Drought Risk in Cambodia: Assessing Costs and a Potential Solution
Bibliographic record
Abstract
The two major natural hazards that threaten Cambodia are flood and drought. Millions of people have been affected by these natural disasters which have put to waste millions of hectares of paddy rice lands on which depend the lifeblood of the rural economy as well as that of the whole country. Given the dire consequences posed by drought to the Cambodian economy, and in light of its short- and long-term development plans aimed at poverty reduction, the government has affirmed its priority for agricultural development. Targeting the most vulnerable areas, this study aims to estimate the costs of drought in two communes in the rural Kampong Speu province, and to assess the costs and benefits of rehabilitating an unused water reservoir. The costs of drought are estimated at the household level. Household questionnaires were used to collect data from households from two rice ecosystems (totally rainfed and supplementary-irrigated) in the Kampong Speu. The study finds that the expected loss from drought for farmers in rainfed areas is USD 51.47 per hectare while that for farmers in supplementary-irrigated areas is USD 23.01 per hectare. Looking at the prospects for rehabilitating a totally damaged reservoir, the study reports that at a 6 percent discount rate, the repair efforts will yield a net present value of around USD 914,834.94 and the benefit-cost ratio is 2.18. The rehabilitated reservoir is seen to serve two significant roles, namely: (1) to stabilize and increase rice production since drought susceptibility among farmers is reduced and food security is ensured and (2) to encourage agricultural diversification.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".