Ramifications of crop residue loading for soil microbial community composition, activity and nutrient supply
Bibliographic record
Abstract
Abstract Variable results have been reported on the effects of crop residue loads on soil microbial properties. We investigated changes in soil bacterial composition, β‐glucosidase enzyme activity and nutrient bioavailability in response to wheat residue loading. The treatments included three levels of above‐ground wheat residues (removed, retained or supplemented), with or without fertilizer N. Bacteroidetes , Firmicutes and Verrucomicrobia (the first two are copiotrophs) were less abundant where residues were removed than where residues were retained or supplemented, but the reverse was true for Actinobacteria, Cyanobacteria, Chloroflexi and Nitrospirae (all oligotrophs, although some Actinobacteria can be copiotrophic). Actinobacteria were also less abundant where fertilizer N was applied, and the abundances of their genera (including Arthrobacter and Mycobacterium ) increased where residues were removed, confirming that they were oligotrophic in this study. β‐diversity showed similar differences in the bacterial community structures because of residue management, but α‐diversity was not affected by residue management or N fertilizer. β‐glucosidase enzyme activities increased as C inputs increased with residue manipulation and N fertilizer. The enzyme activities increased with increasing residue loading in the 0–15 cm soil depth, but decreased with soil depth. Soil K supply increased with increasing residue loading, but nitrate‐N supply was highest with residue retention. These results demonstrate remarkable resilience of soil microbial functioning under a wide range of crop residue inputs, without adverse effects on enzyme activity attributable to inorganic N fertilizer. The increasing β‐glucosidase activity with increasing residue loading probably explains why crop residue return does not always increase soil C stocks.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 | 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 teacher head, 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".