Effects of regulated deficit irrigation on grain yield and water use efficiency of spring wheat in an arid environment
Bibliographic record
Abstract
Grain yield and water use efficiency (WUE) of spring wheat ( Triticum aestivum L.) in arid environments can be improved by applying irrigation selectively to allow soil water deficits to develop at non-critical stages of crop development. Field experiments were conducted on a loam soil in Zhangye district, northwest China in 2003 and 2004 to determine the grain yield, yield components, and water use characteristics of spring wheat in response to regulated deficit irrigation (RDI) schemes. Wheat grown under the RDI schemes produced 17% (in 2004) and 29% (in 2003) higher grain yield than wheat grown under water deficit-free control (5.6 t ha-1 in 2003 and 6.2 t ha-1 in 2004). Among six RDI schemes studied, wheat having a high water deficit at the jointing stage, but free from water def icit from booting to grain-filling produced highest grain yield in both 2003 (7.95 t ha-1) and 2004 (7.26 t ha-1). Compared with the control, wheat plants grown under the RDI schemes received 59 mm (or 15%) less water via irrigation, but they either extrac ted 41 mm more (or 74%) water from the soil profile (in 2003) or lowered (16%) evapotranspiration (ET) (in 2004). Grain yield increased as ET increased from 415 to 460 mm, and declined beyond 460 mm. The WUE values varied from 0.0116 to 0.0168 t ha-1 mm-1, and wheat grown under the RDI had 26% greater WUE compared with the control. Grain yield and WUE of spring wheat can be greatly improved by regulated deficit irrigation with reduced amounts of water. This practice is particularly valuable in arid regions where wheat production relies heavily on irrigation. Key words:
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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 | 0.000 |
| 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".