Soil fertility and fertilization practices affect accumulation and leaching risk of reactive N in greenhouse vegetable soils
Bibliographic record
Abstract
Elevated soil fertility levels induced by continuous chemical fertilizer and (or) manure application may affect N loss potential and redistribution within soil-crop system. A 49-d packed soil column experiment with a factorial design of three soil fertility levels and four fertilization treatments was conducted to evaluate the effects of soil fertility and fertilization treatments on the accumulation and leaching risk of reactive N. The results showed that the 49-d cumulative leaching loss of total reactive N ranged from 176.3 to 499.0 kg N ha−1. The cumulative leaching losses of total dissolved nitrogen (TDN) and dissolved organic nitrogen (DON) in NPK treatment were significantly higher than those in other three treatments in fertility level I and II soils. The cumulative leaching loss of NO3−-N was significantly greater in NPK + M or NPK treatment than in CK treatment in fertility level I or III soils, and it was remarkably greater in M treatment than in other three treatments in fertility level II soil. In total, 64.0% of TDN in soil leachate existed in the form of DON, and 35.1% was nitrate-N among different soil fertility and fertilization treatments. These results indicated that DON was an important component of N leaching loss and could not be neglected in sustainable development of vegetable greenhouse soil.
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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".