Comparing machine learning algorithms for predicting and digitally mapping surface soil available phosphorous: a case study from southwestern Iran

In developing countries like Iran, where information is scarce, understanding the spatial variability of soil available phosphorous (SAP), one of the three major nutrients, is crucial for effective agricultural ecosystem management. This study aimed to predict and digitally map the spatial distribution and related uncertainty of SAP while also assessing the impact of environmental factors on SAP variability in the topsoils. A study area from northern Khuzestan province, Iran was selected as case study area. Three machine learning (ML) models, namely, Random Forest (RF), Artificial Neural Network (ANN), and Support Vector Regression (SVR), were used to develop predictive relationship between surface soil (0–10 cm) SAP content and environmental covariates derived from a digital elevation model and Landsat 8 images. A total of 250 topsoil samples were collected following the conditioned Latin Hypercube Sampling (cLHS) approach and several soil properties were measured in the laboratory. Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Lin’s Concordance Correlation Coefficient (LCCC) were used to determine the accuracy of models. The findings indicated that the RF algorithm demonstrated the most favorable performance, with a mean absolute error (MAE) of 0.85 mg SAP kg⁻¹, the lowest root mean square error (RMSE) of 0.99 mg SAP kg⁻¹, and the highest linear correlation coefficient (LCCC) values of 0.96. This suggests that the RF algorithm had the least tendency to overestimate or underestimate SAP contents compared to other methods. Consequently, the RF algorithm was selected as the optimal choice. Predictive ML models were employed to digitally map SAP contents within the region. Spatial patterns of SAP contents showed an increasing gradient from west to east. The spatial variability information provides a basis for developing sustainable production system in the area.