Evidence map›Paper›PMID 38321581›Full record

ArticleAnimal microbiome2024

An artificial intelligence approach of feature engineering and ensemble methods depicts the rumen microbiome contribution to feed efficiency in dairy cows.

Hugo F Monteiro, Caio C Figueiredo, Bruna Mion, José Eduardo P Santos, Rafael S Bisinotto, Francisco Peñagaricano, Eduardo S Ribeiro, Mariana N Marinho, Roney Zimpel, Ana Carolina da Silva and 13 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Animal microbiome, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

10 citing papers in PubMed.

  1. Review
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

23 authors.

Hugo F MonteiroDepartment of Population Health and Reproduction, School of Veterinary Medicine, University of California, 95616, Davis, CA, USA.
Caio C FigueiredoDepartment of Veterinary Clinical Sciences, Washington State University, Pullman, WA, USA.
Bruna MionDepartment of Animal Biosciences, University of Guelph, Guelph, ON, Canada.
José Eduardo P SantosDepartment of Animal Sciences, University of Florida, Gainesville, FL, USA.
Rafael S BisinottoDepartment of Large Animal Clinical Sciences, University of Florida, Gainesville, FL, USA.
Francisco PeñagaricanoDepartment of Animal and Dairy Sciences, University of Wisconsin, Madison, WI, USA.
Eduardo S RibeiroDepartment of Animal Biosciences, University of Guelph, Guelph, ON, Canada.
Mariana N MarinhoDepartment of Animal Sciences, University of Florida, Gainesville, FL, USA.
Roney ZimpelDepartment of Animal Sciences, University of Florida, Gainesville, FL, USA.
Ana Carolina da SilvaDepartment of Animal Sciences, University of Florida, Gainesville, FL, USA.
Adeoye OyebadeDepartment of Animal Sciences, University of Florida, Gainesville, FL, USA.
Richard R LoboDepartment of Animal Sciences, University of Florida, Gainesville, FL, USA.
Wilson M CoelhoDepartment of Population Health and Reproduction, School of Veterinary Medicine, University of California, 95616, Davis, CA, USA.
Phillip M G PeixotoDepartment of Large Animal Clinical Sciences, University of Florida, Gainesville, FL, USA.
Maria B Ugarte MarinDepartment of Large Animal Clinical Sciences, University of Florida, Gainesville, FL, USA.
Sebastian G Umaña-SedóDepartment of Large Animal Clinical Sciences, University of Florida, Gainesville, FL, USA.
Tomás D G RojasDepartment of Large Animal Clinical Sciences, University of Florida, Gainesville, FL, USA.
Modesto Elvir-HernandezDepartment of Large Animal Clinical Sciences, University of Florida, Gainesville, FL, USA.
Flávio S SchenkelDepartment of Animal Biosciences, University of Guelph, Guelph, ON, Canada.
Bart C WeimerDepartment of Population Health and Reproduction, School of Veterinary Medicine, University of California, 95616, Davis, CA, USA.
C Titus BrownDepartment of Population Health and Reproduction, School of Veterinary Medicine, University of California, 95616, Davis, CA, USA.
Ermias KebreabDepartment of Animal Sciences, College of Agriculture and Life Sciences, University of California, 95616, Davis, CA, USA.
Fábio S LimaDepartment of Population Health and Reproduction, School of Veterinary Medicine, University of California, 95616, Davis, CA, USA. falima@ucdavis.edu.

Funding

Dr. Ribeiro's funding from Food From Thought Thematic Research Fund 499121Dr. Ribeiro's funding from the Ontario Agri-Food Innovation Alliance 030277Dr. Schenkel's Dairy Research Cluster 3- Activity G3United States Department of Agriculture (USDA), National Institute of Food and Agriculture, Foundational Program, Animal Growth, Nutrition, and Lactation 2019-67015-32114
6 · The paper itself

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 CH

Identifiers

PMID38321581
PMCPMC10845535

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.