The study of fungicides application and sowing date, resistance, and maturity of<i>Eragrostis tef</i>for the management of teff rust [<i>Uromyces eragrostidis</i>]
Bibliographic record
Abstract
Teff (Eragrostis tef) is the most important staple cereal food crop in Ethiopia. It accounts for 25% of the annual gross grain production. Teff rust [Uromyces eragrostidis] is the most important and widely distributed teff disease in the country; it causes grain yield losses of 10%–41% annually. Presently, no teff germplasm immune to this disease has been found. The objectives of this study were to determine the resistance of 2000 E. tef landrace accessions and 5000 mutant lines to teff rust and the effects of sowing dates, plant maturity, and fungicides application on teff rust severity. The majority of teff landrace accessions and mutant lines had a high incidence of teff rust infection with susceptible reaction types, i.e., large uredia without chlorosis. None of teff landrace accessions and mutant lines showed complete resistance, but 22 landrace accessions had lower teff rust severity levels. Low rust severity was associated with early sowing dates and early-maturing teff varieties, indicating the beneficial effect of early maturing in escaping the disease. The average yield loss incurred because of teff rust varied from 6% to 33% across three sowing dates. Although, no significant yield differences were observed among the fungicides tested, propiconazole (Tilt® 250 EC and Bumper® 250 EC) was found to reduce more teff rust 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".