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

Xiansheng Xie, Jianfei Qiu, Xinxin Feng, Yanlin Hou, Shuojin Wang, Shugang Jia, Shutian Liu (), Xianda Hou () and Sen Dou
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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
Jianfei Qiu: Jilin Academy of Agricultural Sciences, Changchun 130033, China
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
Shuojin Wang: Guangxi Geographical Indication Crops Research Center of Big Data Mining and Experimental Engineering Technology, 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
Shutian Liu: Guangxi Geographical Indication Crops Research Center of Big Data Mining and Experimental Engineering Technology, 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
Sen Dou: College of Resource and Environmental Science, Jilin Agricultural University, Changchun 130118, China

IJERPH, 2022, vol. 19, issue 24, 1-16

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.

Keywords: soil health; mean annual precipitation; mean annual temperature; hydrothermal condition; estimation model (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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