Nitrous oxide emissions and yields from potato production systems as influenced by nitrogen fertilization and irrigation: A meta‐analysis
Bibliographic record
Abstract
Abstract Potato (Solanum tuberosom) is a globally significant crop in relation to the scale of its consumption, being the third most consumed worldwide. The overall sustainability of global agriculture is increasingly of concern, specifically in relation to increasing anthropogenic emissions of the greenhouse gas nitrous oxide (N2O) emissions from nitrogen fertilizer additions to croplands and its contribution to climate change. Against this backdrop, a meta‐analysis of 119 experimental comparisons from 18 studies—spanning 19 study sites in 10 countries—was employed to investigate the impact of irrigation, cumulative water input, N fertilizer application rate, soil pH, and soil texture on cumulative N2O fluxes and tuber yield in potato production. Compared to non‐fertilized controls, N2O emissions from fertilized potato production decreased by 34% when irrigation provided 61%–90% of total water input (corresponding to averages of 321–473 mm). Likewise, N2O emissions increased by 53% with 200–475 mm seasonal water input and by 37% with N fertilization rates of 101–200 kg N fertilizer ha−1. Furthermore, soil pH between 7.1 and 7.5 reduced emissions by 6%, while medium‐textured soils showed an increase of 2%. Conversely, tuber yields from fertilized potato production were comparatively maximized under 31%–60% of water input as irrigation (7%) and 751–1025 mm cumulative seasonal water input (28%). Alongside 201–300 kg N fertilizer ha−1 (97%), soil pH of 7.1–7.5 (48%), and in coarse‐textured soils (49%). Overall, these findings underscore the importance of considering irrigation and N fertilization options specifically in optimizing potato production for reduced N2O emissions and enhanced tuber yield.
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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 | 0.011 | 0.013 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.049 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".