Multi-level determinants of crop choice to water stress in smallholder irrigation system of Central Nepal
Bibliographic record
Abstract
Change in crop choice is a common adaptation strategy for global change. However, its drivers are not well understood. We investigate the multilevel determinants of smallholders’ crop choice in irrigated agriculture of Central Nepal. We build upon previous studies and consider four levels of determinants: households, irrigation systems, local and regional market systems, and climatic conditions. Using primary survey data of 316 farmers from 9 farmer-managed irrigation systems in the Trishuli-Narayani sub-basin of Central Nepal, among other results, we document that smallholder farmers are likely to choose rice during the monsoon season if they are experienced and farm in the irrigation systems fed by large rivers. Water stress affects the crop choice mainly in two ways. In irrigation systems fed by large rivers, farmers located towards the tail-end of the canal are less likely to plant rice due to water stress. Farmers living in the irrigation systems that are fed by small and medium-size rivers are more likely to choose less water-demanding crops. Market integration is also a key determinant of crop choice. We discuss the implications of our findings for climate-resilient adaptation strategies in Central Nepal.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".