Persistence of Soil Organic Carbon after Plowing a Long‐Term No‐Till Field in Southern Ontario, Canada
Bibliographic record
Abstract
There is abundant evidence that minimizing soil disturbance reduces mineralization of organic matter and can result in larger storage of soil organic carbon (SOC) relative to conventional tillage. However, little is known about the persistence of SOC when no‐till lands are plowed periodically. This study set out to determine the change in SOC when a long‐term (22 yr) no‐till field in southern Ontario, Canada, was plowed once. Four plots were located within three textural classes [sandy loam (SL), sandy clay loam (SCL), and silty clay loam (SiCL)] within two hydrologic conditions (well‐ and poorly drained) in the field. The plots were sampled before and three times after (3 d, 7 mo, and 18 mo) the no‐till field was moldboard plowed. The single tillage event homogenized the SOC through the profile and reduced the stratification. When calculated on an equivalent mass basis beyond the plow depth, there was no significant change in SOC 18 mo after plowing the SCL, SiCL, and SL high SOC plots. However, in the SL plot with low SOC, there was a loss of about 3 Mg SOC ha −1 after 18 mo, and the loss occurred primarily between the 15‐ and 30‐cm depths in the profile. The loss may have accounted for as much as two‐thirds of the SOC gained from no‐tillage. This study also emphasized the need for additional care to account for changes in bulk density when comparing the quantity of soil constituents, such as SOC, after plowing.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 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".