检索项 文章编号 中文标题 英文标题 作者英文名 作者中文名 单位中文名 单位英文名 中文关键词 英文关键词 中文摘要 英文摘要 基金项目 DOI 检索词 1948 1950 1952 1953 1954 1955 1956 1957 1958 1959 1960 1961 1962 1963 1964 1965 1966 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 到 2018 2017 2016 2015 2014 2013 2012 2011 2010 2009 2008 2007 2006 2005 2004 2003 2002 2001 2000 1999 1998 1997 1996 1995 1994 1993 1992 1991 1990 1989 1988 1987 1986 1985 1984 1983 1982 1981 1980 1979 1978 1966 1965 1964 1963 1962 1961 1960 1959 1958 1957 1956 1955 1954 1953 1952 1950 1948
 土壤学报  2021, Vol. 58 Issue (2): 514-525  DOI: 10.11766/trxb201909090428 0

### 引用本文

YANG Jiawei, WANG Tianwei, BAO Yingying, et al. Optimization of the Model for Predicting Cation Exchange Capacity of Clays. Acta Pedologica Sinica, 2021, 58(2): 514-525.

### 作者简介

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

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

 ${\rm{CE}}{{\rm{C}}_{{\rm{7(c)}}}} = \frac{{{\rm{CE}}{{\rm{C}}_7}}}{{{C_c}}} \times 1000$ (1)

1 材料与方法 1.1 研究区概况

1.2 样品采集和数据测定

 图 1 样点分布图 Fig. 1 Soil sampling sites

 \begin{aligned} &\begin{array}{c} \mathrm{P}_{2-50}=-18.39+2.097\left(\mathrm{P}_{2-20}\right)+0.6726\left(\mathrm{P}_{20-2000}\right) \\ -0.0142\left(\mathrm{P}_{2-20}\right)^{2}-0.0049\left(\mathrm{P}_{20-2000}\right)^{2} \\ \left(R^{2}=0.823\right) \end{array}\\ &\text { 如果 } \mathrm{P}_{2-50} <0, \quad \text { 那么 } \mathrm{P}_{2-50}=0.8289\left(\mathrm{P}_{2-20}\right)+\\ & \quad\quad\quad\quad\quad\quad\quad0.0198\left(\mathrm{P}_{20-2000}\right) \end{aligned} (2)

1.3 误差规律确定方法及优化模型形式确定

 $d' = {\rm{CE}}{{\rm{C}}_7} - \frac{y}{{1\;000}} \times {C_c}$ (3)

 ${\rm{CE}}{{\rm{C}}_{(7)}} = \frac{{{\rm{CE}}{{\rm{C}}_7} - {\rm{D'}}}}{{{C_c}}} \times 1\;000$ (4)

1.4 模型精度验证方法

2 结果 2.1 现行模型误差分析及误差因子选取

 注：CEC7：黏粒阳离子交换量。下同。  Note:CEC7:Clay cation exchange capacity. The same below. 图 2 现行模型估测值和实测值散点图 Fig. 2 Scatter plot of measured values and values predicted with the current model

 注：粉粒CEC7指每千克细土中包含粉粒对应的阳离子交换量。下同。  Note:Silt CEC7 refers to cation exchange capacity of the silt contained in each kilogram of fine soil. The same below. 图 3 现行模型误差因子与误差相关性 Fig. 3 Correlation analysis between error factor and error in the current model

2.2 高有机质样本建模

 ${\rm{CEC}}_{(7)}=\left\{ \begin{array}{l}\frac{{\rm{CEC}}_{7}-(0.510\times \rm{OM}-0.899)}{{C}_{c}}\times 1\rm{\hspace{0.17em}}000\rm{\hspace{0.17em}}(黏土类)\\ \frac{{\rm{CEC}}_{7}-(0.817\times \rm{OM}-1.316)}{{C}_{c}}\times 1\rm{\hspace{0.17em}}000\rm{\hspace{0.17em}}(黏壤土类)\\ \frac{{\rm{CEC}}_{7}-(2.492\times \rm{OM}-15.665)}{{C}_{c}}\times 1\rm{\hspace{0.17em}}000\rm{\hspace{0.17em}}(壤土类)\end{array} \right.$ (5)

