effects of all these factors were significant (at least P<0.05) with both models. It is concluded that non-linear models can provide a better applicability over a wider range of intake, and are biologically more reliable for predicting CH4 production as compared to linear models.