Winter Rye Cover Crops Shelter Competent Squash Phyllosphere Bacteria to Reduce <i>Pseudomonas syringae</i> pv. <i>lachrymans</i> Growth and Angular Leaf Spot Symptoms
Bibliographic record
Abstract
Cover crops, a soil conservation practice, can contribute to reducing disease pressure caused by Pseudomonas syringae, considered one of the most important bacterial plant pathogens. We recently demonstrated that the phyllosphere (leaf surface) bacterial community structure changed when squash ( Cucurbita pepo) was grown with a rye ( Secale cereale) cover crop treatment, followed by a decrease of angular leaf spot disease symptoms on squash caused by P. syringae pv. lachrymans. Application of biocontrol agents is a known agricultural practice to mitigate crop losses due to microbial disease. In this study, we tested the hypothesis that some phyllosphere bacteria promoted when squash is grown on cover crops could be isolated and used as a biocontrol agent to decrease angular leaf spot symptoms. We grew squash during a 2-year field experiment using four agricultural practices: bare soil, cover crops, chemically terminated cover crops, and plastic cover. We sampled squash leaves at three different dates each year and constructed a collection of cultivable bacterial strains isolated from squash leaves and rye cover crop material. Each isolated strain was identified by 16S rRNA gene sequencing and used in in vitro (Petri dish) pathogen growth and in vivo (greenhouse) symptom control assays. Four bacterial isolates belonging to the genera Pseudarthrobacter, Pseudomonas, Delftia, and Rhizobium were shown to inhibit P. syringae pv. lachrymans growth and angular leaf spot symptom development. Strikingly, the symptom control efficacy of all strains was stronger on older leaves. This study sheds light on the importance of bacterial isolation from cover crop sources to promote disease control. [Formula: see text] Copyright © 2024 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 distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 teacher head, 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".