DOI: 10.3390/agronomy16212120 ISSN: 2073-4395

Coupling Physical and Biochemical Pathways of Soil Organic Carbon Stabilization Under Conservation Management in Dryland Croplands: A Review

Chang Liu, Lingling Li, Aixia Xu, Zhuzhu Luo, Junhong Xie

Soil organic carbon (SOC) sequestration is vital for sustaining soil health, maintaining agricultural productivity, and combating land degradation in drylands. In these fragile regions, water scarcity, calcareous chemistry, and soil erosion create distinct challenges that traditional climate–substrate–disturbance frameworks fail to fully capture. Although numerous reviews have examined conservation management, they typically evaluate physical, biochemical, and microbial mechanisms in isolation, leaving their cross-regulations largely unresolved. To bridge this gap, we propose a coupled framework that merges physical aggregate protection with biochemical nutrient limitation. By linking ecoenzymatic stoichiometry to the soil aggregate hierarchy, our approach tracks metabolic signals alongside micro-structural changes. Drawing on evidence from the Loess Plateau and comparable semi-arid environments, we suggest that conservation practices stabilize SOC mainly by preserving pre-existing protected carbon rather than by forming new protective structures. Furthermore, the evidence suggests that this physical protection capacity is finite and texture-dependent, and, in the studies reviewed, physical and biochemical indicators together explain a large share of the variation in carbon mineralization. This framework offers an explanation for why four core agronomic practices (no-till duration, straw return rates, subsoiling frequency, and crop rotation combination) often show diminishing or context-dependent returns rather than linear increases, with diminishing responses consistent with capacity saturation. Finally, given the current scarcity of empirical data on tillage-driven nutrient limitations across aggregate fractions, we outline how integrating microbial–mineral carbon pump theory with long-term experimental networks could extend this framework for optimizing dryland agriculture.

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