Causal Transport Bounds for Artificial Intelligence Agriculture Fertilizer Recommendation Under Hidden Plot-Management Confounding: A Computer Policy-Value Guarantee Evaluation

Artificial intelligence (AI) fertilizer recommendation systems trained on observational plot data are routinely deployed across heterogeneous smallholder populations, yet their policy value is threatened by hidden plot-management confounding: unmeasured farmer practices that jointly determine both nutrient application and yield. The framework combines a sensitivity model for hidden management intensity with a bounded-divergence transport constraint between source and target plot populations, yielding a computable lower bound on target policy value that remains valid without identifying the confounder. The bound is expressed as a function of the source policy value, the sensitivity parameter, and a Rényi divergence between covariate distributions, and it is shown to be tight under a class of exponential-family confounding mechanisms. A real-data calibration is performed on World Bank World Development Indicators for 24 African countries over 2000–2023 (N = 560 country-year observations), estimating the association between electricity access, urbanisation and GDP per capita by ordinary least squares with heteroskedasticity-robust (HC1) standard errors. The model yields R-squared = 0.591, adjusted R-squared = 0.5895, F = 184.764 (p = 2.82e-62), with an electricity-access coefficient of 1291.55405 (SE = 194.35159) and an urbanisation coefficient of 113.13952 (SE = 7.77493). The calibration illustrates how the bound degrades as the transport divergence grows, and it clarifies the conditions under which an AI fertilizer recommendation retains a positive policy-value guarantee when plot-management confounding cannot be ruled out.