Evidence map›Paper›PMID 36357461›Full record

ArticleScientific reports2022

Sugarcane cultivation practices modulate rhizosphere microbial community composition and structure.

Ana Paula Corrêa Moneda, Lucas Amoroso Lopes de Carvalho, Luis Guillermo Teheran-Sierra, Michelli Inácio Gonçalves Funnicelli, Daniel Guariz Pinheiro

Abstract read
In one paragraph

Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

  1. Profiling of bacterial community associated with sugarcane rhizosphere.Journal, genetic engineering & biotechnology · 2026
    Article
  2. International journal of microbiology · 2026
    Review
  3. Article
  4. Article
  5. Article
  6. Article
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

5 authors.

Ana Paula Corrêa MonedaLaboratory of Bioinformatics, Department of Agricultural, Livestock and Environmental Biotechnology, School of Agricultural and Veterinary Sciences, São Paulo State University (UNESP), Jaboticabal, SP, 14884-900, Brazil.
Lucas Amoroso Lopes de CarvalhoLaboratory of Bioinformatics, Department of Agricultural, Livestock and Environmental Biotechnology, School of Agricultural and Veterinary Sciences, São Paulo State University (UNESP), Jaboticabal, SP, 14884-900, Brazil.
Luis Guillermo Teheran-SierraLaboratory of Bioinformatics, Department of Agricultural, Livestock and Environmental Biotechnology, School of Agricultural and Veterinary Sciences, São Paulo State University (UNESP), Jaboticabal, SP, 14884-900, Brazil.
Michelli Inácio Gonçalves FunnicelliLaboratory of Bioinformatics, Department of Agricultural, Livestock and Environmental Biotechnology, School of Agricultural and Veterinary Sciences, São Paulo State University (UNESP), Jaboticabal, SP, 14884-900, Brazil.
Daniel Guariz PinheiroLaboratory of Bioinformatics, Department of Agricultural, Livestock and Environmental Biotechnology, School of Agricultural and Veterinary Sciences, São Paulo State University (UNESP), Jaboticabal, SP, 14884-900, Brazil. daniel.pinheiro@unesp.br.

Funding

Conselho Nacional de Desenvolvimento Científico e Tecnológico , Brasil 145962/2019-9Coordenação de Aperfeiçoamento de Pessoal de Nível Superior Finance Code 001Fundação de Amparo à Pesquisa do Estado de São Paulo 2017/09008-5
6 · The paper itself

Abstract

Sugarcane (Saccharum spp.) represents a crop of great economic importance, remarkably relevant in the food industry and energy supply chains from renewable sources. However, its conventional cultivation involves the intensive use of fertilizers, pesticides, and other agrochemical agents whose detrimental effects on the environment are notorious. Alternative systems, such as organic farming, have been presented as an environmentally friendly way of production. Still, the outcomes of different cropping systems on the microbiota associated with sugarcane-whose role in its health and growth is crucial-remain underexplored. Thus, we studied the rhizospheric microbiota of two adjacent sugarcane fields, which differ in terms of the type of farming system. For this, we used the sequencing of taxonomic markers of prokaryotes (gene 16S rRNA, subregions V3-V4) and fungi (Internal transcribed spacer 2) and evaluated the changes caused by the systems. Our results show a well-conserved microbiota composition among farming systems in the highest taxonomic ranks, such as phylum, class, and order. Also, both systems showed very similar alpha diversity indices and shared core taxa with growth-promoting capacities, such as bacteria from the Bacillus and Bradyrhizobium genera and the fungal genus Trichoderma. However, the composition at more specific levels denotes differences, such as the separation of the samples concerning beta diversity and the identification of 74 differentially abundant taxa between the systems. Of these, 60 were fungal taxa, indicating that this microbiota quota is more susceptible to changes caused by farming systems. The analysis of co-occurrence networks also showed the formation of peripheral sub-networks associated with the treatments-especially in fungi-and the presence of keystone taxa in terms of their ability to mediate relationships between other members of microbial communities. Considering that both crop fields used the same cultivar and had almost identical soil properties, we conclude that the observed findings are effects of the activities intrinsic to each system and can contribute to a better understanding of the effects of farming practices on the plant microbiome.

Indexed as

MicrobiotaSaccharumEdible GrainPlant RootsRhizosphereRNA, Ribosomal, 16SSoil MicrobiologyRNA, Ribosomal, 16S

Identifiers

PMID36357461
PMCPMC9649670

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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.