Weather index insurance for managing drought risk in smallholder agriculture: lessons and policy implications for sub-Saharan Africa
Bibliographic record
Abstract
This paper reviews recent advances in, and challenges for, weather index insurance for managing drought risk in smallholder agriculture, with a focus on sub-Saharan Africa. Despite its promise to integrate local agricultural risk smoothing with insurance principles, there remain many challenges to its mainstreaming in low income countries. Scaling up of weather index insurance pilot projects is particularly constrained by high-basis risk, related to the divergence between the calculated weather index and actual productivity loss on the farm. Various options may be considered to enhance uptake of weather index insurance. Linking reliable weather data with location-specific crop and agronomic conditions using flexible geospatial crop modeling tools is one option to reduce the basis risk. The other option is interlinking weather index insurance with credit or safety nets. In the end, insurance should be offered as part of a wider set of business services that provide real value to smallholders. Finally, the review acknowledges that the suggested conceptual solutions, especially interlinking index based weather insurance with credit will require more empirical evidence on the extent to which insurance would reduce the cost of borrowing and make credit more accessible to the smallholder farmers.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".