Prediction of enteric methane production and yield in dairy cattle using a Latin America and Caribbean database

Abstract

Successful mitigation efforts entail accurate estimation of on-farm emission and prediction models can be an alternative to current laborious and costly in vivo CH₄ measurement techniques. This study aimed to: (1) collate a database of individual dairy cattle CH₄ emission data from studies conducted in the Latin America and Caribbean (LAC) region; (2) identify key variables for predicting CH₄ production (g d^-1) and yield [g kg^-1 of dry matter intake (DMI)]; (3) develop and cross-validate these newly-developed models; and (4) compare models' predictive ability with equations currently used to support national greenhouse gas (GHG) inventories. A total of 42 studies including 1327 individual dairy cattle records were collated. After removing outliers, the final database retained 34 studies and 610 animal records. Production and yield of CH₄ were predicted by fitting mixed-effects models with a random effect of study. Evaluation of developed models and fourteen extant equations was assessed on all-data, confined, and grazing cows subsets. Feed intake was the most important predictor of CH₄ production. Our best-developed CH₄ production models outperformed Tier 2 equations from the Intergovernmental Panel on Climate Change (IPCC) in the all-data and grazing subsets, whereas they had similar performance for confined animals. Developed CH₄ production models that include milk yield can be accurate and useful when feed intake is missing. Some extant equations had similar predictive performance to our best-developed models and can be an option for predicting CH₄ production from LAC dairy cows. Extant equations were not accurate in predicting CH₄ yield. The use of the newly-developed models rather than extant equations based on energy conversion factors, as applied by the IPCC, can substantially improve the accuracy of GHG inventories in LAC countries.

Keywords: Diet; Empirical modeling; Enteric methane; GHG inventory; Linear models; Prediction equations.