Predicting methane emissions from feedlot cattle and application of prediction equations to a synthetic feedlot steer population

Abstract

Objective

Our objectives were to develop equations to predict CH₄ emissions specific to feedlot cattle and to apply the equations we derived to a synthetic population of feedlot steers created from a commercial database of close-out data.

Materials and Methods

We built a database from the published literature of 98 treatment means from 32 studies that included measures of enteric CH₄ emissions and diet composition in cattle fed feedlot-type diets. Enteric CH₄ emissions averaged 93.2 g/d, with a mean DMI of 6.6 kg/d, and TDN, NDF, and starch concentrations averaging 82.9%, 20.5%, and 48.5%, respectively. Stepwise regression was used to screen potential dietary variables related to daily CH₄ emissions, followed by mixed-model regression to adjust prediction equations for the random effects of study. Close-out data from 3,483 lots of native steers obtained from commercial feedlots in the High Plains region were used to develop a synthetic multivariate normal population of feedlot steers for application of resulting prediction equations.

Results and Discussion

Two regression equations were derived from the database to predict CH₄ emissions (g/d): one that included only DMI, and one with DMI and TDN. A third equation that included only TDN was derived to predict CH₄ emissions as grams/kilogram of DMI. In addition to regression equations, the use of the database average (adjusted for random effects of study) of 14.11 g of CH₄/kg of DMI also was evaluated. Based on resampling analyses of observed versus predicted values, the equation with both DMI and TDN yielded a lower prediction error (24.7 g/d) and greater r² (0.622) than the DMI-only equation (30.8 g/d and 0.461, respectively). Using the database average of 14.11 g of CH₄/kg of DMI gave similar results to the DMI-only regression equation. When applied to the synthetic feedlot steer population, mean, minimum, and maximum daily CH₄ emissions were 111.2, 64.7, and 183.3 g/d, respectively, for the DMI plus TDN regression equation.

Implications and Applications

The regression equations we developed, which use commonly available animal and diet information, can be applied to commercial feedlot data to monitor enteric CH₄ emissions. As additional CH₄ emission data from cattle fed typical feedlot diets become available, our equations can be updated to provide more accurate and precise predictions.