Potassium, Phosphorus, Sulfur, and Boron Fertilization Effects on Soybean Isoflavone Content and Other Seed Characteristics
Bibliographic record
Abstract
Soybean [Glycine max (L.) Merr.] seeds contain isoflavones that have positive impacts on human health. The objective of this study was to determine the impact of pre-plant mineral fertilization on isoflavone, oil and crude protein concentrations, and seed yield of field-grown soybean. The effects of potassium (0, 50, 100, and 150 kg K ha−1), phosphorus (0, 25, 50, and 75 kg P ha−1), sulfur (0, 15, 30, 45 kg S ha−1), and boron (0, 1.5, 3.0, and 4.5 kg B ha−1) were tested separately, each with two 00 soybean cultivars (‘Golden’ and ‘Grand Prix’) grown in replicated trials at Sainte-Anne-de-Bellevue, Québec, Canada in 2002/3. Seed total and individual isoflavone concentrations were determined by high-performance liquid chromatography. Seed yield, 100-seed weight, and oil and crude protein (CP) contents were determined concurrently. Across years and cultivars, no fertilizer treatments effects were observed for most variables. This overall lack of response to fertilizers was attributed to the relatively high initial fertility of the sandy loam and sandy clay loam soils used. However, total and individual isoflavone concentrations were significantly affected by year and cultivar. Across experiments, total isoflavone concentration was 33% greater on average in 2003 than in 2002, which was characterized by above-average temperatures and severe drought. Cultivar with the greatest isoflavone concentration varied depending on the year. Fertilization does not appear to be a viable strategy to increase isoflavone concentration of soybean seeds on medium-to high-fertility soils.
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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 | 0.000 |
| 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) | 0.001 | 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".