Polymer-Coated Urea in Broadcast or Furrow Application in the Corn-Palisadegrass Intercropping System
Bibliographic record
Abstract
Polymer coated urea (PCU) have the potential to increase nitrogen (N) use efficiency (NUE) by the release of N following crop demand while reducing losses by volatilization, leaching and denitrification. However, the NUE of PCU is still unclear especially in systems of corn-palisadegrass intercropping. Broadcast application of urea result in ammonia volatilization losses, new technologies must be adopted in order to allow broadcast application of urea. The aim of this research was to evaluate NUE from PCU in the corn-palisadegrass intercropping system and the viability of the broadcast application using PCU. Two field trials were carried out in a 4 × 2 factorial design including fertilization treatments and application forms. Fertilization treatments were: Conventional Urea topdressing (CUT); Conventional Urea at corn planting (CUP); PCU applied at planting to release in 30 days (PCU30); PCU mixture applied at planting to release in 30 and 60 days (PCU30+60); and application forms: broadcast placement and in furrow. 15N-urea was used for the determination of NUE. The corn yield and palisadegrass above-ground biomass were not affected by the fertilization treatments or application forms. PCU did not increase N uptake and the NUE by corn or palisadegrass in relation to urea. The N uptake from fertilizer by palisadegrass intercropped with corn is less than 1% of all N uptake on the intercropping system and at most 2% of the N fertilizer applied. The slow release urea does not enable the broadcast application and its efficiency depended upon the same climatic conditions as those of the conventional urea.
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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) | 0.001 | 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".