Imported from lzwei196/KISS-Knowledge-Infrastructure-for-Scientific-Simulation (models/Daisy/SKILL.md). Install upstream with npx skills add lzwei196/KISS-Knowledge-Infrastructure-for-Scientific-Simulation --skill Daisy. Copyright stays with the author.

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:

Check diagnostics/triplets.yaml for a matching error pattern

Try the fix suggested in the triplet's remedy section

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.

Before starting, run: python preflight_check.py (in this KI directory) to verify that the model binary/package and required data are available.

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

Check triplets — diagnostics/triplets.yaml may already cover this error

Read official docs — The model's own documentation for expected formats/units

Find working examples — Check outputs/ or the model's shipped test data

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 — 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/` (5 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` (6 stage docs)	per-stage procedure, verification and traps — the how-to that SKILL.md's overview compresses.
on ANY error, before debugging	`diagnostics/triplets.yaml` (19 entries)	symptom → diagnosis → remedy for this model's known failure modes. Check here FIRST; the answer usually exists. Never renumber or rewrite entries.
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` (17 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.

Projected 2026-08-17 from the KI's actual contents — 9 components present. Refresh: python3 ki_tools_common/generate_skill_map.py --ki_dir <this KI>.

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/convert_soil_to_dai.py`	`KISSPATH_PYTHON_ENV/bin/python {KI}/tools/convert_soil_to_dai.py --help`
`tools/convert_weather_to_dwf.py`	`KISSPATH_PYTHON_ENV/bin/python {KI}/tools/convert_weather_to_dwf.py --help`
`tools/parse_daisy_output.py`	`KISSPATH_PYTHON_ENV/bin/python {KI}/tools/parse_daisy_output.py --help`
`tools/run_daisy.py`	`KISSPATH_PYTHON_ENV/bin/python {KI}/tools/run_daisy.py --help`

5 public tools; _-prefixed helpers and packaging files excluded.

Daisy v7.1.4 (Soil-Crop-Water Simulation Model) — Knowledge Infrastructure

Package: hydrocraft-daisy-soil v1.0.0 Model: Daisy v7.1.4 — Mechanistic simulation of agricultural fields Origin: Agrohydrology Group, University of Copenhagen Last updated: 2026-03-25 Stats: 4 tools | 6 skill documents | 18 diagnostic triplets | ~2,500 lines of validated Python Validation status: example_validated (Taastrup, Denmark, 1986-1988)

Data Preparation

Forcing data

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

Data Validation Reference: See data_ki/CMFD/SKILL.md for meteorological forcing documentation. See data_ki/HWSD/SKILL.md for soil property documentation. See data_ki/RISMA/SKILL.md for soil moisture observations.

Overview

This knowledge infrastructure enables autonomous simulation of agricultural field processes using the Daisy model, covering water, nitrogen, carbon, and pesticide dynamics in the soil-plant-atmosphere system. The 4 validated tools replace manual .dai file editing with a Python pipeline that integrates with global forcing data and standardized soil databases.

What Daisy does: 1D mechanistic model for agricultural field simulation. Simulates:

Soil water transport (Richards equation, macropore flow, preferential flow)
Soil heat transport (conduction, convection)
Nitrogen dynamics (mineralization, nitrification, denitrification, plant uptake)
Carbon turnover (multi-pool organic matter model: SOM, SMB, AOM)
Crop growth (phenology, photosynthesis, root growth, water/N stress)
Pesticide fate (sorption, degradation, transport)
Field management (tillage, fertilization, irrigation, sowing, harvest)
Groundwater coupling (deep drainage, aquifer interaction)
Snow and frost dynamics

Key architectural features:

Lisp-like configuration language (.dai files)
Daisy Weather File format (.dwf) for meteorological forcing
Daisy Log File format (.dlf) for tabular output
Library system for reusable soil, crop, management, and log definitions
Built-in pedotransfer functions (Cosby, HYPRES, van Genuchten)
Batch and spawn modes for multi-scenario runs

Installation

Building from source (Linux)

