Graph Neural Network Yield Forecasting for Artificial Intelligence Agriculture Under Inter-Plot Water Sharing: A Computer Spatial-Leakage Evaluation

Yield forecasting models for smallholder agriculture are increasingly trained on plot-level observations that are not statistically independent, because neighbouring plots share irrigation water, labour and microclimate. When such data are partitioned at random into training and test folds, information about a plot's yield can leak across the partition through its neighbours, inflating reported accuracy. the present analysis formalises that leakage for graph neural network (GNN) yield forecasters and evaluates it as a design problem rather than an empirical one. A spatial adjacency graph is defined over plots, a message-passing forecaster is specified, and the bias induced by random versus spatially blocked cross-validation is derived under a declared variance structure. The operating characteristics of the evaluation design are then characterised by a replication-based power analysis of the before-after-control-impact estimator, using between-cluster SD = 6.0, residual SD = 4.5 and intraclass correlation = 0.64 across 2000 replications per design cell for cluster counts of 8, 16, 24 and 32 and effects of 3, 5 and 8 percentage points. Fifty-two clusters are required for 80% power at a 5-percentage-point effect. The contribution is a leakage-aware evaluation protocol and a formal statement of the conditions under which random partitioning overstates GNN yield-forecasting skill.