Using DayCENT to Simulate Carbon Dynamics in Conventional and No‐Till Agriculture
Bibliographic record
Abstract
The DayCENT model was employed to simulate the effects of conventional tillage (CT) and no‐till (NT) practices on the dynamics of soil organic carbon (SOC) over 9 yr in a rotational cropping system in Southern Ontario, Canada. Observations of site properties and eddy covariance measurements were used to assess crop productivity, net ecosystem productivity (NEP), and SOC changes. The validated model captured the dynamics of grain yield and net primary production, which indicated that DayCENT can be used to simulate crop productivity for evaluating the effects of tillage on crop residues and heterotrophic respiration ( R h ) dynamics. The simulation suggested that CT enhanced the annual R h relative to NT by 38.4, 93.7 and 64.2 g C m −2 yr −1 for corn ( Zea mays L.), soybean [ Glycine max (L.) Merr], and winter wheat ( Triticum aestivum L. ), respectively. The combined effect of incorporating crop residues and increased cultivation factors enhanced R h in CT by 35% relative to NT after disk cultivation in the spring. The simulated NEP varied with crop species, tillage practices, and timing/length of the growing season. The seasonal variation of the total SOC pool was greater in CT than NT because of tillage effects on C transfer from the active surface SOC pool to the active soil SOC pool at a rate of 50 to 100 g C m −2 yr −1 . The NT method practiced during the study period accounted for a 10.7 g C m −2 yr −1 increase in the slow SOC pool. The validated DayCENT model may be applied for longer‐term simulations in similar ecosystems for a variety of climate change experiment.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".