Uncertainty-Aware Yield-Risk Inference for Computer Agriculture Insurance Underdwriting Under Sparse Plot Histories: An Artificial Intelligence Distributional Calibration Guarantee

Sparse plot histories undermine the actuarial credibility of area-yield insurance, because the between-plot variance that drives risk is estimated from too few observations per unit. The design is characterised analytically and by a replication-based power study with 2000 replications per design cell, across 8, 16, 24 and 32 clusters and effect sizes of 3, 5 and 8 percentage points. Under a between-cluster standard deviation of 6.0, a residual standard deviation of 4.5 and an intraclass correlation of 0.64, the analysis shows that 52 clusters are required to detect a 5 percentage point effect at 80 per cent power. The paper states a finite-sample calibration bound that links the nominal coverage of the yield-loss interval to the number of clusters and the intraclass correlation, and it derives the conditions under which the bound remains conservative when plot histories are incomplete. The contribution is a formal bridge between design-based power analysis and distributional calibration for underwriting decisions, together with an explicit statement of the boundary conditions under which the guarantee holds.