Impacts of Farmers’ Adaptation to Extreme Weather Events on Rice Productivity
Bibliographic record
Abstract
Abstract Floods and droughts are major concerns for rice farmers in Thailand, particularly those in the Chao Phraya River Basin (CPRB). To mitigate the impacts of these extreme weather events on their rice cultivation and livelihoods, some farm households have implemented adaptation strategies, such as adjusting the crop calendar and changing rice varieties. Using data from a survey of farm households in the CPRB, this study highlights the adaptation strategies adopted and analyzes their impacts on rice productivity through an endogenous switching model. Our results indicate that adaptation to floods in the CPRB increases wet-season rice productivity. The unconditional impact of adaptation on wet-season rice productivity is approximately 120 kg per rai (about 0.16 hectares). The treatment effect, which captures the hypothetical scenario where farm households that adapted chose not to adapt, shows that the impact of adaptation on wet-season rice productivity is around 31 kg per rai. This means that farm households that adapted to extreme weather events would have produced 31 kg less per rai if they had not adapted.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".