<i>Brassica carinata</i> Seeding Rate and Row Spacing Effects on Morphology, Yield, and Oil
Bibliographic record
Abstract
Core Ideas Carinata growth and yield was influenced more by row spacing than seeding rate. Rows spaced at 36 cm maximized carinata yield. Carinata branching was favored by wider row spacing. Carinata ( Brassica carinata A. Braun) is an oilseed crop with potential as a winter crop in the southeastern United States to diversify crop rotations and provide biofuel production and livestock feed. The objective was to evaluate the effects of row spacing and seeding rate on carinata yield and oil composition. Field experiments were conducted in Jay and Quincy, FL, from 2013 to 2016 evaluating carinata growth, seed and oil yield, and oil composition grown in a factorial arrangement of four seeding rates (3, 6, 9, and 12 kg ha −1 ) and four row spacings (18, 36, 53, and 89 cm). No interactions between seeding rate and row spacing were detected. Seeding rate did not influence any of the variables studied. In contrast, row spacing affected seed and oil yield, branch production, and pods per plant. Seed yield (ranked from highest to lowest) was 2761, 2286, 1851, and 1572 kg ha −1 for rows spaced at 36, 18, 53, and 89 cm, respectively. Branching and pods per plant increased with row spacing. Neither seeding rate nor row spacing affected oil concentration and quality. Oil concentration averaged 40%, of which more than a third was erucic acid (C22:1). Protein concentration was 31%, and glucosinolate concentration was 93 μmol g −1 . The results of the present study demonstrated that carinata can be successfully grown in the southeastern United States, reaching yields and oil quality similar to those reported at other latitudes, and can be a source of biofuel, protein for animal feed, and cropping system diversification for growers.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".