引用本文:杨家伟,王天巍,包莹莹,罗梦雨,李德成.黏粒阳离子交换量估测模型的优化研究[J].土壤学报,2021,58(2):514-525. DOI:10.11766/trxb201909090428
YANG Jiawei,WANG Tianwei,BAO Yingying,LUO Mengyu,LI Decheng.Optimization of the Model for Predicting Cation Exchange Capacity of Clays[J].Acta Pedologica Sinica,2021,58(2):514-525. DOI:10.11766/trxb201909090428
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黏粒阳离子交换量估测模型的优化研究
杨家伟1, 王天巍1, 包莹莹1, 罗梦雨1, 李德成2
1.华中农业大学资源与环境学院, 武汉 430070;2.土壤与农业可持续发展国家重点实验室(中国科学院南京土壤研究所), 南京 210008
摘要:
为了优化土壤黏粒阳离子交换量估测模型,提升估测结果准确性,为土壤系统分类检索提供可靠数据支持,以江西省土壤为主要研究对象,根据有机质、粉粒阳离子交换量对现有估测模型的影响,将有机质含量低于6 g·kg-1的土壤样本定为低有机质样本,高于6 g·kg-1的土壤定为高有机质样本开展分类建模。高有机质样本中主要误差因子为有机质,按照土壤质地不同开展分类建模;低有机质样本中主要误差因子为粉粒CEC7,在实测粉粒CEC7的同时根据pH、年均气温、纬度估测粉粒CEC7大小,建立低有机质样本间接估测模型。分类优化后的黏粒CEC7估测模型的估测值更加接近实测值,提升了土壤系统分类检索的准确度。
关键词:  黏粒阳离子交换量  中国土壤系统分类  优化模型  误差因子  有机质
基金项目:国家自然科学基金项目(41877071,41877008)和国家科技基础性工作专项项目(2014FY110200A16)资助
Optimization of the Model for Predicting Cation Exchange Capacity of Clays
YANG Jiawei1, WANG Tianwei1, BAO Yingying1, LUO Mengyu1, LI Decheng2
1.College of Resources and Environment, Huazhong Agricultural University, Wuhan 430070, China;2.State Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil Science, Chinese Academy of Sciences, Nanjing 210008, China
Abstract:
[Objective] Cation exchange capacity of clays is an important index for determining diagnostic horizons and diagnostic characteristics in the Chinese Soil Taxonomy (3rd edition) and the United States Soil Taxonomy. However, some studies have shown that the values of CEC7 particles predicted using models are often higher than their corresponding measured ones, thus leading to misjudgment of taxon at high taxonomic levels. With the zonal soil in Jiangxi Province taken as the main object, this study aimed to optimize the current CEC prediction model to alleviate the impacts of its error factors, to build up a new model, based on main error factors, for predicting CEC7 of clay particles, so as to improve prediction accuracy and to provide reliable data support for retrieval in the soil taxonomy.[Method] To that end, an idea of how to optimize the current model was put forward, suspicious error factors were screened out based on the previous researches and collated with those in the current model for correlation analysis, and error law in the current model was explored. Then soil samples were classified in line with the law to improve the model in prediction accuracy. Main error factors in each classification sample were searched out and got involved in modeling. Eventually, the optimized model for soil classification was established.[Result] By comparing the value estimated with the current model with the measured one, it is found that the former is generally higher than the latter. Previous studies have shown that soil organic matter, silt CEC7, soil pH and soil free iron oxide content are factors affecting CEC of the fine soil in the B layer of weathered soil. Correlation analysis was performed of the factors with the error, and indicated that organic matter and silt CEC7 were the main ones causing errors. Studies found that in predicting soils higher or lower than 6 g·kg-1 in organic content, errors varied in dispersion. Therefore, in this study, all soil samples were sorted into two groups, high and low in organic matter content for modeling. For the group high in organic matter, errors were ultra-significantly related to soil organic matter content (R2=0.402, n=23). Considering that organic matter may get bonded with clay particles, the group of samples high in organic matter content were further sorted in three subgroups, i.e. "clay soil samples", "clay loam soil samples", and "loam soil samples" and a model was set up for each of the three subgroups. In the subgroup low in organic matter content, errors were ultra-significantly related to CEC7 of silt (R2=0.675, n=23), so a direct model was obtained for soil samples low in organic matter. Considering that CEC7 is not easy to be measured, soil pH, annual mean temperature (Tem℃) and Latitude (Lat) were selected and used in modeling for predicting silt CEC7, and consequently an indirect model based on environmental factors was established for soil samples low in organic matter. Through the accuracy evaluation of the models, it is found that optimization of the models has brought predicted values closer to measured values, and the models for all subgroups of soil samples are good in accuracy. Optimization of the models has raised retrieval of iron-rich soils from 20% to 93.3% in accuracy.[Conclusion] Based on the above findings, it is found that modeling by content of soil organic matter is reasonable. By analyzing sources of the errors with the current model and following the optimization formula, models for predicting cation exchange capacity of clay particles are established by content of soil organic matter with higher accuracy. The models may provide reliable data support for retrieval in the soil taxonomy.
Key words:  Clay cation exchange capacity  Chinese soil classification system  Model optimization  Error factors  Organic matter