Evaluation of Shapley-Value Additivity Audits for Artificial Intelligence Agriculture Yield Models Under Cooperative-Weighted Satellite Fusion: An Interpretability Attribution Bound for Smallholder Insurance Disputes

Smallholder crop-insurance disputes increasingly turn on yield predictions produced by machine-learning models that fuse satellite imagery from several providers, yet the attribution statements those models issue to farmers and adjudicators have no stated error bound. the present analysis treats the Shapley value as an audit instrument rather than a descriptive convenience and asks what additivity failure can be guaranteed to reveal. A cooperative-weighted fusion architecture is formalised in which each satellite source contributes a characteristic function over a coalition of features, and an additivity defect is defined as the gap between the model output and the sum of its Shapley attributions. Under a declared variance structure with an intraclass correlation of 0.64, a between-cluster standard deviation of 6.0 and a residual standard deviation of 4.5, the operating characteristics of a before-after-control-impact audit design were characterised across 2000 replications per design cell for cluster counts of 8, 16, 24 and 32 and effect sizes of 3, 5 and 8 percentage points. Fifty-four clusters were required to detect a 5 percentage point effect at 80 per cent power. The paper derives an attribution bound that converts additivity defect into a defensible interval for dispute adjudication, and argues that the bound, not the point attribution, is the appropriate object of evidentiary weight in smallholder insurance proceedings.