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Record W4211074581 · doi:10.1093/femsec/fiac017

Plastic mulch film residues in agriculture: impact on soil suppressiveness, plant growth, and microbial communities

2022· article· en· W4211074581 on OpenAlexafffund
Yueling Qi, Adam Ossowicki, Étienne Yergeau, Gianpiero Vigani, Violette Geissen, Paolina Garbeva

Bibliographic record

VenueFEMS Microbiology Ecology · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicMicroplastics and Plastic Pollution
Canadian institutionsInstitut National de la Recherche Scientifique
FundersNatural Sciences and Engineering Research Council of CanadaSociety for the Teaching of PsychologyChina Scholarship Council
KeywordsRhizosphereBiologyAgronomyActinobacteriaPlastic mulchMulchAgroecosystemBacteroidetesMicrobial population biologyNutrientRhizoctoniaBiomass (ecology)Rhizoctonia solaniEcologyAgricultureBacteria

Abstract

fetched live from OpenAlex

Plastic mulch film residues have been accumulating in agricultural soils for decades, but so far, little is known about its consequences on soil microbial communities and functions. Here, we tested the effects of plastic residues of low-density polyethylene and biodegradable mulch films on soil suppressiveness and microbial community composition. We investigated how plastic residues in a Fusarium culmorum suppressive soil affect the level of disease suppressiveness, plant biomass, nutrient status, and microbial communities in rhizosphere using a controlled pot experiment. The addition of 1% plastic residues to the suppressive soil did not affect the level of suppression and the disease symptoms index. However, we did find that plant biomasses decreased, and that plant nutrient status changed in the presence of plastic residues. No significant changes in bacterial and fungal rhizosphere communities were observed. Nonetheless, bacterial and fungal communities closely attached to the plastisphere were very different from the rhizosphere communities with overrepresentation of potential plant pathogens. The plastisphere revealed a high abundance of specific bacterial phyla (Actinobacteria, Bacteroidetes, and Proteobacteria) and fungal genera (Rhizoctonia and Arthrobotrys). Our work revealed new insights and raises emerging questions for further studies on the impact of microplastics on the agroecosystems.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.817
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.006
GPT teacher head0.189
Teacher spread0.183 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations58
Published2022
Admission routes2
Has abstractyes

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