Suppression of Verticillium Wilt of Eggplant by Earthworms
Bibliographic record
Abstract
The role of earthworms in plant disease has received little attention. To address whether earthworms would affect the severity of Verticillium wilt of eggplant (Solanum melongena) in the field, we grew eggplants in experimental field plots that were naturally infested with Verticillium dahliae in 2005, 2006, and 2007. Three earthworm treatments were compared: (i) no treatment (untreated control), (ii) earthworm populations reduced via chemical eradicants (carbaryl or hot mustard) (reduced treatment), and (iii) earthworm populations increased by addition of adult Canadian nightcrawlers (Lumbricus terrestris, 11 earthworms per m 2 ) (augmented treatment). Compared to the untreated control, the estimates of the area under the disease progress curve (AUDPC) were reduced while estimates of the canopy growth curve (CGC) and the final plant weights were increased in plots augmented with earthworms in all 3 years. In 2 out of 3 years, eggplant yield (weight and number of fruit) was increased in plots augmented with earthworms. When a carbaryl drench was used to reduce earthworm numbers, the treatment resulted in plants with more disease than in the untreated controls in 2005. However, in 2005 and 2006, carbaryl-treated plants had larger CGC values and higher yield than in the untreated controls and were not significantly different from the augmented plots. When a hot mustard extraction procedure was used to reduce earthworm densities in 2007, plant growth, yield, and disease variables did not differ from the untreated control. Although the effects of reducing earthworms were variable and difficult to explain, our findings suggest that augmenting earthworm populations can suppress Verticillium wilt of eggplant, and strategies that increase earthworm numbers may contribute to disease suppression.
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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) | 0.000 | 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 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".