Differentiable Crop Model Calibration for Artificial Intelligence Agriculture Yield Forecasting Under Unmeasured Canopy Stress: A Computer Identifiability Evaluation

Differentiable calibration of process-based crop models is increasingly proposed as a route to yield forecasting that can absorb the unmeasured canopy stress that statistical learners cannot see. The proposal rests on an identifiability assumption that has not been examined: that the gradient of a differentiable simulator with respect to stress parameters carries enough information to recover those parameters from the yield and phenology observations that are actually available. the present analysis treats that assumption as a formal object. A differentiable crop model is written as a parameter-to-observation map, and structural and practical identifiability of the canopy-stress block are analysed under a declared variance structure with between-cluster standard deviation 6.0, residual standard deviation 4.5 and intraclass correlation 0.64. A before-after-control-impact design is evaluated across 8, 16, 24 and 32 clusters and effects of 3, 5 and 8 percentage points, with 2000 replications per cell. The stress block is structurally identifiable only when the observation operator contains a canopy-sensitive channel, and that under the declared variance structure 52 clusters are required for 80 per cent power at a 5 percentage point effect. The contribution is a set of conditions under which differentiable calibration is informative rather than merely convergent, and a warning that low calibration loss is not evidence of recovered stress.