Evidence for the decision.

Hierarchical spatial models; trend forecasts; stochastic procurement

Select one major crop and comparable NASS county/state yield series. Define a pre-harvest forecast issue date and use only information published by then. Start with historical trend and lagged yield; treat dated NOAA weather and available acreage estimates as separately documented enhancements, not assumed fields in Quick Stats.

Evaluation: Use year-forward and region-held-out tests. Compare trend, historical-average and pooled models using yield error, interval coverage and adverse-year performance. Audit suppressed/revised estimates. Evaluate procurement regret only in a simulator whose prices, capacity and demand are clearly specified.

Boundary: Public aggregate estimates are not farm-level treatment data. Do not use final acreage, realized future weather or revised releases as if available before harvest, or claim causal effects of agronomic practices.

USDA NASS Quick Stats ↗

Markov transitions; spatial-temporal classification; error propagation

Use past-year Cropland Data Layers to predict the next year's crop class on a fixed, compatible grid. Compare persistence and transition-matrix baselines with a spatial-temporal classifier. Harmonize changing resolution, class definitions and alignment; never use the target-year crop map as an input feature.

Evaluation: Hold out future years and large geographic blocks, not random adjacent pixels. Report class-balanced accuracy, transition-specific confusion and county-level acreage error, with uncertainty clustered spatially. Test sensitivity to map resolution and documented label accuracy.

Boundary: CDL labels are themselves remotely sensed estimates, not perfect field ground truth. This study does not estimate farm profit, yield or the causal benefit of crop rotation; nominal pixel count overstates independent evidence.

USDA Cropland Data Layer ↗

Material-balance networks; concentration; robust optimization

Select compatible FAOSTAT production, trade and food-balance modules for one commodity group. Harmonize physical units, reporting years and product definitions; use bilateral trade tables only where available. Construct supply-dependence indicators and a constrained trade-reallocation scenario model.

Evaluation: Compare concentration-only rankings with network-based exposure. Verify mass-balance compatibility and stress alternative shock magnitudes, substitution limits and data-revision treatments. Compare indicators around historical disruptions descriptively, without claiming the model causally explains observed price changes.

Boundary: Country-level annual flows do not reveal a particular company's contracts, inventory or margin. Reallocation capacity and substitution are scenarios; the tool cannot claim realized savings or precise future food prices.

FAOSTAT ↗