Korean Natural Farming practices are dominated by a limited number of microbes and decrease fungal diversity
Bibliographic record
Abstract
Abstract Korean Natural Farming (KNF) practices claim to cultivate and transfer “indigenous micro-organisms” (IMOs) to donor soils as a method of probiotic soil enhancement. We investigated whether IMO cultivation can propagate unique microbiomes and maintain microbial diversity through successive IMO stages for restoration of flood contaminated soils. Employing a balanced study design using soil samples from salt marsh, deciduous forest, and urban greenspace (plus sterilized controls), samples underwent the first two IMO cultivation steps followed by 16S rRNA and ITS metagenomic sequencing. Notably, 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 also revealed distinct clustering by inoculum source (i.e. human-altered, human- transported (HAHT) vs. natural vs. sterile) that persisted throughout cultivation. Our evidence suggests that the IMO process enriches for specific taxa likely adapted to cultivated conditions and fails to maintain fungal diversity, contrasting greatly with KNF’s proposed benefit of propagating locale-specific, fungal-dominated indigenous microbiomes. However, our results demonstrate that early IMO cultures may capture and sustain bacterial diversity in soil, opening the door for future studies of KNF efficacy and sustainability. Sustainability Statement This work is primarily associated with UN Sustainable Development Goal SDG 15 (Life on Land). By scientifically evaluating Korean Natural Farming “indigenous Microorganism” (IMO) practices through metagenomic sequencing, this study provides evidence-based insights into microbial community dynamics relevant to soil restoration and terrestrial ecosystem health. The findings inform low-cost bioremediation strategies for flood- contaminated urban soils, which is of growing importance as climate-driven extreme precipitation events intensify. Additional relevant SDGs include: SDG 13 (Climate Action), SDG 11 (Sustainable Cities and Communities), SDG 2 (Zero Hunger), and SDG 3 (Good Health and Well-being).
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".