Climate Change-Induced Drought Impacts, Adaptation and Mitigation Measures in Semi-Arid Pastoral and Agricultural Watersheds
Bibliographic record
Abstract
Periodic drought is a major challenge in drought-prone areas of South Asia. A sample survey of farmers (n = 400) from South Asia was conducted to study the farmers’ perception about drought impacts on their socio-economic status, agro-biodiversity, and adaptation besides public institutions’ drought mitigation measures. The results revealed reduced surface and groundwater availability, soil degradation, partial or complete crop failure, increased agricultural fallows and wastelands, biodiversity loss, decrease in agricultural yields, pasture lands, and livestock in drought-impacted South Asia. About 16–26% of the farmers perceived a reduction in the agricultural area and production of commercial crops and millets in drought-prone areas, while changes in the cropping of pulses, oilseeds, horticultural, and fodder crops were minimal. About 57–92% of respondents showed a reduction in the consumption of fruits, vegetables, dairy products, and fish. Unemployment, migration, reduced farm income, and malnutrition were major socio-economic impacts among respondents (38–46%). Despite sufficient public support as a mitigation strategy, the farmers had poor participation (8–65%) for agri-information and adaptation (7–36%) against drought impacts. Hence, researchers, extension agents, and policymakers must develop efficient ‘participatory-mode’ drought adaptation and mitigation policies in watershed-based semi-arid pastoral and agricultural regions of South Asia and similar agro-ecologies across the globe.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".