Methane prediction equations including genera of rumen bacteria as predictor variables improve prediction accuracy.

ABSTRACT: Methane (CH₄) emissions from ruminants are of a significant environmental concern, necessitating accurate prediction for emission inventories. Existing models rely solely on dietary and host animal-related data, ignoring the predicting power of rumen microbiota, the source of CH₄. To address this limitation, we developed novel CH₄ prediction models incorporating rumen microbes as predictors, alongside animal- and feed-related predictors using four statistical/machine learning (ML) methods. These include random forest combined with boosting (RF-B), least absolute shrinkage and selection operator (LASSO), generalized linear mixed model with LASSO (glmmLasso), and smoothly clipped absolute deviation (SCAD) implemented on linear mixed models. With a sheep datas

SUBMITTER: Zhang B 

PROVIDER: S-EPMC10693554 | biostudies-literature | 2023 Dec

REPOSITORIES: biostudies-literature