2.3 低有机质样本建模 2.3.1 通过实测粉粒CEC7直接建模

 ${\rm{CE}}{{\rm{C}}_{7({\rm{c}})}} = \frac{{{\rm{CE}}{{\rm{C}}_7} - (0.853 \times {\rm{CE}}{{\rm{C}}_{7({\rm{s}})}} + 0.899)}}{{{C_{\rm{c}}}}} \times 1\;000$ (6)
 图 4 低有机质样本粉粒CEC7与误差相关性分析 Fig. 4 Correlation analysis of silt CEC7 and errors in models for low OM samples

2.3.2 通过环境因子估测粉粒CEC7间接建模

 $\mathrm{CEC}_{7(\mathrm{~s})}=a_{0}+\sum_{i=1}^{n} a_{i} \lambda_{i}$ (7)

 $\mathrm{CEC}_{7(\mathrm{~s})}=1.501 \times L+1.693 \times T+1.775 \times \mathrm{pH}-79.99$ (8)

 ${\rm{CE}}{{\rm{C}}_{7({\rm{c}})}} = \frac{{{\rm{CE}}{{\rm{C}}_7} - \left\{ {0.853 \times (1.501 \times L + 1.693 \times T + 1.775 \times {\rm{pH}} - 79.99) + 0.899} \right\}}}{{{C_c}}} \times 1\;000$ (9)

3 讨论 3.1 按照有机质含量高低分类建模的合理性

3.2 各分类模型精度评价

 图 5 估测模型优化前后与实测值散点图 Fig. 5 Scatter plots predicted values and measured values before and after optimization of the prediction model.

3.3 黏粒CEC7估测模型优化前后对土纲划分的影响

 图 6 不同黏粒CEC7数据来源对应富铁土纲检索数目 Fig. 6 Sources of clay CEC7 data corresponding to number of Ferrosols
3.4 优化后模型的普适性

