Increasing canola and pea crop frequency – cultivar, fungicide, and crop rotation effects on disease/weed pressure and yield
Bibliographic record
Abstract
Field experiments in 1999 at Melfort and Scott in the 2nd year of a 5-year study revealed substantive yield losses associated with canola cultivars having low blackleg resistance, pea and canola in continuous rotations, and the decision not to apply fungicides. These yield reductions could not be attributed to reductions in spring soil water or an increase in weed competition. In a year characterized by above normal precipitation disease was determined to be the main factor contributing to canola and pea yield loss confirming the risks associated with low diversity rotations. At Scott in a canola-canola sequence greater blackleg pressure reduced the yield of a cultivar with little blackleg resistance by an average of 53% compared to the same canola grown on pea or wheat stubble and by 28% when replaced by a cultivar with moderate resistance. Application of Quadris fungicide reduced blackleg severity and incidence and reduced yield loss from 53% to 35% and from 28% to 9%. For pea grown on pea stubble at Scott an increase in mycosphaerella pressure led to a yield reduction of 28% compared to pea on wheat stubble. An application of Quadris reduced that yield loss to only 14%. Despite hail damage at Melfort higher canola yields could also be linked to lower levels of blackleg severity and incidence when a cultivar with greater blackleg resistance was selected and/or Quadris was applied. Although Sclerotinia severity was low at both locations the proportion of more damaging stem infections was greater at Melfort than at Scott. Pea disease assessment at Melfort was complicated by severe hail damage and failed to show a reduction in mycosphaerella blight severity with Quadris although straw and seed yield measurements did indicate a yield increase occurred. These results although preliminary reinforce the benefits of following recommended crop rotations and growing canola cultivars with greater blackleg resistance. Results also underline the importance of applying fungicides when the frequency of growing canola or pea in rotation is \nincreased. Site-specific responses such as greater blackleg pressure at Scott and the potential for greater Sclerotinia induced yield loss at Melfort were also observed.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".