Farmers’ Willingness to Purchase Weather Index Crop Insurance: Evidence from Battambang, Cambodia
Bibliographic record
Abstract
The weather index crop insurance (WICI) scheme was introduced under a pilot project for rice in Cambodia in 2021. The adoption rate was low and the loss ratio was higher than 200%. The increase in farmers’ participation would help reduce the loss ratio, which can sustain the WICI scheme. This study, therefore, examines Cambodian rice farmers’ willingness to purchase WICI in Cambodia. The hypothesis is that the low adoption rate is due to a lack of awareness, lack of understanding of WICI, lack of trust in weather stations, and the problem of basis risk. This study would like to test the influence of those factors on the willingness to purchase in Cambodia. Battambang Province was chosen as the study area as it is the largest area for rice production and has the largest take-up rate of farmers buying WICI. Detailed interviews of 232 farmers were conducted in the districts of Bavel and Moung Ruessei. The probit regression model was used to identify factors that significantly impact farmers’ willingness to purchase WICI. The results indicate that land size, level of trust in weather stations, level of farmers’ understanding of WICI, and joining the WICI awareness program have positive effects on the probability of farmers’ willingness to buy WICI, whereas the number of household laborers and expectation of floods have negative influences. The probability of willingness to purchase by farmers who attended the awareness program on WICI was 38% higher than those who did not. The size of farmland, level of trust in weather stations, and level of understanding of WICI increase in one unit affecting the probability of willingness to purchase WICI by 4%, 16%, and 25%, respectively. On the other hand, the increase in the number of household laborers in the rice field by one person and the increase in the probability of expected flood increase by 0.1 drag back the probability of farmers’ willingness to purchase by 16% and 5%, respectively. The results suggest the government to raise the insurance awareness and understanding of WICI. Development of weather station infrastructure, as well as maintenance of weather stations, is needed to guarantee the accuracy of data generated from the weather station.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".