Xuan Sun, Zhaolin Du*, Jian Ding, Shunan Zheng, Yanpo Yao, Lina Wu, Hongan Chen, Yi An*, Yongming Luo*

ABSTRACT

Biochar-mediated soil organic carbon (SOC) dynam&#x2;ics in cadmium (Cd)-contaminated soils are governed by complex interactions among biochar properties, soil characteristics, and environmental factors. However, the key drivers and causal mechanisms remain unclear, hindering the design of biochar strategies for carbon sequestration. This study integrated machine learning (ML) and partial least-squares path modeling (PLS−PM) to establish an interpretable causal framework. Using a global data set, a high-precision random forest model quantified the primary drivers. Soil properties dominated the predictions (60.27%), with phosphorus (P) (optimal level:<0.7 g="">

KEYWORDS: machine learning, partial least-squares path modeling, cadmium, biochar, soil organic carbon

https://doi.org/10.1021/acs.est.5c12567