Visualizing Results
Running a model is only the first half of the job.
The second half is making the results interpretable.
Why visualization matters in crop-model workflows
Good plots help you answer different kinds of questions:
- Did the model run at all?
- Are the observed and simulated values on the same scale?
- Is the mismatch systematic or occasional?
- Which treatments are hardest for the model?
- Are errors coming from timing, magnitude, or both?
Without plots, calibration can become blind.
What to visualize first
For a beginner, the most useful first figures are:
- observed vs simulated scatter plots
- time-series overlays
- flowering or stage-date comparisons
- summary metric panels
That sequence moves from broad agreement to case-specific diagnosis.
A practical pattern
For real projects, keep figure generation separate from the main run logic.
That separation makes it easier to:
- rerun figures without rerunning every simulation
- audit the data feeding each figure
- debug joins and aggregation choices
The Hopf case-study figures
In the local reproduction workspace, a focused figure script was built around the reproduction outputs rather than around older dashboard-specific code.
The idea is general:
- use the wrapper to generate clean outputs
- save joined time-series and metric tables
- build plots from those saved outputs
That pattern is more portable than a plotting script that is tightly coupled to one personal machine layout.
Common figure types in this project
Observed vs simulated panels
Best for:
- quick agreement checks
- identifying bias
- comparing multiple variables side by side
Time-series overlays
Best for:
- checking whether timing is right
- checking growth trajectory shape
- seeing whether the model diverges early or late
Stage-date alignment plots
Best for:
- phenology validation
- identifying treatment-specific timing issues
Metric summaries
Best for:
- reporting
- comparing variables at a glance
- identifying which variables need more work
Advice for contributors
Treat plotting code as part of the reproducibility workflow, not as a cosmetic extra.
That means:
- keep inputs explicit
- save derived tables
- use consistent naming
- avoid hard-coded personal directories
- document what each figure is meant to prove