A simple model for quantifying change in soil organic C as influenced by tillage and crop rotations on the Canadian prairies
Bibliographic record
Abstract
Simulation models are required for quantifying the impact of crop rotations and tillage on soil organic C dynamics, and for aggregating C sequestration over a relatively large area. However, most current models of soil organic C have been built based on kinetically defined discrete pools with different turnover times. Those pools of soil organic C only exist conceptually. They have not been determined experimentally, thus validation of kinetic models describing soil organic C turnover is usually difficult or not independent from actual measurements. Thus, there is a need to develop a simulation model that can be easily validated and used for estimating future projection of C sequestration under specified management practices. A simple model has been developed to quantify the impact of crop rotations and tillage on soil organic C and validated using long-term field experiments conducted on the Canadian prairies. This simple model required a few input parameters and accurately predicted the change of soil organic C with a relative error of 5% or better. Crop rotation in cereal-dominant cropping systems, affected the amount of soil organic C due to differences in the amount of crop residue inputs. Clay content of soil played a vital role in determining the soil organic C sequestered under conservation tillage compared to tilled systems. This study also showed that the rate constant of soil organic C turnover was about the same for all systems in the drier region of the Canadian prairies, regardless of soil texture and the cropping system.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".