Environmental management in the vegetable sector of Mexico
Bibliographic record
Abstract
The major environmental concerns of agriculture in Mexico are related to water resources and deforestation, in addition to the increasingly important issues of pesticide use, pollution, greenhouse gas emissions and soil erosion. This situation worsens in arid or semiarid regions, such as the state of Zacatecas, where the main source of water is 34 aquifers, 15 of which are overexploited. One option for reducing environmental deterioration is to encourage production units to adopt environmental management systems (EMS). These systems, however, are not well known to growers at the local or national level. The establishment of an EMS in the agricultural sector is relatively new, although it is widely used in other industrial sectors. This study determined the views, drivers and barriers to adopting an EMS in the vegetable sector of the state of Zacatecas, Mexico. A questionnaire was given to 202 technicians or owners of vegetable production units. The data were analyzed using confirmatory factor analysis and structural equation modeling. Improving access to markets was the most important driver for EMS adoption, while the lack of government support was the main barrier. The study demonstrated that views of sustainability are closely related to attitudes toward environmental management actions and environmental sustainability.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".