Spatial variability of available phosphorus for cultivated soil based on GARBF neural network

In order to adjust land management measures, use phosphorus fertilizer properly, minimize phosphorus loss and mitigate non-point source pollution of water, 664 soil samples in cultivate horizon were collected in Gaozhou city, Guangdong Province in this study. The radial basis function network optimized by genetic algorithm (GARBF) and Ordinary Kriging methods were applied to reveal the characteristics of spatial variability of cultivated soil variability phosphorus (AP) and its spatial distribution pattern. The results suggested that spatial variability of surface soil AP of cultivated land in Gaozhou city exhibited semi-variance structure, and its semi-variance function fitted exponential and spherical models well. The analysis showed that the spatial correlation in surface cultivated soil AP was weak in the five sampling scales (training sample points were 100, 200, 300, 400 and 500), while unobvious in wide range. Predictions of soil AP in simulation using GARBF neural network was better than that using radial basis function(RBF) neural network (Near-RBF) prediction model based on several closest neighbors and Ordinary Kriging method. In practical application, the spatial interpolation map by GARBF neural network method with 300 soil samples showed a serious trend of surplus phosphate in cropping in Gaozhou city. Diffusive surplus phosphorus made a serious threat to the water environment in this region. The results provide a theoretical basis and technical support for predicting soil property spatial distribution accurately, using fertilizer properly and mitigating non-point source pollution of water.