The model gave very precise predictions for soils with clay contents lower than 0.3 kg kg− 1, while a moderate over-prediction was observed for soils very high in clay.
Compared with soil testing phosphorus (STP), such as Olsen P, the degree of P saturation (DPS) generally improves the risk prediction for soil P loss.
The addition of the 5000 EB points substantially improved predictions for shallow soil thickness (RMSE 0.9 m) as well as soil thickness in the 2 5 m range, while having negligible impact on predictions of thicker soils.
We compare linear and equivalent-linear site response predictions for a soil layer of varying thickness over bedrock, and assess the effects of varying the bedrock shear-wave velocity (VSb) and quality factor (Q).
Different meteorological models were compared in terms of their prediction errors for soil temperatures at seven observation depths.
The prediction models for soil property mapping performed well, with overall RMSEP values of 10.6, 0.34, 9.1, and 6.5 for SOC, pH, sand, and sum of bases, respectively.
The combination of IR and LG data did not yield a better prediction model for soil aggregate stability values and classes.
This study was designed to develop improved formulae for assessing interrill erosion rate by incorporating the aggregate stability index (As) in the prediction evaluations for soil erodibilites of Ultisols in subtropical China.
Overall, the use of SSPFs by including topographic attributes such as dispersal area, elevation, surface curvature and plan curvature and normalized difference vegetation index (NDVI) could improve the performance of the prediction functions for soil shrinkage indices.
