Long-term tillage and crop rotation effects on soil quality, organic carbon, and total nitrogen
Bibliographic record
Abstract
Van Eerd, L. L., Congreves, K. A., Hayes, A., Verhallen, A. and Hooker, D. C. 2014. Long-term tillage and crop rotation effects on soil quality, organic carbon, and total nitrogen. Can. J. Soil Sci. 94: 303–315. Long-term studies allow for quantification of the effects of crop production practices, such as tillage and crop rotation, on soil quality and soil C and N stores. In two experiments at Ridgetown, ON, we evaluated the long-term (11 and 15 yr) effect of tillage system and crop rotation on soil quality via the Cornell Soil Health Assessment (CSHA) at 0–15 cm and soil organic C (SOC) and total N at 5-, 10-, and 20-cm increments to 120 cm depth. The CSHA soil quality score and SOC and total N were higher with no-till (NT) than fall moldboard plough with spring cultivation (conventional tillage, CT) and rotations with winter wheat [soybean–winter wheat (S-W) and soybean–winter wheat–corn (S-W-C)] compared with rotations without winter wheat. In both long-term trials, NT had ca. 21 Mg ha −1 more or 14% higher SOC than CT in the 0- to 100-cm soil profile, a trend which contrasts previous research in eastern Canada. Thus, the two long-term trial results at Ridgetown suggest that to improve soil quality and storage of C and N, growers on clay loam soil in southwestern Ontario should consider adopting NT production practices and including winter wheat in the rotation.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".