Evaluation of Long-day Onions for Resistance to White Rot Infection Using Greenhouse and Laboratory Techniques
Bibliographic record
Abstract
Breeders have found field screening for onion white rot resistance to be unreliable since consistently moderate to high disease levels that significantly differentiate cultivars, do not occur over field sites and years. The objective was to see if a greenhouse or laboratory technique could predict field reaction of onion accessions. Onion (Allium cepa) accessions were grown in fields naturally infested with the white rot causing fungus (Sclerotium cepivorum) in 1999 and 2000 (New Zealand) and in 2000 and 2001 (Canada). The field disease levels were low at three sites, moderate at two and high at one. Field screening was not a reliable predictor of white rot reaction when disease incidence was low. Onion accessions were screened for resistance in the greenhouse using nonsterile muck soil (NSMS) and sterile muck soil (SMS) with S. cepivorum sclerotia as the inoculum source. Total disease incidence was significantly higher in the NSMS compared to the SMS and accessions showed significant variability for white rot reaction in both soils. Two laboratory-based techniques were used to test the effect of onion volatiles on mycelium growth in culture. The volatiles from susceptible accessions resulted in faster radial growth of S. cepivorum mycelium (on water agar) and height of aerial mycelium (on potato dextrose agar) than volatiles from resistant accessions. Disease incidence in the greenhouse, S. cepivorum culture growth rates on water agar media and aerial mycelial height were all good predictors of field disease incidence in a covariance analysis. The best predictor was aerial mycelial height, which was predictive of field disease incidence in four out of six field sites. Onion breeders can use the methods described in this study in breeding for white rot resistance.
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) | 0.001 | 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".