An empirical model for estimating carbon sequestration on the Canadian prairies
Bibliographic record
Abstract
There is a need to develop verifiable algorithms that can be easily applied to estimate carbon sequestration in soils. A simple process-based empirical model, driven primarily by soil texture and crop residue input, was developed to account for changes in soil organic carbon (SOC) in Chernozemic soils on the Canadian prairies. The model was used to estimate SOC change under no-till and continuous cropping compared with conventional tillage and rotations with fallow. Using this model, C sequestration due to continuous cropping compared with fallow-containing rotations was determined to be 0.09 Mg C ha-1yr-1 for the Brown and Dark Brown, and 0.05 Mg C ha-1 yr-1 for the Black and Dark Gray/Gray soil zones. The rate of C sequestration as a result of continuous cropping was positively related to the frequency of fallow, which decreases on the prairies from the Brown, Dark Brown, and Black to the Dark Gray/Gray soil zones. Using this model average C sequestration when conventional tillage was converted to no-till, was 0.13, 0.23, 0.34, and 0.25 Mg C ha-1 yr-1 for the same soil zones, respectively. Combined gains due to no-till and continuous cropping in comparison with conventional tillage and fallow-containing rotations were determined to be 0.22, 0.32, 0.39, and 0.30 Mg C ha-1 yr-1 for the Brown, Dark Brown, Black and Dark Gray/Gray soil zones, respectively. Based on Agricultural Census of Canada data in 1996 and 2001, the amount of “C sequestered” due to the adoption of no-till was estimated to be 1.23 million Mg of C in 1996 and 1.72 million Mg of C in 2001, which is approximately 10% of the total greenhouse gas emissions from the agricultural sector in Canada. This simple process-based empirical model could serve as a useful tool for soil scientists to use in assessing soil sustainability and C sequestration in the Canadian prairies. It would also assist policy makers in understanding how various scenarios of improved management will influence future greenhouse gas emissions on agricultural soils. Key words: Soil organic carbon, no-till, fallow, crop rotation
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| 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) | 0.002 | 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".