Assessment of winter wheat (<i>Triticum aestivum</i> L.) grown under alternate furrow irrigation in northern China: Grain yield and water use efficiency
Bibliographic record
Abstract
Jia, D.-Y., Dai, X.-L., Men, H.-W. and He, M.-R. 2014. Assessment of winter wheat (Triticum aestivum L.) grown under alternate furrow irrigation in northern China: Grain yield and water use efficiency. Can. J. Plant Sci. 94: 349–359. Increasing water use efficiency (WUE) can improve agricultural production in the north of China, where there is little or no prospect for the expansion of water resources. A field experiment was carried out to investigate the effects of alternate furrow irrigation (AFI) on the physiological response, grain yield, and WUE of winter wheat (Triticum aestivum L.) over two successive growing seasons (2009/2010 and 2010/2011). The irrigation regimes were: W0, non-irrigated; W2, every furrow was irrigated at jointing and anthesis; W3, every furrow was irrigated before wintering and at jointing and grain filling; and AFI, where one of the two neighboring furrows was alternately irrigated before wintering and at grain filling, and every furrow was irrigated during jointing. Our results indicate that the rate of plant transpiration and soil evaporation during grain filling were lower with AFI than when using W3. A reduced biological yield and increased harvest index were achieved under AFI compared with treatment W3. No difference in grain yield was observed between AFI and W3. The photosynthetic WUE, irrigation WUE, and WUE were all higher with AFI than with W3. Therefore, AFI is suggested as an appropriate irrigation schedule that achieves acceptable grain yields and allows for reductions in irrigation water consumption.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 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".