Effect of seed treatments on emergence, yield, and root rot severity of soybean under <i>Rhizoctonia solani</i> inoculated field conditions in Ontario
Bibliographic record
Abstract
Field trials were conducted with soybean at two sites each year from 2001 to 2003 in Ottawa, ON, to determine the effect of seed treatments with various combinations of seven formulated fungicides and the bioagent Yield Shield (Bacillus pumilus GB34) under Rhizoctonia solani inoculated conditions. Controls were untreated seed planted into both non-inoculated (natural) soil and soil inoculated with R. solani. Compared with the non-inoculated control, inoculation significantly increased root rot severity and reduced emergence by 27%, and yield by 31%. Under the inoculated conditions, none of the seed treatments significantly increased emergence or yield in all of the six trials when compared with the control. Allegiance (metalaxyl) plus Vitaflo-280 (carbathiin plus thiram) and Vitaflo-280 alone were the most effective seed treatments, increasing emergence in by 20 and 19% and yield by 21 and 26%, which were significantly better than the control in four and five trials for emergence and three and four trials for yield, respectively. Allegiance plus HEC5725 (HEC5725), Apron Maxx RTA (fludioxonil plus metalaxyl), and Maxim 480FS (fludioxonil) increased both emergence and yield in two trials and TFL RTU (metalaxyl plus triflox ystrobin) plus Yield Shield in one trial. There was no difference between seed treatment with Allegiance and the untreated control for all parameters, confirming that metalaxyl is ineffective to R. solani. It is concluded that carbathiin, thiram, HEC5725, fludioxonil an trifloxystrobin are effective active ingredients protecting soybean from soil-borne R. solani and increasing plant emergence and yield. Key words: Rhizoctonia solani, seed treatment, soybean, Glycine max, fungicide, bioagent
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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 | 0.001 | 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".