Risk-Coverage Evaluation for Computer Agriculture Pest-Diagnosis Advisories Under Shifting Extension-Agent Availability: An Artificial Intelligence Conformal-Abstention Bound

Pest-diagnosis advisories deployed through agricultural extension services face a structural mismatch: the classifier's error rate is fixed at training time, but the number of extension agents available to adjudicate uncertain cases varies across districts and seasons. The bound is distribution-free, holds for any exchangeable calibration and test sequence, and separates the contribution of classifier miscalibration from the contribution of agent scarcity. 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. Coverage of the nominal 95% intervals was 1.0 for both the intercept and the exposure coefficient, estimated bias was 0.01627 and -0.0002 respectively, and RMSE was 0.86914 and 0.01593. The framework proposes that extension services can select abstention thresholds that guarantee a stated risk level whenever realised agent availability exceeds a computable threshold, and that below that threshold no threshold achieves the guarantee. The contribution is a bound that makes the availability constraint explicit in advisory evaluation rather than treating it as an implementation detail.