Economic Assessment of Climate Adaptation Options in Myanmar Rice-Based Farming System
Bibliographic record
Abstract
Agriculture is highly sensitive to climate change and understandings how the adaptation options improve the farming household’s adaptive capacity are critical to the agricultural policies. The study was carried out for the economic assessment of climate adaption options in rice-based farming system of Myanmar. The propensity score matching approach was applied to explore the existing adaptation options and its contribution on the farm income. In addition, the binary probit model was used to analyse the factors influencing those adaptation decisions. The erratic rainfall, especially dry spell period and unexpected rain during the critical crop growth, was the critical challenge of rice-based farming in the study. The timely operation of farm machineries was one of the major adaptation options for the farmers, followed by other options such as use of more agrochemicals and changing rice varieties including early maturity, high yielding and stress tolerant varieties. The combination of those adaptations gave additional 0.86-0.89 ton/ha yield, 152-158 USD/ha total return and 108-124 USD/ha profit to the adapter farmers. The institutional factors such as irrigation access, access to credit, access to weekly weather information and participation to agricultural training were critically important to the adaptation decision. Moreover, the social capital factors like farming experience, farm size and farm income share were also major influencing variables.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".