Comparison of artificial neural networks, geographically weighted regression and Cokriging methods for predicting the spatial distribution of soil macronutrients (N, P, and K)

Abstract

Soil macronutrients (i.e. nitrogen (N), phosphorus (P), and potassium (K)) are important soils components and knowing the spatial distribution of these parameters are necessary at precision agriculture. The purpose of this study was to evaluate the feasibility of different methods such as artificial neural networks (ANN) and two geostatistical methods (geographically weighted regression (GWR) and cokriging (CK)) to estimate N, P and K contents. For this purpose, soil samples were taken from topsoil (0–30 cm) at 106 points and analyzed for their chemical and physical parameters. These data were divided into calibration (n = 84) and validation (n = 22). Chemical and physical variables including clay, pH and organic carbon (OC) were used as auxiliary soil variables to estimate the N, P and K contents. Results showed that the ANN model (with coefficient of determination R 2 = 0.922 and root mean square error RMSE = 0.0079%) was more accurate compared to the CK model (with R 2 = 0.612 and RMSE = 0.0094%), and the GWR model (with R 2 = 0.872 and RMSE = 0.0089%) to estimate the N variable. The ANN model estimated the P with the RMSE of 3.630 ppm, which was respectively 28.93% and 20.00% less than the RMSE of 4.680 ppm and 4.357 ppm from the CK and GWR models. The estimated K by CK, GWR and ANN models have the RMSE of 76.794 ppm, 75.790 ppm and 52.484 ppm. Results indicated that the performance of the CK model for estimation of macro nutrients (N, P and K) was slightly lower than the GWR model. Also, the accuracy of the ANN model was higher than CK and GWR models, which proved to be more effective and reliable methods for estimating macro nutrients.