Changes in the pH of paddy soils after flooding and drainage: Modeling and validation

文献类型： 外文期刊

第一作者： Ding, Changfeng

作者： Ding, Changfeng;Du, Shuyang;Li, Xiaogang;Zhang, Taolin;Wang, Xingxiang;Ma, Yibing;Du, Shuyang;Wang, Xingxiang

作者机构：

关键词： Soil water management; Soil pH change; Predictive models; Organic matter; Cation exchange capacity

期刊名称：GEODERMA （ 影响因子：6.114； 五年影响因子：6.183 ）

ISSN： 0016-7061

年卷期： 2019 年 337 卷

页码：

收录情况： SCI

摘要： The precise determination and characterization of soil acidity was the basis for a robust and realistic assessment of many biogeochemical processes. Samples of twenty paddy soils with varying soil properties were subjected to a successive flooding and drainage period in this work. The soil pH was measured in situ at intervals, and soil samples were collected after each pH measurement during drainage for an analysis of the moisture content. During the flooding period, the pH for soils with an initial pH < 6.5 increased to approximately 7.0, and for soils with an initial pH > 6.5, the pH first decreased and then increased to approximately 7.0. The changes were reversed during the drainage period, as the pH of acidic soils decreased linearly with the decreasing soil moisture content, while neutral-to-alkaline soils showed the opposite pattern. The developed predictive models indicated that the initial soil pH, cation exchange capacity, content of organic matter and flooding or drainage time were the main factors that controlled the change of soil pH after flooding and drainage. The models explained 82% and 67% of the soil pH variability after flooding and drainage, respectively. The predictive model of the soil pH change after flooding was further validated and was found to be reliable on the basis of a number of independent data points for which the predicted soil pH after flooding was within the 95% prediction intervals of the observations. The results suggested that the soil pH, which was determined in the laboratory using air-dried samples, could be corrected to the in situ pH through the developed predictive models.

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