Investigating soil properties and yield variability in farming systems is crucial for delineating Management Zones (MZs). The objectives of study were to investigate the spatiotemporal variability of soil properties, identify spatial and temporal yield-limiting factors of soil and delineate MZs based on these factors. This study was conducted at the Xinghua Rice Smart Farm (33.08°E, 119.98°N) in Jiangsu Province, China, and the experiment covered five consecutive years of soil and rice yield testing from 2017 to 2021, with 933 geo-referenced soil samples and 140 rice yield samples collected annually. Soil samples were analyzed for pH, soil organic matter (SOM), total nitrogen (TN), available phosphorus (AP), available potassium (AK), and apparent soil conductivity (ECa). Spatial and temporal variability of soil properties and RY were analyzed using statistical and geostatistical methods. Ordinary Kriging (OK) interpolation characterized these distributions, and the random forest (RF) algorithm identified key yield-limiting factors. Subsequently, the effectiveness of using all variables to delineate the MZ was compared against the approach of defining MZs based solely on the identified yield-limiting factors. The study also compared Fuzzy C Means (FCM) and Spatial Fuzzy C-Means (sFCM) clustering to evaluate MZs and their temporal stability. Results showed that the coefficients of variation for soil properties ranged from low to medium (7.7-77.4%), with semi-variational function analyses showing moderate to high spatial dependence for most properties. Temporally, soil nutrients and ECa exhibited a slow increase, whereas pH decreased, showing the highest temporal stability for pH and the lowest for AP. RF analysis identified SOM, TN, and ECa as primary influencers of spatial variability of RY, and SOM, pH, and TN as main contributors to its temporal variability. The integration of yield-limiting factors with the sFCM method improves performance of MZ delineation, maintaining stability over the five-year period.