Long-Term Effects of Rotational Tillage On Visual Evaluation of Soil Structure, Soil Quality and Crop Yield
Bibliographic record
Abstract
Tillage and rotation are fundamental factors influencing soil quality and thus the sustainability of cropping systems. Many studies have focused on the effects of either tillage or rotation but few have quantified long term integrated effects tillage and rotation. We studied the issue using a 30 year old long-term rotation and tillage treatment experiment on a Canadian silt loam soil. Measurements were carried out in the topsoil for three different rotations: R1 (C-C-C-C) continuous corn (Zea mays L.), R6. (C-C-O(RC), B(RC)) corn, corn, oats (Avena fatua L.) and spring barley (Hordeum vulgare L.) and R8, (C-C-S-S) corn, corn, soybean (Glycine max L.), soybean. A red clover (Trifolium pretense L.) cover crop was under seeded in oats and spring barley in R6. In 2010, first year corn was grown in R6 and R8. The tillage treatments included no tillage, NT and mouldboard plowing, MP. Topsoil structural quality was visually evaluated early June and mid October. Minimal disturbed soil cores early June and these were used for X-ray CT scanning and more traditional analysis. Soil friability was determined on the soil samples using a drop shatter test. Crop yield was determined and correlated to the soil quality estimates. We found significant effect of both rotation and tillage on visual soil structure at both times of assessment. Poor soil structure was found for NT except when combined with a versatile crop rotation (R6). The soil core pore characteristics data also displayed a significant effect of tillage but only a weak insignificant effect of rotation. The drop shatter results were in accordance with the visual assessment data. Crop yield correlated significantly with the visual soil structure scores. We conclude that a versatile crop rotation was needed for an optimal performance of NT for the studied soil.
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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 | 0.000 | 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".