Aggregate stability in organically and conventionally farmed soils
Bibliographic record
Abstract
Abstract A range of factors that influence aggregate stability and soil erodibility were analysed for soils sampled from land managed under contrasting agricultural methods. These included: an organic farm; a conventional farm that incorporated organic fertilizers; a conventional farm that only used inorganic fertilizers; and a non‐cultivated control site. The stability of aggregates that compose the bulk soil structure (macroaggregates), and aggregates that were mobilized from the soil by simulated rainfall and surface runoff (microaggregates), were evaluated in terms of the soil fragmentation fractal dimension, organic carbon content and ATP (adenosine 5′‐triphosphate; a signature of live biomass) concentration. The results were used to interpret the existing physical condition of the soils, the (microbial) processes that contribute to that physical structure, and how both pedogenic processes and existing soil quality are influenced by agricultural methods. The soils sampled for this study were demonstrated to be multi‐fractal in nature: soils with greater bulk density were composed of more stable macro‐aggregates, which, in turn, fragmented into larger, more stable micro‐aggregates, rendering the entire soil structure less erodible. Soil erodibility and sustainable soil management should therefore be approached at multiple scales. The primary control on both macro‐ and micro‐aggregate stability was determined to be the organic matter input to the soil, as represented by measurements of organic carbon and ATP. Organic content was greatest for the non‐cultivated soil, which reflects the degradation of organic reserves in cultivated soils. For cultivated soils, it was not possible to differentiate aggregate stability for soils managed under organic or conventional (i.e. using biological and inorganic fertilizers) farming practices, but aggregates of soils that only received artificial fertilizers consistently exhibited less stability.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".