Identifying rotation and tillage practices that maintain or enhance soil carbon and its relation to soil health
Bibliographic record
Abstract
Physical fractions of soil organic matter (SOM) are established indicators of management-induced change and have been used to estimate the soil carbon storage capacity and storage potential. Here, we use SOM physical fractions and soil textures to identify management practices that maintain or enhance soil health and carbon storage in agricultural soils in Ontario. Metadata from the National Soil Database were used to estimate carbon storage potentials and calculate carbon deficits. A map was created showing carbon deficits in Ontario's agricultural soils and indicates that these soils have the potential to store an additional 0 to 2 kg m−2 in the top 20 cm of the soil. Tillage system generally had no effect on the size of the carbon deficit at four long-term agricultural experiments (Delhi, Elora, Ottawa, and Ridgetown). There was only a significant tillage effect at Ridgetown and only in the maize–soybean crop rotation, where the carbon deficit was 2.95 g C kg soil−1 under conventional tillage compared to 8.97 g C kg soil−1 with no tillage. A statistically significant effect of crop rotation was detected in Elora and Ridgetown. In Elora, continuous alfalfa had the smallest carbon deficit (7.25 g C kg soil−1) and maize–soybean rotation had the largest deficit (12.07 g C kg soil−1). In Ridgetown, the maize–soybean rotation had the smallest carbon deficit (2.95 g C kg soil−1). Regression analysis showed a weak negative relationship ( R2 = 0.11; P < 0.001) between carbon storage deficits and soil health scores. This suggests that increasing SOM levels alone may not improve soil health.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".