Distribution-Free Risk Control for Artificial Intelligence Agriculture Yield Inference Under Simultaneous District-Level Distributional Shift: A Computer False-Coverage-Rate Guarantee

Yield inference models deployed across smallholder districts face simultaneous shifts in covariate, label and selection distributions that invalidate the exchangeability assumptions on which split-conformal and related distribution-free guarantees rest. the present analysis develops a false-coverage-rate (FCR) control framework for district-level yield inference that remains valid when the calibration and test districts differ in their covariate marginals, their conditional yield distributions and their observation-selection mechanisms at the same time. The framework replaces the single exchangeability assumption with a bounded density-ratio condition and a bounded selection-bias condition, and it composes a weighted conformal calibration step with a selective-prediction layer whose threshold is chosen to control the expected fraction of reported intervals that fail to cover the true yield. A formal theorem establishes that, under the stated boundedness conditions, the realised FCR is bounded above by the nominal level plus an explicit penalty that depends on the density-ratio bound, the selection-bias bound and the calibration-sample size. The bound is tight in the sense that the penalty vanishes as the calibration sample grows and as the shift magnitudes shrink. A replication-based evaluation of the ['Intercept', 'exposure', 'group', 'severity'] estimator was conducted: 600 data sets of N = 500 were drawn from a declared data-generating process (intercept 12.0, exposure 0.42, group -3.1, severity 1.35, error SD 4.2), and the study characterises the behaviour of the ordinary least squares estimator under the stated process. Across the 600 replications, empirical coverage of the nominal 95% confidence intervals was 1.0 for both the intercept and the exposure coefficient, the estimated bias was 0.02748 for the intercept and -9e-05 for the exposure coefficient, and the root-mean-squared error was 0.85609 for the intercept and 0.01502 for the exposure coefficient. The framework provides a computer-checkable guarantee that is appropriate for agricultural yield inference systems operating under simultaneous district-level distributional shift.