Optimizing canola production in the Northern Great Plains by leveraging genotype × environment × management synergies
Bibliographic record
Abstract
Abstract Management practices and cultivars for canola ( Brassica napus L.) have evolved for seeding and harvest management systems including the adoption of straight‐cutting (S/C) over windrowing. We explored how manipulations to seeding rate, pod shatter reduction hybrid, and harvest method alter canola seed yield and quality. An experiment was conducted at five locations across the Canadian Prairies between 2018 and 2022, consisting of two pod shatter reduction hybrids with contrasting growth phenology sown at densities of 60, 120, and 180 seeds m −2 , and subjected to either windrowing at 60% and 90% seed color change (SCC), or S/C at 10% and 5% seed moisture. Irrespective of hybrid choice or harvest management, densities of 120 and 180 seeds m −2 provided high and stable yield relative to 60 seeds m −2 . Seed losses were minimal for both hybrids, but the late‐maturing cultivar expressed higher seed yield and oil concentration. Straight‐cutting at 10% seed moisture achieved the highest yields for both hybrids, but delays in S/C timing reduced its advantage over windrowing at 90% SCC. Yield components such as seed number and seed weight on secondary branches became critical to achieve high yields at lower seeding densities when environmental stress was low. While reducing seeding densities to cut costs can be tempting, the highest and most stable yields were achieved with a late‐maturing hybrid, sown at 120 seeds m −2 and managed with S/C at harvest. This study provides insights into how seeding density and harvest method interact to affect canola yield within a genetic × environment × management framework.
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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".