Epsilon Archive for Student Projects

Abstract

Methane emissions from cattle, influenced by the interaction between their genetics and the rumen
microbiome, pose a significant environmental challenge. This study developed a computational
model to investigate how genetic and microbial factors affect methane production. Utilizing
AlphaSimR, we simulated methane traits under different additive genetic variances and microbial
methane effects. We employed Bayesian models to predict methane emissions and to assess
heritability and microbiability across various scenarios.
The results indicated that lower microbial methane production effects consistently led to reduced
emissions, and heritability values increased with increasing genetic variance. These simulated
results emphasize the host genetics' stronger impact on high additive variance, while microbiability
values decreased under these conditions. The model achieved high prediction accuracy, ranging
from 85% to 91%, and demonstrated that combining genomic and microbial selection effectively
lowered methane emissions over ten generations.
These findings provide valuable insights for breeding programs to minimize methane emissions and
address environmental sustainability in livestock production.