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Spatial variability of soil organic matter and its influencing factors in southern area of Daxing District in Beijing

  

  1. (1. College of Resource and Environment, Fujian Agriculture and Forestry University, Fuzhou 350002, China; 2. National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China; 3. Key Laboratory of AgriInformatics, Ministry of Agriculture, Beijing 100097, China; 4. Beijing Key Laboratory of Intelligent Equipment Technology for Agriculture, Beijing 100097, China)
  • Online:2016-03-25 Published:2016-04-07

Abstract: With the southern plain in Daxing District, Beijing as the study area, a total of 2 272 soil sample sites were selected in agricultural land. Spatial distribution characteristics of soil organic mather (SOM) in the surface layer were analyzed using Geostatistical method, and its influencing factors were revealed by variance analysis and buffer analysis. It was shown that SOM content varied in the range of (11.25±3.68) g·kg-1 with a coefficient of variation being 32.71%. The spatial variability distance of SOM was 7.1 km, and the SOM content was moderately spatial dependent with the nugget effect of 7.02, which suggested that the structural factors exhibited stronger effect than random factors. On the whole, the spatial distribution of SOM content were plaque shape, and its high spot was located in medium loam and light loam soil, and low spot was mainly located in sandy soil. Except residential area, soil texture, soil type, land use type, facility agricultural land and livestock and poultry industry had a significant impact on the spatial distribution of SOM. The argillaceous, high tillage intensity soil was inclined to accumulate SOM. And so was the soil which was within 1 km away from agricultural facilities land and 1.4 km away from livestock and poultry industry. These results could provide references for studies regarding spatial variability of soil organic matter and its influence factors in homogeneous regions.

Key words: soil organic matter, spatial variability, Geostatistics, variance analysis, suburban area