Quantifying how soils resist degradation and recover organic carbon following agricultural abandonment is critical for understanding land restoration trajectories in West Africa. This study applies a Bayesian hierarchical modeling framework to assess resistance (the proportion of soil organic carbon [SOC] retained after land use) and resilience (the rate of SOC recovery) during forest succession across eight chronosequences in Cote d'Ivoire. SOC was measured across three depths (0-30 cm) under fallow, secondary, and old-growth forests. Modeling revealed that soil resistance was about 50 % of their original SOC, with lower resistance under cotton (42 %) and perennial crops (47 %), and higher under annual crops (57 %). Resistance also varied by topography, with lowerslope sites averaging 42 %, versus over 52 % at other positions. SOC resilience was highly influenced by environmental and management factors. Remnant tree density had the strongest positive effect: areas with over 50 remnant trees per hectare had recovery rates twice as high as sites without. Conversely, increasing precipitation, temperature, and their seasonality slowed SOC recovery, highlighting climate-related limits to resilience. SOC recovery also varied by soil type, with Acrisols showing the highest resilience. Overall, the model indicates that up to 90 % of SOC can be recovered within 10 years in favorable conditions. The developed Bayesian approach provides probabilistic estimates of SOC trajectories, offering nuanced insight into ecological and management drivers. Our findings emphasize the importance of preserving remnant trees as ecological anchors in postagricultural landscapes, reinforcing passive regeneration as a cost-effective strategy to restore soil carbon in West Africa.