git:20260826.6c18863 to git:20261003.e425c1f

> **MANDATORY EXECUTION POLICY** — READ BEFORE PROCEEDING

>

> You MUST run the **actual model binary or package** described in this document.

> If the model fails to import, compile, or execute, you MUST:

> 1. Check `diagnostics/triplets.yaml` for a matching error pattern

> 2. Try the fix suggested in the triplet's `remedy` section

> 3. If still failing, report the error to the user with full details

>

> You MUST NOT substitute a simplified Python formula, regression equation,

> or hand-coded approximation in place of the real model. Doing so produces

> scientifically invalid results and defeats the purpose of the KI.

>

> Before starting, run: `python3 preflight_check.py` (in this KI directory). **Use `python3` (3.12, pcse 6.0.12) for ALL tools -- the default `python` (3.11) cannot import pcse (numpy ABI mismatch) and preflight FAILS.**

> to verify that the model binary/package and required data are available.

>

> **DEBUGGING PROTOCOL** — When something goes wrong, follow this order:

> 1. **Check triplets** — `diagnostics/triplets.yaml` may already cover this error

> 2. **Read official docs** — The model's own documentation for expected formats/units

> 3. **Find working examples** — Check `outputs/` or the model's shipped test data

> 4. **Fix the tool** — With knowledge of what "correct" looks like

>

> Do NOT write custom debug scripts. The answers are in the docs and examples.

<!-- KI-MAP:BEGIN (projected by generate_skill_map.py — edit the KI, not this table) -->

## KI map — what to read, and when

| when you need | read | why |

|---|---|---|

| FIRST, always | `preflight_check.py` | run it (`python preflight_check.py`): proves env/binary/data are usable and emits a machine-readable `PREFLIGHT_REPORT=` line. Do not debug a run that never had a healthy environment. |

| to run the pipeline stages | `tools/` (20 tools) | the executable pipeline. Read each tool's argparse (`--help`) before composing a command; SKILL.md's stage table says which tool serves which stage. |

| before running a stage | `docs/s*_*.md` (8 stage docs) | per-stage procedure, verification and traps — the how-to that SKILL.md's overview compresses. |

| to know what an output IS | `dag.yaml` | the model's identity: every output's medium, units, `validation_rank` (1 = the headline variable) and observability. Scoring and obs-binding read THIS — when asked 'what does this model predict', the dag is the answer, not a guess. |

| when building inputs / parsing outputs | `docs/format_spec.yaml` | exact I/O shapes + `known_issues`, projected from dag + triplets. Regenerate with `ki_tools_common/generate_format_spec.py` after changing either — never hand-edit. |

| to judge a run's skill | `docs/validation_convention.yaml` | how this model's field judges it validated: per-`dag_variable` metrics, directions and CITED pass-bands. A run is graded against these, not against intuition. |

| for claims and thresholds | `docs/gathered_papers.json` (22 papers) + `docs/papers_index.md` | the literature this KI is judged by; each entry's `text_path` is fetched full text in the central paper cache. `role: benchmark` marks the model's own skill paper. |

| for a machine-readable summary | `knowledge_infrastructure.yaml` | the manifest (package, pipeline, validation tier, counts) — projected by `ki_tools_common/generate_ki_manifest.py`; regenerate after structural changes, never hand-edit. |

<!-- KI-MAP:END -->

<!-- KI-TOOL-INDEX:BEGIN (projected by generate_skill_map.py — the discoverability contract: every public tool, exact path; PURPOSE stays human-authored elsewhere) -->

### Executable tool index (projected — complete by construction)

Every public tool in this KI, by exact path. What each is FOR lives in the

human-written Tool Inventory above; `--help` on any of these prints its arguments.

