文献类型： 外文期刊

作者： Xie, Xiansheng ¹ ; Qiu, Jianfei ³ ; Feng, Xinxin ⁴ ; Hou, Yanlin ¹ ; Wang, Shuojin ¹ ; Jia, Shugang ¹ ; Liu, Shutian ¹ ; Hou, Xianda ¹ ; Dou, Sen ⁷ ;

作者机构： 1.Nanning Normal Univ, Guangxi Geog Indicat Crops Res Ctr Big Data Min &, Nanning 530001, Peoples R China

2.Chinese Acad Forestry, Res Inst Forestry Policy & Informat, Beijing 100091, Peoples R China

3.Jilin Acad Agr Sci, Changchun 130033, Peoples R China

4.Nanning Normal Univ, Sch Geog & Planning, Nanning 530001, Peoples R China

5.Nanning Normal Univ, Key Lab Environm Change & Resources Use Beibu Gulf, Minist Educ, Nanning 530001, Peoples R China

6.Nanning Normal Univ, Guangxi Key Lab Earth Surface Proc & Intelligent S, Nanning 530001, Peoples R China

7.Jilin Agr Univ, Coll Resource & Environm Sci, Changchun, Peoples R China

关键词： soil health; mean annual precipitation; mean annual temperature; hydrothermal condition; estimation model

期刊名称：INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH （ 影响因子：4.614； 五年影响因子：4.798 ）

ISSN：

年卷期： 2022 年 19 卷 24 期

页码：

收录情况： SCI

摘要： 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 x lgMAP + 0.1312 x 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.