Combining predictive soil mapping and process models to estimate future carbon sequestration potential under no-till
Bibliographic record
Abstract
There is increasing interest in soil organic carbon (SOC) sequestration as a climate change mitigation strategy. There is a need to estimate the quantity of SOC sequestered historically due to no-till, and the remaining sequestration potential in Saskatchewan. To answer this question, predictive soil mapping results were linked with the Century model to predict SOC stock change over time to a depth of 20 cm considering three different future climate change scenarios. Climate scenarios included low, moderate, and high amounts of climate change and included estimated changes to monthly minimum, average, and maximum temperature, total monthly precipitation, and average monthly relative humidity at an 800 m × 800 m resolution. Historically, the modelled average SOC gain for Saskatchewan was 2.8 Mg ha −1 . Future potential simulated SOC was lower over the next 30 years, with average SOC gains estimated to range from 1.4 to 1.7 Mg ha −1 by 2054 and 2.3 to 3.1 Mg ha −1 by 2100. There is also unequal spatial distribution of SOC stock gain potential, with the northern grain growing regions showing lower future potential. The predicted future gains will be at a lower rate than in the past with carbon sequestration rates dropping from 0.06 to less than 0.02 Mg ha −1 year −1 . Additional management practices such as improved residue management and the introduction of crop varieties with increased below ground carbon inputs and more stable residues should be explored to offset the diminishing SOC returns from no-till.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".