Effect of Millet Growth, N Sources and Previous Phosphorus Availability on the Efficiency of P Sources
Bibliographic record
Abstract
The efficiency of phosphate fertilization has been a major challenge for tropical agriculture. Lately, cover plants better adapted to tropical conditions have allowed non-tillage in these areas. Our aim was to investigate the effects of soil and plant (millet) on the efficiency of different P sources combined with N sources in an oxisol with medium and low initial P availability. For this, rhizotubes filled with oxisol under different combinations of P and N source were exposed to the absence and presence of millet plant (Pennisetum glaucum L., Leeke). We characterized the soil before and after, as well the fertilizer sources and plants. There was no effect of medium or low initial P availability in the soil on the P recovery rate (PRR). NH4+ was advantageous mainly combined with a soluble P source, increasing up to 40% the P recovery rate in relation to NO3−. Bayóvar phosphate rock (PR) had higher solubilization than Araxá PR, as well as a significant soil pH increase. Without plants, there was no significant correlation among the variables evaluated, evidencing the plant’s role in dissolving less soluble P sources in an oxisol. Our findings suggest no effect of previous medium P availability on the P recovery rate regardless of phosphorus sources. In addition, an improvement in the P recovery rate when an ammoniacal source is used is observed when soluble phosphate fertilizers are used. Cover plants on oxisols are crucial for increasing the efficiency of phosphorus fertilizers and their plant availability over time.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".