Prediction on spatial distribution of soil selenium in typical karst area of southwest China

Ya Shao, Yiwei Wang, Chongfa Cai, Shunhua Yang, Haitao Zhang

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3 Citations (Scopus)

Abstract

Abstract: 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 (adjusted R2) 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 analysis value was used to measure the prediction accuracy of COK. The amorphous iron oxides prediction value showed significant correlation with analysis value (R=0.62, P<0.01). However, the soil organic matter prediction value was no significantly correlated with analysis value in the researched area. In the end, elevation, amorphous iron oxides, and compound topographic index were selected as variables for spatial prediction of soil Se with geographically weighted regression model (GWR). The predicted results of GWR on soil Se were compared with the results obtained from ordinary Kriging (OK), and mean absolute error (MAE) and root mean square error (RMSE) were adopted to validate the prediction of soil Se by these methods. In order to quantify the improvement on prediction precision, a relative improvement (RI) in correlation coefficient was used to measure the improvement on the prediction accuracy of GWR and OK. Compared to OK, the MAE and RMSE of GWR were 0.19 and 0.24, which reduced by 13.64% and 7.69%, respectively. The correlation coefficient between prediction value and chemical analysis value increased from 0.32 to 0.51. The application of GWR resulted in relative improvement (RI) of 59.38%. The study results showed that the precision of total soil Se prediction could be improved if geographical environment factors and related soil properties were properly selected. The problem of discontinuously distributed auxiliary variables could be resolved by COK method. The spatial distribution of soil selenium was related to topography (elevation) and soil property factors (amorphous iron oxides), which had important influence on selenium chemical behavior in soil. Because of the complexity of terrain in the karst area, the correlation coefficient is still low, and it is worth further research.
Original languageChinese
Pages (from-to)178-183
Number of pages6
JournalNongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering
Volume32
Issue number22
Early online date12 Jun 2016
DOIs
Publication statusPublished - Nov 2016

Keywords

  • soils
  • selenium
  • model
  • chemical behaior
  • co-kriging
  • Geographically weighted regression models
  • spatial distribution prediction
  • Guilin Yongfu

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