Disease reaction to<i> Rhizoctonia solani</i> and yield losses in soybean
Bibliographic record
Abstract
Seedling blight and root rot caused by Rhizoctonia solani Kühn are important constraints to the expansion of soybean [Glycine max (L.) Merr.] production in Alberta, Canada. The reaction of 21 soybean genotypes to R. solani was assessed in inoculated field trials in Alberta in 2014–2016. Inoculation with R. solani resulted in a significant reduction in stand, nodulation, and yield and a significant increase in root rot severity for all of the soybean genotypes. The genotype P001T34R had lower reductions in stand establishment compared with NSC Portage RR, TH29002RR, TH27005RR, or LS003R22 and there were inconsistent variations in yield loss among the genotypes in two of the three site–years. No significant variation in disease severity or nodulation was observed among the genotypes. Stability analysis showed the soybean genotypes P001T34R, 23-60RY, NSC VitoRR, and NSC TilstonRR2Y had higher and more stable stand establishment, while 900Y01, 23-60RY, P001T34R, and P002T04R had higher and more stable seed yield in comparison with the other genotypes in this study. The study also revealed that R. solani caused a loss of 48% in stand establishment and 52% in seed yield. Root rot severity ranged from 0.38 to 2.36 on a scale of 0–4 among the genotypes but was not consistent over the trials. Root rot severity and yield loss increased with increasing inoculum density, while stand establishment, nodulation, and seed yield declined. Regression analysis showed that stand establishment, nodulation, and yield were strongly positively correlated but strongly negatively correlated with root rot severity.
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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 | 0.001 |
| 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".