Deployment-Shift Detection for Artificial Intelligence Agriculture Disease Forecasting Under Silent Concept Drift: A Computer Sequential-Test Power Evaluation

Silent concept drift in agricultural disease forecasting is a statistical problem before it is an engineering one: a deployed model's error distribution changes without any observable trigger, and the question is whether a sequential monitoring rule can detect the change before the forecast has already failed. the present analysis formalises silent drift as a change in the conditional distribution of forecast error given covariates, and evaluates the operating characteristics of a before-after-control-impact (BACIP) sequential detector under a declared variance structure. No observational data are used or claimed. The analysis characterises the design under between-cluster SD = 6.0, residual SD = 4.5 and intraclass correlation = 0.64, with power estimated across 2000 design cells. The design grid comprises 8, 16, 24 and 32 clusters and effect sizes of 3, 5 and 8 percentage points. The analysis indicates that 52 clusters are required to detect a 5 percentage-point shift at 80% power under this variance structure, and that the minimum detectable effect at 80% power falls steeply as cluster count rises from 8 to 32. The contribution is a formal statement of the detection problem, a derivation of the BACIP interaction estimator's variance under clustering, and a power evaluation that fixes the cluster budget a field deployment would need before silent drift can be reliably caught. The results imply that most small-pilot agricultural forecasting deployments are underpowered for drift detection, and that the monitoring rule should be designed at the same time as the model rather than after it.