Evaluation of Composts for Suppression of Dollar Spot (<i>Sclerotinia homoeocarpa</i>) of Turfgrass
Bibliographic record
Abstract
The use of composts in turfgrass disease management allows for a reduction in pesticide use in chemical control practices. Disease suppressive properties of composts rely on a number of factors including microbial activity, microbial population dynamics, nutrient concentrations, and other associated chemical and physical factors. Five composts were evaluated for suppression of dollar spot caused by Sclerotinia homoeocarpa. The dollar spot disease suppressive properties of selected compost formulations prepared in different years was evaluated. A third objective was to examine the effects of storage of compost (1 year) on the suppression of dollar spot. Field experiments were conducted in 1998 with compost prepared in 1997 to 1998. Applications of compost every 3 weeks throughout the season suppressed dollar spot of turf to levels not significantly different than applications of chlorothalonil fungicide applied at the manufacturer's lowest recommended preventative rate of 38.4 ml a.i./100 m2 every 2 weeks (P = 0.05). Single applications of composts at the start of the 1998 season were not effective in reducing disease. Field experiments in 1999 evaluated batches of two selected compost formulations, one batch produced in 1998 to 1999, another stored since production in 1997 to 1998. Composts were effective in suppressing disease to levels not significantly different than the fungicide controls, which showed up to 33% disease in 1998 and up to 31% disease in 1999 (P = 0.05). Storage of composts for up to 1 year did not affect their ability to reduce dollar spot severity (P = 0.05). The use of composts as plant disease suppressants is not likely to replace the use of commercial fungicides in dollar spot management. However, multiple applications of compost may reduce incidence and severity of dollar spot to levels at which chemical control may be reduced or eliminated for a significant portion of the season.
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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) | 0.000 | 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 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".