A Sustainable Management Package to Improve Winter Wheat Production and Competition with Weeds
Bibliographic record
Abstract
Advances in cultivar development and the demand for winter wheat ( Triticum aestivum L.) as an ethanol feedstock has increased winter wheat acreage across the Canadian Prairies. A sustainable production package is required to maintain this renewed interest. Experiments were established in 2002–2004 at Lethbridge and Lacombe, AB, to determine cultivar, seeding rate, and herbicide effects on weed competition and crop yield. Treatments included a factorial combination of four contrasting cultivars (Radiant, CDC Osprey, CDC Falcon, and CDC Ptarmigan), three seeding rates (300, 450, and 600 seeds m −2 ), and two herbicide treatments (fall only or fall plus a spring in‐crop herbicide). CDC Ptarmigan yield was higher (12%) than the other cultivars. This was expected as CDC Ptarmigan has higher yield potential, but its ability to maintain high yields in the presence of weeds was unexpected. The yield of CDC Falcon and Radiant was similar, but CDC Falcon had inferior weed competitive ability, as shown by yield differences, relative to the other three cultivars. CDC Osprey yielded less than the other cultivars. Grain yield was reduced when planted at 600 seeds m −2 by 4%, but weed biomass was less (40%). Spring in‐crop herbicide application reduced weed biomass, but the extra application did not improve grain yield. These results suggest winter wheat yields can be maintained without added inputs of spring herbicides, and greater stability of yield, winter survival, and competitiveness will usually occur with increased seeding rates.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| 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 teacher head, 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".