Characterization and assessment of compost for suppression of selected turfgrass diseases
Bibliographic record
Abstract
The use of composts for turfgrass disease management allows for a reduction of pesticide use in traditional chemical control practices. Up to five composts were characterized and evaluated for suppression of turfgrass diseases. The monitoring of temperature and oxygen throughout the composting process was the best method tested in evaluating compost maturity. Controlled environment experiments with selected compost treatments suppressed dollar spot of turf ('Sclerotinia homoeocarpa' F. T. Bennett) by up to 58% and, in field trials, were not significantly different than fungicide controls ('P' = 0.05). Similarly, fall applications of compost reduced snow mould ('Microdochium nivale' Fr. Samuels and Hallet, ' Typhula ishikariensis' Lasch ex. Fr.) severity to levels not significantly different from fungicide controls and increased green-up of turf (recovery from disease and/or winter dormancy) by up to 63% compared to fungicide and 54% compared to fertilizer controls ('P' = 0.05). Microbial characterization of composts revealed high culturable colony counts. Moreover, 29% of bacteria isolated displayed proteolytic activity. Two bacterial identification systems gave variable results, whereas phospholipid fatty acid (PLFA) analysis was a valuable indicator of microbial community dynamics. Many bacterial isolates tested in the plate challenge experiment displayed antagonistic activity towards selected turfgrass pathogens. Antagonistic activity of composts relies on a number of factors, and although their relative importance varies, microbial activity levels, population dynamics, nutrient aspects, as well as other associated chemical and physical factors all have a part in turfgrass disease suppression.
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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 | 0.001 | 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".