The application of shrub willow chip organic amendments impacts soil microbial community dynamics
Bibliographic record
Abstract
Incorporating shrub willow chips into soil may improve the chemical, physical, and biological properties of soils with low organic matter but the impact on soil microbial communities and their dynamics is not known. We assessed changes in the soil microbial communities in response to willow chip applied at increasing rates (0, 20, 40, and 60 Mg ha−1) in a potato-barley cropping system. Bacterial and fungal community diversity, relative abundance, and potential functions were assessed using amplicon sequencing of 16S and ITS rRNA genes at six time points. High rates (40 and 60 Mg ha−1) of willow chips had no effect on bacterial alpha diversity but significantly decreased fungal alpha diversity (Shannon) while increasing fungal richness (Chao-1). At rates of 40 Mg ha−1 and higher, the relative abundance of copiotrophic bacterial groups increased, while that of copiotrophic fungal groups decreased. The relative abundance of the most dominant microbial phyla and genera varied over time, with copiotrophic groups declining and oligotrophic groups increasing. High willow chip application rates increased bacterial molecular markers related to carbon fixation and degradation, nitrogen fixation, and phosphorus solubilization, while decreasing markers related to cellobiose transport and denitrification. This study demonstrates the ability of willow chips to influence the microbial community composition and potential function over time.
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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 | 0.001 | 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".