Financial Impact of Irrigation and Nitrogen: Topdressing in Rural Enterprises of Sugarcane in Uruaçu, Brazil
Bibliographic record
Abstract
The success of a project depends on a good planning of activities focused on increasing yield and minimizing production costs. The objective of the present work was to assess the economic viability of irrigated sugarcane crops (plant crop and ratoon crop) under nitrogen topdressing, in northern state of Goiás (GO), Brazil. The data needed for the research were obtained in a sugarcane crop area in the Estrela do Lago Farm, which belongs to the Uruaçu industry, in the municipality of Uruaçu, GO. The CTC4 sugarcane variety was used, which has high tillering and yield, and great adaptability to mechanized planting and harvest. The sugarcane was planted in double rows spaced 1.80 m apart. The total nitrogen topdressing rate (100 Kg ha-1) was divided into three applications with 60-day intervals during the crop development. The costs were obtained based on the following items: mechanized operations; manual operations; consumed material, and other expenses. The production cost was calculated using the total production operational cost structure used by the Brazilian Institute of Agricultural Economy. The highest costs for sugarcane in plant crop, with and without nitrogen topdressing, are due to mechanized operations and consumed material, which reached approximately 60% of the costs.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".