Effect of conventional and biodegradable microplastics on the soil-soybean system: A perspective on rhizosphere microbial community and soil element cycling
Bibliographic record
Abstract
As an exogenous carbon input, microplastics (MPs), especially biodegradable MPs, may significantly disrupt soil microbial communities and soil element cycling (CNPS cycling), but few studies have focused on this. Here, we focused on assessing the effects of conventional low-density polyethylene (LDPE), biodegradable polybutylene adipate terephthalate (PBAT), and polylactic acid (PLA) MPs on rhizosphere microbial communities and CNPS cycling in a soil-soybean system. The results showed that PBAT-MPs and PLA-MPs were more detrimental to soybean growth than LDPE-MPs, resulting in a reduction in shoot nitrogen (14.05% and 11.84%) and shoot biomass (33.80% and 28.09%) at podding stage. In addition, dissolved organic carbon (DOC) increased by 20.91% and 66.59%, while nitrate nitrogen (NO– 3-N) significantly decreased by 56.91% and 69.65% in soils treated with PBAT-MPs and PLA-MPs, respectively. PBAT-MPs and PLA-MPs mainly enhanced copiotrophic bacteria (Proteobacteria) and suppressed oligotrophic bacteria (Verrucomicrobiota, Gemmatimonadota, etc.), increasing the abundance of CNPS cycling-related functional genes. LDPE-MPs tended to enrich oligotrophic bacteria (Verrucomicrobiota, etc.) and decrease the abundance of CNPS cycling-related functional genes. Correlation analysis revealed that MPs with different degradation properties selectively affected the composition and function of the bacterial community, resulting in changes in the availability of soil nutrients (especially NO– 3-N). Redundancy analysis further indicated that NO– 3-N was the primary constraining factor for soybean growth. This study provides a new perspective for revealing the underlying ecological effects of MPs on soil–plant systems.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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".