Response of herbicide-tolerant canola (<i>Brassica napus</i> L.) cultivars to four row spacings and three seeding rates in a no-till production system
Bibliographic record
Abstract
Kutcher, H. R., Turkington, T. K., Clayton, G. W. and Harker, K. N. 2013. Response of herbicide-tolerant canola ( Brassica napus L.) cultivars to four row spacings and three seeding rates in a no-till production system. Can. J. Plant Sci. 93: 1229–1236. Appropriate management practices are important to reduce input costs and to optimize yield and crop quality. The objective of this study was to determine the optimum row spacing (23, 31, 46 and 61 cm) and seeding rate (3.2, 6.4 and 9.6 kg ha−1, or 87, 173 and 260 seeds planted m−2) for two herbicide-tolerant canola (Brassica napus L.) cultivars (an open-pollinated and a hybrid) under a no-till production system. Plant density and yield varied with row spacing, with the effect being linear in both cases. Plant density decreased with wider row spacing, from 112 plants m−2 at the 23-cm row spacing to 83 plants m−2 at the 61-cm row spacing. Yield decreased with wider row spacing, from 2397 kg ha−1 at the 23-cm row spacing to 2138 kg ha−1 at the 61-cm row spacing. Results from this study indicate that herbicide-tolerant cultivars of canola grown in no-till production systems under conditions of adequate soil fertility, effective weed management, minimal disease pressure, and good flea beetle control, produced the highest plant densities and yields at row spacing of 24–31 cm, that seeding rates in the range of 3.2–9.6 kg ha−1 had no effects on yield, and that a hybrid cultivar performed better than open-pollinated cultivars in terms of plant density, earliness and duration of flowering, and seed size. The study helps to better define the response of plant density and seed yield in herbicide-tolerant canola cultivars to changes in row spacing and seeding rate. In particular, the results of this study suggest that plant density may not be a reliable predictor of canola yield, although row spacing is an important consideration.
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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 | 0.000 | 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 | 0.001 |
| 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".