Pedotransfer Functions for Field Capacity, Permanent Wilting Point, and Available Water Capacity Based on Random Forest Models for Routine Soil Health Analysis

Figures & data

Figure 1. Distribution of soil samples by state across the continental U.S. within the training dataset for field capacity, permanent wilting point, and available water capacity pedotransfer functions. Soil samples from NAPESHM study sites within the U.S. were used as the validation dataset to test models.

Figure 2. (a-c) measured vs. predicted field capacity water content (θ_FC) for full random forest (RF), reduced RF, and multiple linear regression (MLR) models on the training dataset. (a-b) full and reduced RF models show out of bag predictions. Validation metrics within plots include percent variance explained, out of bag (OOB) root mean square error (RMSE), and OOB normalized RMSE (NRMSE). (d-f) measured vs. predicted field capacity for full RF, reduced RF, and MLR models for the NAPESHM validation dataset. Text within plots includes the regression equation, adjusted R², mean absolute error (MAE), RMSE, and NRMSE.

Figure 3. (a-d) partial dependence plots for individual field capacity water content (θ_FC) predictor variables: sand, silt, clay, and soil organic matter. (e) Multi-predictor partial dependence plot with the interaction between sand and soil organic matter. These partial dependence plots show the influence of individual predictor variables on θ_FC within the full RF model while all other predictor variables are held constant.

Figure 4. (a-c) measured vs. predicted permanent wilting point water content (θ_PWP) for full random forest (RF), reduced RF, and multiple linear regression (MLR) models on the training dataset. (a-b) full and reduced RF models show RF model out of bag predictions. Validation metrics within plots include percent variance explained, out of bag (OOB) root mean square error (RMSE), and OOB normalized RMSE (NRMSE). (d-f) measured vs. predicted permanent wilting point for full RF, reduced RF, and MLR models for the NAPESHM validation dataset. Text within plots includes the regression equation, adjusted R², mean absolute error (MAE), RMSE, and NRMSE.

Figure 5. (a-d) partial dependence plots for individual permanent wilting point water content (θ_PWP) predictor variables: clay, sand, magnesium, and soil organic matter. (e) Multi-predictor partial dependence plot with the interaction between clay and soil organic matter. These partial dependence plots show the influence of individual predictor variables on θ_PWP within the full RF model while all other predictor variables are held constant.

Figure 6. (a-c) measured vs. predicted available water capacity (AWC) for full random forest (RF), reduced random forest, and multiple linear regression models on the training dataset. (a-b) full and reduced random forest models show OOB RF model predictions on the training dataset. Validation metrics within plots include percent variance explained, out of bag (OOB) root mean square error (RMSE), and OOB normalized RMSE (NRMSE). (d-f) measured vs. predicted available water capacity for full random forest, reduced random forest, and multiple linear regression models for the NAPESHM validation dataset. Text within plots includes the regression equation, adjusted R², mean absolute error (MAE), RMSE, and NRMSE.

Figure 7. (a-d) partial dependence plots for individual available water capacity (AWC) predictor variables: sand, silt, clay, and soil organic matter. (e) Multi-predictor partial dependence plot with the interaction between sand and soil organic matter. These partial dependence plots show the influence of individual predictor variables on AWC within the full RF model while all other predictor variables are held constant.