Farmers\' Sustainable Agriculture Perception in the Vietnam Uplands: the Case of Banana Farmers in Quang Tri Province
Bibliographic record
Abstract
Upland farmers in Vietnam are associated with the lowest income and face serious issues of natural resources degradation and environmental pollution because of poor agricultural practices. To persuade the upland farmers to adopt sustainable practices, it is vital first to assess their perception of sustainable agriculture. This study aimed to measure banana farmers’ perception towards sustainable agriculture and its determinants in the Vietnam uplands based on a case study in Quang Tri province. Stratified sample technique was used to randomly select 300 respondents from 2 upland districts of Quang Tri. The primary data were gathered by using a structured questionnaire with Cronbach’s alpha coefficient of 0.84. The results showed that the majority (84.7%) of the farmers had low to mode rate perceptions of sustainable agriculture. Farmers had positive perceptions towards sustainable agriculture in issues related to protection of agricultural resources, negative effects of agrochemicals on human health and the environment, input application, crop rotation, product consumption and roles of farmer groups; whereas, they had moderate perceptions about issues related to production profits, plant residue use and modern technology application. In addition, the study revealed that agricultural programs on TV, education, ethnic group, economic status and credit use were the factors that affected farmers’ sustainable agriculture perceptions.
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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 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".