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

Abstract

Available water capacity (AWC), field capacity (θFC), and permanent wilting point (θPWP) are regarded as key physical soil health indicators that directly capture the soil’s capacity to store plant available water but are expensive components of a comprehensive soil health analysis. To reduce costs, pedotransfer functions for θFC, θPWP, and AWC were developed from a dataset of 7,232 soil samples with texture, soil organic matter (SOM), permanganate-oxidizable carbon, soil respiration, AWC, θFC, θPWP, wet aggregate stability, and extractable potassium, magnesium, iron, and manganese. Three functions were developed for each property: a full random forest (RF) model containing all variables, a reduced RF model and a multiple linear regression model containing texture and SOM. Pedotransfer functions were validated with an independent dataset that contained 1,406 samples. The full RF models for θFC, θPWP, and AWC reduced the root mean square error (RMSE) by 16.3, 13.3, and 12.8%, compared to multiple linear regression models, respectively. Furthermore, the full RF models for θFC, θPWP, and AWC reduced RMSE by 11.6, 6.7, and 12.8%, compared to the reduced RF model, respectively. Permanganate-oxidizable carbon, wet aggregate stability, and extractable magnesium, potassium, and iron were useful novel predictor variables for improving prediction of θFC and AWC. AWC was sensitive in 20/57 long-term experiments, and full RF models were able to replicate 5/20 of those significant results. New RF pedotransfer functions for θFC, θPWP, and AWC can enhance prediction compared to traditional modeling techniques, fits into existing interpretative frameworks, and improves cost-effectiveness of comprehensive assessments of soil health.