A Brief Study on Using pH_H2O to Predict pH_KCl for Acid Soils

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.

DOI: 10.4236/as.2019.102012                

Abstract

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.

Keywords

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

1. Introduction

pH is a basic but important property of soil, it can influence soil other physicochemical properties, microorganism activities and plant growth. For an acid soil (pH < 7.0), usually containing more potential exchangeable Al³⁺ and H⁺, pH measured by KCl extraction (here expressed as pH_KCl) is conventionally used to indicate soil acidity together with pH measured by water extraction (here expressed as ${\text{pH}}_{{\text{H}}_{\text{2}}\text{O}}$ ) [1] . Moreover, pH_KCl is also used to identify Alice property (pH_KCl ≤ 4.0) or Alice evidence (pH_KCl ≤ 4.5) in soil taxonomy [2] [3] .

It is well known that there is internal relationship between pH_KCl and ${\text{pH}}_{{\text{H}}_{\text{2}}\text{O}}$ , but so far little information is available on the quantitative correlation between pH_KCl and ${\text{pH}}_{{\text{H}}_{\text{2}}\text{O}}$ , possibly due to the easiness in measuring pH_KCl as ${\text{pH}}_{{\text{H}}_{\text{2}}\text{O}}$ in the laboratory. However, it is still worthy predicating pH_KCl directly from ${\text{pH}}_{{\text{H}}_{\text{2}}\text{O}}$ on the special conditions, for examples, when there is no information of pH_KCl for soils in the historical literatures, or to save time and cost for a massive measurement in the laboratory. Meanwhile, some studies showed that soil pH is dominated by exchangeable Al³⁺, followed by the exchangeable H⁺ for acid soils in south China [4] [5] , and exchangeable base, the contents of SOC and clays may also influence soil pH [6] [7] [8] [9] , however, it is still unclear that the quantitative contribution of these influential factors to soil pH.

Thus, soil dada of Guangxi and Yunnan in South China obtained in 2014-2015 were used in this study in order to: 1) establish the optimal correlation model between pH_KCl and ${\text{pH}}_{{\text{H}}_{\text{2}}\text{O}}$ , and 2) to quantitatively identity the contribution of exchangeable H⁺, exchangeable Al³⁺, exchangeable base, SOC and clay contents to soil pH.

2. Methods and Materials

2.1. Data of Soil Indexes

Soil Series Database of the National S & T Special Basic Project of Soil Series Survey and Compilation of Soil Series of China (Nos. 2008FY110600 & 2014FY110200) were used in this study. After the comparison of the data completeness of pH_KCl, ${\text{pH}}_{{\text{H}}_{\text{2}}\text{O}}$ , exchangeable H⁺, exchangeable Al³⁺, exchangeable base, SOC and clay contents, and the elimination of abnormal data according to method of μ ± 3σ, 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 (see Figure 1) were used to setup the optimal correlation model between pH_KCl and ${\text{pH}}_{{\text{H}}_{\text{2}}\text{O}}$ , while 110 horizon samples from 30 soil profiles in Yunnan were adopted to quantitatively disclose the influence of exchangeable H⁺, exchangeable Al³⁺, exchangeable base, SOC and clay contents on soil pH.

2.2. Methods to Determine Soil Indexes

For soil indexes adopted in this study, ${\text{pH}}_{{\text{H}}_{\text{2}}\text{O}}$ and pH_KCl were determined by potentiometer method after water extraction (soil: water is 1:2.5) and 1 mol∙L⁻¹ KCl extraction, respectively. Exchangeable H⁺ and Al³⁺ by neutralization titration method after 1 mol∙L⁻¹ KCl extraction, exchangeable base by drying neutralization titration method after 1 mol∙L⁻¹ NH₄OAc extraction, SOC by volume method after K₂CrO₄ digestion, and clay content by pipet method [1] .

Figure 1. Sites of soil profiles in Guangxi (left) and Yunnan (right) surveyed from 2014 to 2015.

2.3. Data Processing, Modeling and Mapping

Microsoft Excel 2016 and IBM Statistics SPSS 20.0 were used for data processing, modeling and mapping.

3. Results

3.1. Statistical Information of Soil Indexes

Table 1 showed that soil pH_KCl ranged from 2.41 to 6.51 for Guangxi and from 3.21 to 5.91 for Yunnan, with a mean of 4.27 for Guangxi and 4.20 for Yunnan, respectively, which prove further that the soils in South China are generally acid [10] [11] .

