Effects of fertilizer rate on yield‐scaled nitrous oxide emissions from two soil types
Bibliographic record
Abstract
Abstract Synthetic N fertilizer application has increased crop yields to meet the growing food demand, but it has also contributed to greater N 2 O emissions from cultivated fields. Best management practices, including the 4Rs (right source, right rate, right time, and right place) of nutrient management, have been proposed to mitigate these emissions; however, there have been inconsistent results regarding the impact of soil texture on yield‐scaled N 2 O emissions. To clarify this issue, a field study was undertaken to evaluate the influence of three nitrogen fertilization rates (140, 180, and 220 kg N ha −1 ) on N 2 O emissions and grain corn ( Zea mays L.) yield from sandy loam and silty clay soil field sites situated in southwestern Quebec, Canada. Crop nitrogen uptake and yields were greater on the sandy loam than on the silty clay. Grain yields increased with N fertilization rate. Cumulative N 2 O emissions from the sandy loam soil were up to threefold greater than those from the silty clay soil due to soil and weather conditions during fertilizer application. No significant differences were found in the N 2 O fluxes among the N rate treatments in either soil. Assessing results from five other studies, we found that under corn production, overall yield‐scaled emissions from poorly drained soils were fivefold greater than well‐drained (coarse‐ and medium‐textured) soils. However, yield‐scaled emissions vary more widely in poorly drained soils, showing both lower and higher values than in well‐drained soils. These results demonstrate the need to consider soil textural differences and the impacts of climate variability on emissions when recommending fertilizer rates to reduce N 2 O emissions.
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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 | 0.001 |
| 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) | 0.001 | 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".