Effect of Phosphate Organomineral Fertilization on the Dry Matter Production and Phosphorus Accumulation of Corn
Bibliographic record
Abstract
Brazilian soils are typically highly weathered, naturally poor in P, and rich in minerals with high P-adsorbing capacity. The objective of the present study was to evaluate the efficiency of peat-based granulated organomineral phosphate fertilizer (OMF) on the phosphorus supply capacity, shoot dry matter production and P accumulation of corn plants, as well as its residual effect on soil, compared to that of monoammonium phosphate (MAP). The experimental was performed using a randomized block design with a 2 × 5 + 1 factorial scheme (two fertilizer: OMF and MAP; five P doses: 15, 30, 45, 60, and 75 mg P2O5 dm-3, and one control treatments (no P fertilizer), and three replicates. Two soils (Ferralsol and Planosol) were used in this study. Soil samples were incubated with limestone for 30 days and then dried, sieved and used to fill plastic pots (3 dm3 soil per pot). Four successive corn cultivations were evaluated and, at the end of each cultivation period, the shoot dry mass (SDM) and P content of the corn were determined. In addition, soil P was measured at the end of the experiment. OMF and MAP had similar effects on SDM, but MAP provided higher P accumulation (SPA) of the first two cultivations, while OMF had higher soil residual P in Ferralsol. However, considering the total accumulated in four crops, SDM and SPA were statistically similar between the two P sources. SDM and SPA in Ferralsol increased linearly with increasing doses, while in Planosol, SPA increased linearly with increasing P dose, regardless of the P source, but SDM was not affected by increasing P doses. According to the results, OMF composed of chemically activated peat and MAP can replace MAP as phosphate fertilizer and maintain the same agronomic efficiency.
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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".