Evaluation of Genuine-Progress Verification Protocols for Artificial Intelligence Agriculture Benchmark Contests Under Ministry Scoreboard Fabrication: A Governance Validity-Degradation Bound for Smallholder Advisory Quality

Ministry scoreboards that report artificial intelligence (AI) agriculture benchmark results are vulnerable to fabrication, and the verification protocols meant to authenticate genuine progress have not been analysed for their statistical operating characteristics. the present analysis treats a genuine-progress verification protocol as a before-after-control-impact (BACI) design and characterises its power to detect real advisory-quality gains when the ministry's reported scoreboard is unreliable. 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 effects of 3, 5 and 8 percentage points. The design required 54 clusters to detect a 5-percentage-point gain at 80% power, a requirement that exceeds the largest cluster count examined. The analysis yields a governance validity-degradation bound: below a threshold number of verified clusters, a protocol cannot distinguish genuine progress from scoreboard fabrication, and the bound tightens as the intraclass correlation rises. The contribution is a formal statement of the conditions under which verification protocols for AI agriculture benchmark contests can and cannot certify smallholder advisory quality, with implications for the design of ministry-facing audit institutions.