Evaluation of Concept-Probe Sufficiency Audits for Artificial Intelligence Agriculture Yield-Forecast Models Under Satellite-Revisit Sparsity: An Interpretability Reliability Bound for Smallholder Crop-Insurance Payouts

Concept-probe sufficiency audits are increasingly proposed as a precondition for deploying artificial intelligence yield-forecast models in smallholder crop-insurance, on the reasoning that a model whose internal representations can be decoded into agronomically meaningful concepts is safe to trust with payout decisions. The audit's inferential warrant is not intrinsic to the probe but depends on the temporal sampling regime of the satellite record that supplies the model's inputs. A formal reliability bound is derived for the sufficiency statistic under satellite-revisit sparsity, treating the probe's estimated concept alignment as a noisy measurement whose variance is inflated by the effective number of independent overpasses per growing season. The bound is then propagated to the payout decision through a before-after-control-impact estimator of the insurance indemnity, and the operating characteristics of that estimator are characterised under a declared variance structure (between-cluster SD = 6.0, residual SD = 4.5, intraclass correlation = 0.64) 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. The sufficiency audit's false-pass rate is governed jointly by revisit density and cluster count, that 54 clusters are required to detect a 5 percentage point effect at 80% power, and that the audit cannot by itself certify payout reliability below a revisit threshold that the bound makes explicit. The contribution is a reliability bound that converts an interpretability claim into a decision-theoretic one, together with the design conditions under which concept-probe sufficiency auditing is and is not a defensible gate for smallholder indemnity.