7.1 Case study — Iowa corn 2024
A 240,000-hectare cooperative in Iowa deployed an ensemble yield-prediction system across the 2024 growing season. The ensemble combined APSIM process-model output, a Sentinel-2 NDVI empirical model, and a gradient-boosted-tree model on historical yield records. Prediction interval coverage at the field level was 81% over the season (vs. the targeted 80%), and aggregated regional forecasts matched USDA NASS post-harvest estimates within their published margin of error. The cooperative used the field-level forecasts to schedule harvest equipment allocation across member farms, reducing late-harvest losses relative to the prior year's manual scheduling.
7.2 Case study — French winegrowing 2024
A consortium of Champagne winegrowers deployed a tree-crop variant of the standard adapted with the alternate-bearing carry-forward feature. The 2024 forecast correctly identified a below-average year for the appellation and informed reserve- wine release decisions. The consortium's annual audit confirmed calibration of the prediction interval; one early-season forecast was retracted after it was discovered that a critical spring frost was missed by the upstream weather data — an example of why the standard's feature-provenance pointers are essential.
7.3 Case study — South Korean rice 2024
The Korean Rural Development Administration ran a parallel- evaluation pilot of WIA-AGRI-008 conformant predictions against their existing field-survey-based forecast. The pilot covered 18 administrative regions across the 2024 season. The model's regional aggregates were within 3% of the survey-based forecasts on average, with the largest discrepancies in mountain-fringe regions where field heterogeneity was poorly captured by the gridded weather inputs. The pilot's outcome was a recommendation to combine the model and survey estimates rather than replace one with the other.
7.4 Best practices distilled from these case studies
- Validate prediction-interval calibration at season end against actual measured yields, not just point-forecast accuracy.
- Retract predictions when upstream feature data is found faulty, rather than letting stale predictions persist in downstream caches.
- Pair model-derived forecasts with field-survey estimates rather than treating either as sole truth.
- Document the feature set used per prediction so consumers can compare apples to apples across regions.
- Re-train at least every 24 months and disclose the training window on each prediction envelope.