Compost Applications Increase Water‐Stable Aggregates in Conventional and No‐Tillage Systems
Bibliographic record
Abstract
Agricultural practices that alter the soil organic matter (SOM) content are expected to cause changes in soil stability and aggregation. The objective of this study was to evaluate short‐term (<2 yr) changes in water‐stable aggregates (WSA) in a silt‐loam soil under different management regimes. The interactive effects of tillage (no‐till and conventional tillage), crop rotations (continuous corn, corn‐soybean rotation) and composted cattle manure applications [0, 15, 30, and 45 Mg (wet weight) ha −1 ] on WSA were assessed in a factorial (tillage × crop rotation) split plot (compost) experiment. The proportion of WSA >4 mm was greater in compost‐amended than unamended soils within 1 yr, and the mean weight diameter (MWD) of aggregates increased with increasing compost application rates. By the second year of the study, no‐till soils under continuous corn and the soybean phase of the corn‐soybean rotation had more WSA >4 mm and a greater MWD than any crop rotation in conventionally tilled soils. Increasing the C input to soil increased the MWD of aggregates. The MWD of aggregates was related to the C content of soils under no‐till, but not conventional tillage, suggesting more physical stabilization of organic matter (OM) in no‐till than conventional tillage agroecosystems. Our findings indicate rapid improvements in aggregation of a silt‐loam in the first 2 yr after compost application and the adoption of no‐tillage practices.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".