Managing tomato vine decline with soil amendments and transplant treatments: fruit yield, quality, and plant-associated microbial communities
Bibliographic record
Abstract
Tomato vine decline (TVD) disease complex results in fruit yield loss, but what soil management strategies might mitigate it? In commercial fields with a history of TVD, five approaches (soil organic amendments and transplant treatments) were evaluated for their impact on fruit yield, fruit quality, and microbial abundance or diversity at four site-years. One site-year had very high TVD pressure and high variability with no yield differences, thus efforts focused on the remaining site-years. Marketable yield was not different among treatments but numerically followed a trend similar to total yield. Amending soil with poultry manure delayed maturity (i.e., increased proportion of green fruit) and had the greatest total yield increases of 17.2%, congruent with decreased abundance of root pathogens (Verticillium dahliae, Rhizopicnis vagum). Microbial DNA fingerprinting data of rhizospheres, roots and (or) stems suggested treatments did not significantly shift the total diversity fungal nor bacterial populations, but the aforementioned pathogen loads were reduced with the application of organic amendments relative to the untreated control. While drenching tomato transplants with pseudomonad culture increased their presence in roots, pathogen load was not reduced relative to the untreated control. Overall, these results show that soil organic amendments were able to improve tomato total yield in two of four site-years without reducing fruit quality (i.e., soluble solids, pH, colour), perhaps, in part, due to their ability to suppress specific root pathogens in commercial fields.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".