Economic Analysis of the Production of Yellow Passion Fruit in an Area With Virose Incidence and Fertilized With NPK
Bibliographic record
Abstract
The objective this work was to evaluate specific economic data of the production of yellow passion fruit under influence of different doses of NPK, in the form of N, P2O5 and K2O, in an area with incidence of virose in the city of Presidente Prudente, State of São Paulo. The following doses of NPK were evaluated: N (150 to 1200 kg ha-1), P2O5 (200 to 1600 kg ha-1) and K2O (100 to 700 kg ha-1). Miyake et al. (2016) describe the methodology used in the formation of seedlings, fertilization and cultural treatments of passion fruit. The data used in the economic analysis were: productivity, commercial production, percentage and fruits of each commercial classification, cost of production and profitability of passion fruit. At the economical part, structures of the COE and TOC and four indicators of profitability were used. It was observed percentage difference in the operational cost of production of 4.0% between the highest and the lowest dose of N, of 5.8% among doses of P2O5 and 1.7% among doses of K2O. The total operating cost ranged from $29,119.77 to $31,113.09 per hectare. The profitability indicators were not favorable. It was concluded that the region of Presidente Prudente-SP, in areas with an incidence of viral infection, it is not recommended the plantation of passion fruits. However, at times with high selling price of fruit (average above R$ 1.95 kg-1), the dose of NPK indicated refers to 300 kg of N, 400 kg of P2O5 and 500 kg of K2O 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 | 0.001 | 0.001 |
| 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) | 0.001 | 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".