Evidence map›Paper›PMID 41681493›Full record

ArticleAnimals : an open access journal from MDPI2026

Rumen Microbial Composition and Fermentation Variables Associated with Methane Production in Italian Simmental Dairy Cows.

Cristina Pavanello, Marcello Franchini, Alberto Romanzin, Lara Tat, Stefano Bovolenta, Mirco Corazzin

Abstract read
In one paragraph

Article in Animals : an open access journal from MDPI, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

6 authors.

Cristina PavanelloDepartment of Agricultural, Food, Environmental and Animal Sciences, University of Udine, Via delle Scienze 206, 33100 Udine, Italy.ORCID 0009-0002-9584-2851
Marcello FranchiniDepartment of Agricultural, Food, Environmental and Animal Sciences, University of Udine, Via delle Scienze 206, 33100 Udine, Italy.ORCID 0000-0003-3050-3859
Alberto RomanzinDepartment of Agricultural, Food, Environmental and Animal Sciences, University of Udine, Via delle Scienze 206, 33100 Udine, Italy.
Lara TatDepartment of Agricultural, Food, Environmental and Animal Sciences, University of Udine, Via delle Scienze 206, 33100 Udine, Italy.ORCID 0000-0003-4375-3398
Stefano BovolentaDepartment of Agricultural, Food, Environmental and Animal Sciences, University of Udine, Via delle Scienze 206, 33100 Udine, Italy.ORCID 0000-0002-6307-6809
Mirco CorazzinDepartment of Agricultural, Food, Environmental and Animal Sciences, University of Udine, Via delle Scienze 206, 33100 Udine, Italy.ORCID 0000-0002-6921-3210

Funding

PNRR Agritech Missione 4 Componente 2, Investimento 1.4 - D.D. 1032 17/06/2022, CN00000022; PNRR M4C2, DM352/2022 in collaboration with Le Tenute Marianis farm
6 · The paper itself

Abstract

The study investigated differences in ruminal and fecal microbiota composition, fermentation traits, and volatile organic compounds (VOC) in Simmental dairy cows classified as high (HME) or low (LME) methane emitters. Methane emissions from 48 cows were quantified using the Laser Methane Smart portable gas detector. The 12 animals with the highest and lowest emissions were selected and assigned to the HME and LME groups, respectively, balanced for body weight, days in milk, and body condition score. Rumen fluid and fecal samples were analyzed for pH, ammonia, volatile fatty acids (VFA), VOC, and microbiota composition. As expected, CH

Indexed as

dairy cowsmethane emissionsmethanogen diversitymicrobial efficiencyrumen microbiome

Identifiers

PMID41681493
PMCPMC12896419

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.