Estimation of Irrigation Water Demand for Barley in Iran: The panel Data Evidence
Bibliographic record
Abstract
In most arid and semi arid regions, as in most parts of Iran, insufficient supply of water has become one of themost important constraints to economic development. In these areas, the main issue in water management is tofulfill the ever-increasing demand for water. The supply of water is usually limited, while the quantity of waterdemanded has increased mainly due to population growth. It is believed that a rationalized water pricing systemwould play a crucial role in the optimal allocation of water resources. Planning for efficient use of water isimportant when there is a severe limit to its availability. The demand elasticity for every good, service, or inputdetermines how a change in price, ceteris paribus, affects users’ quantity demanded. This study investigates thestructure of irrigation water demand by estimating the derived demand for water on one particular crop, barley,in Iran. The analysis is based on deductive econometric method, and on total statistical and panel data. A demandfunction was estimated after performing the relevant statistical tests. The price elasticity of irrigation waterdemand and other elasticities were also computed. Data and information from 2001 to 2006 from 26 provinces inIran was collected from secondary sources.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".