The Agro-Industrial Sugarcane System in Mexico: Current Status, Challenges and Opportunities
Bibliographic record
Abstract
Sugarcane cultivation in Mexico occurs under a wide range of socioeconomic, environmental and agricultural conditions, with the last three harvests (2010/2011, 2011/2012 and 2012/2013) providing yields ranging from 36-125 t ha-1 (variation > 347%), with an average yield of 70.2 t ha-1, which is below the world average of 80 t ha-1. The total area allocated to sugarcane production in Mexico is close to 800 thousand hectares, and could rise to nearly 5 million hectares given adequate conditions for its cultivation. This activity generates approximately 1 million direct jobs, 2.2 million indirect jobs, and more than 2.5 billion dollars (0.4% of GDP) per year. Climate change and the rapid market penetration of high fructose corn syrup are among the greatest threats to this agribusiness, including severe disintegration of production processes in the field, industry, commerce, and consumption of cane sugar. Technology lags, low investment, high processing costs and shortcomings in production sales are issues the industry must address by leveraging their resources and coordinating processing links to be more efficient and competitive. Political influence has imposed a suboptimal policy framework to achieve the projected potential. To overcome current lags in the field and refineries within the country, significant innovations across the value-chain are underway, including a robust breeding program, digitalization of sugarcane fields and novel investments in research and development. The sugarcane value-chain has great potential for Mexico, and exploiting this potential is possible if technological, organizational and commercial management innovations currently in progress in fields and factories are applied.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".