Management impacts on organic carbon under continuous perennial grass, perennial grass-legume mixture, and annual cereals on a thick Black Chernozemic soil
Bibliographic record
Abstract
Impacts of annual and perennial pasture management on soil organic carbon (SOC) and equivalent SOC stocks (equal soil mass basis) were investigated in two trials [CAESA (1994–1997) and BMP (2008–2012) trials] conducted on the same experimental paddocks at Lacombe, AB. The original site was broken from perennial grass in 1992, and the CAESA trial established in 1993. Between 1994 and 1997, half of the paddocks included winter triticale and a mixture of triticale and spring barley; half included smooth and meadow bromegrass; and each paddock was light, medium, or heavily grazed. The BMP trial (2008–2012) on the same paddocks included fertilized, direct seeded barley as silage; grazing and haying of unfertilized meadow bromegrass, fertilized meadow bromegrass, and meadow bromegrass and alfalfa mixture; and unfertilized oldgrass that was continuous since 1994. Between trials (1998–2007), all paddocks received no fertilizer. In the 0–15 cm depth, SOC under oldgrass was constant between 1994 and 2012 and averaged 88 Mg C ha −1 . Under barley silage, SOC decreased from 89 to 72 Mg C ha −1 by 2012. Between 1994 and 2012, SOC decreased in all treatments re-established on original annual forage (1994–1998) but not to the level of barley silage. Light fraction carbon was the highest under oldgrass and the lowest under barley silage. Overall, oldgrass with no fertilizer inputs maintained a constant SOC, although annuals reduced SOC stocks. Re-establishment of perennial grass with grazing may therefore reduce SOC loss, whereas haying perennial grass may not reduce SOC loss.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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".