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  • Title: Enteric Methane Emissions Prediction in Dairy Cattle and Effects of Monensin on Methane Emissions: A Meta-Analysis.
    Author: Marumo JL, LaPierre PA, Van Amburgh ME.
    Journal: Animals (Basel); 2023 Apr 18; 13(8):. PubMed ID: 37106954.
    Abstract:
    Greenhouse gas emissions, such as enteric methane (CH4) from ruminant livestock, have been linked to global warming. Thus, easily applicable CH4 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 CH4 emissions; (ii) identify the principal dietary, animal, and lactation performance input variables that predict enteric CH4 production (g/d) and yield (g/kg of dry matter intake DMI); (iii) develop empirical models that predict CH4 production and yield in dairy cattle; and (iv) evaluate the newly developed models and published models in the literature. A significant reduction in CH4 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 CH4 emissions specifically beyond 21 days of feeding are reported to ensure the monensin effects on the enteric CH4 are needed. In order to explore CH4 predictions independent of monensin, additional studies were added to the database. Subsequently, dairy cattle CH4 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 CH4 production. All databases observed an improvement in prediction abilities in CH4 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 CH4 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 CH4 emission compared to other published equations. Our results indicate that the inclusion of dietary composition along with DMI can provide an improved CH4 production prediction in dairy cattle.
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