Foliar selenium application reduces late blight severity and incidence in potato and acts as a pathogen growth inhibitor and elicitor of induced plant defence
Bibliographic record
Abstract
Late blight caused by Phytophthora infestans is a serious threat to potato production. Currently, solutions to manage late blight in organic systems are scarce. This study was undertaken to evaluate the effect of selenium (Se) on plant emergence from seed pre-treated with Se and the seed decay pathogens Pectobacterium carotovorum ssp. carotovorum and Fusarium sambucinum, and on foliar potato late blight following inoculation with P. infestans and foliar treatment with Se. The potential modes of action of Se were also evaluated. Following inoculation of the seed decay pathogens on potato seed pre-treated with different rates of Se, plant emergence was not affected by Se treatment. By pre-treating potato seed with different Se rates, and applying different foliar Se rates on potato plants subsequently inoculated with the late blight pathogen in a greenhouse setting, we showed that foliar Se application alone or combined with Se seed treatment reduced the severity and incidence of late blight, and activated the production of secondary metabolites in potato leaves and tubers. Further, in vitro studies showed that Se directly inhibited the mycelial growth of P. infestans and other important plant pathogens, including Fusarium graminearum and Sclerotinia sclerotiorum. The data suggest that Se acts as an inducer of plant defences, while also inhibiting fungal growth. The study highlights the potential of Se for managing late blight, and suggests that its application could contribute to improving plant health in greenhouse production systems by helping to control diseases caused by P. infestans, F. graminearum and S. sclerotiorum.
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 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 | 0.000 |
| 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".