Mineralogy and Maximum Phosphorus Adsorption Capacity in Soybean Development
Bibliographic record
Abstract
The low natural fertility of tropical soils and the mineralogy almost dominated by iron and aluminum oxides limit the availability of phosphorus (P) to the plants, causing negative impacts on soybean yield. Objective was to evaluate the effect of phosphate fertilization on soils with different maximum phosphorus adsorption capacities (PAC) in soybean development. The experiment was carried out under greenhouse conditions, using Red-yellow Latosol (RYL) and a Typic Hapludalf (TH) soil as substrate. The analyses were performed by a completely randomized experimental design in a 5 × 2 factorial arrangement with three replications. The treatments consisted of 5 doses of P applied, corresponding to 0, 1, 6, 12, and 24% of PAC of each soil. In the soil, the mineralogy of the clay fraction (hematite, goethite, gibbsite and kaolinite) and crystallographic attributes were characterized. In the plant, we evaluated growth and pod production. The PAC of the soils ranged from 220 to 650 mg dm-3 with higher value in the RYL associated to clayey oxidic mineralogy and texture in relation to the TH of kaolinite origin and sandy texture, where the higher energy of adsorption observed was to TH. Phosphorus application from 16 to 21% of PAC, independently of the soil, promotes the same pattern of response with improvements in soybean development evidenced by increases in P content in plant tissue, plant height, root volume and aerial dry mass.
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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) | 0.000 | 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 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".