摘要
土壤硒精准预测和制图是富硒土壤资源开发利用和环境规划管理的基础。该文以西南典型岩溶区桂林永福百寿河流域为例,在分析影响土壤硒化学行为因子的基础上,通过野外样品采集和室内化学分析以及Arc GIS空间分析,获取了研究区相关地理环境因子和土壤属性因子数据。利用逐步回归方法选择土壤硒空间分布预测的辅助变量,使用协同克里格模型对非连续分布的辅助变量进行插值。在此基础上利用地理加权回归模型对土壤硒空间分布进行预测,同时以普通克里格插值结果作为参照。研究结果表明:使用地理环境因子和影响土壤硒化学行为的土壤属性因子可以提高土壤硒预测精度;协同克里格插值解决了辅助变量数据连续分布的问题;土壤硒的空间分布与地形和影响土壤硒化学行为的因子有关。
Selenium is an essential micronutrient for animals and humans. Extensive studies have been conducted on distribution of selenium, but seldom studies have been done in Se-enriched, longevity, and karst region. Accurately mapping the spatial distribution of soil Se is the basis for Se-enriched soil utilization, planning, and environmental management. To better understand the Se distribution in the soils in a Se-enriched, longevity, and karst area of China, this study focuses on the total Se in soil in the Guilin Yongfu Baishou river catchment, and 226 soils （0-20 cm） samples were collected by random sampling in 180 km2 research area in March, 2015. Coordinates and elevations of the sample sites were recorded using a Garmin GPS. Factors affecting the chemical behavior of soil Se were studied to obtain geographical environment factors and related soil properties in the study area through lab analysis and ArcGIS spatial analysis. Auxiliary variables, including soil properties （pH value, soil organic matter, and amorphous iron oxides） and geographical environment factors （elevation, slope, aspect, curvature, compound topographic index, and stream power index） were preliminary selected for predicting total soil Se through stepwise regression. The adjusted determination coefficient （adjustedR2） was used to select the regression model, and then elevation, soil organic matter, amorphous iron oxides and compound topographic index were selected as variables. Co-kriging （COK） was used for interpolation of discontinuously distributed auxiliary variables （soil organic matter, amorphous iron oxides）, and the correlation coefficient between prediction value and chemical analysisvalue was used to measure the prediction accuracy of COK. The amorphous iron oxides prediction value showed significant correlation with analysisvalue （R=0.62,P＆lt;0.01）. However, the soil organic matterprediction value was no significantly correlated with analysisvalue in the researched area. In the end, elevation, amorphous
出处
《农业工程学报》
EI
CAS
CSCD
北大核心
2016年第22期178-183,共6页
Transactions of the Chinese Society of Agricultural Engineering
基金
国家自然科学基金资助项目(41261082)
关键词
土壤
硒
模型
化学行为
协同克里格
地理加权回归模型
空间分布预测
桂林永福
soils
selenium
model
chemical behavior
co-kriging
geographically weighted regression model
spatialdistribution prediction
Guilin Yongfu
