End-to-End Intern Exercise: From Raw Notes to a Finished Run

This exercise is designed for a beginner who knows little or nothing about:

  • agronomy
  • DSSAT
  • hemp
  • weather files
  • soil profiles

The goal is not only to finish a run.

The goal is to learn how to think from raw field information all the way to a traceable simulation result.

Exercise goal

By the end of this exercise, the intern should be able to:

  1. organize raw field notes into DSSAT input categories
  2. identify missing information and explicit assumptions
  3. build weather, soil, and management tables in a DSSAT-oriented way
  4. connect those inputs to a project file
  5. launch a run with the wrapper
  6. inspect the outputs and explain what happened at a beginner level

The starting packet

Imagine the intern receives this packet.

Field note summary

Site:

  • site code: TRN1
  • latitude: 29.70 N
  • longitude: 82.41 W
  • elevation: 32 m

Crop:

  • crop: industrial hemp
  • cultivar: IH Williams
  • objective: evaluate stem and total biomass under moderate nitrogen

Management:

  • planting date: 2022-05-12
  • population: 60 plants m-2
  • row spacing: 0.76 m
  • planting depth: 2.5 cm
  • nitrogen applied:
    • 60 kg N ha-1 on planting day
    • 120 kg N ha-1 on 2022-06-10
  • irrigation applied:
    • 20 mm on 2022-05-14
    • 25 mm on 2022-06-01

Observation dates:

  • 2022-06-20
  • 2022-07-05
  • 2022-07-22
  • 2022-08-15

Soil notes:

  • sandy loam surface
  • sandy clay loam subsoil
  • profile depth 120 cm
  • moderate initial moisture

Weather source:

  • nearby station export with daily radiation, minimum temperature, maximum temperature, and rainfall

Part 1: organize the packet

Task 1

Create a four-part note with headings:

  • weather
  • soil
  • management
  • genotype

Expected result

The intern should place:

  • station coordinates and daily weather under weather
  • texture, depth, and water status clues under soil
  • planting, fertilizer, irrigation, and observation dates under management
  • cultivar name under genotype

Why this matters

This is the first step in learning to think like a modeler rather than like a reader of mixed field notes.

Part 2: build a weather preparation sheet

Task 2

Create a table with columns:

  • Date
  • SRAD
  • TMIN
  • TMAX
  • RAIN

Populate it from the station export and check:

  1. no missing dates
  2. no duplicate dates
  3. TMAX >= TMIN
  4. rainfall units are daily totals
  5. radiation units are consistent

Deliverable

A cleaned daily weather table plus a one-paragraph note describing:

  • source of data
  • date coverage
  • any corrections made

Common beginner trap

Do not jump straight into a DSSAT weather file before doing the quality checks.

Part 3: build a soil preparation sheet

Task 3

Turn the soil notes into a layered table with at least five layers down to 120 cm.

Suggested columns:

  • Layer bottom
  • Texture
  • Bulk density
  • LL
  • DUL
  • SAT
  • Organic C

Deliverable

A provisional soil profile table plus a note that states clearly which values were:

  • measured
  • estimated
  • borrowed from standard references or nearby profiles

Common beginner trap

Do not pretend estimated hydraulic values are measured truths.

Part 4: build a management preparation sheet

Task 4

Create separate tables for:

  • planting
  • fertilizer
  • irrigation
  • harvest or observation schedule

Deliverable

A management packet that could later be encoded in an experiment file.

Common beginner trap

Do not mix destructive biomass harvest dates with routine standing observations without labeling the difference clearly.

Part 5: document assumptions

Task 5

Write a short list of assumptions such as:

  • initial soil moisture estimated from field note "moderately moist"
  • weather station assumed representative of the field
  • cultivar name mapped to the intended DSSAT cultivar entry

Deliverable

A plain-language assumptions log.

Why this matters

Good model work is not about hiding uncertainty. It is about making uncertainty visible and manageable.

Part 6: prepare the run context

For the first execution exercise, the intern does not need to hand-author every DSSAT file from scratch.

Instead, the intern should work from a known project template and inspect how its file structure maps onto the prepared notes.

Task 6

Open a known hemp project file and identify:

  • weather linkage
  • soil linkage
  • planting section
  • fertilizer section
  • cultivar section
  • simulation controls

Deliverable

A short annotation note explaining where each of those pieces lives.

Why this step is important

This builds the bridge between the preparation sheets and the real DSSAT file ecosystem.

Part 7: launch a first run

Once the intern has a valid project file and a working DSSAT installation, they can launch a test run with the wrapper.

Example run pattern

source("C:/path/to/DSSAT-wrapper/R/DSSAT_omniwrapper.R")

result <- DSSAT_omniwrapper(
  model_options = list(
    DSSAT_path = "C:/path/to/DSSAT48",
    DSSAT_exe = "DSCSM048.EXE",
    project_file = "C:/path/to/project/Hemp/TRN1.HMX",
    suppress_output = TRUE
  ),
  situation = "TRN1_1",
  var = "CWAD"
)

Task 7

Run the project and record:

  • whether the wrapper returned without error
  • how many rows were in the simulation output
  • which variables were available
  • the first and last simulated dates

Deliverable

A short run note summarizing the simulation status.

Part 8: perform a first interpretation

Task 8

Look at the simulation output and answer:

  1. Did the crop simulate through the intended season?
  2. Is the requested biomass variable present?
  3. Does the output look like a daily time series?
  4. Does the timing appear plausible relative to planting and harvest dates?

Deliverable

A one-page beginner interpretation note.

Example interpretation language

Good:

  • "The run completed and produced a daily biomass series through the expected season."
  • "I still need to verify whether flowering timing and biomass magnitude are realistic."

Not good:

  • "The model is correct because it ran."

Part 9: optional observation comparison

If observation files are available, the intern can take one more step and read them through the wrapper:

obs <- DSSAT_omni_read_obs(
  model_options = list(
    DSSAT_path = "C:/path/to/DSSAT48",
    DSSAT_exe = "DSCSM048.EXE",
    project_file = "C:/path/to/project/Hemp/TRN1.HMX"
  ),
  situation = "TRN1_1",
  read_end_season = TRUE
)

Task 9

Check whether observation data were found and whether their dates overlap with the simulated series.

Deliverable

A note answering:

  • Were observations present?
  • Were they in-season, end-season, or both?
  • Are the dates aligned enough for later plotting or metrics?

What a successful exercise submission should contain

A strong intern submission should include:

  1. an organized raw-notes summary
  2. a cleaned weather table
  3. a layered soil table
  4. a management table set
  5. an assumptions log
  6. a project-file annotation note
  7. a run summary
  8. a first interpretation note

What this exercise is really teaching

This is not just a software exercise.

It teaches the intern how to move across four ways of thinking:

  • field observation
  • structured data preparation
  • simulation setup
  • scientific interpretation

That transition is the heart of practical crop-model work.