| tool (exact path) | invocation |

|---|---|

| `tools/calib_run.py` | `KISSPATH_PYTHON_ENV/bin/python {KI}/tools/calib_run.py --help` |

| `tools/s1_crop_params/load_crop_parameters.py` | `KISSPATH_PYTHON_ENV/bin/python {KI}/tools/s1_crop_params/load_crop_parameters.py --help` |

| `tools/s1_crop_params/validate_crop_params.py` | `KISSPATH_PYTHON_ENV/bin/python {KI}/tools/s1_crop_params/validate_crop_params.py --help` |

| `tools/s2_soil_params/convert_hwsd_to_pcse_soil.py` | `KISSPATH_PYTHON_ENV/bin/python {KI}/tools/s2_soil_params/convert_hwsd_to_pcse_soil.py --help` |

| `tools/s2_soil_params/validate_soil_params.py` | `KISSPATH_PYTHON_ENV/bin/python {KI}/tools/s2_soil_params/validate_soil_params.py --help` |

| `tools/s3_weather_prep/create_csv_weather_file.py` | `KISSPATH_PYTHON_ENV/bin/python {KI}/tools/s3_weather_prep/create_csv_weather_file.py --help` |

| `tools/s3_weather_prep/validate_weather_data.py` | `KISSPATH_PYTHON_ENV/bin/python {KI}/tools/s3_weather_prep/validate_weather_data.py --help` |

| `tools/s4_agromanagement/generate_agromanagement_yaml.py` | `KISSPATH_PYTHON_ENV/bin/python {KI}/tools/s4_agromanagement/generate_agromanagement_yaml.py --help` |

| `tools/s4_agromanagement/validate_agromanagement.py` | `KISSPATH_PYTHON_ENV/bin/python {KI}/tools/s4_agromanagement/validate_agromanagement.py --help` |

| `tools/s5_engine_config/configure_pcse_engine.py` | `KISSPATH_PYTHON_ENV/bin/python {KI}/tools/s5_engine_config/configure_pcse_engine.py --help` |

| `tools/s5_engine_config/validate_engine_config.py` | `KISSPATH_PYTHON_ENV/bin/python {KI}/tools/s5_engine_config/validate_engine_config.py --help` |

| `tools/s6_execution/check_simulation_status.py` | `KISSPATH_PYTHON_ENV/bin/python {KI}/tools/s6_execution/check_simulation_status.py --help` |

| `tools/s6_execution/run_wofost_simulation.py` | `KISSPATH_PYTHON_ENV/bin/python {KI}/tools/s6_execution/run_wofost_simulation.py --help` |

| `tools/s7_output_parsing/export_output_csv.py` | `KISSPATH_PYTHON_ENV/bin/python {KI}/tools/s7_output_parsing/export_output_csv.py --help` |

| `tools/s7_output_parsing/parse_wofost_output.py` | `KISSPATH_PYTHON_ENV/bin/python {KI}/tools/s7_output_parsing/parse_wofost_output.py --help` |

| `tools/s8_yield_analysis/compare_wofost_dssat.py` | `KISSPATH_PYTHON_ENV/bin/python {KI}/tools/s8_yield_analysis/compare_wofost_dssat.py --help` |

| `tools/s8_yield_analysis/compute_gridded_yield.py` | `KISSPATH_PYTHON_ENV/bin/python {KI}/tools/s8_yield_analysis/compute_gridded_yield.py --help` |

| `tools/s8_yield_analysis/generate_yield_map.py` | `KISSPATH_PYTHON_ENV/bin/python {KI}/tools/s8_yield_analysis/generate_yield_map.py --help` |

| `tools/s8_yield_analysis/validate_against_faostat.py` | `KISSPATH_PYTHON_ENV/bin/python {KI}/tools/s8_yield_analysis/validate_against_faostat.py --help` |

*20 public tools; `_`-prefixed helpers and packaging files excluded.*

<!-- KI-TOOL-INDEX:END -->

---

## Data Preparation

### Forcing data

**Data Sources**: Use `from ki_tools_common.load_forcing import load_daily_forcing` for CMFD/MSWX/NASA POWER.

WOFOST forcing tools in this KI:

- `validate_weather_data.py` — Validates weather data ranges and completeness

### Soil properties

- `convert_hwsd_to_pcse_soil.py` — Converts HWSD to PCSE soil parameters

**Data Validation Reference**: See `data_ki/CMFD/SKILL.md` for CMFD unit documentation and known traps.

See `data_ki/HWSD/SKILL.md` for soil property documentation.

