Improving pedotransfer functions for predicting soil mineral associated organic carbon by ensemble machine learning

Soil organic carbon (SOC) sequestration is a promising natural climate solution for capturing atmospheric CO2, improving soil functions and services at the same time. A deep understanding of SOC response to environmental changes requires additional information on SOC fractions with distinct characteristics such as particulate organic carbon (POC) and mineral associated organic carbon (MAOC). Pedotransfer function (PTF) is a good strategy to estimate missing soil properties, while its application in SOC fractions has been poorly explored. Based on 352 representative mineral topsoil samples (0-20 cm) across Europe, we evaluated the potential of MAOC prediction using machine learning based PTF (random forest (RF), Cubist and gradient boosted machine (GBM)) together with predictor selection methods (recursive feature elimination (RFE) and forward recursive feature selection (FRFS)). RFE can effectively reduce the number of predictors from 21 to 12 with comparable performance to the models using all predictors. The proposed FRFS algorithm had the best model parsimony with only 6 predictors (SOC, silt+clay, nitrogen, nitrogen deposition, soil erosion and sand) and performed similar to or even better than RFE. The Cubist in combination with FRFS performed best among three machine learning models (R2 of 0.9, RMSE of 2.994 g kg-1). Our results also showed that five model ensemble methods had similar model performance and can improve model accuracy and robustness compared to a single machine learning model.

XIAO Yi;  XUE Jie;  ZHANG Xianglin;  WANG Nan;  HONG Yongsheng;  JIANG Yefeng;  ZHOU Yin;  TENG Hongfeng;  HU Bifeng;  LUGATO Emanuele;  RICHER-DE-FORGES Anne;  ARROUAYS Dominique;  SHI Zhou;  CHEN Songchao; 

2022-11-30

ELSEVIER

JRC129664

0016-7061 (online),   

https://www.sciencedirect.com/science/article/pii/S0016706122005158,    https://publications.jrc.ec.europa.eu/repository/handle/JRC129664,   

10.1016/j.geoderma.2022.116208 (online),