Soil organic matter dynamics in long-term temperate agroecosystems: rotation and nutrient addition effects
Bibliographic record
Abstract
Soil organic matter (SOM) is a major driver of key agroecosystem functions. Our objective was to examine the dynamics of organic matter in whole soil, particulate (POM; >53 μm size), and mineral-associated (MAOM) fractions under varying crop rotations and nutrient managements at two long-term experimental sites (Breton and Lethbridge). Soil samples were collected from simple (2 yr) and complex (5 or 6 yr) crop rotations at the 5–10 cm depth. We found associations between SOM pools versus microbial community and soil aggregation. Compared to cropped soils, an adjacent forest exhibited a significantly higher soil total organic carbon (TOC) and a shift in SOM fractions with substantially higher POM. However, the forest soil had the lowest microbial biomass C among all the assessed land use systems (P < 0.05), suggesting that other factors than the amount of labile SOM (i.e., POM-C) were controlling the microbial community. When contrasted to simple 2 yr rotations, the complex rotations including perennials and legumes significantly raised TOC and soil total nitrogen as well as the stable SOM fraction (i.e., MAOM-C and -N) consistently for both Breton and Lethbridge sites. Our findings highlight that varying land managements have profound feedbacks on soil quality as mediated by alterations in long-term SOM dynamics.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".