# Dependencies
apt install g++ cmake libsuitesparse-dev libboost-filesystem-dev python3-pybind11

# Clone and build
git clone https://github.com/daisy-model/daisy.git
cd daisy
mkdir -p build/linux-gcc-portable
cmake . -B build/linux-gcc-portable --preset linux-gcc-portable
cmake --build build/linux-gcc-portable -j $(nproc)

# Binary location
build/linux-gcc-portable/daisy

# Verify
build/linux-gcc-portable/daisy -v

Pre-built packages

deb:     apt install ./daisy_7.1.4_amd64.deb
flatpak: flatpak install --user daisy-7.1.4.flatpak

Python dependencies (for KI tools)

numpy, pandas, matplotlib, pyyaml

Pipeline Stages

The Daisy simulation pipeline consists of 6 stages:

Stage	Name	Tool	Input	Output
S1	Weather Preparation	`convert_weather_to_dwf.py`	Global forcing (CSV/NetCDF)	`.dwf` file
S2	Soil Definition	`convert_soil_to_dai.py`	HWSD/texture data	Soil `.dai` file
S3	Setup Generation	Manual / template	Weather + Soil + Management	Main `.dai` file
S4	Execution	`run_daisy.py`	Main `.dai` file	`.dlf` output files
S5	Output Analysis	`parse_daisy_output.py`	`.dlf` files	CSV + figures
S6	Validation	Manual	Observed + simulated	Metrics + figures

7. Tool Inventory

Tool	Purpose	Inputs	Outputs
`tools/convert_weather_to_dwf.py`	Convert meteorological forcing into Daisy weather format	Global forcing CSV/NetCDF	`.dwf` weather file
`tools/convert_soil_to_dai.py`	Convert soil texture/profile data into Daisy soil definitions	HWSD or custom soil texture/profile data	Soil `.dai` file
`tools/run_daisy.py`	Execute the actual Daisy model binary	Main `.dai` setup file and libraries	`.dlf` output files and run logs
`tools/parse_daisy_output.py`	Parse Daisy log files into analysis-ready tables	`.dlf` output files	CSV files, summaries, and figures

Shared Utilities (`ki_tools_common`)

Tools should use these shared helpers instead of writing raw data extraction or metric code:

from ki_tools_common.load_forcing import load_daily_forcing
from ki_tools_common.metrics import all_metrics
from ki_tools_common.validation import validate_forcing_ranges
from ki_tools_common.units import convert

3. Input Format Reference

Exact machine-readable shapes live in docs/format_spec.yaml, projected from dag.yaml and diagnostics/triplets.yaml. Regenerate that file after changing the dag or triplets; do not hand-edit it. This section explains the model-facing intent and common traps.

Weather File (`.dwf`)

The Daisy Weather File is a custom text format with header metadata and columnar data.

Header section (keyword: value pairs):

dwf-0.0 -- Description text
Station: Taastrup
Elevation: 30 m
Longitude: 12 dgEast
Latitude: 56 dgNorth
TimeZone: 15 dgEast
Surface: reference
ScreenHeight: 2.0 m
Begin: 1962-04-01
End: 2008-10-31
Timestep: 24 hours
NH4WetDep: 0.9 ppm
NH4DryDep: 2.2 kgN/ha/year
NO3WetDep: 0.6 ppm
NO3DryDep: 1.1 kgN/ha/year
TAverage: 7.8 dgC
TAmplitude: 8.5 dgC
MaxTDay: 209 yday

Data section (tab-separated, after dashed line):

Year  Month  Day  GlobRad  AirTemp  Precip  RefEvap
year  month  mday W/m^2    dgC      mm/d    mm/d
1962  4      1    120.4    2.8      0.0     1.3

Required columns: Year, Month, Day, GlobRad (W/m^2), AirTemp (dgC), Precip (mm/d) Optional columns: RefEvap (mm/d), Wind (m/s), RelHum (%), VapPres (Pa), etc.

Soil Definition (`.dai`)

Soil is defined hierarchically: horizons → column.

