In Situ Nitrogen Mineralization, Nitrification, and Ammonia Volatilization in Maize Field Fertilized with Urea in Huanghuaihai Region of Northern China
Bibliographic record
Abstract
Nitrogen (N) fertilization potentially affects soil N mineralization and leaching, and can enhance NH3 volatilization, thus impacting crop production. A fertilizer experiment with five levels of N addition (0, 79, 147, 215 and 375 kg N ha(-1)) was performed in 2009 and 2010 in a maize field in Huanghuaihai region, China, where > 300 kg N ha(-1) has been routinely applied to soil during maize growth period of 120 days. Responses of net N mineralization, inorganic N flux (0-10 cm), NH3 volatilization, and maize yield to N fertilization were measured. During the growth period, net N mineralization and nitrification varied seasonally, with higher rates occurring in August and coinciding with the R1 stage of maize growth. Soil NO3(-)-N contributed to more than 60% of inorganic N flux during maize growth. Cumulative NH3 volatilization increased with N additions, with total NH3 volatilization during maize growth accounting for about 4% of added N. Relative to the control, mean maize yield in the fertilizer treatments increased by 17% and 20% in 2009 and 2010, respectively. However, grain yield, aboveground biomass, and plant N accumulation did not increase with added N at levels > 215 kg N ha(-1). These results suggest that the current N rate of 300 kg N ha(-1) is not only excessive, but also reduces fertilizer efficacy and may contribute to environmental problems such as global warming and eutrophication of ground water and streams.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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 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".