The Grain-for-Green Program (GGP) has been widely demonstrated to be an effective ecological restoration initiative for accelerating soil organic carbon (SOC) accumulation in the post-agricultural period on the Loess Plateau. However, the spatiotemporal patterns of SOC dynamics across different soil depths and vegetation types remain insufficiently understood. In this study, we constructed an SOC database for the study area, comprising 1,221 observed SOC stocks collected from 86 peer-reviewed publications following the GGP. By integrating trend analysis, machine learning models, and mechanism-oriented approaches, we systematically simulated and compared the effects of four typical GGP vegetation types (grassland, sea buckthorn, Caragana shrub, and black locust) on SOC dynamics across different soil depths. The Michaelis–Menten analysis indicated relatively high long-term net SOC stock-gain potentials in the 0–20 cm soil layer under sea buckthorn and grassland. After subtracting the cropland SOC baseline of 13.4 Mg C ha –1 , the estimated maximum Δ SOC stock gains were approximately 20.1 and 19.2 Mg C ha –1 , respectively (both fitted relationships, p < 0.01). The SOC accumulation rate in the 0–20 cm layer accounted for about 40% of the total SOC in the 0–100 cm depth and was roughly twice that in the 20–40 cm layer. Although SOC accumulation rates decrease markedly with increasing soil depth, black locust maintained a relatively high accumulation rate in the 60–100 cm soil layer, reaching 0.2 Mg C ha –1 yr –1 . By 2025, cropland-to-vegetation conversions potentially associated with the GGP were associated with an estimated total net ΔSOC stock gain of 5.4 ± 1.2 Tg C relative to cropland across the 0–100 cm soil profile. We further identified distinct SOC dynamics pathways across vegetation types, whereby grasslands were primarily driven by climatic factors, whereas Caragana shrub and sea buckthorn were more strongly controlled by soil-mediated processes. Overall, our findings provide new insights into vegetation selection for ecological restoration aimed at SOC dynamics.
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Shengli et al. (2026) studied this question.
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