A meta-analysis establishes bromoform dose and forage-based models to evaluate the antimethanogenic effects of Asparagopsis spp. feed additive

Asparagopsis spp., two red macroalgae, have been widely studied as antimethanogenic feed additives due to their production of bromoform (CHBr₃) and other bioactive compounds, reducing methane (CH₄) emissions in ruminants by up to 98.9%. As their integration into livestock systems expands, there is a growing need for predictive models to estimate CH₄ reduction as an alternative to resource-intensive in situ methods. Existing models often omit the CHBr₃ concentration or lack sufficient data on consistent dose-response relationships across diets and animal types. This meta-analysis addresses these gaps by incorporating individual animal-level data to examine the relationships between CHBr₃ dose (mg/kg dry matter intake [DMI]) and enteric CH₄ yield (g/kg DMI), and dietary covariates. In accordance with PRISMA guidelines, 12 studies were included, with raw individual animal-level data obtained from the authors for seven studies; for the remainder, means and standard errors were utilized. Statistical analysis revealed a CHBr₃ threshold of 49.81 mg/kg DMI as a point of diminishing returns, beyond which reductions plateaued and risks of overestimation increased. Correlation analysis revealed significant associations between CHBr₃ dose, crude protein (CP), neutral detergent fiber (NDF), and CH₄ yield reduction. Significant differences were observed by animal type (p = 0.0217), prompting eight stratified linear mixed-effects models, and CH₄ detection technology (p = 5.21e-09), highlighting the need for improved accuracy at lower detection limits for antimethanogenic feed additives. The models estimate that in beef cattle, an average CHBr₃ dose of 14.95 mg/kg DMI reduces the methane yield by 34.8%, with a maximum dose of 35.70 mg/kg resulting in an 83.6% reduction. In dairy cows, an average dose of 11.25 mg/kg DMI led to a 16.9% reduction, whereas the maximum dose of 27.40 mg/kg resulted in a 43.1% reduction. The incorporation of CP and NDF marginally improved model performance, particularly for beef, with CP also improving predictions in dairy cows, thereby reducing the reliance on in vivo feed composition trials. Future research should expand dairy datasets and assess dietary fat as a predictor variable.