Dates Palm Farming Systems Sustainability and Risk Efficiency in Oman
Bibliographic record
Abstract
Date Palm is the most important cultivar in Oman and occupies 35% of total cultivated area and 78% of the total fruit trees area. The Ministry of Agriculture and Fisheries attempted to improve Date production but due to climate change and environmental constrains agricultural production has shown instability in production and generated a desire to build a sustainable farming systems. The study used stochastic model to analyze Date Palm Farming Systems in Oman and identify the most sustainable and risk efficient one. Stochastic Efficiency with Respect to Function (SERF) technique use certainty equivalent concept to rank a set of risk efficient alternatives. The study found Batinah Region is the most risk efficient region and got a positive NPV with a probability of 88% followed by Dakhiliyah Region 77% and Dahirah Region 67%. The study also indicates Batinah and Dakhiliyah Regions which cultivate 52% of total Date Palm area in Oman is located in risk efficient farming system areas. Risk premium analysis performed and shows Dakhiliyah Region farmers can pay up to RO 59 for replanting Date Palm tree and move to Batinah Region farming practices with less risky farming system. The farmer in Dahirah Region is willing to pay RO 144 per Date Palm tree to shift to more efficient farming system practices such as Batinah Region. Date Palm Farming System analysis should be used as a basis and foundation for replanting program and resources and environmental constrains need be considered. A right economic incentives to be given to encourage production of selected Date Palm varieties for each region to increase ecosystem resilience and economic benefit.
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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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; 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".