Combining optimized irrigation with reduced N fertilization increases wheat N use efficiency by increasing soil N cycling and plant N uptake
Bibliographic record
Abstract
With the aim of maximizing nitrogen use efficiency (NUE) of wheat in the North China Plain by optimizing irrigation and nitrogen application, a field experiment with a split-plot design was conducted. The main plots were subjected to three irrigation levels: bringing soil water content in the 0–40 cm profile to 65% (I1), 75% (I2) and 85% (I3) of field water capacity. The subplots were subjected to three nitrogen application rates: 150 (N150), 210 (N210) and 270 (N270) kg N ha −1 . Compared with the N270, N210 treatment enhanced grain yield, NUE, and net income by 4.5%, 6.2%, and 5.8%, respectively (two-year averages). Additionally, it reduced soil nitrate reductase activity, the abundance of denitrification-related bacteria, and loss rate of fertilizer nitrogen by 12.9%, 53.3%, and 16.3%, respectively. Compared with the N150, N210 treatment increased grain yield, grain nitrogen accumulation, and net income by 15.9%, 14.2%, and 26.3%. Relative to I1 and I3, I2 treatment increased root length density in the 20–60 cm soil layer, uptake rate of fertilizer nitrogen, grain yield, and net income. Overall, the combination of irrigation to 75% of field capacity with nitrogen application at 210 kg N ha −1 increased wheat’s capacity for nitrogen uptake and remobilization and thereby grain nitrogen accumulation, and increased NUE by reducing nitrogen loss rate.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".