Water-related limits to growth for agriculture in Iran
Bibliographic record
Abstract
Globally, agriculture is the primary water consumption sector. This study used water footprint ( W F ) as a bottom-up tool and satellite imagery as a top-down tool to estimate the internal water use ( W U ) in the agricultural sector in an innovative way to show the effects of water-intensive use in agriculture in an arid country. The W F of Iran has been quantified for 19 main crops and for related agricultural products exported from Iran to partner countries. Using a bottom-up approach, Iran's total yearly agriculture net water consumption is estimated to be 42.43 billion cubic meters (BCM) per year. Out of 42.43 BCM total net internal water use, only 1.61 BCM is virtual-water export related to these 19 products, and the remaining 40.82 BCM is for internal use. Our results using satellite imagery show that in case of using all possible lands for agriculture, it would require 77.4 BCM. However, not all these lands are within human reach, and the maximum available water is way lower than this amount. Using satellite imagery, the total evaporation from agricultural lands shows 55.27 BCM for 2020, which agrees with national reports during 2005–2014. This study shows that agricultural water consumption tends to use internal water resources at a maximum level for export and national use, significantly impacting renewable and non-renewable water resource availability, especially in groundwater.
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 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".