A Competitiveness Index of Soil Tillage and Planting Among Sugarcane Mills and Suppliers: The Benefits of Cost Reduction and High Production Strategies
Bibliographic record
Abstract
Sugarcane mills (SCMs) and sugarcane suppliers (SCSs) use different production systems. To increase competitiveness, these systems use cost reduction, high productivity investment, and technology strategies according to their scale of production, that is, small (S), medium (M), or large (L). The question that arises is: which of the three production scales, among SCMs and SCSs, have the best competitiveness index in activities related to soil tillage and sugarcane planting? The objective of this research was to analyze and compare a competitiveness index built by using the values of four variables: planted area, sugarcane replanted area, cost of soil tillage, and cost of planting. The study was conducted with data corresponding to the 2017/18 harvest season from 31 SCMs and 42 SCSs located in Brazil. In addition, Monte Carlo Simulation was used to analyze the level of certain costs and profits through relative frequency. Small scale suppliers showed the highest productivity and lowest cost in soil tillage, while the medium scale sugarcane mills revealed the smallest sugarcane replanting cycle area and the lowest cost of planting. However, the competitiveness index showed that SCSs are more competitive than SCMs, with both kind of sugarcane producers taking the benefits of using cost reduction and high productivity strategies.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".