Relationships of Isoflavone, Oil, and Protein in Seed with Yield of Soybean
Bibliographic record
Abstract
Ideal soybean [ Glycine max (L.) Merr.] production systems achieve both high seed yield and high concentrations of desired seed quality components. However, the relationships between seed quality and yield of soybean are largely unknown. This study sought to determine the relationships of isoflavone, oil, and protein with seed yield of soybean across a wide range of yield levels. Field experiments involving soybean response to K fertilizer applications in alternate tillage and soybean row‐width treatments were conducted at five locations in Ontario, Canada, from 1998 through 2000. Soybean yield and the concentrations and yields of oil, protein, daidzein, genistein, glycitein, and total isoflavone in seed were determined from a total of 13 trials. Oil concentration in seed decreased 4.2 g kg −1 with each megagram per hectare of increased seed yield. The relationship between protein concentration and seed yield was not significant. Concentrations of daidzein, glycitein, genistein, and total isoflavone increased by 249, 11, 164, and 427 mg kg −1 with each megagram per hectare of increased seed yield. Overall, oil and protein concentrations were much less responsive to seed yield increases compared with individual and total isoflavone concentrations. Daidzein was the most variable and glycitein the most stable isoflavone component. In addition, yields of individual and total isoflavones, and yields of oil and protein, were all positively related to seed yield. Our results suggest that high soybean seed yield can be accompanied by high concentrations of isoflavones without any substantial declines in oil and protein concentrations.
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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".