2025 Drought in Nepal's Madhesh Province: A rapid situational analysis
Bibliographic record
Abstract
Madhesh Province, Nepal’s “Grain Basket,” is facing a severe and prolonged drought in mid-2025 due to deficient winter rains and weak monsoon performance. Rainfall deficits of 30–50% have critically reduced groundwater recharge, dried up more than 30% of boreholes, and caused widespread water shortages, especially in Mahottari, Dhanusha, and Siraha districts. Satellite data and ground reports show significant crop stress, with delayed rice transplantation (only 52% completed vs. 92% last year) and an estimated shortfall of 400,000–450,000 metric tons of rice, potentially cutting national rice supply by 10%. The crisis is already leading to income losses for farmers, rising food prices, and risks of large-scale food insecurity. The government has declared Madhesh a disaster zone and initiated emergency measures such as deep and shallow tubewell installations and small irrigation schemes. ICIMOD is supporting with Earth observation–based drought monitoring and technical advice. Recommendations include: Short-term: seed replacements, alternative irrigation (mobile pumps, drip, sprinklers), crop diversification, food and water aid, and health support. Long-term: direct seeding methods, sustainable groundwater recharge (nature-based solutions, canal rehabilitation, Sunkoshi Marin Diversion Project), crop diversification, and stricter Chure Hills conservation. The report calls for urgent, coordinated action among government, development partners, farmers, and scientific bodies to mitigate the crisis and strengthen climate resilience.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".