Extreme weather and agricultural management decisions among smallholder farmers in rural Thailand and Vietnam
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it