Soil Properties Affecting Rainfall Water Use Efficiency (RWUE) in Wheat Dry-Farming Lands, NW Iran
Bibliographic record
Abstract
Effective use of rainfall water is a key issue in agricultural development in the arid and semi-arid regions since rainfall water is a precondition for crop production there. This study was conducted in a semi-arid agricultural region with 900 km2 in area in Hashtroud, northwest of Iran to determine the relationship between rainwater use efficiency (RWUE) and soil properties. Winter wheat yield and soil properties were determined at 108 plots (40.41 m2 in area) installed in thirty six dry-farming lands. RWUE of each plot obtained from the ratio of crop dry matter per unit of abstracted rainfall water volume (ARWV). ARWV was computed from deduction of the rainfall and runoff volume during a two-growth period. Runoff data for each land was obtained from field measurements at the plots under natural rainfalls. Analysis of rainfalls uniformity using four rain gauge stations data showed that spatial distributions of rainfalls were homogeneous in the area. The RWUE values in the lands were ranged from 0.35 kg m-3 to 1.49 kg m-3 with an average of 0.84 kg m-3. Soil properties which considerably affected either the infiltration capacity or the available water controlled the RWUE in the study area. Multi-regression analysis indicated that the RWUE significantly related to silt, organic matter and lime (R2=0.82, p < 0.001). Maintaining crop residues and incorporating with the soil can be proper techniques and sustainable strategies to improve the soil properties and enhance the RWUE in the dry-farming lands.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".