Perception of Climate Change and Farmers’ Adaptation: A Case Study of Poor and Non-Poor Farmers in Northern Central Coast of Vietnam
Bibliographic record
Abstract
Successful implementation of national climate change agriculture adaptation policy in Vietnam requires rural communities to be able to respond to government programs. Critical players in ensuring this include provincial government officials and local farmers. Program success depends on strong uptake by farmers, which in turn depends on strong understanding of climate change and its agricultural and environmental impacts. Small-scale farming is dominant in Vietnam, and therefore the perceptions of such farmers regarding climate change and variability, necessary farming practice adjustment, and barriers to adaptation are important. However, there has been very little research devoted to understanding the factors that may influence farmers’ responses to climate change in Vietnam. The objectives of this paper are, therefore, to: (i) identify the of understanding and awareness of climate change among small-scale farmers in Vietnam, as it may affect their continuing practice as farmers; (ii) evaluate farmers’ understanding of adaptation to climate change; and (iii) record small-scale farmers’ responses to climate change adaptation, and therefore the capacity for rural communities to respond meaningfully to government climate change adaptation programs. Drawing on interviews of 172 small-scale farmers and six agricultural officers, we find that the majority of the farmers are, indeed, aware of local climate change. Both poor and non-poor farmers hold similar perceptions of changes in local climatic conditions. Importantly, however, these two groups differ significantly in terms of their perceptions and understandings of adaptation measures, barriers to adaptation, and factors influencing decisions. These differences reflect differences in income, financial capacity and education. Adaptation measures taken by poor farmers typically comprise relatively simple and minimal collective actions, and are typically low cost options. These are likely to have relatively low impacts in terms of their efficacy in responding to climate change. Non-poor farmers, on the other hand, tend to adopt more sophisticated responses, which require greater knowledge, skills and investment costs. These farmers are more likely to be able to respond to climate change with greater efficacy.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".