Climate Overrides Effects of Fertilizer and Straw Management as Controls of Nitrous Oxide Emissions After Oilseed Rape Harvest
Bibliographic record
Abstract
Oilseed rape (Brassica napusL.) is an important bioenergy crop that contributes to the diversification of renewable energy supply and mitigation of fossil fuel CO2emissions. Typical oilseed rape crop management includes the use of nitrogen (N) fertilizer and the incorporation of oilseed rape straw into soil after harvest. However, both management options risk increasing soil emissions of nitrous oxide (N2O). The aim of this 2-years field experiment was to identify the regulating factors of N cycling with emphasis on N2O emissions during the post-harvest period. As well as the N2O emission rates, soil ammonia (NH4+) and nitrate (NO3−) contents, crop residue and seed yield were also measured. Treatments included variation of fertilizer (non-fertilized, 90 and 180 kg N ha−1) and residue management (straw remaining, straw removal). Measured N2O emission data showed large intra- and inter-annual variations ranging from 0.5 (No-fert + str) to 1.0 kg N2O-N ha−1(Fert-180 + str) in 2013 and from 4.1 (Fert-90 + str) to 7.3 kg N2O-N ha−1(No-fert + str) in 2014. Cumulative N2O emissions showed that straw incorporation led to no difference or slightly reduced N2O emissions compared with treatments with straw removal, while N fertilization has no effect on post-harvest N2O emissions. A process-based model, CoupModel, was used to explain the large annual variation of N2O after calibration with measured environmental data. Both modeled and measured data suggest that soil water-filled pore space and temperature were the key factors controlling post-harvest N2O emissions, even though the model seemed to show a higher N2O response to the N fertilizer levels than our measured data. We conclude that straw incorporation in oilseed rape cropping is environmentally beneficial for mitigating N2O losses. The revealed importance of climate in regulating the emissions implies the value of multi-year measurements. Future studies should focus on new management practices to mitigate detrimental effects caused by global warming, for example by using cover crops.
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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) | 0.001 | 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 | 0.001 |
| 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".