安徽省土壤阳离子交换量传递函数构建与精度评价  

Construction and Precision Evaluation of the Pedotransfer Model of Soil Cation Exchange Capacity in Anhui Province

作　　者：潘启凤 曾荣 PAN Qi-feng;ZENG Rong(School of Geographical Sciences,Nanjing University of Information Science&Technology,Nanjing 210044,China)

机构地区：[1]南京信息工程大学地理科学学院,江苏南京210044

出　　处：《土壤通报》2024年第2期374-382,共9页Chinese Journal of Soil Science

基　　金：国家自然科学基金项目(42107322,41877008)资助。

摘　　要：[目的]由于化学测定方法费时费力,很多历史土壤资料缺少阳离子交换量(CEC)信息。本研究旨在基于易获取的变量建立安徽省土壤CEC的预测模型。[方法]利用覆盖安徽全省区域的711个土样的有机质含量、颗粒组成和土壤pH的信息,采用逐步多元线性回归方法建立CEC预测模型;并按土壤层次、土壤类型、母质、土地利用方式、质地和石灰性六种分组方式检验了其对模型预测精度的影响。[结果](1)利用全省未分组数据建立的预测模型精度较低,调整R^(2)仅为0.33;(2)按土壤类型、土地利用方式和石灰性进行分组,整体上可提高模型的预测精度,调整R2提升至0.44~0.93;但按土壤层次、母质和质地进行分组,模型精度未得到明显改善甚至有所下降;(3)预测安徽省土壤CEC的参数重要性依次为土壤黏粒含量、土壤有机质含量和土壤pH。[结论]基于未分组数据集上建立的安徽省土壤CEC模型精度很低,依据土壤类型、土地利用方式和石灰性进行分组可提高CEC的预测精度,对CEC预测最重要的变量是土壤黏粒含量,其次是土壤有机质含量和土壤pH。[Objective]The traditional chemical method for determining soil cation exchange capacity(CEC)is a time-consuming and laborious work,so many historical soil data were lack of CEC information.The study aims to develop a predictive model of soil CEC in Anhui Province based on easily accessible variables.[Method]The soil CEC prediction model was established by using the information of soil organic matter content,particle composition and pH of 71l soil samples covering Anhui Province.A stepwise multiple linear regression method was used to establish the CEC prediction model,and its influence on the prediction accuracy of the model was examined by six groupings:soil horizon,soil type,parent material,land use,texture and calcareousness.[Result]The accuracy of the prediction model built using ungrouped data from the province was low,with an adjusted R^(2) of only 0.33.②Grouping soil samples by soil type,land use and calcareousness improved the prediction accuracy of the model overall,with an adjusted R^(2) ranged from 0.44 to 0.93.However,grouping soil samples by soil horizon,parent material and texture did not improve the model accuracy significantly or even decreased.③The important parameters for prediction of soil CEC in Anhui Province are soil clay content,followed by organic matter content and pH.[Conclusion]The prediction accuracy of the CEC model based on the unsorted dataset for soils in Anhui Province is quite low.Grouping based on soil type,land use pattern and calcareousness can enhance the prediction accuracy of CEC.The most important variable for CEC prediction is clay content,followed by SOM and pH.

关 键 词：土壤 阳离子交换量 传递函数 有机质 黏粒 PH 

分 类 号：S714.2[农业科学—林学]