Cultivating Sustainable Green Belts with ADW and RWH in Iraq's Arid Zones
Bibliographic record
Abstract
Climate changes and water scarcity are forcing arid and semi-arid countries to search for non-conventional alternatives of renewable water resources such as Agriculture Drainage Water (ADW) and Rainwater Harvesting (RWH).Bio-saline Agriculture (that defined as the production and growth of plants irrigated by saline waterin water scarce location) is introduced to achieve food security.The phenomenon of dust storms is commonly seen in arid zones that is affected by climate changes.Protection of these areas requires the establishment of windbreaks and sustainable green belts to reduce wind speed and soil erosion.This research aims to study the area that can be planted by orchards of palm and olives using ADW and RWH around Main Outflow Drain (MOD) in Iraq.Two alternatives are proposed according to the possibility of using the rainwater-harvesting technique in order to expand the irrigated areas; to reduce the quantities of saline water in irrigation and reclamations the soil from the excess quantities of salinity.It was found using only 30% of MOD saline water achieves the cultivation of a net green belt width of palm and olive of 9.74 km on both sides of MOD of 526 km length from north of Baghdad to the Basra city.The accumulated salinity at steady state condition of using ADW was estimated according to WATSUIT model is within the range of orchards and high tolerant winter crops like barley.This research demonstrates a viable strategy for mitigating soil erosion and dust storms in arid regions, offering a model for sustainable agricultural practices in the face of climate change.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".