ArticleMolecular ecology2026
An Interpretable Machine Learning Approach to Ecologically Characterize Soil Carbon and Structure From Multi-Kingdom Microbiome, Texture and Climate.
Article in Molecular ecology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Soil physical structure is a critical determinant of agricultural landscape resilience, yet standard pedotransfer functions estimate soil hydraulic and structural properties using static abiotic variables, often overlooking the biological mechanisms that actively organize soil structure. This study evaluates the predictive power of multi-kingdom microbiome data (prokaryotes, fungi and microeukaryotes) for three key soil functions: soil organic carbon (SOC) stock, mean weight diameter (MWD) and macroporosity. Using a dataset of 2251 agricultural soil samples from Quebec, Canada, we benchmarked four machine learning algorithms (HGBR, RFR, XGBoost, SVR) and four data aggregation strategies. The integration of microbiome data with texture and climate variables achieved high peak predictive accuracy (
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