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Farmland is one of the most important and active components of the soil carbon pool. Exploring the controlling factors of farmland soil organic carbon density (SOCD) and its sequestration rate (SOCDSR) is vital for improving carbon sequestration and addressing climate change. Present studies provide considerable attention to the impacts of natural factors and agricultural management on SOCD and SOCDSR. However, few of them focus on the interaction effects of environmental variables on SOCD and SOCDSR. Therefore, using 64 samples collected from 19 agricultural stations in China, this study explored the effects of natural factors, human activities, and their interactions on farmland SOCD and SOCDSR by using geographical detector methods. Results of geographical detectors showed that SOCD was associated with natural factors, including groundwater depth, soil type, clay content, mean annual temperature (MAT), and mean annual precipitation. SOCDSR was related to natural factors and agricultural management, including MAT, groundwater depth, fertilization, and their interactions. Interaction effects existed in all environmental variable pairs, and the explanatory power of interaction effects was often greater than that of the sum of two single variables. Specifically, the interaction effect of soil type and MAT explained 74.8% of the variation in SOCD, and further investigation revealed that SOCD was highest in Luvisols and was under a low MAT (<6 °C). The interaction effect of groundwater depth and fertilization explained 40.4% of the variation in SOCDSR, and fertilization was conducive to SOCD increase at a high groundwater depth (<3 m). These findings suggest that low soil temperature, high soil moisture, and fertilization are conducive to soil carbon accumulation. These findings also highlight the importance of agricultural management and interaction effects in explaining SOCD and SOCDSR, which promote our knowledge to better understand the variation of SOCD and its dynamics.

Accurate mapping of farmland soil organic carbon (SOC) provides valuable information for evaluating soil quality and guiding agricultural management. The integration of natural factors, agricultural activities, and landscape patterns may well fit the high spatial variation of SOC in low-relief farmlands. However, commonly used prediction methods are global models, ignoring the stratified heterogeneous relationship between SOC and environmental variables and failing to reveal the determinants of SOC in different subregions. Using 242 topsoil samples collected from Jianghan Plain, China, this study explored the stratified heterogeneous relationship between SOC and natural factors, agricultural activities, and landscape metrics, determined the dominant factors of SOC in each stratum, and predicted the spatial distribution of SOC using the Cubist model. Ordinary kriging, stepwise linear regression (SLR), and random forest (RF) were used as references. SLR and RF results showed that land use types, multiple cropping index, straw return, and percentage of water bodies are global dominant factors of SOC. Cubist results exhibited that the dominant factors of SOC vary in different cropping systems. Compared with the SOC of paddy fields, the SOC of irrigated land was more affected by irrigation-related factors. The effect of straw return on SOC was diverse under different cropping intensities. The Cubist model outperformed the other models in explaining SOC variation and SOC mapping (fitting R2 = 0.370 and predicted R2 = 0.474). These results highlight the importance of exploring the stratified heterogeneous relationship between SOC and covariates, and this knowledge provides a scientific basis for farmland zoning management. The Cubist model, integrating natural factors, agricultural activities, and landscape metrics, is effective in explaining SOC variation and mapping SOC in low-relief farmlands.