Effect of Silicon Absorption on Soybean Resistance to <i>Phakopsora pachyrhizi</i> in Different Cultivars
Bibliographic record
Abstract
Silicon (Si) is recognized for its prophylactic role in alleviating diseases when absorbed by plants and has been proposed as a possible solution against soybean rust, caused by Phakopsora pachyrhizi. However, little is known about its potential effects on soybean (Glycine max) because the plant's ability to absorb Si is poorly defined. In this work, our objectives were to evaluate and quantify the absorption of Si in leaves of different soybean cultivars and to determine if such absorption was able to enhance resistance to soybean rust. In a first set of experiments with cv. Williams 82, hydroponic plants were supplied or not with Si and inoculated with urediniospores of P. pachyrhizi. Chemical analyses revealed no significant differences in the plants' Si content regardless of the treatment, which translated into no effect on rust incidence. However, in a second set of experiments with different cultivars, plants of Korean cultivar Hikmok sorip absorbed nearly four times more Si than those of Williams 82. At the same time, plants from this cultivar exhibited a near absence of disease symptoms when supplied with Si. This resistance appeared to be the result of hypersensitive (HR) reactions that were triggered when plants were fed with Si. These results support the concept that a plant's innate ability to absorb Si will dictate the benefits conferred by a treatment with Si and provide evidence that Si can protect soybean plants against soybean rust through mediated resistance.
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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".