It is well known that pH_KCl is lower than ${\text{pH}}_{{\text{H}}_{\text{2}}\text{O}}$ because KCl could extract more Al³⁺ from soil particles than water [1] , however, Table 1 showed that the lower extents are different in different regions, for examples, pH_KCl was 0.07 - 1.99 units with a mean of 0.99 units lower than ${\text{pH}}_{{\text{H}}_{\text{2}}\text{O}}$ for Guangxi, while 0.03 - 1.90 units with a mean of 0.89 units lower than ${\text{pH}}_{{\text{H}}_{\text{2}}\text{O}}$ for Yunnan.

Table 1 also showed that only ${\text{pH}}_{{\text{H}}_{\text{2}}\text{O}}$ of Guangxi presented as normal distribution (±0.1 < skewness < ±0.1), while ${\text{pH}}_{{\text{H}}_{\text{2}}\text{O}}$ of Yunnan, pH_KCl of Guangxi and Yunnan as extremely-skewed normal distribution (skewness > ±0.3), The values of kurtoses of ${\text{pH}}_{{\text{H}}_{\text{2}}\text{O}}$ and pH_KCl of both Guangxi and Yunnan ranged from −0.44 to 0.42, indicating the probability density curves of ${\text{pH}}_{{\text{H}}_{\text{2}}\text{O}}$ and pH_KCl were very flat (kurtosis < 0.67) [12] .

3.2. Optimal Correlation Model between pH_KCl and $p{H}_{H_{2}O}$

IBM Statistics SPSS 20.0 is used to find the optimal correlation between pH_KCl and ${\text{pH}}_{{\text{H}}_{\text{2}}\text{O}}$ . It can be seen from Figure 2 that: 1) The values of R² showed again that there was significant positive correlation between pH_KCl and ${\text{pH}}_{{\text{H}}_{\text{2}}\text{O}}$ for both Guangxi and Yunnan (p < 0.01). 2) The optimal correlation models were

Figure 2. Optimal correlation models between measured pH_KCl and ${\text{pH}}_{{\text{H}}_{\text{2}}\text{O}}$ .

Table 1. Statistical information of measured soil pH_KCl and ${\text{pH}}_{{\text{H}}_{\text{2}}\text{O}}$ .

different in Guangxi and Yunnan, quadratic model is found for Guangxi while exponential model for Yunnan, which may be attributed to the differences in climatic condition, soil types and land use types of the two regions. Values of MAE and RSME showed that the model accuracies were different in Guangxi and Yunnan, and the model accuracy is higher for Guangxi than Yunnan (Figure 3).

3.3. $p{H}_{H_{2}O}$ Threshold for pH_KCl ≤ 4.0 or 4.5

It is our concern to know ${\text{pH}}_{{\text{H}}_{\text{2}}\text{O}}$ corresponding to pH_KCl ≤ 4.0 or 4.5, which is the threshold of Alice property or Alice evidence in soil taxonomy. It can be seen from Table 2 that the estimated pH_KCl would be most likely lower than 4.0 or 4.5 when the measured ${\text{pH}}_{{\text{H}}_{\text{2}}\text{O}}$ ≤ 5.0 or 5.5 for a soil in south China judged from the optimal correlation models in Figure 2.

Here, ${\text{pH}}_{{\text{H}}_{\text{2}}\text{O}}$ and pH_KCl dada of 211horizons of 58 acid soil profiles surveyed from 2009 to 2010 in Guangdong were used to validate the proposed ${\text{pH}}_{{\text{H}}_{\text{2}}\text{O}}$ threshold (≤5.0 or ≤5.5), the results showed that pH_KCl was within 3.08 - 4.21 (n = 173, pH_KCl of 98.6% soil samples lower than 4.0) and from 3.08 - 5.00 (n = 202, pH_KCl of all soil samples lower than 4.5) with a mean of 3.67 or 3.74 respectively when ${\text{pH}}_{{\text{H}}_{\text{2}}\text{O}}$ ≤ 5.5 or 5.0, indicating the proposed ${\text{pH}}_{{\text{H}}_{\text{2}}\text{O}}$ thresholds are reliable.

3.4. Contribution of Influential Factors to Soil pH

Table 3 showed the statistical information of values of pH_KCl, clay, SOC, free Fe₂O₃, exchangeable H⁺, Al³⁺ and base of 110 horizon samples of 30 soil profiles

Figure 3. Comparison between simulated and measured pH_KCl. MAE is mean absolute error, while RMSE is root mean square error.

Table 2. ${\text{pH}}_{{\text{H}}_{\text{2}}\text{O}}$ thresholds for pH_KCl ≤ 4.5 or 4.0.