4 结论

 [1] Soil System Classification Research Group & China Soil System Classification Research Cooperation Group, Institute of Soil Science, Chinese Academy of Sciences. Chinese soil taxonomy search (In Chinese). 3rd ed[M]. Hefei: University of Science and Technology of China Press, 2001. [中国科学院南京土壤研究所土壤系统分类课题组, 中国土壤系统分类课题研究协作组. 中国土壤系统分类检索[M]. 3版. 合肥: 中国科学技术大学出版社, 2001.] (0) [2] 牟经瑞.富铁土、淋溶土在鄂赣地区分布与分界的研究[D].武汉: 华中农业大学, 2016. Mou J R. Study of ferrosols and argosols in Hubei and Jiangxi Province[D]. Wuhan: Huazhong Agricultural University, 2016. (0) [3] Chen Z C, Gong Z T, Zhao W J, et al. Some characteristics and taxonomic classification of Ferrallitic soils in eastern part of tropical and subtropoical zone of China (In Chinese)[J]. Acta Pedologica Sinica, 1995, 32(Supplement): 53-68. [陈志诚, 龚子同, 赵文君, 等. 我国热带亚热带东部富铁铝化土壤特性及系统分类[J]. 土壤学报, 1995, 32(增刊): 53-68.] (0) [4] Zhang M K, Zhu Z X. Effect of powder on cation exchange capacity of soil (In Chinese)[J]. Soils and Fertilizers, 1993(4): 41-43. [章明奎, 朱祖祥. 粉粒对土壤阳离子交换量的影响[J]. 土壤肥料, 1993(4): 41-43.] (0) [5] Xu M G, Zhang J X, Zhang H, et al. Study on effect factors of cation exchange capacity in Dark felty soils and Yellow-cinnamon soils (In Chinese)[J]. Chinese Journal of Soil Science, 1991, 22(3): 108-110, 127. [徐明岗, 张建新, 张航, 等. 黑垆土、黄褐土等土壤阳离子交换量影响因素的研究[J]. 土壤通报, 1991, 22(3): 108-110, 127.] (0) [6] Xu M G, An Z S, Zhang J X. Contribution of organic matter and clay complex to cation exchange capacity in soils (In Chinese)[J]. Acta Universitatis Agriculturalis Boreali – occidentalis, 1991, 19(4): 79-84. DOI:10.3321/j.issn:1671-9387.1991.04.015 [徐明岗, 安战士, 张建新. 有机质与粘粒复合对土壤阳离子交换量的贡献[J]. 西北农业大学学报, 1991, 19(4): 79-84.] (0) [7] Wang W Y, Zhang L P, Liu Q. Distribution and affecting factors of soil cation exchange capacity in watershed of the loess plateau (In Chinese)[J]. Journal of Soil and Water Conservation, 2012, 26(5): 123-127. [王文艳, 张丽萍, 刘俏. 黄土高原小流域土壤阳离子交换量分布特征及影响因子[J]. 水土保持学报, 2012, 26(5): 123-127.] (0) [8] Bayat H, Davatgar N, Jalali M. Prediction of CEC using fractal parameters by artificial neural networks[J]. International Agrophysics, 2014, 28(2): 143-152. DOI:10.2478/intag-2014-0002 (0) [9] Liu S Q, Pu Y L, Zhang S R, et al. Spatial change and affecting factors of soil cation exchange capacity in Tibet (In Chinese)[J]. Journal of Soil and Water Conservation, 2004, 18(5): 1-5. DOI:10.3321/j.issn:1009-2242.2004.05.001 [刘世全, 蒲玉琳, 张世熔, 等. 西藏土壤阳离子交换量的空间变化和影响因素研究[J]. 水土保持学报, 2004, 18(5): 1-5.] (0) [10] Soil Fertilizer Station, Hubei Department of Agriculture. Records of soil species in Hubei Province (In Chinese). Shenyang: Liaoning University Press, 2015. [湖北省农业厅土肥站. 湖北省土种志[M]. 沈阳: 辽宁大学出版社, 2015.] (0) [11] Xu J S, Liu X, Liu K S, et al. Jiangxi Soils (In Chinese). Beijing: China Agriculture Science and Technology, 1991: 373-388. [许菊生, 刘勋, 刘开树, 等. 江西土壤[M]. 北京: 中国农业科技出版社, 1991: 373-388.] (0) [12] Zhang G L, Li D C. Manual of description and sampling (In Chinese). Beijing: Science Press, 2016. [张甘霖, 李德成. 野外土壤描述与采样手册[M]. 