Welcome to Chinese Journal of Applied Ecology! Today is Share:

Chinese Journal of Applied Ecology ›› 2017, Vol. 28 ›› Issue (2): 439-448.doi: 10.13287/j.1001-9332.201702.023

• Special Features for 2016 Annual Meeting of Ecological Society of China • Previous Articles     Next Articles

Bayesian geostatistical prediction of soil organic carbon contents of solonchak soils in nor-thern Tarim Basin, Xinjiang, China.

WU Wei-mo1, 2, WANG Jia-qiang2, CAO Qi2, WU Jia-ping3*   

  1. 1College of Environmental and Resources Sciences, Zhejiang University, Hangzhou 310058, China;
    2College of Plant Science, Tarim University, Alar 843300, Xinjiang, China;
    3Institute of Islands and Coastal Ecosystems, Zhejiang University, Zhoushan 316021, Zhejiang, China.

  • Received:2016-07-06 Online:2017-02-18 Published:2017-02-18
  • Contact: * E-mail: jw67@zju.edu.cn
  • Supported by:
    This work was supported by the National Science Foundation of China (40961028).

Abstract: Accurate prediction of soil organic carbon (SOC) distribution is crucial for soil resources utilization and conservation, climate change adaptation, and ecosystem health. In this study, we selected a 1300 m×1700 m solonchak sampling area in northern Tarim Basin, Xinjiang, China, and collected a total of 144 soil samples (5-10 cm). The objectives of this study were to build a Baye-sian geostatistical model to predict SOC content, and to assess the performance of the Bayesian model for the prediction of SOC content by comparing with other three geostatistical approaches [ordinary kriging (OK), sequential Gaussian simulation (SGS), and inverse distance weighting (IDW)]. In the study area, soil organic carbon contents ranged from 1.59 to 9.30 g·kg-1 with a mean of 4.36 g·kg-1 and a standard deviation of 1.62 g·kg-1. Sample semivariogram was best fitted by an exponential model with the ratio of nugget to sill being 0.57. By using the Bayesian geostatistical approach, we generated the SOC content map, and obtained the prediction variance, upper 95% and lower 95% of SOC contents, which were then used to evaluate the prediction uncertainty. Bayesian geostatistical approach performed better than that of the OK, SGS and IDW, demonstrating the advantages of Bayesian approach in SOC prediction.