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−1and 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 m3 Mg−1, with an average of 695 m3 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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".