Horizon (texture fractions are dimensionless 0-1 or percent with [%]):

(defhorizon "My Ap" USDA3            ; or FAO3, ISSS4
  (clay 0.107)                        ; fraction or [%]
  (silt 0.222)
  (sand 0.671)
  (humus 0.024)
  (dry_bulk_density 1.45 [g/cm^3])
  (C_per_N 11.0 [g C/g N])
  (hydraulic M_vG                     ; van Genuchten-Mualem
    (Theta_res 0.0)
    (Theta_sat 0.392)
    (alpha 0.0385)                    ; [cm^-1]
    (n 1.211)
    (K_sat 7.52 [cm/h])))

Texture classification systems:

USDA3: clay, silt, sand (3 fractions, USDA system)
FAO3: clay, silt, sand (3 fractions, FAO system)
ISSS4: clay, silt, fine_sand, coarse_sand (4 fractions, ISSS system)

Column (soil profile = stack of horizons):

(defcolumn MySite default
  (Soil (MaxRootingDepth 100 [cm])
        (horizons (-30 [cm] "My Ap")    ; depths are NEGATIVE from surface
                  (-250 [cm] "My C")))
  (Groundwater deep)                     ; or: aquitard, fixed
  (OrganicMatter original
    (init (input 1400 [kg C/ha/y])
          (root 480 [kg C/ha/y])
          (end -20 [cm]))))

Management Definition (`.dai`)

(defaction "My Management" activity
  (wait_mm_dd 3 05)                          ; wait until March 5
  (fertilize (N25S (weight 115 [kg N/ha])))  ; mineral fertilizer
  (plowing)
  (wait_mm_dd 4 05)
  (seed_bed_preparation)
  (sow "Spring Barley")
  (wait (or (crop_ds_after "Spring Barley" 2.0)  ; DS 2.0 = ripe
            (mm_dd 08 20)))
  (harvest "Spring Barley" (stub 8 [cm]) (stem 0.70)))

Main Setup File (`.dai`)

(input file "tillage.dai")
(input file "crop.dai")
(input file "log.dai")

(defprogram MySimulation Daisy
  (column MySite)
  (weather default "my-weather.dwf")
  (time 1986 12 1 1)                   ; start: YYYY MM DD HH
  (stop 1988 4 1 1)                    ; end:   YYYY MM DD HH
  (manager activity ...)
  (output harvest
    ("Field nitrogen" (when monthly))
    ("Soil nitrogen" (when daily))
    ("Field water" (when monthly))
    ("Soil water" (when daily))
    ("Crop" (crop "Spring Barley"))))

(run MySimulation)

6. Output Description

This section restates dag.yaml; if this section and the dag disagree, dag.yaml wins.

Headline output (validation_rank: 1):

sorg_DM — Harvested storage-organ (grain) dry matter — the primary crop yield output. (Mg DM/ha)

Output variable (dag `var`)	Rank	Unit	Notes
`sorg_DM`	1	Mg DM/ha	Harvested storage-organ (grain) dry matter — the primary crop yield output.
`harvest_index`	dag output	see `dag.yaml`	Other dag output.
`Harvest_N`	dag output	see `dag.yaml`	Other dag output.
`Leaching`	dag output	see `dag.yaml`	Other dag output.
`Denitrification`	dag output	see `dag.yaml`	Other dag output.
`Evapotranspiration`	dag output	see `dag.yaml`	Other dag output.
`Drain / Percolation`	dag output	see `dag.yaml`	Other dag output.
`Theta`	dag output	see `dag.yaml`	Other dag output.
`Soil temperature`	dag output	see `dag.yaml`	Other dag output.
`DS`	dag output	see `dag.yaml`	Other dag output.
`LAI`	dag output	see `dag.yaml`	Other dag output.

Output Format Reference

Daisy Log File (`.dlf`)

Tab-separated text with metadata header:

dlf-0.0 -- Harvest (defined in 'log-std.dai').

VERSION: 7.1.4
LOGFILE: harvest.dlf
RUN: Mon Mar 25 12:00:00 2026

COLUMN: *
SIMFILE: test.dai
SIM: AndebyFarm

----
year  month  mday  hour  column  crop  stem_DM  ...