---

# WOFOST/PCSE Knowledge Infrastructure

**Model**: WOFOST 7.2 (WOrld FOod STudies) via PCSE 6.0 (Python Crop Simulation Environment)

**Domain**: Crop growth simulation / food production modelling

**Created by**: Zhang Jianyun Research Group, Hohai University

---

## Overview

WOFOST is the EU standard crop growth model developed at Wageningen University & Research. PCSE is its pure-Python implementation (`pip install pcse`), providing a clean programmatic API without compiled executables or batch files. This makes it an ideal complement to DSSAT for multi-model crop yield ensemble estimation within HydroCraft.

**Key advantage over DSSAT**: No compilation, no Fortran, no fixed-width column formatting. Everything is Python objects and YAML configuration. The entire simulation can be run in 15 lines of Python code.

**Key differences from DSSAT**:

| Aspect | WOFOST/PCSE | DSSAT |

|--------|-------------|-------|

| Language | Pure Python | Fortran + batch scripts |

| Phenology | DVS (0→1→2) continuous | V-stage/R-stage discrete |

| Soil model | Single-layer bucket (SMW/SMFCF/SM0) | Multi-layer (SLLL/SDUL/SSAT per layer) |

| Radiation units | **J/m2/day** (IRRAD) -- see WARNING below | MJ/m2/day (SRAD) |

| Precipitation units | **CSV column: mm/day** (CSVWeatherDataProvider divides /10 -> cm); internal var: cm/day (RAIN) | mm/day (RAIN) |

| Configuration | YAML files + Python dicts | Fixed-width text files (FileX) |

| Weather providers | NASAPower, CSV, Excel, custom | .WTH fixed-format files |

| Execution | `engine.run_till_terminate()` | `dscsm048` executable |

---

## Installation

```bash

pip install pcse

# PCSE 6.0 requires Python 3.8+

# Dependencies: numpy, PyYAML, SQLAlchemy, xlrd, openpyxl, requests

```

Verify installation:

```python

import pcse

print(pcse.__version__) # Should print 6.0.x

```

---

## Pipeline Stages

| Order | Stage | Skill Document | Key Tools |

|-------|-------|---------------|-----------|

| 1 | Crop Parameter Configuration | [s1_crop_params_skill.md](docs/s1_crop_params_skill.md) | `load_crop_parameters`, `validate_crop_params` |

| 2 | Soil Parameter Setup | [s2_soil_params_skill.md](docs/s2_soil_params_skill.md) | `convert_hwsd_to_pcse_soil`, `validate_soil_params` |

| 4 | Agromanagement Definition | [s4_agromanagement_skill.md](docs/s4_agromanagement_skill.md) | `generate_agromanagement_yaml`, `validate_agromanagement` |

| 5 | Engine Configuration | [s5_engine_config_skill.md](docs/s5_engine_config_skill.md) | `configure_pcse_engine`, `validate_engine_config` |

| 6 | Simulation Execution | [s6_execution_skill.md](docs/s6_execution_skill.md) | `run_wofost_simulation`, `check_simulation_status` |

| 7 | Output Parsing | [s7_output_parsing_skill.md](docs/s7_output_parsing_skill.md) | `parse_wofost_output`, `export_output_csv` |

| 8 | Yield Analysis & Ensemble | [s8_yield_analysis_skill.md](docs/s8_yield_analysis_skill.md) | `compute_gridded_yield`, `compare_wofost_dssat`, `generate_yield_map` |

**Stages 1-4 can run in parallel.** Stage 5 depends on all of 1-4. Stage 6 depends on 5. Stages 7-8 are sequential after 6.

---

## Output Description

**Source of truth**: `dag.yaml`. The dag is the model identity for outputs; if this section and the dag disagree, the dag wins.

