| --- |
| license: cc-by-4.0 |
| language: |
| - en |
| task_categories: |
| - text-generation |
| - question-answering |
| tags: |
| - agriculture |
| - korean-natural-farming |
| - knf |
| - regenerative-agriculture |
| - tropical-farming |
| - smallholder |
| - philippines |
| - instruction-tuning |
| pretty_name: ROOTMODEL KNF Philippines v1 |
| size_categories: |
| - n<1K |
| --- |
| |
| # rootmodel-knf-philippines-v1 |
|
|
| Adaptive agricultural instruction dataset for **regenerative tropical farming informed by Korean Natural Farming (KNF)**, built from a working farm in Nabua, Camarines Sur, Bicol, Philippines. |
|
|
| Released for the **AutoScientist Challenge — Agriculture (Part 2, 2026)**. To the maintainer's knowledge, no equivalent KNF-specific instruction dataset currently exists in the public domain. |
|
|
| > "Modern AI was trained on the internet. ROOTMODEL is trained on living soil." |
|
|
| ## What this dataset is |
|
|
| 59 instruction → response records teaching the decision logic of a KNF-informed biological farming system: read the signal (crop stage, symptom, soil and weather condition), correct soil biology first, then apply the stage-appropriate biological input at the right dilution, method and timing — and record the outcome. |
|
|
| JSONL, with fields: |
|
|
| | field | meaning | |
| |---|---| |
| | `task` | reasoning task (`stress_classification`, `input_selection`, `fermentation_readiness`, `outcome_comparison`, `recovery_prediction`, `field_observation`, `microbial_imbalance`, `land_history`, `pest_cycle`, `knf_input_function`, `knf_knowledge`) | |
| | `source` | provenance — see below | |
| | `instruction` | task prompt | |
| | `input` | field or lab context | |
| | `output` | target response | |
|
|
| ## Data provenance |
|
|
| Records are split by provenance and never blended: |
|
|
| - **`nabua_field_log`** (37 records) — real observations from the Nabua farm: dated interventions and outcomes, substrate readings, fermentation batches, and documented management transitions (2016 chemical → organic → KNF; 2019 pig-litter integration; ASF culling; Typhoon Kristine 2024; Typhoon Inday July 2026; 2026 reboot). |
| - **`knf_canonical`** (22 records) — established KNF domain knowledge: input functions (IMO, LAB, FPJ, FFJ, OHN), standard dilution ranges, fermentation-readiness signals, stage-to-input mapping, biological-first decision logic. |
| |
| This separation lets a reviewer see exactly which records are general knowledge and which are ground-truth field data. |
| |
| ## Field observation schema |
| ``` |
| Date · Plot ID · Crop · Growth Stage · Weather · Symptom · |
| Soil Condition · KNF Input Applied · Dilution Ratio · |
| Application Method · Time Applied · Result After 72h · Notes |
| ``` |
| |
| ## KNF inputs covered |
| |
| | Input | Function | Stage | |
| |---|---|---| |
| | IMO | Soil microbial inoculation | Land prep, transplanting | |
| | LAB | Fermentation activator, anaerobe suppression | Foliar spray, soil drench | |
| | FPJ | Vegetative growth stimulation | Vegetative | |
| | FFJ | Flowering and fruiting support | Reproductive | |
| | OHN | Pest resistance, stress support | Stress response | |
| |
| ## Recording discipline |
| |
| - No date is estimated. Precision is recorded as exact, approximate or month-only. |
| - No outcome is written before its 72-hour window has elapsed. |
| - "No visible change" and "identification unconfirmed" are valid records. |
| - Failures, confounds and interrupted observation days are logged rather than omitted. |
| - Identifications are revised in place when better evidence arrives, with the revision noted. |
| |
| ## Limitations |
| |
| - Small corpus. Field observations centre on IMO application and fermentation readiness; other KNF inputs are represented canonically. |
| - Dilution ranges are standard practice and should be validated against local results. |
| - Specific to humid, typhoon-affected lowland tropical Philippines. Transfer to temperate or arid systems is not claimed. |
| - Several observations carry stated confounds (rainfall, co-introduced organisms, weed-seed load) recorded alongside the result. |
| |
| ## Citation |
| @dataset{rootmodel_knf_philippines_v1_2026, |
| title = {ROOTMODEL KNF Philippines v1}, |
| author = {Sales, Ralph Anthony}, |
| year = {2026}, |
| note = {Nabua, Camarines Sur, Bicol, Philippines.}, |
| url = {https://huggingface.co/datasets/GreenRalph/rootmodel-knf-philippines-v1} |
| } |
| |
