Extreme weather and agricultural management decisions among smallholder farmers in rural Thailand and Vietnam
Bibliographic record
Abstract
Abstract In this article, we explore whether and to what extent smallholder farmers in Northeastern Thailand and Central Vietnam adjust their farm‐level management strategies in response to droughts. We hereby consider adjustments in flexible adaptive strategies including water management, fertilizer and pesticide application, labor, and machine use in response to a contemporaneous drought, and adjustments in crop diversification and investments in response to a previous year drought. To that end, we combine longitudinal household data from the two regions from 2007 to 2017 with monthly high‐resolution rainfall and temperature data to characterize droughts at the subdistrict level. We find that Thai farmers scale down input costs in terms of fertilizer and hired labor and outsource tasks to service providers with equipment such as a combine, especially when exposed to extreme droughts. Their diversification and investment response seems, however, muted. While Vietnamese farmers are also reducing fertilizer use, they are expanding both the number of hired laborers and rented machinery services. They are also diversifying their cropping portfolio and investing in agricultural equipment.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".