AI-driven precision agriculture for enteric methane mitigation: cross-farm validation with an essential oil-based feed additive
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 emission...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2025-04-01
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| Series: | Frontiers in Sustainable Food Systems |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fsufs.2025.1548223/full |
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| author | Yaniv Altshuler Yaniv Altshuler Tzruya Calvao Chebach Shalom Cohen Joao Gatica |
| author_facet | Yaniv Altshuler Yaniv Altshuler Tzruya Calvao Chebach Shalom Cohen Joao Gatica |
| author_sort | Yaniv Altshuler |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-214985e16e5045f694f2f0bdf9608fe7 |
| institution | DOAJ |
| issn | 2571-581X |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Sustainable Food Systems |
| spelling | doaj-art-214985e16e5045f694f2f0bdf9608fe72025-08-20T03:05:21ZengFrontiers Media S.A.Frontiers in Sustainable Food Systems2571-581X2025-04-01910.3389/fsufs.2025.15482231548223AI-driven precision agriculture for enteric methane mitigation: cross-farm validation with an essential oil-based feed additiveYaniv Altshuler0Yaniv Altshuler1Tzruya Calvao Chebach2Shalom Cohen3Joao Gatica4Massachusetts Institute of Technology, Cambridge, MA, United StatesMetha Artificial Intelligence, Tel Aviv, IsraelMetha Artificial Intelligence, Tel Aviv, IsraelMetha Artificial Intelligence, Tel Aviv, IsraelMetha Artificial Intelligence, Tel Aviv, IsraelRuminant 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.https://www.frontiersin.org/articles/10.3389/fsufs.2025.1548223/fullenteric methane emissionsdairyAI modelpredictive modelprecision agriculturefeed additive |
| spellingShingle | Yaniv Altshuler Yaniv Altshuler Tzruya Calvao Chebach Shalom Cohen Joao Gatica AI-driven precision agriculture for enteric methane mitigation: cross-farm validation with an essential oil-based feed additive Frontiers in Sustainable Food Systems enteric methane emissions dairy AI model predictive model precision agriculture feed additive |
| title | AI-driven precision agriculture for enteric methane mitigation: cross-farm validation with an essential oil-based feed additive |
| title_full | AI-driven precision agriculture for enteric methane mitigation: cross-farm validation with an essential oil-based feed additive |
| title_fullStr | AI-driven precision agriculture for enteric methane mitigation: cross-farm validation with an essential oil-based feed additive |
| title_full_unstemmed | AI-driven precision agriculture for enteric methane mitigation: cross-farm validation with an essential oil-based feed additive |
| title_short | AI-driven precision agriculture for enteric methane mitigation: cross-farm validation with an essential oil-based feed additive |
| title_sort | ai driven precision agriculture for enteric methane mitigation cross farm validation with an essential oil based feed additive |
| topic | enteric methane emissions dairy AI model predictive model precision agriculture feed additive |
| url | https://www.frontiersin.org/articles/10.3389/fsufs.2025.1548223/full |
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