Differentially Private computed Plot Generation for Artificial Intelligence Agriculture Yield Model Pretraining Under Confidential Cooperative Records: A Computer Membership-Inference Risk Evaluation

Cooperatives hold plot-level yield records that could support pretraining of artificial-intelligence yield models, but confidentiality constraints and membership-inference risk limit data sharing. The framework combines a bounded sensitivity mechanism for continuous yield attributes with a discrete exponential mechanism for categorical plot descriptors, and it derives an upper bound on membership-inference advantage as a function of the privacy budget and the number of pretraining records. A replication-based evaluation of the before-after-control-impact estimator was conducted under a declared variance structure (between-cluster SD = 6.0, residual SD = 4.5, intraclass correlation = 0.64) across 2000 design cells, with cluster counts of 8, 16, 24 and 32 and effect sizes of 3, 5 and 8 percentage points. The analysis characterised the operating characteristics of the design rather than estimating effects from observational data. Power reached conventional levels at 56 clusters for a 5-percentage-point effect at 80% power, and the minimum detectable effect declined monotonically with cluster count. The derived membership-inference bound indicates that privacy budgets below 1.0 keep advantage under 0.05 for pretraining sets of 10,000 records, while larger budgets permit advantage above 0.10. The framework provides a computable basis for cooperatives to release computed pretraining corpora without exposing individual plot records.