Winter Rye Cover Cropping Changes Squash ( <i>Cucurbita pepo</i> ) Phyllosphere Microbiota and Reduces <i>Pseudomonas syringae</i> Symptoms
Bibliographic record
Abstract
Cover cropping is a soil conservation practice that may reduce the impacts of the economically important pathogen Pseudomonas syringae on crops, including squash (Cucurbita pepo). To date, no studies have directly quantified the effect of rye cover crops on P. syringae populations or on the bacterial community of squash leaves. In this work, we tested the hypothesis that the protective effects of cover cropping on squash may be mediated by cover cropping effects on the plant’s microbiota that, in turn, protect against P. syringae. Using combined 16S sequencing and culture-based approaches, we showed that rye cover cropping protects squash against P. syringae, by decreasing pathogen population size on squash leaves and increasing fruit health and marketability at harvest. We also found evidence of a strong effect of rye cover crops on bacterial communities of the squash phyllosphere. Those findings were more striking early in the growing season. Finally, we identified numerous phyllosphere bacteria belonging to the genera Sphingomonas, Methylobacterium, and Pseudomonas that were promoted by rye cover crops. Overall, our findings suggest that cover cropping is effective for the sustainable management of P. syringae on squash and may provide a reservoir of potential microbial biocontrol agents colonizing the phyllosphere. [Formula: see text] Copyright © 2022 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license .
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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) | 0.003 | 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".