Miscanthus litter additions induce a successional change in the soil micro-food web with apparent decreases in soil nitrogen
Bibliographic record
Abstract
The rapid loss of soil carbon (C) from cultivated peatland soils is leading to the use of high lignin, C-rich litter amendments as a potential solution to slow C losses. These chemically recalcitrant litter inputs are expected to cause microbial nitrogen (N) immobilization as a result of changes in the soil micro-food web, but whether bioavailable N is altered by litter as it is decomposed by the micro-food web remains unclear. We monitored changes in the soil nematode , fungal, and bacterial communities over time and across space (the rhizosphere and bulk soil) after field-applying different types of ligneous litter from miscanthus , ash, willow, or larch to a cultivated peatland soil. We found that miscanthus grass (C:N = 118) induced succession from fast-growing nematodes (cp-1) to slower-growing, cp-3 and cp-4 nematodes and this corresponded to reduced N availability. This lower soil N was likely due to relatively higher microbial biomass we observed with miscanthus, combined with a decrease in fast-growing bacterivores , limiting N mineralization from nematode grazing. We did not observe strong effects on the soil micro-food web or microbial biomass N for the other woody litters that had much higher C:N. This indicates that the changes in nematode community composition following ligneous litter inputs and subsequent impacts on soil N depend on litter type but are independent of litter C:N. Miscanthus amendments also corresponded to the lowest lettuce yield of all the amendments and thus caution is raised when using miscanthus straw as a widely-applied litter. Our results provide a useful reference to predict the effect of litter amendments on cultivated peatland soils through soil micro-food web dynamics, and bioavailable N for the crop.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 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".