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Monteiro HF, Figueiredo CC, Mion B, Santos JEP, Bisinotto RS, Peñagaricano F, Ribeiro ES, Marinho MN, Zimpel R, da Silva AC, Oyebade A, Lobo RR, Coelho WM, Peixoto PMG, Ugarte Marin MB, Umaña-Sedó SG, Rojas TDG, Elvir-Hernandez M, Schenkel FS, Weimer BC, Brown CT, Kebreab E, Lima FS. An artificial intelligence approach of feature engineering and ensemble methods depicts the rumen microbiome contribution to feed efficiency in dairy cows. Anim Microbiome 2024; 6:5. [PMID: 38321581 PMCID: PMC10845535 DOI: 10.1186/s42523-024-00289-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2023] [Accepted: 01/17/2024] [Indexed: 02/08/2024] Open
Abstract
Genetic selection has remarkably helped U.S. dairy farms to decrease their carbon footprint by more than doubling milk production per cow over time. Despite the environmental and economic benefits of improved feed and milk production efficiency, there is a critical need to explore phenotypical variance for feed utilization to advance the long-term sustainability of dairy farms. Feed is a major expense in dairy operations, and their enteric fermentation is a major source of greenhouse gases in agriculture. The challenges to expanding the phenotypic database, especially for feed efficiency predictions, and the lack of understanding of its drivers limit its utilization. Herein, we leveraged an artificial intelligence approach with feature engineering and ensemble methods to explore the predictive power of the rumen microbiome for feed and milk production efficiency traits, as rumen microbes play a central role in physiological responses in dairy cows. The novel ensemble method allowed to further identify key microbes linked to the efficiency measures. We used a population of 454 genotyped Holstein cows in the U.S. and Canada with individually measured feed and milk production efficiency phenotypes. The study underscored that the rumen microbiome is a major driver of residual feed intake (RFI), the most robust feed efficiency measure evaluated in the study, accounting for 36% of its variation. Further analyses showed that several alpha-diversity metrics were lower in more feed-efficient cows. For RFI, [Ruminococcus] gauvreauii group was the only genus positively associated with an improved feed efficiency status while seven other taxa were associated with inefficiency. The study also highlights that the rumen microbiome is pivotal for the unexplained variance in milk fat and protein production efficiency. Estimation of the carbon footprint of these cows shows that selection for better RFI could reduce up to 5 kg of diet consumed per cow daily, potentially reducing up to 37.5% of CH4. These findings shed light that the integration of artificial intelligence approaches, microbiology, and ruminant nutrition can be a path to further advance our understanding of the rumen microbiome on nutrient requirements and lactation performance of dairy cows to support the long-term sustainability of the dairy community.
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Affiliation(s)
- Hugo F Monteiro
- Department of Population Health and Reproduction, School of Veterinary Medicine, University of California, 95616, Davis, CA, USA
| | - Caio C Figueiredo
- Department of Veterinary Clinical Sciences, Washington State University, Pullman, WA, USA
- Department of Large Animal Clinical Sciences, University of Florida, Gainesville, FL, USA
| | - Bruna Mion
- Department of Animal Biosciences, University of Guelph, Guelph, ON, Canada
| | | | - Rafael S Bisinotto
- Department of Large Animal Clinical Sciences, University of Florida, Gainesville, FL, USA
| | | | - Eduardo S Ribeiro
- Department of Animal Biosciences, University of Guelph, Guelph, ON, Canada
| | - Mariana N Marinho
- Department of Animal Sciences, University of Florida, Gainesville, FL, USA
| | - Roney Zimpel
- Department of Animal Sciences, University of Florida, Gainesville, FL, USA
| | | | - Adeoye Oyebade
- Department of Animal Sciences, University of Florida, Gainesville, FL, USA
| | - Richard R Lobo
- Department of Animal Sciences, University of Florida, Gainesville, FL, USA
| | - Wilson M Coelho
- Department of Population Health and Reproduction, School of Veterinary Medicine, University of California, 95616, Davis, CA, USA
| | - Phillip M G Peixoto
- Department of Large Animal Clinical Sciences, University of Florida, Gainesville, FL, USA
| | - Maria B Ugarte Marin
- Department of Large Animal Clinical Sciences, University of Florida, Gainesville, FL, USA
| | - Sebastian G Umaña-Sedó
- Department of Large Animal Clinical Sciences, University of Florida, Gainesville, FL, USA
| | - Tomás D G Rojas
- Department of Large Animal Clinical Sciences, University of Florida, Gainesville, FL, USA
| | | | - Flávio S Schenkel
- Department of Animal Biosciences, University of Guelph, Guelph, ON, Canada
| | - Bart C Weimer
- Department of Population Health and Reproduction, School of Veterinary Medicine, University of California, 95616, Davis, CA, USA
| | - C Titus Brown
- Department of Population Health and Reproduction, School of Veterinary Medicine, University of California, 95616, Davis, CA, USA
| | - Ermias Kebreab
- Department of Animal Sciences, College of Agriculture and Life Sciences, University of California, 95616, Davis, CA, USA
| | - Fábio S Lima
- Department of Population Health and Reproduction, School of Veterinary Medicine, University of California, 95616, Davis, CA, USA.
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