Measuring and Modeling the Long‐Term Impact of Crop Management on Soil Carbon Sequestration in the Semiarid Canadian Prairies
Bibliographic record
Abstract
Agricultural management practices which promote soil organic carbon (SOC) sequestration can contribute to the long‐term productivity of soils, thus research must quantify and predict SOC dynamics in response to crop management. Using long‐term (1967–2009) data from 10 cropping systems on a Brown Chernozem (Aridic Haploboroll) in the Canadian semiarid prairies at Swift Current, Saskatchewan, we assessed the effect of fertilizer, cropping frequency, and crop type on SOC dynamics in the 0‐ to 15‐cm depth. Three models: Campbell, introductory carbon balance model (ICBM), and DayCent were evaluated, all of which produced fairly accurate predictions of SOC content and sequestration rates (R2 of 0.64–0.82); however, DayCent had the highest correlation and lowest errors of prediction and was deemed superior. Residue inputs of 0.87 to 1.13 Mg C ha−1 yr−1 maintained the SOC level, and SOC content was directly related to factors which increased C inputs. The SOC content and sequestration rates were lowest for wheat (Triticum aestivum L.)‐based rotations which were frequently fallowed and included flax (Linum usitatissimum L.), but highest for systems which were frequently cropped, well‐fertilized, and included rye (Secale cereale L.) or pulse crops in rotation. For systems with high C input, DayCent projected SOC gains of 12 Mg C ha−1 from 2009 to 2100, indicating that the soil at Swift Current had not reached maximum C capacity. This study was the first to rigorously test and demonstrate the strength of the DayCent for simulating SOC under different cropping systems on the Canadian prairies.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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 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".