Climate Change Adaptation Strategies in Agriculture: Cases from Southeast Asia
Bibliographic record
Abstract
Climate change has become apparent and been threatening more and more in Southeast Asia. Its impacts on agriculture and adaptation strategies at household level in farming systems areas are explored. The study focused on better understanding of climate change impacts and adaptation practices in four villages of Myanmar and Cambodia. Household questionnaire survey, focus group discussions and key informant interviews were used for data collection at household and community level supplemented with secondary data. Dry Zone farmers of Myanmar reported increase in crop diversity while in the Kampong Speu province in Cambodia, the number of rice growing farmers increased together with cultivation of other crops, such as cassava, palm fruit, sugarcane, mangoes, watermelons and vegetables. Farmers changed their cultivation practices as adaptation strategies in various ways: change in cropping calendar, crop varieties, machinery for cultivation practice, and change in area for cultivation. The shift in cropping calendar has occurred from two weeks to one month. Diverse strategies were reported in adapting to water scarcity in agriculture, such as system of rice intensification and water pumping. Farmers also adopted strategies for coping with declining soil productivity. These include animal manure application, compost making and application, crop rotation and crop residues retention. In particular, Dry Zone farmers prefer to apply animal manure rather than other practices because of its vast benefits, such as buffering capacity, effectiveness for plant growth and cheaper price. Cooperative actions are becoming increasingly needed when an individual could not afford adaptation strategies. However, barriers to adaptation strategies are to be reduced to promote climate adaptive practices in agriculture.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".