Nitrogen leaching and grey water footprint affected by nitrogen fertilization rate in maize production: a case study of Southwest China
Bibliographic record
Abstract
Abstract BACKGROUND Effective nitrogen (N) management measures are required to control environmental problems caused by N fertilizer use in intensive maize production systems. Soil N losses associated with high precipitation and over‐fertilization in maize production can cause substantial environmental problems, whereas there is a lack of quantitative data and effective study countermeasures. A 2‐year field study was conducted in the subtropical maize production system in Southwest China to quantify N leaching under varying N application rates of 0, 90, 180, 270 and 360 kg N ha −1 yr −1 . RESULTS The results indicated that N leaching accounted for 16–38% of N fertilizer input. For farmer practice treatment (360 kg N ha −1 yr −1 ), N leaching loss was high at 110 kg N ha −1 yr −1 and accounted for 31% of the N applied. As an indicator of the ambient water quality pollution, the grey water footprint across all treatments ranged from 376 to 1092 m 3 Mg −1 , with an average of 695 m 3 Mg −1 . Reducing N rate to agronomically optimized treatment (180 kg N ha −1 yr −1 ) significantly decreased N leaching by 77%, and maintained high grain yield of 8.1 Mg ha −1 . The grey water footprint was reduced by 52–63% with N rates from 270 or 360 kg N ha −1 yr −1 to 180 kg N ha −1 yr −1 . CONCLUSION Nitrogen surplus (applied N rate minus N uptake by maize) resulted in higher soil residual nitrate concentration and consequently high N leaching. High precipitation and low soil pH were the main ecological factors leading to high N leaching. © 2021 Society of Chemical Industry.
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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 | 0.000 |
| 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 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".