Integrated disease management of leaf spots and crown rust of oat
Bibliographic record
Abstract
Crown rust and leaf spots can reduce the yield and quality of oats. The objective of this research was to determine the effect of conventional fungicides, Actigard® and oat cultivars that vary in resistance to crown rust on leaf spot and crown rust severity, and oat yield and quality. Two experiments were established at two locations in Saskatchewan: Saskatoon and Melfort. Experiment 1 consisted of three oat varieties: AC Morgan (crown rust susceptible), CDC Dancer (intermediate) and CDC Morrison (resistant) and three fungicide treatments: check (unsprayed), propiconazole and pyraclostrobin. Experiment 2 consisted of the application of Actigard® at two rates: 8.75 g ai/ha and 26.25 g ai/ha; three crop growth stages: seedling, boot and heading; on two varieties: CDC Dancer and CDC Morrison, with an unsprayed check for each variety. At Saskatoon, crown rust was observed while leaf spot severity was low. At Melfort, no crown rust was observed and leaf spot severity was low. Fungicide reduced the severity of crown rust and increased yield and quality of oat at Saskatoon for the susceptible variety (AC Morgan) and somewhat for the moderately susceptible variety (CDC Dancer). The crown rust resistant variety (CDC Morrison) did not benefit from fungicide. Leaf spots were reduced by fungicide application at Melfort, but little increase in yield or quality was detected. There was little difference between AC Morgan and CDC Morrison for leaf spot symptoms, but CDC Dancer appeared to suffer slightly more than the other varieties. There was no impact of fungicide on beta-glucan content at either location, although there were differences among varieties, but only at Saskatoon. Actigard® was not observed to have any positive or negative effects on disease severity (crown rust or leaf spots) or any of the factors measured, including nutritional characteristics, at either location, although there were differences among varieties for many of the factors measured.
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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 | 0.001 | 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".