Graph-Neural Inference for Computer Agriculture Intercropping Yield Estimation Under Unobserved Root Competition: An Artificial Intelligence Identifiability Bound Analysis

Intercropping yield estimation from above-ground observations is complicated by root competition, a latent process that is rarely measured and that confounds the relationship between observed canopy traits and realised yield. The intercropping system is formalised as a graph whose nodes carry above-ground traits and whose edges encode neighbour relations; root competition enters as a latent node-level variable that is not observed. A graph-neural estimator is specified, and the question addressed is whether its parameter is identifiable from above-ground data alone. The parameter is identifiable only up to an equivalence class whose size is governed by the graph's neighbourhood structure and by the variance of the latent competition term. A bound on the estimation error is derived, and the bound is shown to be tight when the latent term is exchangeable across neighbours. The design analysis, which characterises the operating characteristics of a before-after-control-impact estimator under a declared variance structure (between-cluster SD = 6.0, residual SD = 4.5, intraclass correlation = 0.64), indicates that 52 clusters are required for 80% power at a 5 percentage-point effect. The contribution is a formal statement of what above-ground intercropping data can and cannot identify about yield under root competition, together with the design implications that follow.