Benchmarking Evaluation of Delay-Aware Reinforcement Learning for Computer Agriculture Variable-Rate Nitrogen Scheduling Under Leaching-Risk Uncertainty: An Artificial Intelligence Regret Audit for Groundwater Compliance

Variable-rate nitrogen scheduling in computer agriculture must reconcile two competing demands: maximising crop uptake and limiting nitrate leaching into groundwater. Reinforcement learning (RL) offers a data-driven alternative to rule-based schedulers, but its deployment is complicated by actuation delays and by uncertainty in the leaching-risk signal. Because no field data are available, the study is framed as a methodological and comparative analysis: it formalises the scheduling problem as a constrained Markov decision process with delayed actions, defines a regret metric that penalises both yield loss and compliance violations, and derives a finite-sample bound on the regret gap between the delay-aware learner and an oracle scheduler. The bound is then evaluated across a design grid of 8, 16, 24 and 32 management clusters, effect sizes of 3, 5 and 8 percentage points, and 2000 replications per cell, using a before-after-control-impact variance structure with an intraclass correlation of 0.64, a between-cluster standard deviation of 6.0 and a residual standard deviation of 4.5. The regret gap widens nonlinearly as the delay horizon grows relative to the decision interval, and that the compliance penalty dominates the yield penalty once the leaching-risk signal is sufficiently noisy. The implication is that many reported RL gains in agricultural scheduling may be artefacts of underpowered evaluations.