Evaluation of Verifiable-Delay Commitments for Artificial Intelligence Agriculture Procurement Auctions Under Ministerial Bid-Suppression Windows: A Governance Timing-Integrity Bound for Smallholder Award Equity

Ministerial bid-suppression windows in artificial intelligence (AI) agriculture procurement create a timing-integrity problem: a minister can delay the public opening of tenders until smallholder-favourable bids are withdrawn, and no existing commitment device makes that delay verifiable to losing bidders. The bound is derived analytically from a two-period auction model with a ministerial suppression option and is then characterised through a replication-based design evaluation of the before-after-control-impact (BACIP) estimator that a regulator would use to audit VDC compliance. No observational data are used or claimed. Under a declared variance structure with between-cluster standard deviation 6.0, residual standard deviation 4.5 and intraclass correlation 0.64, power was estimated across 2000 design cells for cluster counts of 8, 16, 24 and 32 and effect sizes of 3, 5 and 8 percentage points. Fifty-four clusters are required for 80 per cent power at a 5-percentage-point effect.