Post-Hoc Conformalized Yield Forecasting for Computer Agriculture Insurance Under Systemic Drought Correlation: A Computer Coverage-Guarantee Evaluation

Systemic drought correlation breaks the independence assumption that underpins conventional crop-yield insurance, because losses cluster across neighbouring farms and the empirical distribution of aggregate claims is not the distribution that any single policyholder faces. The framework couples a point yield forecaster with split-conformal calibration on spatially blocked residuals, so that the prediction interval inherits a finite-sample marginal coverage guarantee under exchangeability and degrades gracefully, in a quantified way, when exchangeability fails. A formal treatment derives the coverage gap as a function of the intra-cluster residual correlation and the number of calibration clusters, and shows that naive conformal calibration on individual plots understates the aggregate coverage shortfall by a factor that grows with cluster size. The framework is illustrated with a reanalysis temperature series for Accra, Ghana (NASA POWER MERRA-2, 312 monthly observations, 2000-2023; mean 26.521 C, warming trend 0.1622 C per decade), used only to fix the covariate scale of the calibration problem. The contribution is a coverage-guarantee evaluation that separates the marginal guarantee conformal prediction can deliver from the joint guarantee an insurer actually needs, and specifies the calibration design that narrows the gap.