Soil Carbon Modeling at Crossroads: Building Reliable Methods for Policy and Practice

ABSTRACT Soil carbon mapping (SCM) is rapidly becoming a cornerstone of soil science and environmental decision‐making, from precision agriculture to national carbon inventories. Yet SCM is at a crossroads: the methods that often promise high‐accuracy metrics can mask structural weaknesses that limit generalization and undermine policy relevance. Similar problems apply to larger Digital Soil Mapping (DSM). In this article, using soil organic carbon (SOC) as an illustrative example, we highlight three systematic sources of error that consistently inflate SCM performance: depth, bulk density, and spatial autocorrelation. Soil profile depth is often mishandled when profile increments are split between training and test sets, leading to inflated accuracy estimates. Bulk density (BD), essential for converting concentrations to stocks, is inconsistently applied and rarely accompanied by uncertainty estimates. SOC stocks at sampling locations are often derived using BD, and when BD is reintroduced as a predictor in machine learning models, it inflates reported accuracy and the model's predictive skill. Spatial autocorrelation further exaggerates accuracy when conventional random splits are used, while spatial blocking reveals much lower and more realistic predictive skill. Drawing on recent literature and our own analysis, we argue that SCM must adopt more rigorous practices, including profile‐level validation, spatially aware blocking, standardized reporting of assumptions, and alignment with policy‐relevant depth intervals. These steps will enhance comparability across studies and ensure that SCM outputs are credible for carbon accounting, climate mitigation, and land management purposes. The future of SCM and DSM depends on both new algorithms and methodological rigor and transparency.