Influence of Supplemental Irrigation and Applied Nitrogen on Wheat Water Productivity and Yields
Bibliographic record
Abstract
A field experiment was conducted for three growing seasons to study the effects of seasonal water use and applied N fertilizer on yield attributes and water productivity indices of wheat in an arid region of Iran. The results revealed that yield attributes were significantly affected by irrigation and nitrogen treatments and year, and their interactions. Crop height, maximum leaf area index and biological yields were increasingly affected by the available water and N fertilizer. The findings indicated that the grain yield response to N was associated with water application levels. The water productivity indices were influenced by irrigation strategies and deficit irrigation effectively boosted productivity of irrigation water (WI). The highest WI was obtained at a seasonal irrigation water of 156 mm for different levels of applied nitrogen. For levels of applied N1 (application 70% of the required nitrogen), N2 (required nitrogen), and N3 (application 120% of the required nitrogen), WI ranged between 0.93 and 2.28, 1.30 and 2.75, and 0.98 and 2.47 kg m-3, respectively. The data generated here suggest that under deficit irrigation, maximum water productivity (WET) would be achieved when 98 kg N ha?1 is combined with a 156 mm of supplemental irrigation. In this seasonal water use, WET value may be increased to 30% with N appropriate practice (practice N2). Consequently, when limited irrigation water is combined with N fertilizer appropriate management, wheat water productivity can be substantially and consistently increased in the region.
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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 | 0.000 | 0.000 |
| 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".