Data & Model Evaluation (agsy-data-and-model-evaluation)
A model is only as credible as its evaluation. AgSy reviewers are systems-modelling experts: they
want to see the model tested against independent data, its sensitivity and uncertainty
characterized, and the trade-offs the system exhibits — not a single tuned run presented as truth.
Model description and choice live in agsy-systems-framing-and-modeling; this skill covers testing and
reporting.
When to trigger
- Reporting how well the model reproduces observations
- Running sensitivity / uncertainty analysis
- Building scenario comparisons and trade-off analyses
- A reviewer asked for validation, sensitivity, uncertainty, or alternative scenarios
Evaluation norms AgSy expects
- Independent evaluation. Compare observed vs. simulated on data not used for calibration. Report standard fit statistics — RMSE, RRMSE, bias/ME, modelling efficiency (NSE), index of agreement (d), R² — and show the 1:1 plot. State what "good enough" means for the decision.
- Sensitivity analysis. Identify the parameters/inputs that drive outputs (local one-at-a-time and, where feasible, global methods — Morris, Sobol). Report which assumptions matter most.
- Uncertainty. Propagate uncertainty from inputs, parameters, and structure to the outputs that carry the conclusions. Present ranges/intervals, not point estimates dressed as certainty.
- Trade-off & scenario analysis. This is where AgSy papers earn their place: compare scenarios or management/design options on multiple objectives (yield, profit, environment, risk) and show the trade-offs and synergies — Pareto fronts, trade-off curves, multi-indicator profiles.
- Scaling. When you aggregate from field to farm to region, state how, and check that aggregation does not hide compensating errors.
Stochastic & data-driven components
- For Monte Carlo, ABM, or stochastic weather/price generators: set and report seeds, run enough replicates, and report the distribution, not one realization.
- For surrogate/emulator or ML components: validate against the full model and report where they fail.
Reproducibility while you work (not at the end)
- One master workflow regenerates every figure/table from inputs + model runs.
- Pin model version, parameter sets, and software versions; record calibration vs. evaluation splits.
- Keep table/figure numbers matched to script/model outputs — the data/code/model are deposited (see
agsy-reproducibility-and-data-policy).
Anti-patterns
- Calibrating and "validating" on the same data; reporting fit only on the training period
- A single tuned run with no sensitivity or uncertainty analysis
- Reporting R² alone (insensitive to bias) — pair it with RMSE/NSE/bias
- Scenario tables with no trade-offs surfaced (just "scenario B is best")
- Aggregating across scales without checking for compensating errors
Output format
【Evaluation data】independent of calibration? [Y/N] 【Fit statistics】RMSE / NSE / bias / d / R² + 1:1 plot 【Sensitivity】which inputs/parameters dominate 【Uncertainty】propagated to the conclusion-bearing outputs? [Y/N] 【Trade-offs】scenarios compared on multiple objectives (Pareto/curve) 【Reproducible】master workflow + pinned model/version + seeds? [Y/N] 【Next】agsy-figures-and-tables
Supplementary resources
../../resources/external_tools.md— calibration, sensitivity, uncertainty, and multi-objective packages../../resources/official-source-map.md— research-data/model reproducibility policy
