Calibration-Aware Decision Fusion for Artificial Intelligence Agriculture Drought Early Warning Under Compound Forecast Misspecification: A Computer False-Alarm Cost Evaluation

Drought early-warning systems that fuse multiple forecast sources must decide when to issue an alert, and that decision is a calibration problem before it is a classification problem. The framework treats each forecast source as a probability-forecast channel, estimates a per-channel calibration map, and fuses calibrated channels through a cost-weighted log-odds rule whose threshold is set by the relative cost of a missed drought and a false alarm. Compound misspecification is formalised as simultaneous miscalibration and dependence between channels, and a bound on the excess false-alarm rate is derived. The framework is illustrated on NASA POWER monthly reanalysis air temperature at 2 m for Harare, Zimbabwe (-17.83, 31.05) over 2000-2023, comprising 312 monthly observations with a mean of 18.914 C, a standard deviation of 3.107 C and a warming trend of 0.2001 C per decade. The analysis is a methodological and theoretical contribution: it specifies the fusion rule, derives the false-alarm bound, and identifies the conditions under which calibration-aware fusion reduces false-alarm cost relative to uncalibrated fusion. The results are propositions and derived bounds rather than field-validated alert outcomes, and the paper states the boundary conditions under which the framework should be tested prospectively.