Evaluating an Investment Opportunity Under Risk and Uncertainty Environment: A Case Study About Profitability and Risk in Desert Farming at Najed Area
Bibliographic record
Abstract
<p>Fodder is continuously cultivated in coastal area of Salalah and Batinah region of Sultanate of Oman. However, this created a negative impact on the overall agriculture system and production. The government authority stopped the cultivation of Rhodes grass in coastal area and support farmers with incentive systems in order to increase fodder production investment at Najed area. Due to irrigation water policy regulations, new technical solutions required, underground water availability, fodder investors have little data to help in making investment decisions. In addition, fodder production investments are characterized by much uncertainty due to the nature of the desert farming which is relying on many factors that cannot be controlled. In this paper a dynamic evaluation model was formed and developed as a method of analyzing the economic feasibility of fodder cultivation investment project with regard to project profitability under risk environment. Economic feasibility of the investment is evaluated through calculation of the Net Present Value and IRR by using Monte Carlo Simulation models. Our objective is to formulate a dynamic programming simulation model for the investment decision with incorporating this uncertainty in a probabilistic manner. Our simulation model result shows that the dynamic models are able to help investors and policy makers in taking optimum investment decision. Taking the risk into account leads to a significant decrease of the economic attractiveness of stakeholders and more Government support is needed to achieve water policy objectives and project sustainability at new developed area at Najed.</p>
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.067 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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; both teacher heads agree on what is shown here.
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".