Table 3. Statistics of basic properties of soil samples of Yunnan (n = 110).

in Yunnan. Table 4 showed Pearson correlation coefficients between pH_KCl with other soil indexes. It can be seen that there were extremely significant correlations between pH_KCl with exchangeable H⁺ and Al³⁺, while significant correlation between pH_KCl with exchangeable base, which indicate that exchangeable Al³⁺, H⁺ and base are the influential factors of soil pH, and their optimal correlation models with pH_KCl were all in quadratic form (see Table 3 and Figure 4).

The linear regression model between pH_KCl with exchangeable Al³⁺, H⁺ and base was obtained by using IBM Statistics SPSS 20.0 as pH_KCl = 4.087 − 0.448H⁺ − 0.042Al³⁺ − 0.001 Base (R = 0.764, R² = 0.583, F = 49.41, p < 0.01), which could be simplified as pH_KCl = 4.087 − 0.448H⁺ − 0.042Al³⁺ since the exchangeable base could be neglected.

The linear regression model between pH_KCl with the normalized exchangeable H⁺ and Al³⁺ was obtained as pH_KCl = 4.064 − 0.211H⁺ − 0.522 Al³⁺ (R = 0.763, R² = 0.581, F = 49.08, p < 0.01). The contribution of Al³⁺ and H⁺ to pH_KCl were 71.1% and 28.7% after normalizing the coefficients of Al³⁺ and H⁺, which indicate further pH_KCl is mainly dominated by exchangeable Al³⁺, followed by exchangeable H⁺ [4] - [9] .

Figure 4. Optimal correlation models between pH_KCl with exchangeable H⁺, Al³⁺ and base.

Table 4. Pearson correlation coefficients between pH_KCl and other indexes (n = 110).

**Significant at 0.01 level, *Significant at 0.05 level.

3.5. Discussion

Soil acidification is a natural process in tropical and subtropical regions due to higher rate of weathering and leaching under hot and humid climatic conditions [13] [14] [15] , it is accelerated greatly due to acidic deposition and excess application of acid fertilizer in agricultural system [16] [17] [18] [19] . Our data showed that soil pH_KCl ranged from 2.41 to 6.51 for Guangxi and from 3.21 to 5.91 for Yunnan, which proves further that the soils in South China are generally acid [6] [10] [11] .

Although it is well known that pH_KCl certainly has inner relation with ${\text{pH}}_{{\text{H}}_{\text{2}}\text{O}}$ , so far there is no quantitative model between the two indexes. Our study not only proved further the quantitative relation between pH_KCl and ${\text{pH}}_{{\text{H}}_{\text{2}}\text{O}}$ , but also established the correlation models and further found that the correlation models are different in different regions, it is useful to predict soil pH_KCl directly from ${\text{pH}}_{{\text{H}}_{\text{2}}\text{O}}$ when there is no available information of pH_KCl or to save time and cost for a massive measurement in the laboratory.

Our study found the optimal correlation models between soil pH and exchangeable Al³⁺ and H⁺ are in quadratic form, other studies showed the optimal correlation models between soil pH and exchangeable acid are different, which include power [8] or quadratic forms [9] . Different optimal models may be attributed to the differences in climatic conditions, soil types and land use types for the different studied regions.

Studies already proved that the main form of exchangeable acidity is found in the form of exchangeable Al³⁺ in acid soils and disclosed the mechanism of soil acidification [6] [19] [20] [21] [22] . Our study showed that the content of Al³⁺ of soil samples in Yunnan were 5.29 ± 2.93 cmol (+) kg⁻¹, higher than H⁺, which was 0.25 ± 0.41 cmol (+) kg⁻¹, meanwhile, exchangeable Al³⁺ has more significant correlation (R² = 0.716) with soil pH compared with exchangeable H⁺ (R² = 0.487), Thus, it is normal that exchangeable Al³⁺contributes more (71.1%) to soil pH compared to exchangeable H⁺ (28.7%).

4. Conclusion

By using the database of soil properties of Guangxi and Yunnan, this study discloses that pH_KCl is meanly 1.0 unit lower than ${\text{pH}}_{{\text{H}}_{\text{2}}\text{O}}$ . There is significant positive correlation between pH_KCl and ${\text{pH}}_{{\text{H}}_{\text{2}}\text{O}}$ , but the optimal correlation models are in quadratic or exponential forms for different regions. There are significant negative correlations between pH_KCl with exchangeable H⁺ and Al³⁺, and exchangeable Al³⁺ and H⁺ contribute 71.1% and 28.7% to soil pH, respectively.

Acknowledgements

This study was supported by projects of the National Natural Science Foundation of China (Grant No. 41877008) and the National S & T Basic Special Foundation Project (No. 2014FY110200). We would like to express thanks to the contribution from all the participants in field soil survey and laboratory work.

Conflicts of Interest

The authors declare no conflicts of interest regarding the publication of this paper.

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