Spatial modeling using Gaussian random fields is effective for understanding soil properties. By analyzing soil samples from northern Ethiopia, researchers used kriging to predict soil properties across a field. They found that predictions using kriging and conditional simulations matched well with observed data, especially near sampling locations. The study highlights the accuracy of kriging in predicting soil properties and the importance of considering spatial variability in soil analysis.