The Potential of Switchgrass and Miscanthus to Enhance Soil Organic Carbon Sequestration—Predicted by DayCent Model
Bibliographic record
Abstract
Warm season perennial C4 grasses (WSGs), switchgrass (Panicum virgatum L.) and miscanthus species (Miscanthus spp.), have been reported to positively influence short-term (15–20 years) soil organic carbon (SOC). In this study, the DayCent model was used to predict changes in long-term SOC stocks under WSGs for moderate (Representative Concentration Pathway (RCP) 4.5) and high (RCP 8.5) warming climate change scenarios in southern Ontario, Canada, and to determine how long the enhanced SOC stock will last when WSGs are converted back to annual crop rotation. The model predicted that a consistent corn–corn–soybean–winter wheat (CCSW) rotation prevented SOC from depletion over the 21st century. Under WSGs, the model predicted high rates of SOC sequestration during the first 20–30 years which then tended to stabilize after 50–60 years. However, the rate of SOC sequestration over 90 years for RCP 4.5 was 0.26 and 0.94 Mg C ha−1 yr−1 for switchgrass and miscanthus, respectively. If 40-year stands of WSGs are converted back to CCSW, the model predicted SOC decline to the previous level in 40–50 years. DayCent predicted that under RCP 8.5 scenario in the second half of the 21st century and in the future, there will be a reduction in SOC stocks, especially under miscanthus stands.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".