Equiprobable realizations of soil textural maps can be drawn using Sequential Indicator Simulation (SIS), which reflects the probability of occurrence of each texture and is constrained by the observed textures at the observation sites. However, the SIS is not an error-free technique, and the accuracy of these maps should be checked before they are used as basic information for precision agricultural- and environmental-related studies. This article assesses the accuracy of using SIS in the three-dimensional prediction of soil texture. A soil data set (139 profiles) with five types of textures distributed in a 15-km2 region was first collected and then randomly sub-divided into a training set (85 profiles) and a validation set (54 profiles). Second, 100 realizations were obtained by SIS using the training set. Finally, the prediction capacity was assessed using independent validation set and probability of correct prediction as criterion. Results show that 43.59% of total observations can be correctly predicted while the accuracy varies among textures and depths. The dominant textures in the data set have higher accuracy (>42.49%), while the textures with less proportion (<28.86%) were poorly predicted. The SIS performed better for the near-surface depth (0–0.5 m) than deeper depths (0.5–2.0 m). Therefore, further improvement in simulation of soil texture is necessary as correct predictions of these minor textures and deeper depth textures were very low.
1Department of Soil and Water Sciences, College of Resources and Environmental Sciences, China Agricultural University, Beijing, China.
2Semiarid Prairie Agricultural Research Centre, Agriculture and Agri-Food Canada, Swift Current, Saskatchewan, Canada.
3School of Resource Management, The University of Melbourne, Melbourne, Victoria, Australia.
4Department of Soil Science, University of Saskatchewan, Saskatoon, Saskatchewan, Canada.
Address for correspondence: Dr. Kelin Hu, Department of Soil and Water Sciences, College of Resources and Environmental Sciences, China Agricultural University, Beijing 100193, People’s Republic of China. E-mail: email@example.com
Financial Disclosures/Conflicts of Interest: The study was funded by the National Key Basic Research Special Funds (2009CB118607), the Natural Science Foundation of China (no. 40401025), the Program for New Century Excellent Talents in University (NCET-07-0809), and China Scholarship Council. The authors gratefully acknowledge support for this research from the Visiting Fellowships in Canadian Government Laboratories Program, managed by the Natural Science and Engineering Research Council of Canada.
Received July 22, 2011.
Accepted for publication January 4, 2012.