Risk and Economic Analysis of Greenhouse Cucumber and Tomato Cropping Systems in Oman
Bibliographic record
Abstract
The agriculture investment decision affected by risk of capital and operation cost, yield and sale price of planted crops. This study examined risk of investment in green-house cucumber and tomato production and optimum mix of crop pattern at Al Batinah, Al Sharqiya Regions of Oman. The net present value with Monte Carlo simulation models are used to test risk efficiency and project viability. The result indicated that investment in two green-houses and growing one tomato crop and two cucumber crops (Tom1Cuc2) per year is more profitable and risk aversion. Stochastic Efficiency with Respect to a Function (SERF) performed and confirmed that (Tom1Cuc2) is the most risk efficient cropping system and got a positive NPV with 62% probability followed by growing tomato crop in two seasons with a positive NPV with probability of 58%. The study concluded tomato and cucumber producers are faced with different production and financial situations and their risk preferences play an important role in determining their production decisions. Risk premium analysis shows that greenhouse tomato growers need to be paid up to RO 2 847 to keep growing tomato instead of (Tom1Cuc2) cropping system. Greenhouse cucumber growers can sacrifices of RO 5 373 to justify not to switch from planting cucumber to grow (Tom1Cuc2) cropping system. Government subsidy should be given to farmers to construct new greenhouses to maximize their resource use efficiency, benefit from extended cropping season, protect their crops from adverse environmental conditions and increase food security.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 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".