**Headline output** (dag `validation_rank: 1`):

> `TWSO` — Total crop storage-organ biomass (harvestable yield) (`kg ha-1`)

| Output variable (dag `var`) | Rank | Unit | Description |

|-----------------------------|------|------|-------------|

| `TWSO` | 1 | `kg ha-1` | Total crop storage-organ biomass (harvestable yield) |

Other dag outputs currently exposed by this KI: `DVS`, `DOA`, `DOM`, `LAI`, `LAIMAX`, `TAGP`, `TWLV`, `TWST`, `TWRT`, `TRA`, `CTRAT`, `SM`, `RD`, `WWLOW`.

---

## Unit Conversion Table

**Exact I/O shapes live in `docs/format_spec.yaml`; output identity and output units live in `dag.yaml`.** This table captures the known unit conversions and output-unit facts that are surfaced in this skill document.

| Variable | Source or context | Source unit | Model or output unit | Conversion / handling |

|----------|-------------------|-------------|----------------------|-----------------------|

| `TWSO` | dag output | model output | `kg ha-1` | No conversion stated here; use dag output unit. |

| `E0` / `ES0` / `ET0` | Hargreaves evapotranspiration output | `mm/day` | `cm/day` | Divide by 10. |

| `RAIN` | `CSVWeatherDataProvider` CSV column | `mm/day` | internal model variable `cm/day` | PCSE divides the CSV value by 10 internally. |

| `VAP` | weather input vapor pressure | `hPa` or `mbar` when supplied that way | `kPa` | `1 kPa = 10 hPa = 10 mbar`; convert to kPa before model use. |

---

## Quick Start — Minimal WOFOST Simulation

```python

import pcse

from pcse.models import Wofost72_WLP_FD

from pcse.input import NASAPowerWeatherDataProvider, YAMLCropDataProvider

from pcse.input import WOFOST72SiteDataProvider # NOTE: moved from pcse.util in PCSE 6.0

from pcse.base import ParameterProvider

import yaml, datetime

# 1. Weather — automatic from NASA POWER

# NOTE: NASAPowerWeatherDataProvider may fail behind a proxy. If so, use

weather = NASAPowerWeatherDataProvider(latitude=52.0, longitude=5.5)

# 2. Crop parameters — built-in PCSE database

crop = YAMLCropDataProvider() # loads all built-in crop YAMLs

crop.set_active_crop('wheat', 'Winter_wheat_101')

cropdata = crop

# 3. Soil parameters — example for medium-textured soil

soildata = {

'SMW': 0.10, # wilting point (cm3/cm3)

'SMFCF': 0.30, # field capacity (cm3/cm3)

'SM0': 0.45, # saturation (cm3/cm3)

'CRAIRC': 0.04, # critical air content

'K0': 10.0, # saturated hydraulic conductivity (cm/day)

'SOPE': 1.0, # max percolation root zone (cm/day)

'KSUB': 1.0, # max percolation subsoil (cm/day)

'RDMSOL': 120.0, # max rootable depth (cm)

'IFUNRN': 0, # infiltration flag (0=no runoff)

'SSMAX': 0.0, # max surface storage (cm)

'SSI': 0.0, # initial surface storage (cm)

'WAV': 20.0, # initial available water (cm)

'NOTINF': 0.0, # fraction not infiltrating

'SMLIM': 0.30, # soil moisture limit for reduction

}

# 4. Site parameters — NOTE: CO2 is NOT accepted in PCSE 6.0 SiteDataProvider

sitedata = WOFOST72SiteDataProvider(WAV=20)

# 5. Assemble ParameterProvider

params = ParameterProvider(cropdata=cropdata, soildata=soildata, sitedata=sitedata)

# 6. Agromanagement — YAML string

agro_yaml = """

- 2000-10-01:

CropCalendar:

crop_name: wheat

variety_name: Winter_wheat_101

crop_start_date: 2000-10-15

crop_start_type: sowing

crop_end_date:

crop_end_type: maturity

max_duration: 365

TimedEvents: null

StateEvents: null

"""

agro = yaml.safe_load(agro_yaml)

# 7. Run simulation

engine = Wofost72_WLP_FD(params, weather, agro)

engine.run_till_terminate()

# 8. Get results

output = engine.get_output()

summary = engine.get_summary_output()

import pandas as pd

df = pd.DataFrame(output).set_index('day')

print(f"Yield (TWSO): {df['TWSO'].iloc[-1]:.0f} kg/ha")

print(f"Max LAI: {df['LAI'].max():.2f}")

print(f"Final DVS: {df['DVS'].iloc[-1]:.2f}")

