Accumulation of nitrate N in the soil profile and its implications for the environment under dryland agriculture in northern China: A review
Bibliographic record
Abstract
Nitrate (NO 3 - ) leaching and water contamination have become a worldwide concern. In this review, some examples are presented to show the extent and magnitude of NO 3 - accumulation in the soil profiles and its potential effects on contamination of ground water and surface water under dryland farming in northern China. Climatic and management factors affecting NO 3 - leaching are also discussed. In northern China, rainfall is relatively sparse, but the high intensity of precipitation and porous soils play an important role in the accumulation of NO 3 N in soil and its subsequent leaching in the soil profile. There is a risk of nitrate accumulation and leaching when high rates of fertilizer N are applied to improve crop yields, and it becomes even worse when conventional land use is changed from cereal crops to vegetable crops and fruit orchards. Under such conditions, shallow ground water might be polluted by NO 3 - . This suggests that more attention should be paid to prevent this problem by using best management practices, especially by controlling the amount of N fertilizer input, balanced fertilization, split N application, inclusion of crops with deep taproots in the rotation and minimizing summer fallow (especially tilled) frequency. Key words: Accumulation, contamination, dryland farming, ground water, leaching, nitrate, northern China
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 teacher head, 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".