Modelling soil organic carbon stock change for estimating whole-farm greenhouse gas emissions
Bibliographic record
Abstract
Modelling soil organic carbon (SOC) stock changes in agroecosystems can be performed with different approaches depending on objectives and available data. Our objective in this paper is to describe a scheme for developing a dynamic SOC algorithm for calculating net greenhouse gas emissions from Canadian farms as a function of management and local conditions. Our approach is flexible and emphasizes ease of use and the integration of available knowledge. Using this approach, we assessed the performance of several SOC models having two or more compartments for some common agroecosystems in Canada. Analysis of long-term data for conventional management practices at different sites (n = 36) in Canada, including recent model applications in the literature on some of those data, indicated that the results obtained with two-compartment models, such as the Introductory Carbon Balance Model (ICBM) and Modified Woodruff Model (MWM), yielded results comparable to those of a multi-compartment model (CENTURY). The analysis also showed that a model such as ICBM need stuning to be applied to management and conditions across Canada. Two-compartment models programmable in a simple spreadsheet format, though they may not supplant more complex models in allapplications, offer advantages of simplicity and transparency in whole-farm analyses of greenhouse gas emissions. Key words: Virtual Farm, soil organic carbon, soil disturbance, C inputs, Introductory Carbon Balance Model (ICBM), CENTURY, Modified Woodruff Model (MWM).
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".