Long-Term Cover Cropping Suppresses Foliar and Fruit Disease in Processing Tomatoes
Bibliographic record
Abstract
While links between soil and plant health are implied, there are few opportunities to empirically evaluate this due to inherent differences among sites. An exception is a long-term experiment established in 2007 (repeated in 2008) in Ridgetown, ON, where improved soil health scores and changes in soil microbial communities were observed in the medium-term with annual cover crops (CC). This led us to hypothesize that CC-induced changes in soil health might affect bacterial spot (Xanthomonas hordorum pv. gardneri) and anthracnose (Colletotrichum coccodes) development in processing tomato. Five CC treatments (no CC control, winter cereal rye, oat, radish, and mix of radish + rye) planted after winter wheat harvest were evaluated in 2019 and 2020 (CC grown nine times over 12 years). Fruit yields and net revenue were similar or greater with CC than without. In 2019, there was greater defoliation (area under the disease progress stairs = 4,370 ± 204), percent red fruit (71.0% ± 5.38), and rots (1.91% ± 0.5) in no CC than with radish (3,410, 39.1%, and 0.62%, respectively, P ≤ 0.0366), indicating earlier fruit maturity in no CC plots. Similarly, no CC had a greater incidence of red fruits with anthracnose (25.8% ± 2.89) compared with all CCs but rye (7.4 to 12.1% ± 2.89; P = 0.0029). Environmental conditions in 2020 were less favourable for disease development. Defoliation was not affected by CC treatment (P = 0.1254), and anthracnose incidence was low (≥90.3 ± 1.22% healthy fruit), which may have limited the ability to detect treatment effects (P = 0.2922). Long-term cover crops have the potential to produce greater or equivalent tomato yield with decreased defoliation and anthracnose fruit rot.
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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) | 0.001 | 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".