```

---

## Critical Domain Knowledge (Non-Obvious Facts)

### 1. Unit Traps (SILENT ERRORS) — VERIFIED 2026-03-19

- **E0/ES0/ET0 are cm/day, NOT mm/day** — Spain DB values: ET0=0.08-0.88 cm/d. If Hargreaves gives mm/day, **divide by 10**. Values >1.5 cm/d cause extreme water stress. **(dt_v006)**

- **RAIN unit is context-dependent** -- In a **CSVWeatherDataProvider CSV the RAIN column is mm/day** (PCSE divides it by 10 -> cm internally); only the *internal model variable* / a hand-built WeatherDataContainer use cm/day. Writing the CSV in cm makes rainfall 10-100x too low -> drought-stressed crop, low yield. Ground truth: create_csv_weather_file.py docstring + dt_004.

- **VAP is kPa, NOT hPa or mbar** — Vapor pressure must be in kPa. 1 kPa = 10 hPa = 10 mbar.

| Crop | WOFOST | DSSAT | AquaCrop |

|------|:---:|:---:|:---:|

| Winter wheat | **3,543 kg/ha** | 3,217 | 6,307 |

| Grain maize | **6,102 kg/ha** | 5,780 | 14,281 |

WOFOST matches DSSAT within 10% for both crops with default parameters.

### ⚠️ Validating against FAOSTAT NATIONAL yields — scale/trend mismatch (added 2026-06-08)

Point or few-point WOFOST runs compared to a FAOSTAT NATIONAL yield series are a **domain/scale mismatch**, not a fair skill test. The national aggregate is spatially smoothed across many agro-climates, partly irrigated, and rises on a multi-decade technology trend (improved hybrids, fertiliser, management). A fixed-parameter, weather-driven point sim has far higher interannual variance (drought-year crop failures -> TWSO≈0; wet years -> high yield) than the buffered national mean, so **raw NSE/KGE are intrinsically very negative and raw r is low regardless of model quality.**

**Correct framing when only national obs exists:**

- Prefer sub-national / admin-unit yield obs at the simulated scale if available.

- Otherwise **detrend BOTH series and report detrended r** as the skill metric (removes the technology trend the model cannot reproduce).

- Pick a **rainfed semi-arid country+crop in WLP_FD** to maximise the weather-driven signal.

**Empirical anchor (Morocco wheat, 3 points, 1985-2024, WLP_FD, fixed Nov sowing, default Winter_wheat_107):** raw r=0.278, **detrended r=0.287** — i.e. detrending does NOT lift this case above the 0.5 skill threshold. The residual gap is structural (point-vs-national aggregate) + uncalibrated fixed-calendar agromanagement (Mediterranean wheat sowing follows the autumn rains, not a fixed date), which is **calibration / human-engineering territory, not a KI artifact bug.** Do not treat a low r on a point-vs-national comparison as a forcing/physics failure.

> **CANONICAL TOOL (added 2026-06-08):** Use `tools/s8_yield_analysis/validate_against_faostat.py SIM_CSV CROP COUNTRY OUT_JSON [UNITS]` for FAOSTAT-national validation. It is **obs_shape-aware**: it classifies the comparison as `regional_aggregate_time_series` and reports ONLY the dag-valid metric families — `magnitude_accuracy` (pbias, decadal_mean_pbias, mean_abs_pct_err) and `trend_match` (detrended r, first-difference r, trend-slope ratio). Raw NSE/r/KGE are relegated to an `invalid_for_obs_shape` block (the pre-retry gate REJECTs them as `REJECT_WRONG_METRIC` if reported as the verdict — see triplet dt_faostat_obs_shape). SIM_CSV needs columns `year,sim_tha`. The other s8 tools (compute_gridded_yield / compare_wofost_dssat / generate_yield_map) remain for gridded/cross-model work.

