  In an era of increasing pressure to achieve sustainable agriculture, the
optimization of livestock feed for enhancing yield and minimizing environmental
impact is a paramount objective. This study presents a pioneering approach
towards this goal, using rumen microbiome data to predict the efficacy of feed
additives in dairy cattle.
  We collected an extensive dataset that includes methane emissions from 2,190
Holstein cows distributed across 34 distinct sites. The cows were divided into
control and experimental groups in a double-blind, unbiased manner, accounting
for variables such as age, days in lactation, and average milk yield. The
experimental groups were administered one of four leading commercial feed
additives: Agolin, Kexxtone, Allimax, and Relyon. Methane emissions were
measured individually both before the administration of additives and over a
subsequent 12-week period. To develop our predictive model for additive
efficacy, rumen microbiome samples were collected from 510 cows from the same
herds prior to the study's onset. These samples underwent deep metagenomic
shotgun sequencing, yielding an average of 15.7 million reads per sample.
Utilizing innovative artificial intelligence techniques we successfully
estimated the efficacy of these feed additives across different farms. The
model's robustness was further confirmed through validation with independent
cohorts, affirming its generalizability and reliability.
  Our results underscore the transformative capability of using targeted feed
additive strategies to both optimize dairy yield and milk composition, and to
significantly reduce methane emissions. Specifically, our predictive model
demonstrates a scenario where its application could guide the assignment of
additives to farms where they are most effective. In doing so, we could achieve
an average potential reduction of over 27\% in overall emissions.
