Effect of precision planting and seeding rates on canola plant density and seed yield in southern Alberta
Bibliographic record
Abstract
Precision planters are recently being adopted for seeding canola to improve crop establishment and seed yield. This study determined the effect of seeding canola using precision planters (30.5 and 50.8 cm seeding row width) and conventional air drill seeders at different rates (20, 40, 60, 80, and 160 seeds m−2) on plant density and seed yield. The study was conducted for 4 yr (2016 to 2019) at three locations in southern Alberta. Plant density increased with higher seeding rates following the negative exponential function distribution. The yield-density relationship was non-linear asymptotic in nature and weak-to-moderate in strength at most site-years. The parameters of yield-density relationship did not show statistically significant differences among the air drill and precision planters. When averaged among seeding rates, canola yield was higher for the narrow row precision planter at 5 site-years and for the air drill at 2 site-years out of a total of 12 site-years. Under irrigated and high-precipitation conditions, seed yield in narrow-row precision planted canola was higher than air drill seeded canola. There was an average increase of 463 kg ha−1 (10%) in the seed yield in narrow-row precision planted canola compared with the air drill seeded canola among irrigated systems; however, under water-limited conditions, seed yield in air drill seeded canola was comparable or higher than the precision planted canola. Wide-row planter led to poor crop establishment and seed yield under both irrigated and dryland conditions, attributed to higher in-row plant density due to wider row spacing.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".