Cultivar and soil effects on growing season and production-scaled N<sub>2</sub>O emissions from Prairie Potato systems
Bibliographic record
Abstract
Agricultural soils are a significant source of nitrous oxide (N2O), a potent greenhouse gas. Potatoes, with their low nitrogen use efficiency (NUE) and high nitrogen (N) demand, present a high risk of N2O emissions. Investigating factors influencing N2O production, including crop cultivar and soil environment, is needed to reduce emissions from Prairie potato production. This 2-year study in Saskatchewan monitored N2O emissions from three potato cultivars (Clearwater Russet, Dark Red Norland, and Sangre) on two sites (clay loam and sandy loam), and evaluated the effect of soil micro-topography by measuring emissions from hill and furrow positions. Daily N2O flux, cumulative growing season emissions, yield-scaled, and tuber N-scaled emissions were quantified. Cumulative growing season emissions ranged from 164 to 848 g N2O N ha−1, aligning with other Prairie research but lower than more humid regions, likely due to lower precipitation on the Prairies. Although cultivars produced similar cumulative emissions, production-based analyses revealed Dark Red Norland and Sangre had 35%–67% lower yield-scaled N2O than Clearwater Russet. Emissions were higher from hills than furrows at three of four site-years, suggesting variability in N2O production processes, likely related to substrate availability and moisture. This study highlights the complexity of soil and environmental interactions—encompassing differences in soil micro-topography, growing seasons, and soil characteristics—in driving N2O emissions. Significant negative relationships were found between NUE (tuber NUpE) and N2O emissions, demonstrating that selecting cultivars for agronomic performance (high yield and high NUE) may be a practical strategy to reduce production-based N2O emissions from potato production.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".