Key Output Files

File	Content	Key Variables	Units
`harvest.dlf`	Crop harvest events	stem_DM, leaf_DM, sorg_DM, stem_N, sorg_N, harvest_index	Mg DM/ha, kg N/ha
`field_nitrogen.dlf`	N balance	Fertilizer, Fixation, Harvest_N, Denitrification, Leaching	kg N/ha
`field_water.dlf`	Water balance	Precipitation, Irrigation, Evapotranspiration, Drain, Percolation	mm
`soil_nitrogen.dlf`	Soil N profile	NH4, NO3, org_N per layer	kg N/ha
`soil_water.dlf`	Soil water profile	Theta per layer	mm
`<crop>.dlf`	Crop development	DS, LAI, Height, Root_Depth, WLeaf, WStem, WSOrg	various

Harvest Output Variables

Variable	Description	Unit
`stem_DM`	Harvested stem dry matter	Mg DM/ha
`leaf_DM`	Harvested leaf dry matter	Mg DM/ha
`dead_DM`	Harvested dead leaf matter	Mg DM/ha
`sorg_DM`	Harvested storage organ (grain) dry matter	Mg DM/ha
`stem_N`	Nitrogen in harvested stems	kg N/ha
`sorg_N`	Nitrogen in harvested grain	kg N/ha
`water_stress_days`	Days with water stress	d
`nitrogen_stress_days`	Days with nitrogen stress	d
`harvest_index`	Ratio of grain to total aboveground DM	dimensionless

8. Unit Conversion Table

This table documents the unit conversions used by the Daisy KI pipeline. Verify source data attributes before running a new dataset; docs/format_spec.yaml is the exact machine-readable contract.

Variable / parameter	Source unit	Daisy model unit	Conversion	Type
`GlobRad`	MJ/m^2/d daily total	W/m^2 daily mean	÷ 0.0864 (= ×11.574)	multiplicative
`AirTemp`	K	dgC (°C)	− 273.15	additive
`AirTemp`	dgC (°C)	dgC (°C)	identity	passthrough
`Precip`	mm/3h (CMFD)	mm/d	× 8	multiplicative
`Precip`	kg/m^2/s	mm/d	× 86400	multiplicative
`Precip`	mm/d	mm/d	identity	passthrough
`RefEvap`	mm/d	mm/d	identity	passthrough
`Wind`	m/s	m/s	identity, but check measurement height	passthrough
`RelHum`	fraction (0–1)	% (0–100)	× 100	multiplicative
`VapPres`	Pa	Pa	identity	passthrough
Elevation	m	m	identity	passthrough
Longitude	degrees (−180 to 180)	dgEast	identity if east; 360−abs(value) if west	convention
Latitude	degrees (−90 to 90)	dgNorth	identity	passthrough
Clay, silt, sand	percent (0–100)	fraction (0–1)	÷ 100	multiplicative
Clay, silt, sand	fraction (0–1)	fraction (0–1)	identity	passthrough
Bulk density	kg/m^3	g/cm^3	÷ 1000	multiplicative
`K_sat`	m/s	cm/h	× 360000	multiplicative
van Genuchten alpha	m^-1	cm^-1	÷ 100	multiplicative
Horizon depth	positive depth from surface	negative cm from surface	negate	sign convention
Fertilizer N	g N/m^2	kg N/ha	× 10	multiplicative
Organic input	g C/m^2/y	kg C/ha/y	× 10	multiplicative
Timestep	source frequency	24 hours	aggregate or resample to daily	temporal

8c. Sign Conventions and Output Units

These conventions are checked during input preparation and post-processing because sign or accumulation mistakes can silently invalidate validation metrics.

