Evaluation of models of enteric methane emissions in finishing steers

Accurate estimation of enteric CH₄ emissions (i.e., MJ or g of CH₄/ day) in the ruminant sector is necessary for properly determining greenhouse gas emissions and developing measuring, reporting, and verification programs. However, measuring enteric CH₄ emissions under commercial conditions presents challenges due to technical and economic constraints. Thus, using prediction models allows for estimating individual enteric CH₄ emissions according to animal and dietary characteristics. When evaluated in independent datasets, there is limited information regarding the accuracy and precision of the reported equations to predict enteric CH₄ emissions in steers fed a finishing diet. This study evaluated the predictive performance of various reported equations for estimating enteric CH₄ production in finishing steers. Data used to assess the prediction equations came from 446 steers from five experiments during the finishing phase. Gas flux, nutrient consumption, and animal growth performance were evaluated in each experiment. Seventy-two equations were compared based on the mean square prediction error (MSPE), the decomposition of the root MSPE (RMSPE), and the concordance correlation coefficient (CCC). Prediction equations for estimating enteric CH₄ emissions showed lower sensitivity with RMSPE (as a percentage of the observed mean) ranging from 17.79 to 99.21 and CCC ranging from -0.07 to 0.21. The decomposition of the RMSPE showed mean bias (as a percentage of the RMSPE) ranging from 0.14 to 94.87 and slope bias (as a percentage of the RMSPE) ranging from 0 to 25.24. In addition, 49 equations underpredicted (ranging from 0.6 to 50.1%) and 23 overpredicted (ranging from 3.7 to 96.1%) enteric CH₄ emissions. The prediction of enteric CH₄ production showed greater CCC and lower MSPE when the intake of DM and ether extract were included as predictors. These results suggest a limited ability to predict enteric CH₄ by steers during the finishing phase. Further efforts are required to generate sensitive models to accurately predict enteric CH₄ emissions in finishing steers.