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ORIGINAL RESEARCH article
Front. Sustain. Food Syst.
Sec. Climate-Smart Food Systems
Volume 9 - 2025 | doi: 10.3389/fsufs.2025.1548223
This article is part of the Research Topic Measurements and Modelling of Methane Emissions from Agricultural Cropping Systems View all articles
AI-driven precision agriculture for enteric methane mitigation: cross-farm validation with an essential oil-based feed additive
Provisionally accepted- 1 Massachusetts Institute of Technology, Cambridge, United States
- 2 Metha.AI, Tel Aviv, Israel
Ruminant livestock production depends on microorganisms to ferment forages into valuable dairy and meat products. However, this process also generates enteric methane emissions, a significant contributor to anthropogenic greenhouse gases. Despite various strategies aimed at reducing methane emissions, success has been limited. In previous work, we developed an AI-driven model based on deep microbiome sequencing, which predicts the effect of feed additives on methane emissions. The model uses sequenced rumen samples from a given herd to construct microbiome networks to identify biomarkers associated with feed additive effectiveness in the reduction of methane emissions. In this study, we validated the model supplying a commercial methane-mitigating feed additive and performing hundreds of in-situ methane measurements across several commercial dairy farms. The results highlight the model's robustness and precision, demonstrating its effectiveness in predicting enteric methane reductions and enhancing feed additive performance. Additionally, the model serves as a critical tool for data-driven decision-making, playing a pivotal role in advancing precision agriculture practices.
Keywords: Enteric methane emissions, Dairy, AI model, predictive model, precision agriculture, feed additive
Received: 19 Dec 2024; Accepted: 11 Mar 2025.
Copyright: © 2025 Altshuler, Calvao Chebach, Cohen and Gatica. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Yaniv Altshuler, Massachusetts Institute of Technology, Cambridge, United States
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