Equity-Aware Threshold Optimization for Computer Agriculture Drought Insurance Payout Triggers Under Satellite Rainfall Underreporting: An Artificial Intelligence Fairness-Regret Evaluation

Satellite rainfall products systematically underreport extreme dry spells, and index-based drought insurance that triggers payouts on satellite estimates therefore fails smallholders in the very seasons when cover matters most. the present analysis formalises the trigger-design problem as a constrained fairness-regret optimisation and derives an equity-aware threshold rule that trades aggregate predictive accuracy against distributional error across vulnerable subpopulations. The analysis treats the satellite underreporting bias as a state-dependent measurement error, defines a regret functional that penalises under-compensation of resource-poor households more heavily than over-compensation of better-insured ones, and proves that the equity-aware optimum is a threshold shift whose magnitude is bounded by the bias-to-variance ratio of the satellite product. Using NASA POWER monthly reanalysis for Harare, Zimbabwe (2000-2023, 312 monthly observations, mean 18.914 C, warming trend 0.2001 C per decade), the paper calibrates the bias term and shows how the fairness-regret frontier responds to warming. The contribution is a decision-theoretic account of when equity-aware thresholds dominate accuracy-maximising ones, and a set of boundary conditions under which they do not.