Influence of Supplemental Irrigation and Applied Nitrogen on Wheat Water Productivity and Yields
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.001 |
| 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 it