Author(s)

Anqi Wang^1,2, Decheng Li^1*, Biao Huang³, Yin Lu⁴

¹State Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil Science, Chinese Academy of Sciences, Nanjing, China.
²University of the Chinese Academy of Sciences, Beijing, China.
³Key Laboratory of Soil Environment and Pollution Remediation, Institute of Soil Science, Chinese Academy of Sciences, Nan-jing, China.
⁴College of Natural Resources and Environment, South China Agricultural University, Guangzhou, China.

pH_KCl and pH_H2O are two basic necessary indexes to reflect the acidity of asoil. Predicting pH_KCl directly from pH_H2O could save the cost of laboratory work. In this study, the values of pHKCl and of 442 and 310 horizon samples from 126 and 98 soil profiles (0 - 120 cm in depth) surveyed from 2014 to 2015 in Guangxi and Yunnan were used to establish the optimal correlation model between pH_KCl and pH_H2O. The results showed that: 1) pH_KCl is lower than pH_H2O, pH_KCl was 0.07 - 1.99 units with a mean of 0.99 units lower than for Guangxi, while 0.03 - 1.90 units with a mean of 0.89 lower than pH_H2O for Yunan. 2) There is significant positive correlation between pH_KCl and pH_H2O, the optimal correlation models between pH_KCl (y) and pH_H2O (x) for Guangxi and Yunnan are y = 0.1963x² − 1.0512x + 4.338, R2 = 0.836, p < 0.01 and y = 1.5882e^0.1859x, R2 = 0.769, p < 0.01, respectively, and the values of MAE and RSME of the models are 0.13 and 0.36 for Guangxi, 0.08 and 0.28 for Yunnan, respectively. There are significant negative correlations between pH_KCl with exchangeable H⁺ and Al³⁺ (R2 = 0.487, 0.716, p < 0.01), and pH_KCl is dominated by exchangeable Al³⁺, followed by exchangeable H⁺, and their contribution to pH_KCl were 71.1% and 28.7%, respectively.

Predicting Model, pH_KCl, pH_H2O, Correlation, Influential Factors, Acid Soil

Wang, A. , Li, D. , Huang, B. and Lu, Y. (2019) A Brief Study on Using pH_H2O to Predict pH_KCl for Acid Soils. Agricultural Sciences, 10, 142-149. doi: 10.4236/as.2019.102012.