>

> **Validated reference (USA Corn Belt maize, 3-pt avg, PP mode, 1991-2023, default Grain_maize_205):** magnitude_accuracy pbias +6.6% (decadal +3.4%); trend_match detrended r=0.506, first-diff r=0.611. This PASSES under the correct framing — raw NSE=-1.4/r=0.09 are structurally meaningless for this obs_shape and must NOT be the verdict.

### Performance Metrics — Convention Bars

**Source of truth**: `docs/validation_convention.yaml`. A null band in the convention is written here as `no cited threshold`; do not substitute remembered thresholds.

| Dag variable | Metric | Direction | Satisfactory band | Citation key |

|--------------|--------|-----------|-------------------|--------------|

| `DVS` | `nse` | maximize | no cited threshold | none |

| `DOA` | `pbias` | zero_centered | no cited threshold | none |

| `DOM` | `pbias` | zero_centered | no cited threshold | none |

### PCSE 6.0 API Changes (validated 2026-04-10)

| Change | Old (PCSE 5.x) | New (PCSE 6.0) |

|--------|----------------|----------------|

| SiteDataProvider import | `from pcse.util import WOFOST72SiteDataProvider` | `from pcse.input import WOFOST72SiteDataProvider` |

| CO2 parameter | `WOFOST72SiteDataProvider(WAV=20, CO2=400)` | CO2 **removed** — handled internally via crop CO2 tables |

| SNOWDEPTH | Optional | Must not be NaN — set to 0.0 in custom weather |

### Maize Variety Selection Guide

| Variety | TSUM1 | TSUM2 | Best for | Yield range |

|---------|-------|-------|----------|-------------|

| Grain_maize_201 | 750 | 859 | Short season, N Europe | Low at 32°N |

| Grain_maize_204 | 855 | 900 | Medium season, C Europe / China | Good for Bengbu |

| Grain_maize_205 | 900 | 1000 | Long season, NE China / US Corn Belt | Best for Harbin |

### Spring Wheat Note

PCSE has no dedicated spring wheat variety. Use `Winter_wheat_107` (VERNSAT=5 days, effectively spring type) for spring-sown sites like Harbin.

### 2. Vernalization for Winter Crops

- Winter wheat/barley REQUIRE vernalization parameters: VERNSAT (days), VERNBASE (base temp), VERNDVS (DVS to force-stop vernalization).

- If VERNSAT is too high and winter is mild, DVS gets permanently stuck before anthesis — **the crop never matures, yield is zero, NO error message**.

- VERNDVS is a safety cutoff: forces vernalization to complete at this DVS, even if VERNSAT days not reached. Default ~0.30.

- If simulating winter crops sown in autumn, the campaign start date must precede the sowing date (e.g., campaign starts Oct 1, sowing Oct 15).

### 3. Simulation Mode Confusion

- **Wofost72_PP** — Potential Production: no water stress, no soil. Use for theoretical maximum yield.

- **Wofost72_WLP_FD** — Water-Limited Production, Free Drainage: includes water balance, water stress affects growth. Most common for real-world simulations.

- **Wofost72_WLP_CWB** — Water-Limited, Classic Water Balance: includes groundwater influence.

- Using PP mode when you want water-limited results gives unrealistically high yields — **silent error**.

### 4. crop_name and variety_name Are Case Sensitive

- `"Wheat"` fails. `"wheat"` works.

- `"winter_wheat_101"` fails. `"Winter_wheat_101"` works.

- Mismatch produces a `KeyError` that does not clearly explain the case-sensitivity issue.

### 5. AFGEN Tables Must Be Monotonic in X

- PCSE uses AFGEN (table interpolation) for many parameters (AMAXTB, SLATB, FOTB, etc.).

- Tables are defined as `[x1, y1, x2, y2, ...]` pairs. The x values MUST be monotonically increasing.

- Non-monotonic x values cause silent interpolation errors or runtime exceptions.

