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Construction of Pedotransfer Function for Predicting Soil Bulk Density in Cultivated Land of Northeast China Using Random Forest
Author:
Affiliation:

1.Co‑Innovation Center for Sustainable Forestry in Southern China, Nanjing Forestry University, Nanjing 210037, China;2.State Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil Science, Chinese Academy of Sciences, Nanjing 210008, China;3.College of Advanced Agricultural Sciences, University of Chinese Academy of Sciences, Beijing 100049, China

Fund Project:

Supported by the National Key R&D Program of China (No. 2021YFD1500102), Strategic Priority Research Program of the Chinese Academy of Sciences (No. XDA28010102), and the National Natural Science Foundation of China (No. 42107145)

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    Abstract:

    【Objective】Soil bulk density (BD) is crucial for understanding the physical condition of black soil in cultivated land of Northeast China and advancing its utilization and protection. The traditional cutting ring method for determining BD is time-consuming and laborious, making the evaluation of BD on a large spatial scale difficult. The pedo-transfer function (PTF) can estimate BD information using readily available soil variables. However, there is currently a lack of research on PTF models specifically targeting the whole of Northeast China, and the importance of potential soil attribute variables for PTF model construction remains to be elucidated.【Method】By incorporating soil organic matter (SOM), moisture content (MC), and soil texture-related variables as input features, we constructed PTF models capable of predicting BD on a large scale. Furthermore, we delved into the significance of these soil attribute variables in the constructed PTF models. Additionally, we assessed the suitability of existing published PTF models for BD prediction in the black soil of Northeast China.【Result】The optimal predicted R2 values of published PTF were 0.17, 0.22, and 0.26, respectively, for the topsoil, subsoil, and all soil samples, and Root Mean Squared Errors ( RMSE) were 0.16, 0.13, and 0.15 g·cm-3, respectively. Also, the optimal predicted R2 values of PTF for the topsoil, subsoil, and all soil samples based on the proposed RF method were 0.22, 0.45, and 0.37, respectively, while the RMSE values were 0.16, 0.11, and 0.14 g·cm-3, respectively.【Conclusion】The published PTF models had low BD prediction accuracy and were difficult to use for BD prediction on the scale of black soil in Northeast China whereas the PTF model constructed in this study has the potential to predict BD on the scale of black soil in Northeast China. Among the variables, SOM was the most important variable for predicting BD in the black soil of Northeast China, followed by MC, while soil texture-related variables had a relatively small impact.

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History
  • Received:June 18,2024
  • Revised:October 22,2024
  • Adopted:November 18,2024
  • Online: November 22,2024
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