Nitrous oxide emissions and productivity of irrigated potato: Effects of nitrogen fertilization options
Bibliographic record
Abstract
Abstract Improved N management is needed in intensive agriculture to mitigate nitrous oxide (N2O) emissions while sustaining high yields. We assessed the effectiveness of polymer‐coated urea (PCU); nitrification inhibitor 2,4‐dimethylpyrazol succinic acid (DMPSA), a biostimulant; and their combinations with granular urea and ammonium sulfate nitrate (ASN) fertilizers to reduce N2O emissions and to improve potato (Solanum tuberosum L.) productivity under irrigation. Sites were located in Lethbridge and Brooks, Alberta, Canada over two growing seasons. Tuber yield, grade, specific gravity, and N uptake were quantified. We used the chamber method to measure N2O fluxes from potato hills and furrows. The N2O emissions from furrow positions were at least two‐fold greater than those from hills at the Lethbridge site. Peak N2O emissions and increased N concentrations in potato petiole and soils occurred shortly after fertilizer applications. The overall average emission factor (EF) of N2O was 0.056% kg N2O–N kg−1 N fertilizer (accounting for emissions from unfertilized controls). Urea alone commonly exhibited the highest N2O fluxes. Admixing DMPSA with either urea or ASN lowered N2O emissions in only certain cases. For instance, in one growing season at the Brooks site, adding DMPSA to urea reduced the N2O emissions by 57%. Likewise, in one of the four site‐years in the study, 36% higher potato marketable yields were obtained when applying either ASN treated with DMPSA or PCU compared with the unfertilized controls (45 vs. 33 Mg ha−1). Results showed that under specific conditions, N application strategies using DMPSA admixed with either urea or ASN can maintain high potato yields while reducing N2O emissions relative to soils receiving these fertilizers without this additive.
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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".