The researchers studied soil in a hilly area in Southern China to map out the levels of soil organic carbon (SOC) and total nitrogen (TN). They found that SOC and TN varied across different depths, with SOC being influenced by both external and internal factors, while TN was mainly affected by internal factors. By using a method called ordinary kriging, they were able to accurately predict the distribution of SOC and TN in the soil. The results showed that SOC and TN levels were higher on the edges of the area and lower in the middle. In the future, incorporating probabilistic and machine learning methods could improve the accuracy of predicting soil properties.