Straw from Different Crop Species Recruits Different Communities of Lignocellulose-Degrading Microorganisms in Black Soil
Bibliographic record
Abstract
The biological degradation of plant residues in the soil or on the soil surface is an integral part of the natural life cycle of annual plants and does not have adverse effects on the environment. Crop straw is characterized by a complex structure and exhibits stability and resistance to rapid microbial decomposition. In this study, we conducted a microcosm experiment to investigate the dynamic succession of the soil microbial community and the functional characteristics associated with lignocellulose-degrading pathways. Additionally, we aimed to identify lignocellulose-degrading microorganisms from the straw of three crop species prevalent in Northeast China: soybean (Glycine max Merr.), rice (Oryza sativa L.), and maize (Zea mays L.). Our findings revealed that both the type of straw and the degradation time influenced the bacterial and fungal community structure and composition. Metagenome sequencing results demonstrated that during degradation, different straw types assembled carbohydrate-active enzymes (CAZymes) and KEGG pathways in distinct manners, contributing to lignocellulose and hemicellulose degradation. Furthermore, isolation of lignocellulose-degrading microbes yielded 59 bacterial and 14 fungal strains contributing to straw degradation, with fungi generally exhibiting superior lignocellulose-degrading enzyme production compared to bacteria. Experiments were conducted to assess the potential synergistic effects of synthetic microbial communities (SynComs) comprising both fungi and bacteria. These SynComs resulted in a straw weight loss of 42% at 15 days post-inoculation, representing a 22% increase compared to conditions without any SynComs. In summary, our study provides novel ecological insights into crop straw degradation by microbes.
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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) | 0.001 | 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 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".