Soybean Tocopherol Concentrations Are Affected by Crop Management
Bibliographic record
Abstract
Soybeans are an important source of tocopherols, which have health-beneficial properties. Previous studies have demonstrated that environmental factors may affect soybean tocopherol concentrations; the impact of specific crop management strategies, however, remains poorly understood. Experiments were conducted for 2 years at three sites in Quebec to determine the impact on soybean tocopherol concentrations of seeding rate, row spacing, seeding date, cultivar, and P and K fertilization. Total and alpha-, gamma-, and delta-tocopherol concentrations were determined by high-performance liquid chromatography. Overall, alpha-tocopherol was the most responsive to the factors evaluated; the response of other tocopherols was often lower or inconsistent across environments. The seeding rate affected alpha-tocopherol concentrations in three out of five environments; seeding at a rate of 40 seeds m(-2) resulted in 4% higher concentrations than seeding at a higher rate. Wide row spacing (more than 36 cm) resulted in two out of five environments in 6% higher alpha-tocopherol concentrations as compared to narrower row spacing. The seeding date had a greater impact; mid- to late-May seeding across four environments resulted in 45% greater alpha-tocopherol concentrations than seeding at later dates. Phosphorus and K fertilization had a negligible impact on tocopherol concentrations. Across experiments, large differences were observed between environments; plants grown in northern environments consistently had lower concentrations of alpha- and gamma-tocopherols but higher concentrations of delta-tocopherol. Differences between cultivars were also consistent, ranging between 10 and 30%, depending on the tocopherol. Results demonstrate that soybean tocopherol concentrations are affected by crop management and thus suggest that specific recommended agronomic practices may need to be established for the production of soybeans for the functional food market.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".