Higher soil nitrous oxide production in landscape depressions linked to soil and hydrological legacy effects
Bibliographic record
Abstract
Background and aims Eastern Denmark’s agricultural landscapes feature numerous topographic depressions that are frequently flooded during late winter and spring. These poorly drained, carbon- and nitrogen-rich depression soils receive eroded material from adjacent slopes. Fertilization and water saturation create N 2 O emission hotspots. However, the potential legacy effects of these topographic locations on microbial communities involved in N 2 O production and reduction remain unclear. One approach to mitigating high denitrification rates (as a source of N 2 O) is to alter microbial pathways by adding nonhazardous levels of copper. Methods We conducted an incubation study using upland and depression soils from the same site, incorporating varying Cu levels (0, 130, and 260 mM) and water levels (60% and 90% water holding capacity). Results Depression soils emitted eight times more N 2 O than upland soils at 90% WHC. Cu addition did not reduce cumulative N 2 O emissions but delayed or lowered the flux peak. Depression soils exhibited 3,000- and 4,000-fold higher 16S rRNA and nosZ clade I abundances, respectively, compared to upland soils. Cu addition significantly decreased 16S rRNA abundance, eliminated AOB amoA in upland soils, and slightly reduced the tested gene abundances in depression soils. The nosZ gene community structure differed significantly between the two soils. Conclusions Overall, our study suggests that erosional differentiation of soil properties, together with frequent waterlogging conditions, can result in distinct microbial communities, fostering legacy effects that lead to differences in N 2 O emissions between upland and depression soils. Adding Cu to these intensively managed soils is unlikely to be an effective strategy for mitigating N 2 O emission hotspots in arable fields.
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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".