Abstract: To elucidate the spatial variations and key drivers of soil bulk density (BD) in typical farmland across Jiangsu Province, we collected soil samples from 611 sites at depths of 0-10 cm, 10-20 cm, and 20-40 cm. By combining measured BD data with a suite of physicochemical properties and climatic variables, we analyzed the spatial variations of BD and its main influencing factors, and then simulated BD using random forest (RF) and neural network (NN) models. Results showed that soil BD increased significantly with soil depth. The spatial patterns of BD in the 0-10 cm and 20-40 cm layers were largely consistent, whereas the 10-20 cm layer exhibited greater spatial randomness due to straw incorporation. Areas with high soil BD values were mainly concentrated in southern Jiangsu, Xuzhou, and coastal zones. Among the examined 11 crop planting systems, significant differences in BD were observed only in the 0-10 cm layer. The spatial variation of BD in the 0-20 cm soil layer was influenced primarily by tillage practices and straw incorporation, while BD in the 20-40 cm layer was mainly governed by parent material and hydrothermal conditions. BD was negatively correlated with soil organic carbon and total nitrogen, and positively correlated with pH and C/N. Soil depth, located region, mean annual temperature, and latitude jointly influence BD value. The RF model based on 10 soil physicochemical traits, environmental factors, and climatic variables explained 62% of BD variation, while the NN model based on 7 key variables explained 53% of BD variation. In the RF model, latitude, longitude, and mean annual temperature were the most important predictors, followed by C/N and soil organic carbon. In the NN model, mean annual precipitation and C/N ranked the highest among all variables. Our findings suggested that farmland BD is jointly controlled by soil physicochemical properties, geographic location, cropping systems and climate factors, and that the RF model offers higher simulation performance of BD for 0-40 cm soil layer, enabling convenient simulation of farmland BD in the Yangtze River Delta region using readily available variables.

Key words: soil bulk density, farmland soil, spatial distribution, machine learning