Attempts to rescue yield loss in continuous canola with agronomic inputs
Bibliographic record
Abstract
Recent canola acreage and production in western Canada have reached record high levels. Field experiments were conducted from 2014 to 2016 on land previously seeded to continuous canola for 6 yr at three Canadian Prairie sites. We determined that more intensive seed inputs, fertilizer, fungicide, tillage, or chaff removal could increase continuous canola yields compared with a “standard practice” (SP) treatment or match yields compared with canola in rotation. Recommended or 50% higher fertility levels alone or in combination with a higher seeding rate, tillage, chaff removal, or additional fungicide were applied to the same plots in three successive years to determine effects after 1, 2, and 3 yr of treatment imposition. In continuous canola, blackleg incidence and severity were both reduced by fungicide treatment. In rotations where canola was preceded by wheat, blackleg incidence and severity were much lower than in continuous canola. None of the treatments improved continuous canola yields compared with SP after a single year of treatment imposition. In subsequent years, canola yield increases compared with SP usually occurred as a result of additional fertilizer, seed, or fungicide. In the final year, canola yield loss due to continuous canola was largely rescued by additional seed and fertilizer. Without additional inputs, and when preceded by a different crop, canola yields averaged 11% greater than the average of all continuous canola treatments. High fertility regimes generally reduced oil content and increased protein content relative to standard fertility in continuous canola or relative to canola preceded by wheat.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".