Yield, Oil Content, and Composition of Sunflower Grown at Multiple Locations in Mississippi
Why this work is in the frame
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Bibliographic record
Abstract
Sunflower ( Helianthus annuus L.) is not a common crop in Mississippi and southeastern United States. There is potential for sunflower (for production of cooking oil or biodiesel) to fit into traditional cropping systems of Mississippi and to improve economic sustainability of agriculture in the region. The objective of the research was to evaluate the effect of N (0, 67, 134, and 202 kg ha −1 ), hybrid (DKF3875, DKF2990, DKF3510, and DKF3901), and their interaction on seed yield, oil content, and oil composition of sunflower grown at five locations in Mississippi (Newton, Starkville, Stoneville, and two locations in Verona). Oleic acid concentration in the original planting seed oil was 29% (DKF3875), 26% (DKF2990), 85% (DKF3510), and 41% (DKF3901). In Stoneville, Newton, and Verona 2, DKF3510 had the highest seed yields. DKF2990 had lower yields in Stoneville and in Verona 2. Seed oil concentration was higher in DKF3875 and DKF2990 (43–47%) and lower in DKF3510 and DKF3901 (40–44%) at Stoneville and Verona 2. At Newton however, oil concentration was highest in DKF2990, lower in DKF3510 and DKF3901 and lowest in DKF3875. Overall, increasing N rates reduced seed oil concentration but increased seed yields and subsequently oil yield per area. Relative to the concentration in the original seed used for planting, oleic acid generally increased in all locations and hybrids. There was a corresponding decrease in the concentration of linoleic acid. Oil yields varied between 483 and 1388 L ha −1 and calculated biodiesel oil per area varied from 386 to 1110 L ha −1 with the different N treatments and locations. Sunflower can be a viable crop in most parts of Mississippi for production of cooking oil or biodiesel.
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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 | 0.000 | 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) | 0.000 | 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