Effect of split application of fertilizer nitrogen on N<sub>2</sub>O emissions from potatoes
Bibliographic record
Abstract
The timing of fertilizer nitrogen (N) application influences the availability of NO3− as a substrate for denitrification. This study examined the effect of split application of fertilizer N on N2O emissions and denitrification rate in potato (Solanum tuberosumL.) production over 2 yr. Three treatments were used: 0 or 200 kg N ha-1 at planting, and 120 kg N ha-1 at planting plus 80 kg N ha-1 at final hilling. Fertilizer N application increased cumulative N2O emissions. Split fertilizer N application decreased cumulative N2O emissions in 2003, but not in 2002, compared with all fertilizer N applied at planting. A greater proportion of N2O emissions occurred between planting and hilling in 2003 (67%) compared with 2002 (17%). In 2003, the higher emissions during this period resulted from the coincidence of high soil NO3− availability and increased rainfall resulting in reduced aeration. Split N application was effective in reducing N2O emissions by minimizing the supply of NO3− when demand for terminal electron acceptors was high. N2O emissions were higher in the potato hill relative to the furrow; however, denitrification rate was higher in the furrow. Nitrate intensity (NI) expresses the exposure of the soil microbial population to NO3− and was calculated as the summation of daily soil nitrate concentration over the monitoring period. Cumulative N2O emissions were positively related to NI across year, N fertility treatment and row location. Denitrification was not related to NI, reflecting the primary role of NO3− in influencing the N2O:N2 ratio of denitrification rather than the magnitude of the overall process. Split N application was an effective strategy for reducing N2O emissions in years where there was significant rainfall during the period between planting and hilling. Key words: Denitrification, nitrous oxide, aeration, nitrate intensity, split N application, terminal electron acceptor, Solanum tuberosum
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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".