Benchmarking Evaluation of Distributionally Robust Crop-Yield Estimators for Computer Agriculture Insurance Under District-Level Distribution Shift: An Artificial Intelligence Worst-Case Calibration Bound for Payout Solvency

Crop-yield estimators used to set agricultural insurance payouts are typically calibrated on historical district records and then deployed into seasons whose covariate distribution has shifted. The framework is evaluated through a replication-based benchmark in which 600 data sets of N = 500 were drawn from a declared data-generating process with intercept 12.0, exposure 0.42, group -3.1, severity 1.35 and error SD 4.2, and the behaviour of the ordinary least squares estimator was characterised under that process. Across the 600 replications the estimated bias was -0.06802 for the intercept and 0.00106 for the exposure coefficient, the root mean squared error was 0.89252 and 0.01617 respectively, and empirical coverage of the nominal 95% confidence intervals was 1.0 for both parameters. The bound shows that payout solvency is governed not by average calibration error but by the tail of the calibration loss over the ambiguity set, and that a nominal coverage of 1.0 in the in-distribution benchmark is compatible with material shortfall once the district-level shift exceeds the radius the estimator was calibrated against. The contribution is a benchmark protocol and a formal solvency criterion that together make the robustness of a crop-yield estimator auditable before it is used to price a payout.