Farmers’ perceptions of sustainable agriculture in the Red River Delta, Vietnam
Bibliographic record
Abstract
While economic growth and food security in Vietnam's Red River Delta are heavily reliant on agriculture, the intensive use of agricultural land has resulted in various negative impacts on the environment, such as soil degradation, water pollution, biodiversity loss, and health effects on humans and animals. The current situation emphasizes an increased need for sustainable agriculture practices in the region. Understanding farmers' decision-making processes and identifying factors that influence their choices is crucial in order to promote their adoption of sustainable agriculture practices. This study examines the impact of attitudes, subjective norms, perceived behavioral control, age, and gender on farmers' intention to adopt sustainable agriculture practices using the Theory of Planned Behavior and Partial Least Squares Structural Equation Modeling. The results show that attitude towards sustainable agriculture practices showed a path coefficient of 0.310 (p < 0.001), and perceived behavioral control had a coefficient of 0.305 (p < 0.001), indicating strong positive relationships with intention. However, subjective norms, despite a positive coefficient, did not significantly affect intentions (path coefficient 0.099, p > 0.05). Age was found to have a moderating effect; older farmers are less likely to adopt sustainable agriculture practices compared to their younger counterparts. Gender, however, did not present a significant influence. In light of these findings, policymakers face a challenge in creating incentives to encourage farmers' engagement in sustainable agriculture practices in the Red River Delta and at the same time discourage youth out-migration from the agricultural sector more generally. Overall, this study enriches our theoretical understanding of the factors influencing sustainable agriculture adoption in developing countries and offers practical insights for policymakers and agricultural stakeholders in the Red River Delta to promote more effective and targeted sustainable agriculture practices.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".