Adapting or Chasing Water? Crop Choice and Farmers' Responses to Water Stress in Peri‐Urban Bangalore, India
Bibliographic record
Abstract
Abstract Unregulated groundwater extraction has led to declining groundwater tables and increasing water scarcity in the Indian subcontinent. Understanding how farmers respond to this scarcity is important from multiple perspectives—equity in access, livelihood security and resource sustainability. We present a case from the rapidly urbanizing Arkavathy sub‐basin near Bangalore city in southern India where irrigation is fully groundwater dependent. Using cross‐sectional data from a stratified random sample of 333 farmers from 15 villages, we investigated the factors that determine their choice of crops under conditions of water scarcity and urbanization. Binary logit analysis showed that farmers with a large landholding respond by tapping deep groundwater using borewells. Multinomial logit analysis revealed that access to groundwater, variation in the proximity to the product market (city) and labour availability influence crop choice decisions. We observe that current responses indicate what has been characterized in the literature as chasing strategies. These largely favour well‐off farmers and hence are inequitable. While the choice of water‐intensive crops and unregulated pumping have aggravated water stress, the uptake of water‐saving technologies among irrigated farmers has been low, showing that resource sustainability may not be a concern where non‐farm diversification opportunities exist. © 2018 John Wiley & Sons, Ltd.
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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 | 0.001 |
| 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".