Yield and quality response of autumn-planted sunflower (<i>Helianthus annuus</i> L.) to sowing dates and planting patterns
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it