Comparison of multiple statistical techniques to predict soil phosphorus

- Advanced statistical methods were tested to determine the best model to predict soil P.
- The best models were GA and PLS.
- Models could play an important role to predict soil changes at reduced cost and time.
- This is especially important in developing nations with limited finances.

Soil scientists have tested various models to predict soil physical, chemical and biological properties over the last few decades. Determination of soil phosphorus (P) in soils is difficult due to the sensitivity of its measurement, especially in developing nations because of lack of sufficient facilities and limitation of financial resources. In this study, advanced statistical methods (intelligent and regression models) were evaluated, such as genetic algorithm (GA), artificial neural network (ANN), fuzzy inference system (FIS), adaptive neuro-fuzzy inference system (ANFIS), partial least squares (PLS), principal components regression (PCR), ordinary least squares (OLS) and multiple regression (MR), to determine the best model to predict P. This research was carried out at Mazandaran Research Center of Agricultural and Natural Resources, Sari, Iran. Four properties of soils, clay, sand, soil organic matter (SOM) and pH were presented to the models as independent parameters to predict soil P. Such advanced quantitative models have never been compared with each other in order to find the best model for prediction. The results revealed that PLS (among regression models) and GA and ANN (among intelligent models) are promising approaches for the estimation of soil P with higher R2 and value account for (VAF) and lower mean absolute percentage error (MAPE) and root mean square error (RMSE) compared to the other models. The ANN model predicted soil P more accurately than the other models with R2 = 0.912 and RMSE = 4.019. The GA and PLS models both provided formulas to predict soil P with good fits. Results of the sensitivity analysis showed that SOM was a more effective factor predicting soil P relative to the other variables and SOM provides an important source of P in soil.