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Assessing Spatial Variation and Driving Factors of Available Phosphorus in a Hilly Area (Gaozhou, South China) Using Modeling Approaches and Digital Soil Mapping

Author

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  • Wenhui Zhang

    (College of Natural Resources and Environment, South China Agricultural University, Guangzhou 510642, China)

  • Liangwei Cheng

    (College of Natural Resources and Environment, South China Agricultural University, Guangzhou 510642, China)

  • Ruitao Xu

    (College of Natural Resources and Environment, South China Agricultural University, Guangzhou 510642, China)

  • Xiaohua He

    (College of Natural Resources and Environment, South China Agricultural University, Guangzhou 510642, China)

  • Weihan Mo

    (College of Natural Resources and Environment, South China Agricultural University, Guangzhou 510642, China)

  • Jianbo Xu

    (College of Natural Resources and Environment, South China Agricultural University, Guangzhou 510642, China)

Abstract

Soil fertility plays a crucial role in crop growth, so it is important to study the spatial distribution and variation of soil fertility for agricultural management and decision-making. However, traditional methods for assessing soil fertility are time-consuming and economically burdensome. Moreover, it is hard to capture the spatial variation of soil properties across continuous geographic space using the conventional methods. As key techniques of digital soil mapping (DSM), spatial interpolation techniques have been widely applied in soil surveys and analysis in recent years, since they can predict soil properties at unknown points in continuous space based on limited sample points. However, further research is needed on spatial interpolation models for DSM in regions with variable climates and complex terrains, which are characterized by strong spatial variation in both environmental variables and soil fertility. In this study, taking a typical hilly area in a subtropical monsoon climate, i.e., Gaozhou, Guangdong Province, China, as an example, the performances of four popular spatial interpolation models (Random Forest (RF), Ordinary Kriging, Inverse Distance Weighting, and Radial Basis Function) for digital soil mapping on available phosphorus (AP) are compared. Based on RF, the spatial variation and its driving factors of the AP of Gaozhou are then analyzed. Furthermore, by selecting three typical truncation lines from different directions, the correlations between environmental variables and AP in different spatial positions are demonstrated. The root mean square error (RMSE) results of the above four models are 32.01, 32.08, 32.74, and 33.08, respectively, which indicate that the RF has a higher interpolation accuracy. Based on the mapping results of RF, the minimum, maximum, and mean values of AP in the study area are 38.90, 95.24, and 64.96 mg/kg, respectively. The high-value areas of AP are mainly distributed in forested and orchard areas, while the low-value areas are primarily found in urban and cultivated areas in the eastern and western regions. Vegetation and topography are identified as the key factors shaping the spatial variations of AP in the study area. Furthermore, the spatial heterogeneity of the influence strength of altitude and EVI is revealed, providing a new direction for further research on DSM in the future, i.e., spatial interpolation models considering the spatial heterogeneity of the influence of environmental variables.

Suggested Citation

  • Wenhui Zhang & Liangwei Cheng & Ruitao Xu & Xiaohua He & Weihan Mo & Jianbo Xu, 2023. "Assessing Spatial Variation and Driving Factors of Available Phosphorus in a Hilly Area (Gaozhou, South China) Using Modeling Approaches and Digital Soil Mapping," Agriculture, MDPI, vol. 13(8), pages 1-18, August.
  • Handle: RePEc:gam:jagris:v:13:y:2023:i:8:p:1541-:d:1208983
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    References listed on IDEAS

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    1. Rumi Wang & Runyan Zou & Jianmei Liu & Luo Liu & Yueming Hu, 2021. "Spatial Distribution of Soil Nutrients in Farmland in a Hilly Region of the Pearl River Delta in China Based on Geostatistics and the Inverse Distance Weighting Method," Agriculture, MDPI, vol. 11(1), pages 1-12, January.
    2. Zhifan Chen & Sen Zhang & Wencai Geng & Yongfeng Ding & Xingyuan Jiang, 2022. "Use of Geographically Weighted Regression (GWR) to Reveal Spatially Varying Relationships between Cd Accumulation and Soil Properties at Field Scale," Land, MDPI, vol. 11(5), pages 1-18, April.
    3. Chiao, Ling-Yun & Hsieh, Chih-hao & Chiu, Tai-Sheng, 2012. "Exploring spatiotemporal ecological variations by the multiscale interpolation," Ecological Modelling, Elsevier, vol. 246(C), pages 26-33.
    4. Azamat Suleymanov & Ilyusya Gabbasova & Mikhail Komissarov & Ruslan Suleymanov & Timur Garipov & Iren Tuktarova & Larisa Belan, 2023. "Random Forest Modeling of Soil Properties in Saline Semi-Arid Areas," Agriculture, MDPI, vol. 13(5), pages 1-11, April.
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    Cited by:

    1. Liangwei Cheng & Mingzhi Yan & Wenhui Zhang & Weiyan Guan & Lang Zhong & Jianbo Xu, 2024. "Interpretable Digital Soil Organic Matter Mapping Based on Geographical Gaussian Process-Generalized Additive Model (GGP-GAM)," Agriculture, MDPI, vol. 14(9), pages 1-18, September.

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