Estimating soil carbon sequestration potential with mid-IR spectroscopy and explainable machine learning

Soil carbon sequestration refers to the process of capturing atmospheric carbon through plant photosynthesis and storing it in soil as organic carbon. The primary mechanism for carbon sequestration is the adsorption of organic carbon molecules onto the mineral surfaces of the soil's fine fraction (clay + silt ≤ 20 µm), forming mineral-associated organic carbon (MAOC). Soil has a finite capacity to stabilise and sequester organic carbon, known as carbon saturation capacity, which depends on the proportion of reactive minerals in the soil. The difference between the current MAOC content and the carbon saturation capacity is referred to as the organic carbon saturation deficit ( C def ) or sequestration potential. Fourier-transformed (FTIR) mid-infrared (mid-IR) spectroscopy can simultaneously measure soil properties relevant to carbon stabilisation: organic carbon functional groups, clay and iron-oxide mineralogy and particle size. Therefore, we hypothesise that mid-IR spectroscopy can effectively and accurately estimate C def . Here, we aim to (i) develop spectroscopic models to estimate the MAOC and C def of 482 Australian topsoil samples, (ii) model MAOC and C def using mid-IR spectra and an interpretable machine learning algorithm, and (iii) further interpret the MAOC and C def models using SHapley Additive exPlanations (SHAP). Using frontier line analysis, we fitted a function to the upper envelope of the MAOC vs. clay + silt relationship to derive C def . We recorded mid-IR spectra of the samples and used the regression trees method CUBIST to model MAOC content and C def . We interpreted these models by examining the regression trees and using SHAP. The models were unbiased and estimated MAOC content with R 2 of 0.86 and RMSE of 2.77 (g kg soil −1 ), and C def with R 2 of 0.89 and RMSE of 3.72 (g kg soil −1 ). Model interpretation showed that C def estimates relied on negative interactions with absorptions from organic matter functional groups and positive interactions with absorptions from clay minerals. Our results demonstrate that mid-IR spectra can effectively estimate MAOC and soil C def , providing a rapid, cost-effective method for assessing and monitoring this critical soil function.