Biomass Accumulation and Industrial Yield of Irrigated Sugarcane Submitted to Sources and Doses of Nitrogen Grown in Cerrado Oxisol
Bibliographic record
Abstract
The sugar and alcohol sector have invested heavily in technologies to increase the productivity of sugarcane and consequently the gross income of sugar and alcohol; among these practices irrigation and fertilization stands out. Based on the hypothesis that the source and the availability of nitrogen influence the growth, development and yield of irrigated sugarcane in the cerrado region, this study aimed to evaluate the accumulation of biomass and yield to define the best source and dose of nitrogen fertilization in irrigated sugarcane, in the cane-plant cycle, in a very clayey dystrophic Red Latosol, cerrado phase. The experiment was carried out at the Raízen Plant, located in the municipality of Jataí-GO. Brazil. The variety IACSP95-5000 was used in a randomized block experimental design, analyzed in a split-split-plot scheme, with four replicates. The factors evaluated were in the plots of four N rate (0, 60, 120 and 180 kg ha-1); In the split-plot two N sources (urea and ammonium nitrate) and as split-split-plot were represented by four evaluation periods (210, 250, 290 and 330 days after the planting-DAP). The irrigation was by sprinkling, performed by a central pivot. The highest gross sugar yield and gross alcohol yield in the average source of 131.72 kg N ha-1 had an average increase of 32.19%, compared to without N application 0 kg N ha-1.
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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".