Variable	Convention in this KI	Common alternative	Impact if wrong
`sorg_DM`	Mg DM/ha harvested storage-organ dry matter	fresh mass or kg/ha	Crop yield magnitude and dry-matter comparisons are wrong.
`harvest_index`	dimensionless ratio of grain to total aboveground dry matter	percent	Ratios are off by 100 if interpreted as percent.
`Harvest_N`	kg N/ha harvested nitrogen	g N/m^2	Nitrogen removal is off by 10.
`Leaching`	kg N/ha nitrogen loss in field nitrogen outputs	concentration or flux rate	Nitrogen balance and validation metrics are not comparable.
`Denitrification`	kg N/ha nitrogen loss in field nitrogen outputs	rate per day	Period totals are misread as instantaneous rates.
`Evapotranspiration`	mm water-balance output	m or kg/m^2/s	Water balance magnitude is wrong.
`Drain / Percolation`	mm water-balance output	m or mm/s	Drainage magnitude and timing are wrong.
`Theta`	soil water profile output; see `dag.yaml` and `.dlf` headers for exact unit	percent or volumetric fraction without checking	Soil moisture comparisons can be scaled incorrectly.
`Soil temperature`	soil profile temperature; see `dag.yaml` and `.dlf` headers for exact unit	K	Temperature bias is shifted by 273.15.
`DS`	dimensionless crop development stage	calendar day or phenological class	Crop timing diagnostics are invalid.
`LAI`	leaf area index, dimensionless area ratio	percent cover	Canopy comparisons are not comparable.

Output unit verification checklist:

Read dag.yaml before binding observations or scoring outputs.
Read .dlf headers and unit rows before parsing a new Daisy log definition.
Print the first values from each parsed output and check order of magnitude.
For fluxes and balances, verify whether values are timestep rates or period totals.
For validation, compare sorg_DM in Mg DM/ha dry matter against observations in the same basis.

Unit Trap Table

These are the most dangerous unit conversion pitfalls when preparing Daisy inputs:

Parameter	Daisy Expects	Common Source Unit	Conversion	Severity
GlobRad	W/m^2 (daily mean)	MJ/m^2/d (daily total)	÷ 0.0864 (= ×11.574)	CRITICAL
AirTemp	dgC (°C)	K (Kelvin)	− 273.15	CRITICAL
Precip	mm/d	mm/3h (CMFD)	× 8	CRITICAL
Precip	mm/d	kg/m^2/s	× 86400	CRITICAL
Wind speed	m/s	m/s	identity (but check height)	MEDIUM
RelHum	% (0–100)	fraction (0–1)	× 100	HIGH
Elevation	m	m	identity	LOW
Longitude	dgEast	degrees (−180 to 180)	identity if E, 360−	val
Latitude	dgNorth	degrees (−90 to 90)	identity	LOW
Clay/silt/sand	fraction (0–1)	percent (0–100)	÷ 100	CRITICAL
Bulk density	g/cm^3	kg/m^3	÷ 1000	CRITICAL
K_sat	cm/h	m/s	× 360000 (from m/s to cm/h)	CRITICAL
Alpha (vG)	cm^-1	m^-1	÷ 100	HIGH
Horizon depth	negative cm from surface	positive depth	negate	HIGH
Fertilizer N	kg N/ha	g N/m^2	× 10	HIGH
Organic input	kg C/ha/y	g C/m^2/y	× 10	HIGH
Timestep	24 hours	—	must match data freq	MEDIUM

9. Diagnostic Triplets

On any error, inspect diagnostics/triplets.yaml before writing new debugging code. The full triplet corpus stays in YAML to avoid drift; this document only points to the workflow.

Step	Action	Reason
1	Match the observed symptom against `diagnostics/triplets.yaml`.	Known Daisy/KI failures are documented there with remedies.
2	Apply the listed `remedy` exactly when a triplet matches.	The remedy is part of the KI's validated debugging path.
3	If no triplet matches, read the relevant `docs/s_.md` stage document.	Stage docs contain format expectations and verification traps.
4	Compare against working files in `outputs/` or shipped Daisy examples.	The correct `.dai`, `.dwf`, and `.dlf` shapes are easiest to confirm from examples.
5	Only then fix the tool or report the full error.	Avoid replacing the actual Daisy model with a hand-coded approximation.

