Abstract

Accurate prediction of soil carbon dynamics requires mechanistic understanding of causal relationships among environmental factors, management practices, and biogeochemical processes. This study evaluates advanced causal modeling techniques for improving soil carbon prediction accuracy and process generalization across diverse ecosystems. We analyzed 89 long-term soil carbon datasets spanning 23 countries using structural equation modeling (SEM), directed acyclic graphs (DAGs), and machine learning causal inference methods including causal forests and double machine learning (DML). Results demonstrate that causal modeling approaches achieve 23-34% higher prediction accuracy compared to traditional correlational models when applied to independent validation datasets. Structural equation modeling revealed climate variables (temperature, precipitation) as primary drivers explaining 42% of carbon variance, while management practices contributed 28% and soil properties 19%. Causal forest analysis identified critical interaction effects between temperature and soil texture (coefficient: 0.67, P<0.001) and precipitation and organic amendments (coefficient: 0.54, P<0.01). Mediation analysis through SEM showed that 65% of management effects operate indirectly through soil biological processes rather than direct carbon inputs. Cross-validation using geographically independent sites demonstrated superior generalization of causal models with mean absolute error (MAE) of 0.31 Mg C ha⁻¹ compared to 0.47 Mg C ha⁻¹ for machine learning models and 0.52 Mg C ha⁻¹ for process-based models. Causal discovery algorithms identified previously unrecognized relationships including bidirectional causality between microbial diversity and carbon stability (correlation: 0.73), and unexpected negative effects of certain tillage practices under specific moisture conditions. Economic analysis reveals that improved prediction accuracy could enhance carbon market valuations by $78-145 ha⁻¹ through reduced uncertainty premiums. However, causal modeling requires larger sample sizes (n>500) and comprehensive variable measurement, limiting applicability in data-sparse regions. These findings demonstrate that causal modeling techniques provide superior mechanistic understanding and prediction accuracy for soil carbon dynamics, supporting evidence-based management decisions and policy development for climate change mitigation strategies.