Crop yields under no-till in Canada: implications for soil organic carbon change
Bibliographic record
Abstract
Crop yields directly affect carbon (C) inputs into soils. Tillage management can influence crop performance, and as such should be considered when quantifying soil organic carbon (SOC) change, and thus net greenhouse gas emissions from croplands for national greenhouse gas inventory reporting. We conducted a meta-analysis of the effects of no-tillage (NT) and conventional tillage (CT) on crop yields for multiple crop species, soil types, and climatic regions of Canada. Yield response to NT varied between western and eastern Canada. Regardless of crop type, experiment duration, soil texture, and residue management, experiments in eastern Canada showed an average of 6% lower yields ( p ≤ 0.005) under NT compared to CT. In western Canada, crop type had an important effect on yields between NT and CT with wheat, canola, and legumes exhibiting 10% ( p ≤ 0.001), 7% ( p ≤ 0.05), and 9% ( p ≤ 0.05) higher yields on average under NT compared to CT, respectively. In western Canada, higher yields would be reflected in a similar scale of higher C inputs to NT systems. A recent meta-analysis of the effects of tillage management on SOC in Canada showed an 8% higher storage of SOC under NT systems compared to CT, a difference limited to western Canadian soils. Incorporating the effect of tillage on C inputs will lead to improvements in the accuracy of the effects of tillage management on SOC change in Canadian cropland. The activity data can be improved by applying weightings to the yield data by site-specific assessment of tillage practices across Canada.
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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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.008 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".