Comparison of the influence of inoculum sources on the development of fusarium head blight and the deoxynivalenol content in spring wheat in a disease nursery
Bibliographic record
Abstract
The screening for genetic resistance to fusarium head blight (FHB) in wheat has been challenged by the lack of efficacy of various artificial inoculation techniques in disease nurseries. In 2001-2003, we examined the influence of inoculum sources (conidial suspension, infested barley and corn kernels, or infested wheat debris) and inoculation frequency on the development of FHB and the deoxynivalenol (DON) content. The study was conducted on three spring wheat genotypes in a disease nursery of Ottawa, Ontario. The development of FHB was monitored by visually estimating disease severity on a scale of 0 to 9, six times during each growing season. Severity of FHB over time was summarized as the area under the disease-progress curve (AUDPC). Symptoms of FHB were also rated, at a critical developmental stage (soft dough), as critical-time disease severity (DS), percentage of infected spikelets (IS), and FHB index. The percentage of Fusarium-damaged kernels (FDK) and DON content in the wheat kernels were assessed after harvesting. All inoculation treatments had significantly greater AUDPC and DS in 2002, FHB index and IS in 2001 and 2002, and FDK in all three years than the noninoculated control. Regardless of inoculum source, two inoculations produced as much FHB, FDK, and DON as three inoculations. Among the inoculum sources, the conidial suspension or infested kernels yielded significantly greater AUDPC, DS, FHB index, IS, and DON content than the infested debris. Inoculation with conidial suspension resulted in greater FDK than inoculation with infested kernels or infested debris, between which there were no differences. The results of the present study confirm that conidial suspension and infested kernels are equally effective inoculants for FHB nurseries. Infested debris, though a natural source of inoculum, is less effective.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".