Development of a Soil pH Prediction Model for South Korean Agricultural Fields Using Digital Soil Mapping

Agricultural soils in South Korea are prone to acidification due to the leaching of exchangeable bases caused by intensive rainfall. To mitigate this issue, the government subsidizes lime fertilizers based on soil test data. However, for untested fields lacking analytical data, a standardized application rate is uniformly applied, which limits the precision of soil management. This study aims to develop an optimal prediction model and establish environmental and geographical covariates for the precise estimation of soil pH in untested fields using Machine Learning (ML)  techniques. A dataset comprising 203,941 soil pH measurements collected between 2022 and 2024 was utilized for model training. Based on the SCORPAN framework, we constructed environmental and geographical covariates across a grid covering South Korea, incorporating Soil (s), Climate (c), Organisms (o), Relief (r), Parent material (p), and Spatial position (n) factors. The Random Forest (RF) algorithm was employed as the primary prediction model. Furthermore, to account for spatial autocorrelation, a hybrid model combining RF with Ordinary Kriging (OK) of the prediction residuals (RF+OK) was developed, and its performance was compared with the standalone RF model. Variable importance analysis indicated that geographical variables (distance information) had the most significant influence on pH prediction, followed by organism variables (NDVI and crop cultivation density) and climatic variables. In the model performance evaluation, the standalone RF model achieved an R² of 0.509 and an RMSE of 0.550. However, the hybrid RF+OK model demonstrated significantly improved prediction precision, achieving an R² of 0.590 and an RMSE of 0.492. The big data-driven pH prediction model established in this study is expected to eliminate policy blind spots in areas lacking soil test information and enable the precise calculation of lime requirements for sustainable soil management.