Effect of tillage and crop rotations on the light fraction organic carbon and carbon mineralization in Chernozemic soils of Saskatchewan
Bibliographic record
Abstract
Light fraction of soil organic C (LFOC) represents a major portion of labile soil organic C (SOC) and is a key attribute of soil quality. Soil respiration (Cmin) is an important index depicting the potential activity of the labile SOC. Six field experiments, varying in duration (8 to 25 yr), in location (Brown, Dark Brown and Black Chernozemic soil zones of Saskatchewan) and soil texture, were conducted to evaluate the impact of tillage and crop rotations on crop production and soil quality. We sampled the 0-7.5-cm depth of soil in these experiments to determine the treatment effects on LFOC, the proportion of LFOC in the SOC (LFOC/SOC) and Cmin. Increasing the frequency of summer fallow in cropping systems decreased the LFOC in all soil zones; it also decreased the proportion of LFOC in SOC and Cmin. Tillage had little impact on LFOC in the Brown and Dark Brown Chernozemic soil zones, although it significantly decreased LFOC in the Black Chernozemic soil zone. Thus, crop rotation had a greater impact on LFOC than tillage. Tillage did not influence Cmin in any soil zone. Because adoption of no-till management increased SOC in all soil zones, we concluded that LFOC was not a sensitive indicator of the impact of tillage on this soil quality attribute for these Chernozemic soils in Saskatchewan. We also found that LFOC/SOC is directly proportional to sand content. This relationship may assist us in partitioning SOC pools with differing turnover times when modeling SOC dynamics. Key words: Soil organic C, light fraction organic C, tillage, crop rotations, texture, mineralizable C
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".