Plastic mulch film residues in agriculture: impact on soil suppressiveness, plant growth, and microbial communities
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".