Modelling and Interpreting Thermal Stability Indices to Understand Soil Carbon Stabilization Using Soil Properties Data

Abstract. Soil organic carbon (SOC) sequestration and nutrient cycling are related to the susceptibility of soil organic matter to biological decomposition. Several studies have demonstrated associations between biological stability and thermal stability, as assessed using programmed pyrolysis. We sought to develop parsimonious machine learning (ML) models to predict SOC stability indices from measured soil properties. The study analyzed indices such as S1, S2, and S3; the oxygen index (OI); the hydrogen index (HI); and T50, which reflect SOC composition, thermal behaviour, and stability. A total of 203 soil samples collected at 0–15cm depth increments from agricultural, forest, and wetland landscapes in New Brunswick, Canada, were analyzed. Feature selection techniques optimized predictive models, and a random forest (RF) was used to develop one. Correlation results revealed that HI was negatively associated with pH (r = –0.35) and bulk density (r = –0.33), whereas OI showed a positive correlation with pH (r = 0.34). Thermal indices were more strongly related to soil chemistry and texture, with S1 closely correlated with total carbon (r = 0.88) and nitrogen, and S2 negatively associated with sand (r = –0.66) but positively associated with clay (r = 0.24) and POXC (r = 0.84). T50 showed positive correlations with both pH (r = 0.48) and bulk density (r = 0.36), indicating greater thermal stability in higher pH, compacted soils, though these patterns varied by land use. Random Forest (RF) model predicted S1 S3 indices with high accuracy (CCC = 0.83–0.86), while HI and OI were more difficult to model (CCC = 0.44–0.48), suggesting missing biological or environmental predictors; NH₄⁺ and POXC emerged as key predictors. Structural equation modeling (SEM), after addressing multicollinearity, supported a hypothesis driven model that explained ~54% of T50 variation. Clay dissolved organic carbon, pH, and aluminum showed significant direct associations with T50 (β for pH = 0.44), whereas bulk density showed no meaningful relationship. Our study demonstrates that ML and SEM can reveal patterns and associations between soil properties and thermal stability indices, offering insight into understanding the SOC stability under a changing climate as well as presenting a framework for rapid estimation of SOC stability proxies.