Abstract: Soil texture is a qualitative classification tool used in both the field and laboratory to determine the classes of agricultural soils based on physical texture. Surface soil texture reflects soil physical and chemical properties, which affects not only soil fertility and farming/production performance but also crop quality and yield. Precision agriculture requires reliable data on the variations in field soil properties for effective management decisions. The most common way to do this is to predict the values for un-sampled places using observed samples and represent the variations in maps. The optimal sampling method is importation in the evaluation of spatial variations in soil texture, which is more critical for fertilization or irrigation in precision agriculture. The acquisition of precise soil data which are representative of an entire survey area is critical for irrigation and fertilization in precision agriculture. Here, we compared the ability of three sampling methods used in estimating the precision agriculture practices and predict the spatial distribution of soil texture with the goal of choosing the optimal sampling method. About 289 soil samples were collected from the field at 0 20 cm depth in 16 m grid cells in the Southeast Pengshui County of Chongqing City. The geostatistics method and Geographic Information System (GIS) was used to evaluate the accuracy of the 16 m grid-cell sampling (a total of 253 sampling points), 32 m grid-cell sampling (a total of 115 sampling points) and random sampling (a total of 115 sampling points). The results showed that the largest component of the soil texture was silt and the lowest was sand. While sand and clay exhibited a medium variation, silt showed a low variation. Based on Kolmogorov-Smirnov test, sand, silt and clay were all normally distributed. Results of geostatistics analysis suggested that larger sampling intervals were needed under grid-cell sampling while lower sampling intervals could be used under random sampling of spatial variability of soil texture in the study area. Cross validation showed that the interpolation precision was highest for soil texture components under experimental control (16 m grid-cell sampling). This was followed by 32 m grid-cell sampling, while then random sampling had the lowest interpolation precision. The research indicated that based on the factors considered (including interpolation precision, cost effectiveness and timeliness), random sampling was the optimal method for analyzing soil texture in the study area.