Estimating the Tipping Point between N2O Emissions and C Sequestration in Soil using the DNDC v. CAN Model
Bibliographic record
Abstract
Increasing the grassland proportion in the crop rotation has been considered as an effective approach to sequester carbon (C) in the soil. However, its climate mitigation benefits may be overestimated because the associated impact of long-term C sequestration on nitrous oxide (N2O) emissions remains uncertain. Mechanistic models, such as the DeNitrification and DeComposition model (DNDC v. CAN 9.5.0), are used to simulate changes in soil organic carbon (SOC) and N2O emissions. This provides the opportunity to estimate future emission trends and to enhance our understanding of the interactions between SOC and N2O emissions under different levels of grass/clover proportion in arable crop rotations. We hypothesize that increases in N2O emissions will offset the benefits from the increased SOC over time. The objectives of this study are to (1) calibrate and validate the DNDC model, and (2) estimate and predict the potential tipping point at which the negative climate forcing of N2O emissions offsets the benefits of C sequestration over long-term timescales. For this, we used long-term measurements of biomass, SOC, and N2O emissions from two crop rotations with either two or four years of grass-clover in a six-year rotation in Denmark. Preliminary results showed that the DNDC model simulated crop biomass production with fair to high accuracy as indicated by an index of agreement (d) of 0.98, a Nash-Sutcliffe efficiency (NSE) of 1, and a normalized root mean square error (nRMSE) of less than 30%. The simulated biomass was slightly underestimated as shown by a negative mean bias error (MBE). Conversely, the simulations for N2O fluxes and SOC exhibited poorer agreement, with d-values below 0.7 and nRMSE exceeding 30%. These findings suggest that while the DNDC model effectively predicts crop growth, including annual crops and grass/clover ley, its ability to simulate SOC and N2O fluxes requires substantial improvement. Our future efforts will focus on refining and optimizing model parameters for SOC and N2O, with an emphasis on calibration to enhance the model performance and the capacity to predict management-induced long-term dynamics under future climate scenarios. Results of these updated model simulations will be shown at the conference.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".