Biochar contributes to resistance against root rot disease by stimulating soil polyphenol oxidase
Bibliographic record
Abstract
Abstract Biochar has been considered an effective approach as soil amendment for decreasing incidences of disease and regulating microbial populations in continuous-cropping soil. Although researches have extensively focused on changes of soil microbes and unbalance of nutrition in continuous-cropping soil, the relationship between soil properties and pathogens by biochar application remains poorly understood. In this study, we applied ITS ribosomal RNA gene profiling to analyze tobacco root microbiota of biochar and non-biochar treatment in a 3-year continuous-cropping tobacco field, comparing firstly planting tobacco as control. We found that biochar application decreased the relative abundance of the soil fungal pathogens ( Ceratobasidium and Monosporascus ), which are the prime pathogens of tobacco root rot in continuous-cropping soil. Using RDA, co-occurrence and PLS-PM approaches, we provided evidence that there was a negative correlation between fungal genera (especially for Ceratobasidium and Monosporascus ) and soil polyphenol oxidase (PPO) activity (R 2 incidence rate = − 0.930, R 2 disease index = − 0.905, both p < 0.001). The PPO was up-regulated by different biochar treatment intensities. Together, we demonstrated that biochar in continuous-cropping soil regulated the soil PPO activity to suppress pathogens, and further decrease incidence of root rot. Notably, biochar application forward continuous cropping was more effective for the continuous-cropping soil improvement than the other treatments. The data should help in appropriate timing of biochar application for alleviating continuous-cropping obstacle. Graphical abstract
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 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 | 0.000 | 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".