Tool Reference

1. `convert_weather_to_dwf.py` — Forcing Converter

Converts global meteorological data (CSV with columns for date, temperature, radiation, precipitation, etc.) into Daisy's .dwf weather file format.

Key conversions:

Radiation: MJ/m^2/d → W/m^2 (÷ 0.0864)
Temperature: K → °C (− 273.15) or passthrough if already °C
Precipitation: mm/3h → mm/d (× 8) or kg/m^2/s → mm/d (× 86400)
Validates: no negative radiation, temperature range −60 to +60°C, precip ≥ 0

2. `convert_soil_to_dai.py` — Soil/Parameter Converter

Converts HWSD or custom soil texture data into Daisy horizon and column definitions.

Key conversions:

Texture fractions from % to 0–1
Bulk density from kg/m^3 to g/cm^3
Depths from positive to negative (Daisy convention)
Auto-selects texture system (USDA3 for 3-fraction, ISSS4 for 4-fraction)
Optionally estimates hydraulic parameters via built-in pedotransfer functions

3. `run_daisy.py` — Execution Wrapper

Runs the Daisy binary with a given .dai setup file and captures output/errors.

Features:

Locates daisy binary (build dir, system PATH, or explicit path)
Validates that required input files (.dwf, .dai libraries) exist
Runs with timeout protection
Captures stdout/stderr and parses daisy.log for errors
Returns exit code and paths to generated .dlf files

4. `parse_daisy_output.py` — Output Parser

Parses .dlf (Daisy Log File) output into pandas DataFrames and CSV files.

Features:

Reads DLF header metadata (version, run time, parameters)
Parses tab-separated data section with proper column types
Extracts harvest summary, water balance, N balance, crop development
Computes derived metrics (total yield, N use efficiency, water productivity)
Generates time series CSV for downstream analysis

Execution Reference

Command Line

# Run a simulation
daisy test.dai

# Run with version info
daisy -v

# Run with info
daisy --info

# Run batch (multiple scenarios)
daisy batch.dai

Required Files for a Simulation

Main .dai file — defines the program with column, weather, manager, output
Weather .dwf file — meteorological forcing data
Library .dai files — crop.dai, tillage.dai, fertilizer.dai, log.dai (from lib/)
Soil .dai file — if soil defined in separate file

Library Files (installed with Daisy)

File	Content
`crop.dai`	Standard crop parameterizations (wheat, barley, maize, pea, etc.)
`tillage.dai`	Tillage operations (plowing, seed bed preparation, etc.)
`fertilizer.dai`	Fertilizer types (mineral: N25S, AmmoniumNitrate; organic: slurry)
`log.dai`	Standard output log definitions
`vegetation.dai`	Vegetation parameters

10. Coupling Interfaces

Upstream source	Variable exchanged	Unit	Temporal resolution
CMFD/MSWX/NASA POWER via `load_daily_forcing`	Weather forcing: radiation, temperature, precipitation, optional evapotranspiration, wind, humidity, vapor pressure	Daisy `.dwf` units	Daily
HWSD or custom texture/profile data	Soil texture, bulk density, hydraulic parameters, horizon depths	Daisy `.dai` units	Static profile
Management templates or user setup	Tillage, fertilization, sowing, harvest, irrigation	Daisy `.dai` action units	Event-based

Downstream consumer	Variable exchanged	Unit	Temporal resolution
Validation workflow	`sorg_DM`	Mg DM/ha	Harvest event / period summary
Water-balance analysis	`Evapotranspiration`, `Drain / Percolation`, `Theta`	see `dag.yaml` and `.dlf` headers	Daily to monthly, depending on log definition
Nitrogen-balance analysis	`Harvest_N`, `Leaching`, `Denitrification`	see `dag.yaml` and `.dlf` headers	Daily to monthly, depending on log definition
Crop-development analysis	`DS`, `LAI`, `harvest_index`	see `dag.yaml` and `.dlf` headers	Daily to harvest event, depending on log definition

11. Validated Results

The KI status line records example_validated for Taastrup, Denmark, 1986-1988. The current body campaign is pending; do not invent achieved metric values. When a real validation run is scored, judge it against docs/validation_convention.yaml, not intuition.

