Assessing cropping system effects on carbon footprint on the Canadian prairies
Bibliographic record
Abstract
Abstract Crop rotations are considered a promising strategy for mitigating greenhouse gas (GHG) emissions and enhancing soil organic matter in agricultural land. However, studies often focused solely on either GHG emissions or soil organic carbon (SOC) changes, rather than integrating both indicators. We conducted a 4‐year (2018–2021) crop rotation study to examine effects of six rotation systems in three ecoregions (sub‐humid, sub‐semiarid, and semiarid) on GHG emissions, SOC changes, and C footprints in Saskatchewan, Canada. The six rotation systems include (i) control, (ii) intensified, (iii) diversified, (iv) market‐driven, (v) high‐risk, and (vi) soil‐health cropping system. GHG emissions were estimated using the Holos model, and SOC changes were estimated using the Campbell model, and C footprints were calculated as the difference between GHG emissions and SOC changes. The 4‐year cumulative GHG emissions, expressed as CO 2 equivalent (CO 2 e), were highest in the sub‐humid ecoregion due to higher background SOC levels, nitrogen (N) fertilizer inputs, and precipitation. The diversified and soil‐health systems reduced GHG emissions by reduced N fertilizer inputs. Carbon footprints revealed net CO 2 e emissions for the market‐driven system but net CO 2 e withdrawals for the soil‐health and diversified systems. The results indicated that the diversified systems significantly mitigated GHG emissions, increased soil C stocks, and withdrew CO 2 e.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.001 | 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".