### 6. DVS Development Stage Interpretation

- DVS = 0: emergence

- DVS = 1: anthesis (flowering)

- DVS = 2: maturity (harvest)

- If DVS never reaches 2.0, the crop did not mature. Check TSUM1/TSUM2 (too high for the climate?) or vernalization (stuck?).

---

## Error Handling

When errors occur during WOFOST/PCSE simulation, consult the diagnostic triplets at [`diagnostics/triplets.yaml`](diagnostics/triplets.yaml). The triplets cover 15 failure patterns across 12 domains including YAML parse errors, unit conversion traps, vernalization misconfiguration, zero yield silent death, and PCSE API version changes.

---

## Model Couplings

See [`docs/model_couplings.yaml`](docs/model_couplings.yaml) for formal coupling definitions:

- **HWSD → PCSE soil**: HWSD raster/MDB → pedotransfer → WOFOST soil parameters

- **WOFOST ↔ DSSAT ensemble**: Run both models per grid cell → average yields → uncertainty bounds

- **WOFOST LAI → VIC feedback**: WOFOST daily LAI → VIC vegetation parameter update (experimental)

---

## File Structure

```

knowledge_infrastructure/

├── SKILL.md # this file — agent entry point

├── knowledge_infrastructure.yaml # schema with all stages, tools, docs, triplets

├── workflow/

│ └── workflow.md # agent-readable workflow document

├── tools/ # Layer 1: validated tools

│ ├── s1_crop_params/

│ │ ├── load_crop_parameters.py

│ │ └── validate_crop_params.py

│ ├── s2_soil_params/

│ │ ├── convert_hwsd_to_pcse_soil.py

│ │ └── validate_soil_params.py

│ ├── s3_weather_prep/

│ │ ├── create_csv_weather_file.py

│ │ └── validate_weather_data.py

│ ├── s4_agromanagement/

│ │ ├── generate_agromanagement_yaml.py

│ │ └── validate_agromanagement.py

│ ├── s5_engine_config/

│ │ ├── configure_pcse_engine.py

│ │ └── validate_engine_config.py

│ ├── s6_execution/

│ │ ├── run_wofost_simulation.py

│ │ └── check_simulation_status.py

│ ├── s7_output_parsing/

│ │ ├── parse_wofost_output.py

│ │ └── export_output_csv.py

│ └── s8_yield_analysis/

│ ├── compute_gridded_yield.py

│ ├── compare_wofost_dssat.py

│ └── generate_yield_map.py

├── docs/ # Layer 2: skill documents

│ ├── s1_crop_params_skill.md

│ ├── s2_soil_params_skill.md

│ ├── s3_weather_prep_skill.md

│ ├── s4_agromanagement_skill.md

│ ├── s5_engine_config_skill.md

│ ├── s6_execution_skill.md

│ ├── s7_output_parsing_skill.md

│ ├── s8_yield_analysis_skill.md

│ └── model_couplings.yaml

└── diagnostics/ # Layer 3: diagnostic triplets

└── triplets.yaml

```

---

*This knowledge infrastructure package was created using the Knowledge Dissection Toolkit developed by the Zhang Jianyun Research Group, Hohai University.*

---

## Crop Calendar Reference (China)

| Region | Latitude | Winter Wheat | Summer Maize | Rice |

|--------|----------|-------------|-------------|------|

| Northeast | >40°N | — | May-Sep | — |

| North China | 35-40°N | Oct-Jun | Jun-Sep | — |

| Huang-Huai | 32-35°N | Oct-Jun | Jun-Oct | — |

| Yangtze | 28-32°N | Nov-May | — | Apr-Oct |

| South | <28°N | — | — | Mar-Jul, Jul-Nov |

**Data sources on server:**

- GGCMI Crop Calendar: `KISSPATH_HOME/Crop_model_dataset/GGCMI_phase3_crop_calendar/`

- China Phenology GeoTIFF: `KISSPATH_HOME/Crop_model_dataset/8313530/`

- SPAM crop distribution: `KISSPATH_HOME/Crop_model_dataset/dataverse_files/`