Comparison of DayCent and DNDC Models: Case Studies Using Data from Long‐Term Experiments on the Canadian Prairies
Bibliographic record
Abstract
Two prominent process-based models, DNDC (i.e., Denitrification–Decomposition) and DayCent, were investigated for their ability to capture the inter-annual variability in spring wheat grain yields, soil carbon change, and trace gas emissions for three long-term experiments conducted on the Canadian prairies. The cropping systems included continuous spring wheat (Triticum aestivum L.) receiving N and P fertilizer [Cont-W(N+P)], continuous wheat receiving P [Cont-W(+P)], and fallow–wheat receiving N and P [F-W(N+P)]. These studies were conducted on three distinct soils: a Brown Chernozen (silt loam), a Dark Brown Chernozem (sandy clay loam) and a thin Black Chernozem (heavy clay). Both models were effective in estimating variations in grain yield for the Cont-W(N+P) rotation, particularly for drier to normal periods, with coefficients of determination (R 2) ranging from 0.56 to 0.73 and 0.51to 0.70 for DayCent and DNDC, respectively. When crop N requirements were not met fully by fertilizer inputs [i.e., rotations Cont-W(+P) and F-W(N+P)], the models demonstrated decreased accuracy in predicting yields, which suggests deficiencies in the estimated N balance. Crop water use was generally well estimated for both models (AREs < 6% and < 15% for DayCent and DNDC) considering the marked differences in each models conceptualization of the soil profile and water movement. Nitrous oxide estimates by both models compared well with measurements and captured the relative differences between fertilized and unfertilized N systems. The utilization of this long-term dataset proved to be highly informative in assessing each model's characterization of C and N dynamics while highlighting further development needs.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".