Abstract:【Objective】Soil cationic exchange capacity (CEC) directly reflects capacity of the soil supplying and buffering cation nutrients, and hence plays a very important role in conserving soil fertility. The knowledge about spatial distribution of soil CEC and effects of its control factors (i.e., clay, soil organic matter (SOM), and soil pH) at a regional scale may help precisely regulate soil fertility in the region. This paper explored soil CEC and its related control factors (i.e., Clay, SOM and soil pH) in the topsoil (0~20 cm) and subsoil (20~40 cm) of Jinxian County, Jiangxi Province, China for analysis of spatial non-stationary relationships between them, with a view to providing some critical information for the region to formulate specific soil fertility building measures.【Method】In the past, the traditional least squares regression (OLS) method was used to explore effects of relevant factors on soil CEC. The method, however, is a population regression one, and assumes that the relationships between soil CEC and its control factors are constant, thus ignoring spatial non-stationary relationships between soil CEC and its control factors across the region. Geographically weighted regression (GWR) - a local spatial regression model, can be used to solve this problem. With this model, spatial locations of the data are embedded into the linear regression model in exploring spatial non-stationary relationships between variables. And the regression coefficients of the model have been estimated separately by spatial data location. Therefore, compared with the OLS model, GWR is obviously advantageous in exploring spatial non-stationary relationship between soil CEC and its related control factors.【Result】Results of the descriptive statistical analysis show that soil CEC varies moderately in Jinxian County. The topsoil is higher than the subsoil in soil nutrient retention capacity. Soil CEC is relatively high in the western part of the county, but lower in the northern and southeastern parts and always higher in the topsoil than in the subsoil. The GWR analysis shows that the relationships between soil CEC and its related control factors (i.e., soil pH, clay soil, and SOM) were not constant and varied spatially, demonstrating the existence of certain spatial non-stationarity. The effects of the control factors on soil CEC varied with the soil layer. For example, clay in the topsoil affected soil CEC more in the southwest than in the northeast; while that in the subsoil did more in the southeast than in the northwest. Furthermore, effects of the factors varied from sub-region to sub-region. For instance, in topsoil, the effect of SOM was low in the south, but quite high in the northwest. The soil CEC spatial distribution map and the regression coefficient map of soil CEC and its control factors demonstrates that in the northern region where soil CEC is quite low, soil CEC is more sensitive to changes in SOM than in any other regions. In this case, more organic manure should be applied to improve soil CEC and hence soil nutrient retention capacity. However, in the southeastern region where soil CEC is relatively low, clay is the major factor affecting soil CEC in both soil layers, and pH in the topsoil is another. In this case, either alteration of soil texture or application of alkaline fertilizer in this region would effectively increase soil CEC.【Conclusion】The findings show that the control factors vary sharply in effect on soil CEC with sub-region and soil depth. Meanwhile, the model of GWR effectively reveals that spatial non-stationary relationships exist between soil CEC and its related control factors. Based on the soil CEC spatial distribution map, it is recommended that more organic manure and/or alkaline fertilizer be applied to alter soil texture and improve soil fertility in the northern and southeastern regions.