Enteric Methane Emissions Prediction in Dairy Cattle and Effects of Monensin on Methane Emissions: A Meta-Analysis

SIMPLE SUMMARY: Enteric methane (CH(4)) emissions are a global concern and have been associated with climate change. Thus, sustainable, easily applicable CH(4) mitigation strategies should be in place without having an adverse effect on animal productivity. We (i) developed a series of dairy cattle enteric CH(4) production (g/d) and yield (g/kg of dry matter intake, DMI) models using combined (lactating and non-lactating cows) and lactating data, (ii) investigated the effects of monensin on enteric CH(4) emissions in dairy cattle, and (iii) evaluated the proposed and published models. Monensin reduced daily CH(4) production and CH(4) yield by 5.4% and 4.0%, respectively. Further, long-term in vivo studies on monensin feeding of ≤24 mg/kg DM with CH(4) measurements taken to account for bacterial adaptation in the rumen are needed. Overall, DMI is the significant driver of CH(4) emissions in dairy cattle and a model that included DMI, dietary forage proportion, and the quadratic term of dietary forage proportion was the best model for both combined (lactating and non-lactating) and lactating cows. The methane yield was best predicted with dietary forage only for combined data, while a combination of dietary forage proportion, milk fat, and milk protein yields was the best model for lactating cows. This indicates that the inclusion of dietary composition along with DMI can provide a better CH(4) production prediction in dairy cattle. The selected developed models outperformed the published models. ABSTRACT: Greenhouse gas emissions, such as enteric methane (CH(4)) from ruminant livestock, have been linked to global warming. Thus, easily applicable CH(4) management strategies, including the inclusion of dietary additives, should be in place. The objectives of the current study were to: (i) compile a database of animal records that supplemented monensin and investigate the effect of monensin on CH(4) emissions; (ii) identify the principal dietary, animal, and lactation performance input variables that predict enteric CH(4) production (g/d) and yield (g/kg of dry matter intake DMI); (iii) develop empirical models that predict CH(4) production and yield in dairy cattle; and (iv) evaluate the newly developed models and published models in the literature. A significant reduction in CH(4) production and yield of 5.4% and 4.0%, respectively, was found with a monensin supplementation of ≤24 mg/kg DM. However, no robust models were developed from the monensin database because of inadequate observations under the current paper’s inclusion/exclusion criteria. Thus, further long-term in vivo studies of monensin supplementation at ≤24 mg/kg DMI in dairy cattle on CH(4) emissions specifically beyond 21 days of feeding are reported to ensure the monensin effects on the enteric CH(4) are needed. In order to explore CH(4) predictions independent of monensin, additional studies were added to the database. Subsequently, dairy cattle CH(4) production prediction models were developed using a database generated from 18 in vivo studies, which included 61 treatment means from the combined data of lactating and non-lactating cows (COM) with a subset of 48 treatment means for lactating cows (LAC database). A leave-one-out cross-validation of the derived models showed that a DMI-only predictor model had a similar root mean square prediction error as a percentage of the mean observed value (RMSPE, %) on the COM and LAC database of 14.7 and 14.1%, respectively, and it was the key predictor of CH(4) production. All databases observed an improvement in prediction abilities in CH(4) production with DMI in the models along with dietary forage proportion inclusion and the quadratic term of dietary forage proportion. For the COM database, the CH(4) yield was best predicted by the dietary forage proportion only, while the LAC database was for dietary forage proportion, milk fat, and protein yields. The best newly developed models showed improved predictions of CH(4) emission compared to other published equations. Our results indicate that the inclusion of dietary composition along with DMI can provide an improved CH(4) production prediction in dairy cattle.