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

Abstract

Methane (CH4) produced from enteric fermentation in ruminants has a noticeable impact on climate change. Prediction models are an alternative to current laborious and costly in vivo CH4 measurement techniques. The objectives of this study were to: (1) collate a database of individual sheep records from CH4 emission studies conducted in the Latin America and Caribbean (LAC) region; (2) identify key variables for predicting CH4 production (g/d) and CH4 yield [g/kg 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 in the LAC region. After removing outliers, the final database retained 219 individual sheep records from 11 studies, 48.2% of the original database. Models were developed using a sequential approach, by incrementally adding different variables with increasing complexity. Production and yield of CH4 were predicted by fitting mixed-effects models with a random effect of study. The predictive accuracy of fitted CH4 prediction models was evaluated using a leave-one-out cross-validation. Overall, increasing model complexity improved the predictive performance of CH4 production and yield equations. Feed intake was the most important predictor of sheep CH4 production. Our best-developed CH4 production models outperformed Tier 2 equations from the Intergovernmental Panel on Climate Change (IPCC) in the growing lambs and mature sheep subsets, whereas they performed slightly worse in the complete subset. Methane yield can be predicted using dietary forage content only, or with an increased complexity model combining body weight, feeding level, and dietary forage content. The use of the newly-developed models rather than IPCC Tier 2 equations can substantially improve the accuracy of GHG inventories from LAC countries.