N<sub>2</sub>O emissions from spring barley production as influenced by fertilizer nitrogen rate
Bibliographic record
Abstract
Usage of mineral nitrogen (N) fertilizers for agricultural crop production systems is a major contributor to anthropogenic nitrous oxide (N2O) emissions. As part of a national study to quantify N2O emissions under different cropping systems and in different eco-regions, this study quantified the effect of fertilizer N rate on spring barley (Hordeum vulgare L.) on N2O emissions in 3 yr in a cool maritime climate with humid soil moisture regimes. Treatments were 0, 75 and 150 kg N ha-1 as ammonium nitrate applied as a pre-plant broadcast. N2O emissions were increased by fertilizer N application in each year. In 2003 and 2005, elevated N2O emissions occurred in the 6-wk period following fertilizer application when soil NO3-N concentrations were high. However, in 2004 and 2005, peak N2O emissions occurred near crop harvest. Elevated N2O emissions at this time were attributed to increased carbon availability due to re-wetting of dry soil. Therefore, the effect of fertilizer N management on N2O emissions may not necessarily occur immediately after treatment application. This emphasizes the importance of measuring N2O emissions outside of the crop growth period. Fertilizer-induced cumulative N2O emissions averaged 0.011 and 0.021 kg N kg-1 N when fertilizer N rate was increased from 0 to 75 kg N ha-1 and from 75 to 150 kg N ha-1, respectively, indicating increased N2O emissions when fertilizer is applied at above optimal rates. N2O emissions increased linearly with nitrate intensity, the summation of daily NO3-N concentrations for 0- to 15-cm depth. This suggests that the non-linearity in the relationship between fertilizer N rate and N2O emissions can be explained by the decreasing efficiency in crop NO3-N uptake at high fertilizer N rates. Key words: Hordeum vulgare, soil nitrate, denitrification, carbon availability
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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".