Simulation of the availability index of soil copper content using general regression neural network

Excessive soil copper (Cu) availability leads to plant growth retardation and leaf chlorosis, and the contamination of Cu in the food chain would be detrimental to human and animal health. The most important path for Cu accumulation in plants is uptake from soils. It is therefore important to understand the availability of soil Cu and its controlling factors to modify Cu availability and prevent excessive Cu from entering the food chain. The present study proposed a general regression neural network (GRNN) to simulate the availability index of soil Cu (available heavy mental concentrations/total heavy metal concentrations), based on the influencing factors of total Cu concentration, pH, organic matter (OM), available phosphorus (AP), and readily available potassium (RAK). Results showed that total Cu concentration, combined with OM and AP, achieved the lowest RMSE value (0.0524) for the modeled value of the availability index of soil Cu. The simulated results by GRNN and the ground truth values had better agreement (R ² = 0.7760) than that by a linear model (R ² = 0.6464) for 23 test samples. Moreover, GRNN obtained lower averaged relative errors than linear model. This demonstrated that GRNN could be used to simulate the availability index of soil heavy metals and gained better results than linear model.