<scp>Biodiesel Co‐Product</scp> ( <scp>BCP</scp> ) amendment drives beneficial soil microbiome assembly promoting acid soil health
Bibliographic record
Abstract
Abstract Biodiesel Co‐Product (BCP) amendment has been shown to decrease both nitrate leaching and nitrous oxide (N 2 O) emissions in acidic soil; however, the effects of BCP on the soil microbiome have not been investigated thoroughly. In this study, we investigated the response of prokaryotic and fungal communities in aspects of structure, diversity, and co‐occurrence network to the BCP amendment following complete mixing application (0–18‐cm depth) of 1.5 mg BCP‐C g −1 and surface application (0–6‐cm depth) of 4.5 mg BCP‐C g −1 via high‐throughput 16S rRNA and internal transcribed spacer (ITS) amplicon sequencing. The amendment altered microbial communities significantly by increasing the relative abundances of Proteobacteria ( Burkholderia ) and Ascomycota ( Trichoderm a) in prokaryotic and fungal communities, respectively. Only a higher rate application (4.5 mg BCP‐C g −1 ) decreased prokaryotic alpha diversity, whereas all rates of amendment decreased fungal diversity. The co‐occurrence network of prokaryotes had more nodes and links and a higher average degree and clustering coefficient than the fungal network with BCP addition. The majority of keystone species in prokaryotic and fungal networks were from Proteobacteria and Ascomycota taxa. Of note, the BCP amendment significantly increased the OTU numbers of potential biocontrol agents, including Trichoderma ( T. ) spirale , T. koningiopsis , and T. virens , etc., while decreased OTU numbers related to plant pathogens species, particularly in the complete mixing application. Our work highlights the potential for BCP amendments to promote the assembly of a healthy soil microbiome by enhancing the abundance of potential biocontrol microbes while reducing plant pathogens species, which may contribute to soil health.
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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) | 0.002 | 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".