Integrating APSIM model with machine learning to predict wheat yield spatial distribution

Traditional simulation models are often point based; thus, more research is needed to emphasize spatial simulation, providing decision‐makers with fast recommendations. Combining machine learning algorithms with spatial process‐based models could be considered an appropriate solution. We created a spatial model in R (APSIMx_R) to generate fine‐resolution data from coarse‐resolution data, which is typically available at the regional level. The APSIM crop model outputs were then deployed to train and test the artificial neural network, creating a hybrid modeling approach for robust spatial simulations. The APSIMx_R package facilitates preparing the required model inputs, executes the prediction, processes, and analyzes the APSIM crop model outputs. This note demonstrates the use of a new approach for creating reproducible crop modeling workflows with the spatial APSIM next‐generation model and machine learning algorithms. The tool was deployed for spatial and temporal simulation of potential wheat yield under different nitrogen rates and various wheat cultivars. The spatial APSIMx_R was validated by comparing the simulated yield at 100 kg N ha−1 to the analogues' actual yield at the same grid points, which showed good agreement (d = 0.89) between the spatially predicted and actual yield. The hybrid approach increased such precision, resulting in higher agreement (d = 0.95) with actual yield. When the interaction between cultivars and nitrogen levels was considered, it was found that the novel cultivar Sakha95 is nitrogen voracious, exhibiting a larger drop in yield (65%) under minimal nitrogen treatment (0 kg N ha−1) relative to the potential yield. Core Ideas Crop models are frequently point based, while developing spatial models is required. We developed a spatial Agricultural Production System Simulation model in R to generate fine‐resolution data. The spatial model‐based R was integrated with an artificial neural network, creating a hybrid approach. The developed approach is used to determine the yield heterogeneity at scale. The hybrid approach's simulated yield correlated positively with farmer yield.