Yield and quality response of autumn-planted sunflower (<i>Helianthus annuus</i> L.) to sowing dates and planting patterns
Bibliographic record
Abstract
Sowing time and sowing methods are often used to overcome environmental constraints on crop production. Information on the effect of these agronomic techniques on sunflower (Helianthus annuus L.) oil quality is, however, scarce. A field study was conducted to evaluate the effect of sowing dates and planting patterns, and their interaction, on seed yield and oil quality of hybrid sunflower. Sunflower hybrid Hysun-33 was sown at four dates beginning with the first week of August with fortnightly intervals under three planting patterns, viz., flat sowing (60 cm apart lines), ridge sowing (60 cm apart ridges) and bed sowing (90/30 cm) for 2 yr (2002 and 2003). The performance of the August sowing dates was significantly better with respect to yield and yield components than the September-sown crop. Among the three sowing dates in August, there was variable performance of the crop in the 2-yr study. On average, the sowing of sunflower from mid-August to the last week of August yielded better than early August sowing dates. The evaluation of quality parameters revealed greater content of achene oil in the September-sown crop followed by the crop sown in the last week of August. Delayed sowing lowered oleic acid content, but increased stearic and linoleic acid levels. Planting pattern treatments affected head diameter, achenes per head, 1000-achene weight and achene yield. Conclusively, sunflower sown on ridges during the second fortnight of August encountered favourable environmental conditions and gave significantly higher economic yield. Key words: Helianthus annuus , plantingg eometry, plantingpatterns, quality, sowingdates, sunflower yield
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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".