Retrenchment under climate-driven risks in subsistence farming communities
Bibliographic record
Abstract
Increasing climate risks introduce new sources of uncertainty to smallholder farmers' livelihood decisions. While farmers in different development contexts tend to accurately perceive long-term climatic trends, livelihood diversification as a climate resilience strategy has generally lagged behind awareness of climate risks. In this study, we investigate potential mechanisms behind this lagged response through a survey of 500 farming households in Nepal's Chitwan Valley, a region that is highly dependent on subsistence agriculture and highly exposed to several climate-driven hazards. Specifically, we employ a suite of cross-sectional and time series econometric techniques to analyze how farmers' information sources, social capital, and previous exposure to climate hazards shape climate risk perceptions and livelihood decisions. We find that climate-driven risks are highly salient to household perceptions of farming risks; however, they also drive higher perceived risks of common livelihood diversification strategies, including rural-urban migration and off-farm employment. Further, while farming households generally maintain diversified income portfolios, exposure to droughts and/or floods leads to persistent increases in the reliance on farming income, which we term a "retrenchment" response. We find evidence for both financial and psychological mechanisms behind this response, which may exacerbate environmentally driven poverty traps. Our results indicate that efforts to build farmers' resilience to climate risks should especially account for perceived risks of livelihood alternatives, financial constraints, and loss-averse behavior in response to income shocks. Supplementary Information: The online version contains supplementary material available at 10.1007/s11111-025-00493-8.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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".