ORIGINAL RESEARCH article
Front. Sustain. Food Syst.
Sec. Climate-Smart Food Systems
This article is part of the Research TopicMitigating Agricultural Greenhouse Gas Emissions Through Bio-Inputs and Innovative PracticesView all 6 articles
AI-Based Predictive Modeling for Enteric Methane Mitigation: Cross-Farm Validation Using an Allicin Based Essential Oil
Provisionally accepted
- 1Massachusetts Institute of Technology, Cambridge, United States
- 2Metha AI, Tel Aviv, Israel
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Greenhouse gas emissions are a major global concern and reducing them is a key objective for governments and organizations worldwide. One of the largest agricultural contributors to greenhouse gas emissions is enteric methane, produced as a byproduct of microbial fermentation in the rumen during forage digestion. As previously reported, we developed an AI-driven model to address this challenge by predicting the efficacy of feed additives in mitigating methane emissions. Since the wide variety of feed additives available in the market, validating the model across a diverse range of additives is critical for its adoption in commercial farming practices. In this study, we extensively validate the model across ten commercial farms over a three-month period, involving 339 Holstein cows, and using an allicin-based essential oil (Allimax), an organosulfur compound obtained from garlic with potential to reduce enteric methane emissions. The validation process simulated two hypothetical scenarios: (i) a naive scenario in which the feed additive is applied uniformly across all participating farms, and (ii) an optimized scenario, based on a precision agriculture approach, in which the additive is supplied only to farms where the AI-driven model predicted a significant reduction in enteric methane emissions. Our results revealed two key findings: first, the AI-driven model demonstrated high accuracy in predicting the additive's effect on enteric methane emissions at the farm level. Second, the optimized scenario achieved greater overall methane reductions compared to the naive scenario, underscoring the value of a precision agriculture strategy that incorporates rumen microbiome genetics to guide additive applications. These results, which align with a previous validation using a different commercial feed additive, support the adoption of data-driven, customized additive strategies that enhance sustainability and productivity, promote precision agriculture practices, and facilitate compliance with evolving environmental regulations.
Keywords: AI-driven model, predictive model, Enteric methane emissions, feed additive, Dairy, precision agriculture
Received: 29 May 2025; Accepted: 03 Nov 2025.
Copyright: © 2025 Altshuler, Calvão 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, yanival@mit.edu
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.