To what extend can soil moisture and soil contamination stresses affect greenhouse gas emissions? An attempt to calibrate a nitrification/denitrification model
Bibliographic record
Abstract
Abstract. Continental biogeochemical models are commonly used to prospect the effect of land use, exogenous organic matter input or climate change on soil greenhouse gas emission. However, they can still not be used to investigate the effect of soil contamination while it is known to affect several soil processes and to concern a large fraction of land surface. We implemented a commonly used model estimating soil nitrogen (N) emission, the DeNitrification DeCompostion (DNDC) model, with a function taking into account soil copper (Cu) contamination in nitrate production modulation. Then, we aimed at using this model to predict N-N2O, N-NO2 NOx and N-NH4 emissions in the presence of contamination and in the context of changes in precipitations. For that, incubations of soils were performed at different soil moistures in order to mimic expected rainfall patterns during the next decades and in particular drought and excess of water. The effect of this double stress on soil nitrate production was studied using a bio-assay. Then, data of nitrate production obtained under each moisture treatment were used to parameterize the DNDC model and estimate soil N emission considering the various effect of Cu. Whatever the moisture preincubation, experimental results showed a N-NO3 decreasing production when Cu was added but with different sharpness depending on soil moisture. The DNDC-Cu version we proposed was able to reproduce these observed Cu effects on soil nitrate concentration with r2 > 0.99 and RMSE < 10 % for all treatments in the DNDC-Cu calibration range (> 40 % of the water holding capacity) but showed poor performances for the dry treatments. We modelled a Cu-effect inducing an increase in N-NH4 soil concentration and emissions due to a reduced nitrification activity, and therefore a decrease in N-NO3, N-N2O and N-NOx concentrations and emissions. The effect of added Cu was larger on N-N2 and N-N2O emissions than on the other N species and larger for the soils incubated under constant than variable moisture.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".