The Effect of Peat Moss Amended with Three Engineered Wood Substrate Components on Suppression of Damping-Off Caused by Rhizoctonia solani
Bibliographic record
Abstract
The use of wood-derived materials in soilless substrates for horticultural crop production is increasing; however, there is little information about the effects of wood on the incidence and severity of soilborne diseases of container-grown plants. The objectives of this research were to compare three differently processed wood substrate components blended with sphagnum peat and to investigate the effect of the peat:wood blend ratio on damping-off disease caused by Rhizoctonia solani using radish as a model system. In objective one, raw sphagnum peat was blended with three types of processed pine wood, screw-extruded, twin disc-refined, and hammer-milled, at a volumetric ratio of 70:30 and compared to a 70:30 peat:perlite mix. Radish plants grown in the hammer-milled wood and disc-refined wood had significantly lower damping-off disease severity compared to plants grown in the peat–perlite control. In objective two, sphagnum peat was blended with the three types of processed wood at a volumetric ratio of 90:10, 80:20, and 70:30 and compared to a 70:30 peat–perlite mix. The effect of the blend ratio varied by wood processing type. Higher percentages of Forest Gold and pine tree substrate resulted in lower disease severity. In both objectives, radish plants grown in any of the substrate treatments containing wood infested with R. solani tended to have lower disease severity compared to plants in the control. Results of this study indicate that the blending of processed pine wood-derived components into peat may enhance the natural suppression of damping-off disease of radish. Further research is needed to elucidate the mode of action of wood-derived materials on disease suppression in container-grown crops and to study the effects for other plant pathogens and crop species.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".