Cultivar considerations and pod sealants for straight-combining canola in Saskatchewan
Bibliographic record
Abstract
While the recommended and preferred harvest practice in western Canada is to swath \ncanola (Brassica napus L.), there is appreciable interest in straight-combining this crop. \nIn a recent study, five cultivars were harvested according to one of four harvest \ntreatments and evaluated for seed yield, yield loss due to shattering, percent green seed and seed size. The cultivars included four B. napus hybrids and an open-pollinated canola quality B. juncea variety. Harvest strategies were swathed, straight-combined without a pod sealant, straight-combined with Pod Ceal DC® and straight-combined with Pod-Stik®. While average yields ranged from 894-3066 kg ha-1, cultivar rankings for yield were generally consistent across sites. Seed yields were equal when averaged across harvest treatments and sites, but swathed yields differed from straight-combined yields 50% of the time for individual sites. At two sites, straight-combining produced 142-370 kg ha-1 higher yields than swathing while, when harvest was delayed due to unfavourable weather, swathed yields were 276-413 kg ha-1 higher. A 217 kg ha-1 yield increase occurred with pod sealants at one site, but there were no differences amongst the two products and pod sealants did not affect yields of straight-combined canola at the remaining seven sites. Pod sealants did not have a measurable effect on shattering losses, even under high shattering conditions. In contrast, cultivar effects on seed loss were generally significant with losses from one of the napus cultivars being particular and consistently low, especially when overall shattering losses were high. On average, losses for all cultivars were 4% of the total yield when harvest was completed reasonably close to the optimal stage. Straight-combining resulted in a small but significant increase in percent green seed and seed size but pod sealants did not affect seed quality in any cases. In conclusion, straight-combining can be a viable alternative to swathing, but substantial yield losses may occur if harvest is delayed too long. Important varietal differences in shattering losses were detected and cultivar selection appears to be a factor of greater importance than pod sealants for growers planning to straight-combine canola.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".