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Spatial Distribution and Estimation Model of Soil pH in Coastal Eastern China

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  • Xiansheng Xie

    (Guangxi Geographical Indication Crops Research Center of Big Data Mining and Experimental Engineering Technology, Nanning Normal University, Nanning 530001, China
    Research Institute of Forestry Policy and Information, Chinese Academy of Forestry, Beijing 100091, China
    These authors contributed equally to this work.)

  • Jianfei Qiu

    (Jilin Academy of Agricultural Sciences, Changchun 130033, China
    These authors contributed equally to this work.)

  • Xinxin Feng

    (School of Geography and Planning, Nanning Normal University, Nanning 530001, China)

  • Yanlin Hou

    (Guangxi Geographical Indication Crops Research Center of Big Data Mining and Experimental Engineering Technology, Nanning Normal University, Nanning 530001, China
    Key Laboratory of Environment Change and Resources Use in Beibu Gulf (Ministry of Education), Nanning Normal University, Nanning 530001, China
    Guangxi Key Laboratory of Earth Surface Processes and Intelligent Simulation, Nanning Normal University, Nanning 530001, China)

  • Shuojin Wang

    (Guangxi Geographical Indication Crops Research Center of Big Data Mining and Experimental Engineering Technology, Nanning Normal University, Nanning 530001, China
    Key Laboratory of Environment Change and Resources Use in Beibu Gulf (Ministry of Education), Nanning Normal University, Nanning 530001, China
    Guangxi Key Laboratory of Earth Surface Processes and Intelligent Simulation, Nanning Normal University, Nanning 530001, China)

  • Shugang Jia

    (Guangxi Geographical Indication Crops Research Center of Big Data Mining and Experimental Engineering Technology, Nanning Normal University, Nanning 530001, China
    Key Laboratory of Environment Change and Resources Use in Beibu Gulf (Ministry of Education), Nanning Normal University, Nanning 530001, China
    Guangxi Key Laboratory of Earth Surface Processes and Intelligent Simulation, Nanning Normal University, Nanning 530001, China)

  • Shutian Liu

    (Guangxi Geographical Indication Crops Research Center of Big Data Mining and Experimental Engineering Technology, Nanning Normal University, Nanning 530001, China
    Key Laboratory of Environment Change and Resources Use in Beibu Gulf (Ministry of Education), Nanning Normal University, Nanning 530001, China
    Guangxi Key Laboratory of Earth Surface Processes and Intelligent Simulation, Nanning Normal University, Nanning 530001, China)

  • Xianda Hou

    (Guangxi Geographical Indication Crops Research Center of Big Data Mining and Experimental Engineering Technology, Nanning Normal University, Nanning 530001, China
    Key Laboratory of Environment Change and Resources Use in Beibu Gulf (Ministry of Education), Nanning Normal University, Nanning 530001, China
    Guangxi Key Laboratory of Earth Surface Processes and Intelligent Simulation, Nanning Normal University, Nanning 530001, China)

  • Sen Dou

    (College of Resource and Environmental Science, Jilin Agricultural University, Changchun 130118, China)

Abstract

Soil pH is an essential indicator for assessing soil quality and soil health. In this study, based on the Chinese farmland soil survey dataset and meteorological dataset, the spatial distribution characteristics of soil pH in coastal eastern China were analyzed using kriging interpolation. The relationships between hydrothermal conditions and soil pH were explored using regression analysis with mean annual precipitation (MAP), mean annual temperature (MAT), the ratio of precipitation to temperature (P/T), and the product of precipitation and temperature (P*T) as the main explanatory variables. Based on this, a model that can rapidly estimate soil pH was established. The results showed that: (a) The spatial heterogeneity of soil pH in coastal eastern China was obvious, with the values gradually decreasing from north to south, ranging from 4.5 to 8.5; (b) soil pH was significantly correlated with all explanatory variables at the 0.01 level. In general, MAP was the main factor affecting soil pH ( r = −0.7244), followed by P/T ( r = −0.6007). In the regions with MAP < 800 mm, soil pH was negatively correlated with MAP ( r = −0.4631) and P/T ( r = −0.7041), respectively, and positively correlated with MAT ( r = 0.6093) and P*T ( r = 0.3951), respectively. In the regions with MAP > 800 mm, soil pH was negatively correlated with MAP ( r = −0.6651), MAT ( r = −0.5047), P/T ( r = −0.3268), and P*T ( r = −0.5808), respectively. (c) The estimation model of soil pH was: y = 23.4572 − 6.3930 × lgMAP + 0.1312 × MAT. It has been verified to have a high accuracy ( r = 0.7743, p < 0.01). The mean error, the mean absolute error, and the root mean square error were 0.0450, 0.5300, and 0.7193, respectively. It provides a new path for rapid estimation of the regional soil pH, which is important for improving the management of agricultural production and slowing down soil degradation.

Suggested Citation

  • Xiansheng Xie & Jianfei Qiu & Xinxin Feng & Yanlin Hou & Shuojin Wang & Shugang Jia & Shutian Liu & Xianda Hou & Sen Dou, 2022. "Spatial Distribution and Estimation Model of Soil pH in Coastal Eastern China," IJERPH, MDPI, vol. 19(24), pages 1-16, December.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:24:p:16855-:d:1004205
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    References listed on IDEAS

    as
    1. Hui Wei & Yalan Liu & Huimin Xiang & Jiaen Zhang & Saifei Li & Jiayue Yang, 2019. "Soil pH Responses to Simulated Acid Rain Leaching in Three Agricultural Soils," Sustainability, MDPI, vol. 12(1), pages 1-12, December.
    2. E. W. Slessarev & Y. Lin & N. L. Bingham & J. E. Johnson & Y. Dai & J. P. Schimel & O. A. Chadwick, 2016. "Water balance creates a threshold in soil pH at the global scale," Nature, Nature, vol. 540(7634), pages 567-569, December.
    3. Zhenju Chen & Xingyuan He & Nicole K. Davi & Xianliang Zhang, 2016. "A 258-year reconstruction of precipitation for southern Northeast China and the northern Korean peninsula," Climatic Change, Springer, vol. 139(3), pages 609-622, December.
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    Cited by:

    1. Yue Zhang & Huichun Ye & Ronghao Liu & Mingyao Tang & Chaojia Nie & Xuemei Han & Xiaoshu Zhao & Peng Wei & Fu Wen, 2024. "Spatial and Temporal Variations of Soil pH in Farmland in Xinjiang, China over the Past Decade," Agriculture, MDPI, vol. 14(7), pages 1-18, June.

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