Models for predicting enteric methane emissions from dairy cows in North America, Europe, and Australia and New Zealand

There are several models in the literature for predicting enteric methane (CH4) emissions. These models were often developed on region or country-specific data and may not be able to predict the emissions successfully in every region. The majority of extant models require dry matter intake (DMI) of individual animals, which is not routinely measured. The objectives of this study were to (i) evaluate performance of extant models in predicting enteric CH4 emissions from dairy cows in North America (NA), Europe (EU), and Australia and New Zealand (AUNZ) and (ii) explore the performance using estimated DMI. Forty extant models were challenged on 55, 105, and 52 enteric CH4 measurements (g per lactating cow per day) from NA, EU, and AUNZ, respectively. The models were ranked using root mean square prediction error as a percentage of the average observed value (RMSPE) and concordance correlation coefficient (CCC). A modified model of Nielsen etal. (Acta Agriculturae Scand SectionA, 63, 2013 and 126) using DMI, and dietary digestible neutral detergent fiber and fatty acid contents as predictor variables, were ranked highest in NA (RMSPE=13.1% and CCC=0.78). The gross energy intake-based model of Yan etal. (Livestock Production Science, 64, 2000 and 253) and the updated IPCC Tier 2 model were ranked highest in EU (RMSPE=11.0% and CCC=0.66) and AUNZ (RMSPE=15.6% and CCC=0.75), respectively. DMI of cows in NA and EU was estimated satisfactorily with body weight and fat-corrected milk yield data (RMSPE<12.0% and CCC>0.60). Using estimated DMI, the Nielsen etal. (2013) (RMSPE=12.7 and CCC=0.79) and Yan etal. (2000) (RMSPE=13.7 and CCC=0.50) models still predicted emissions in respective regions well. Enteric CH4 emissions from dairy cows can be predicted successfully (i.e., RMSPE<15%), if DMI can be estimated with reasonable accuracy (i.e., RMSPE<10%).