Korean Natural Farming practices are dominated by a limited number of microbes and decrease fungal diversity
Bibliographic record
Abstract
Abstract Purpose Korean Natural Farming (KNF) practices claim to cultivate and transfer “indigenous micro-organisms” (IMOs) to donor soils as a method of probiotic soil enhancement, yet lack peer-reviewed scientific validation. We investigated whether IMO cultivation can propagate unique microbiomes and maintain microbial diversity through successive IMO stages for restoration of flood contaminated soils. Methods We employed a balanced study design using soil samples from three New York metropolitan ecological sites: salt marsh, deciduous forest, and urban greenspace, plus sterilized controls. Samples underwent the first two IMO cultivation steps via inoculation on rice and fermentation in unrefined sugar. We extracted environmental DNA and performed 16S rRNA and ITS metagenomic sequencing using the AVITI platform and analyzed 84 libraries with QIIME2. Dynamics of microbial community composition and soil biodiversity, as a function of both ecological source and IMO cultivation, were assessed using Jaccard Index, Unweighted UniFrac, and ANCOM-BC analysis. Results IMO cultivation was dominated by limited bacterial taxa ( Enterobacterales, Pseudomonadales, Bacillales ) and fungal taxa ( Rhizopodaceae , particularly R. oryzae ). While bacterial diversity was maintained or increased during two IMO stages, fungal diversity consistently decreased. Principal Coordinates Analysis revealed distinctclustering by inoculum source (i.e. manmade vs. natural vs. sterile) that persistedthroughout cultivation. Conclusions Our evidence suggests that the IMO process selects for specific taxa likely adapted to cultivated conditions and fails to maintain fungal diversity. This greatly contrasts IMO’s proposed benefit of propagating locally-specific, fungal-dominated indigenous microbiomes. However, our results demonstrate that IMO cultures may capture and sustain bacterial diversity in soil, while it is unclear how such diversity mayarise. The possibility remains that the early stages of IMO studied here could fosterfurther microbial community expansions, but further study is required.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".