Effects of Consumer Preferences on Environmentally Friendly Tomatoes in Myanmar
Bibliographic record
Abstract
Environmentally and economically sustainable agricultural production systems are crucial to conserve natural resources and the environment as well as to protect human health. In recent years, Myanmar, one of the agricultural-resource-rich developing countries, is confronting land degradation, environmental pollution, and food safety issues due to intensive agricultural methods that use high dosages of agro-chemical inputs. Myanmar environmental farming systems and the market for environmentally certified products are still under developed. Determining consumers’ preferences and willingness to pay for environmentally certified products are vitally important to develop safe food markets. In this study, the choice experiment method was applied to examine consumer preference and the potential demand for environmentally friendly tomatoes. Using a sructured questionnaire in face to face interviews, the study collected information from 332 consumers in 8 supermarkets, and 4 open markets in Yangon city. Our results informed that most of the respondents in both markets have a positive WTP for an increase in each attribute. The supermarket respondents paid attentions to food safety labels, and it had the highest MWTP 2067.170 MMK (1.53 USD) relative to the other attributes. Our results suggest that policymakers and producers must enhance consumers’ knowledge of what is an eco-product and how to differentiate it in the market place and emphasize the improvement of food safety certification programs.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.003 | 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".