Straw retention efficiently improves fungal communities and functions in the fallow ecosystem
Bibliographic record
Abstract
BACKGROUND: Straw retention is a substitute for chemical fertilizers, which effectively maintain organic matter and improve microbial communities on agricultural land. The purpose of this study was to provide sufficient information on soil fungal community networks and their functions in response to straw retention. Hence, we used quantitative real-time PCR (qRT-PCR), Illumina MiSeq (ITS rRNA) and FUNGuild to examine ITS rRNA gene populations, soil fungal succession and their functions under control (CK) and sugarcane straw retention (SR) treatments at different soil layers (0-10, 10-20, 20-30, and 30-40 cm) in fallow fields. RESULT: The result showed that SR significantly enhanced ITS rRNA gene copy number and Shannon index at 0-10 cm soil depth. Fungi abundance, OTUs number and ACE index decreased with the increasing soil depth. The ANOSIM analysis revealed that the fungal community of SR significantly differed from that of CK. Similarly, significant difference was also observed between topsoil (0-20 cm) and subsoil (20-40 cm). Compared with CK, SR decreased the relative abundance of the pathogen, while increased the proportion of saprotroph. Regarding soil depth, pathogen relative abundance in topsoil was lower than that in subsoil. Besides, both sugarcane straw retention and soil depths (topsoil and subsoil) significantly altered the co-occurrence patterns and fungal keystone taxa closely related to straw decomposition. Furthermore, both SR and topsoil had higher average clustering coefficients (aveCC), negative edges and varied modularity. CONCLUSIONS: Overall, straw retention improved α-diversity, network structure and fungal community, while reduced soil pathogenic microbes across the entire soil profile. Thus, retaining straw to improve fungal composition, community stability and their functions, in addition to reducing soil-borne pathogens, can be an essential agronomic practice in developing a sustainable agricultural system.
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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 | 0.000 | 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".