Prediction of enteric methane emission from buffaloes using linear and non-linear statistical models

Methane (CH₄) production from world buffalo population contributes a substantial share to the global greenhouse gas production by livestock after cattle. The annual growth rate of enteric methane from buffaloes is higher than the growth rate of enteric methane emission from cattle. However, there is no model for predicting enteric CH₄ production in buffaloes, though there are several models developed for prediction of enteric CH₄ from cattle. Thus, the objective of this study was to develop linear and nonlinear statistical models to predict CH₄ production from dietary and animal characteristic variables. A database from 24 publications was constructed, which included 64 mean observations of CH₄ outputs measured on 394 buffaloes. Extant equations developed for cattle were also evaluated for suitability of those CH₄ prediction equations in buffaloes. The simple linear equations that predicted with high precision and accuracy were CH₄ (MJ/day) = 1.29_(±0.576) + 0.788_(±0.099) × dry matter (DM) intake (kg/day) [RMSPE = 19.4%, with 94% of mean square prediction error (MSPE) being random error; R² = 0.81] and CH₄ (MJ/day) = 0.135_(±0.767) + 1.717_(±0.233) × neutral detergent fiber (NDF) intake (kg/day) [RMSPE = 18.3%, with 99.7% of MSPE being random error; R² = 0.79]. Multiple regression equations that predicted CH₄ slightly better than simple prediction equations were CH₄ (MJ/day) = -0.436_(±0.665) + 0.678_(±0.184) × DM intake (kg/day) + 0.697_(±0.347) × NDF intake (kg/day) [RMSPE = 16.1%, with 99.9% of MSPE from random error; R² = 0.85] and CH₄ (MJ/day) = -0.819_(±0.801) + 0.690_(±0.432) × crude protein (CP) intake (kg/day) + 1.527_(±0.215) × NDF intake (kg/day) + 0.930_(±0.413) × non-fibrous carbohydrate (NFC) intake (kg/day) [RMSPE = 16.5%, with 99.7% of MSPE accounting random error; R² = 0.84]. Among the nonlinear equations developed, monomolecular model, CH₄, MJ/day = 39.99_(±17.23) × {1 – exp(- 0.0276_(±0.0132) × DM intake (kg/day) ) [RMSPE = 19.1%, with 99.9% of MSPE accounting random error; R² = 0.80], performed better than other nonlinear models, but the predictability and robustness of the equation did not improve compared with the linear models. Extant equations overestimated the methane production, and had low accuracy and precision. There was no significant mean and linear biases (P>0.05) for all models except for non-linear models. The mean and slope biases of the non-linear equations, although significant statistically resulted in a maximum bias of less than 1.13 and 1.52 MJ/day over the full range of predicted values for monomolecular and Gompertz equations, respectively. In contrast, the mean and linear biases of two best extant equations (one each from linear and non-linear models) were significant (P<0.001) and resulted in a maximum bias of 3.28 to 4.31 MJ/day over the full range of predicted values. The equations developed in this study would be useful for national inventory preparation to improve an estimation of methane production in buffaloes particularly for tropical feeding situations.

 

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