Response of sunflowers (<i>Helianthus annuus</i> L.) to varying seeding rates and nitrogen fertilizer rates in a no-till cropping system in Saskatchewan
Bibliographic record
Abstract
In the past, most sunflower research was conducted in tilled cropping systems and was based on wide row configurations established using precision planters. Little agronomic information is available for the no-till systems predominant in Saskatchewan, where crops are typically seeded in narrow rows using an air drill. Two studies were conducted in Saskatchewan to determine the optimum seeding and nitrogen (N) rates for short-season sunflowers in a no-till cropping system. The N rate study used 5 N rates (10, 30, 50, 70, and 90 kg N ha−1) with the hybrid 63A21. The seeding rate study used 7 seeding rates (37 000, 49 000, 61 000, 74 000, 86 000, 98 000, and 111 000 seeds ha−1) with two cultivars, AC Sierra (open pollinated) and 63A21 (hybrid). There was a linear yield increase as the N rate increased from 10 to 90 kg N ha−1. Based on the N rates tested in this study and current N fertilizer costs below $1 kg−1, sunflower yields and gross returns were most favorable at 90 kg N ha−1. Future N response research with a wider range of N rates is warranted to best determine the optimum N rate. The optimum seeding rate was between 98 000 and 111 000 seeds ha−1 for AC Sierra and between 74 000 and 86 000 seeds ha−1 for 63A21. The optimum plant density, approximately 70 000 to 75 000 plants ha−1, was similar for both cultivars. These results are higher than the current recommended seeding rates for wide-row precision planting systems in areas with a longer growing season.
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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 | 0.000 |
| 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".