Environmental factors affecting soil organic carbon, total nitrogen, total phosphorus under two cropping systems in the Three Gorges Reservoir area

He-Shuang Wan, Wei-Chun Zhang,Wei Wu,Hong-Bin Liu

Journal of Soils and Sediments(2022)

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Abstract
Purpose The present study aimed to reveal the spatial distribution of soil nutrients in southwest China and to quantitatively evaluate the effect of environmental factors on spatial variability of soil nutrients under different cropping systems. Methods Semivariogram, random forest (RF), and partial dependence plots (PDP) were applied to investigate the relationships between environmental factors and soil nutrients variability. A total of 142 samples were collected from topsoil under rice and maize cropping systems in southwest China. Results Soil organic carbon (SOC), total nitrogen (TN), and total phosphorus (TP) in the topsoil of the study area were higher than their corresponding background and geochemical baseline values of China. Semivariogram analysis indicated that SOC and TN have weak spatial dependence, and TP has strong spatial dependence. The soil nutrient contents were predicted well by the RF models. The mean absolute error, root mean square error, and coefficient of determination were 0.14–0.47 g/kg, 0.19–3.67 g/kg, and 0.30–0.46, respectively. For different soil nutrients, the relative importance of environmental variables varied greatly. Cropping system, topographic wetness index (TWI), and slope were critical factors that controlled SOC variability. Cropping system, TWI, and mean annual temperature explained most variation of TN. Soil pH, aspect, and cropping system were dominant factors affecting TP variability. Conclusion In this study, SOC and TN had weak spatial autocorrelation, while TP had strong spatial autocorrelation. Meanwhile, cropping system and topography had greatly impact on the variability of soil nutrients, which should be considered in formulating agricultural measures in the future.
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Key words
Nutrient variation,Cropping system,Relative importance,Random forest,Partial dependence plot
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