Optimal Nitrogen Management Enhanced External Chemical Nitrogen Fertilizer Recovery and Minimized Losses in Soil-Tomato System
Bibliographic record
Abstract
Excess chemical nitrogen (N) fertilization is widespread in intensive greenhouse vegetable production in China and has resulted in low recovery efficiency and high losses of chemical N fertilizer. Understanding the fate of chemical N fertilizer is crucial for best management of chemical N fertilizer. Using the technique of stable isotope 15N-labeled urea, a micro-plot experiment was conducted to estimate the recovery of 15N-labeled urea in tomato, residues in soil and losses in soil-tomato system. The treatments included the conventional N management with chemical N rate (1000 kg N/ha), named FP and optimal N management with chemical N rate (500 kg N/ha), combined with maize straw and drip irrigation, named OPT. Compared with the FP, total dry matter yield increased by 6.5%–9.3% for the OPT in the autumn-winter season (AW) and winter-spring seasons (WS), respectively. There was a significantly higher recovery efficiency (20.7%) of 15N-labeled urea in the OPT compared to the FP (11.3%; P < 0.05). The amount of residual NO3--N derived from 15N-labeled urea was significantly higher in the FP than in the OPT (P < 0.05). More inorganic N derived from 15N-labeled urea was incorporated into the stable fraction of organic matter in the OPT and had a positive effect on reducing the N leaching with increased time during the season. The loss rate of N derived from 15N-labeled urea was 46.8% in the FP, 25.8% greater than in the OPT. Optimal N management improved tomato yields, enhanced chemical N recovery efficiency, while minimizing losses in the soil-tomato system. It will be practical for maintaining the sustainability of greenhouse-based intensive vegetable systems.
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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".