北京: 科学出版社, 2016.] (0) [13] Wu K N, Zhao R. Soil texture classification and its application in China (In Chinese)[J]. Acta Pedologica Sinica, 2019, 56(1): 227-241. [吴克宁, 赵瑞. 土壤质地分类及其在我国应用探讨[J]. 土壤学报, 2019, 56(1): 227-241.] (0) [14] Bao S D. Analysis for soil and agro-chemistry (In Chinese). 3rd ed[M]. Beijing: China Agriculture Press, 2000. [鲍士旦. 土壤农化分析[M]. 3版. 北京: 中国农业出版社, 2000.] (0) [15] Minasny B, McBratney A B. The Australian soil texture boomerang:A comparison of the Australian and USDA/FAO soil particle-size classification systems[J]. Soil Research, 2001, 39(6): 1443-1451. DOI:10.1071/SR00065 (0) [16] Ni J H, Luo W H, Li Y X, et al. Simulation of leaf area and dry matter production in greenhouse tomato (In Chinese)[J]. Scientia Agricultura Sinica, 2005, 38(8): 1629-1635. DOI:10.3321/j.issn:0578-1752.2005.08.019 [倪纪恒, 罗卫红, 李永秀, 等. 温室番茄叶面积与干物质生产的模拟[J]. 中国农业科学, 2005, 38(8): 1629-1635.] (0) [17] Zhao Z Z. Study on relationship between organic matter, soil fractions and CEC in Qinghai soil (In Chinese)[J]. Science and Technology of Qinghai Agriculture and Forestry, 2004(4): 4-6. DOI:10.3969/j.issn.1004-9967.2004.04.002 [赵之重. 青海省土壤阳离子交换量与有机质和机械组成关系的研究[J]. 青海农林科技, 2004(4): 4-6.] (0) [18] Zhang X N, Jiang N H, Shao Z C, et al. Studies on electrochemical properties of soils-Ⅵ. Adsorption of ions by red soils in relation to the electric charge of the soil (In Chinese)[J]. Acta Pedologica Sinica, 1979, 16(2): 145-156. [张效年, 蒋能慧, 邵宗臣, 等. 土壤电化学性质的研究——Ⅵ.红壤对离子的吸附特点与其电荷性质的关系[J]. 土壤学报, 1979, 16(2): 145-156.] (0) [19] Huang C Y. Soil science (In Chinese). Beijing: China Agriculture Press, 2000. [黄昌勇. 土壤学[M]. 北京: 中国农业出版社, 2000.] (0) [20] Lü Q R, Wang X J. Clay minerals in fine-grained sediments at Changjiang estuary and their geochemical characteristics (In Chinese)[J]. Acta Sedimentologica Sinica, 1985, 3(4): 141-153. [吕全荣, 王效京. 长江口细颗粒沉积物的粘土矿物及地球化学特征[J]. 沉积学报, 1985, 3(4): 141-153.] (0) [21] Zhao W J, Chen Z C. Establishment of ferrisol order in Chinese soil taxonomic classification (In Chinese)[J]. Acta Pedologica Sinica, 1995, 32(Supplement): 21-33. [赵文君, 陈志诚. 论富铁土纲的设立[J]. 土壤学报, 1995, 32(增刊): 21-33.] (0) [22] Jing C W, Zhang M K, Zhi J J, et al. Referencing between soils under GSCC and CST and CST soil mapping in Zhejiang Province (In Chinese)[J]. Acta Pedologica Sinica, 2013, 50(2): 260-267. [荆长伟, 章明奎, 支俊俊, 等. 浙江省土壤发生分类与系统分类参比及制图研究[J]. 土壤学报, 2013, 50(2): 260-267.] (0) [23] Ouyang N X, Zhang Y Z, Sheng H, et al. Attribution of typical soils derived from quaternary red clay of eastern Hunan in Chinese soil taxonomy (In Chinese)[J]. Soils, 2018, 50(4): 841-852. [欧阳宁相, 张杨珠, 盛浩, 等. 湘东第四纪红色黏土发育的典型土壤在中国土壤系统分类中的归属[J]. 土壤, 2018, 50(4): 841-852.] (0) [24] Luo M Y, Bao Y Y, Yang J W, et al. Characteristics of References between GSCC and CST for Typical Red Soil in Jiangxi (In Chinese)[J]. Chinese Journal of Soil Science, 2019, 50(5): 1-7. [罗梦雨, 包莹莹, 杨家伟, 等. 江西红壤在系统分类中的参比特征研究[J]. 土壤通报, 2019, 50(5): 1-7.] (0) [25] Ouyang N X, Zhang Y Z, Sheng H, et al. Taxonomy of granite-derived red soils in eastern Hunan (In Chinese)[J]. Soils, 2017, 49(4): 828-837. [欧阳宁相, 张杨珠, 盛浩, 等. 湘东地区花岗岩红壤在中国土壤系统分类中的归属[J]. 土壤, 2017, 49(4): 828-837.] (0)