Test Site: Taastrup

Property	Value
Location	Taastrup, Denmark
Period	1986-1988
Validation status	`example_validated`
Headline output	`sorg_DM`
Headline unit	Mg DM/ha

Performance Metrics — judged against the field's bar

Convention for sorg_DM: metric pbias; direction zero_centered; citation moriasi2015. The convention's null bands are stated as no cited threshold.

Bar for sorg_DM (pbias, per moriasi2015): satisfactory within 25.0 of zero; good: no cited threshold (moriasi2015); very good: no cited threshold (moriasi2015). Achieved: body campaign pending.

Metric	Calibration	Validation	Full Period	Bar (convention, cited)
PBIAS (%)	body campaign pending	body campaign pending	body campaign pending	satisfactory: zero-centered 25.0 (`moriasi2015`); good: no cited threshold (`moriasi2015`); very good: no cited threshold (`moriasi2015`)

Data Replacement Tracking

Component	Source	Status	Notes
Forcing	Pipeline	Pending for body campaign	Use `convert_weather_to_dwf.py` and verify with `preflight_check.py`.
Soil	Pipeline	Pending for body campaign	Use `convert_soil_to_dai.py` and verify Daisy units/sign conventions.
Management	Daisy setup/template	Pending for body campaign	Keep `.dai` actions in Daisy syntax and units.
Observations	User/site data	Pending for body campaign	Bind to `sorg_DM` in `Mg DM/ha` dry matter for headline scoring.
Outputs	Daisy `.dlf` logs	Pending for body campaign	Parse with `parse_daisy_output.py`; do not hand-code replacement formulas.

12. Parameter Selection by Region

These are physically informed starting points, not calibration results. Prefer site-specific Daisy documentation, local agronomic management records, and the validated Taastrup example when no local calibration exists.

Climate / region	Key parameters	Rationale
Temperate Northern Europe	Built-in Daisy crop libraries, local sowing/harvest dates, measured soil horizons where available	Daisy crop parameterizations are primarily calibrated for temperate agricultural systems.
New sites with HWSD-only soils	Texture fractions, bulk density, pedotransfer hydraulic parameters	Provides a reproducible starting soil profile while preserving unit checks.
Irrigated fields	Irrigation actions and water-balance logging	Management timing and water additions must be explicit in `.dai` setup files.

Quick Start Example

# 1. Copy sample files
cp -r /path/to/daisy/sample /tmp/daisy-test
cd /tmp/daisy-test

# 2. Run the tutorial simulation
daisy test.dai

# 3. Check outputs
cat harvest.dlf          # Crop harvest results
cat field_water.dlf      # Water balance
cat field_nitrogen.dlf   # Nitrogen balance
cat sbarley.dlf          # Spring barley crop development

Expected output files: harvest.dlf, field_nitrogen.dlf, field_water.dlf, soil_nitrogen.dlf, soil_water.dlf, sbarley.dlf, checkpoint-*.dai, daisy.log

Common Crop Models Available

Crop	Dai Library	Typical Yield (Mg DM/ha)
Spring Barley	crop.dai / dk-sbarley.dai	4–7
Winter Wheat	crop.dai / dk-wwheat.dai	6–10
Winter Barley	crop.dai / dk-wbarley.dai	5–8
Winter Rape	crop.dai / dk-wrape.dai	3–5
Silage Maize	crop.dai / dk-maize.dai	10–18
Pea	pea.dai	3–5
Grass	grass.dai	8–14 (4 cuts/yr)
Potato	potato.dai	6–12
Sugar Beet	sugarbeet.dai	10–16

Known Limitations

1D only — no lateral flow between fields (except 2D experimental GP2D mode)
Daily or hourly — sub-hourly forcing not supported in standard mode
Temperate focus — crop models calibrated primarily for Northern European conditions
No built-in calibration — parameter optimization requires external tools
Lisp-like syntax — steep learning curve for configuration files
No GUI — command-line only (VSCode extension available for syntax highlighting)

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