Ecological Modelling 181 (2005) 1–15 The application of classification tree analysis to soil type prediction in a desert landscape P. Scull a,∗ , J. Franklin b , O.A. Chadwick c a Department of Geography, Colgate University, 13 Oak Drive, Hamilton, NY 13346, USA b Department of Biology, San Diego State University, San Diego, CA 92182-4614, USA c Department of Geography, University of California, Santa Barbara, CA 93106, USA Received 3 April 2003; received in revised form 13 May 2004; accepted 1 June 2004 Abstract Classification tree analysis is evaluated as a predictive soil mapping technique for developing a preliminary soil map for neighboring site from samples extracted from an existing soil map. The objective of the research is to help guide future soil mapping in a nearby area. In order to determine the best overall modeling approach several variations were explored: the dependent variable (soil map class) was grouped at several hierarchical levels (according to Soil Taxonomy), sensitivity analysis was performed on the predictor variables (environmental variables acting as surrogates for soil forming factors), and the study area was divided into meaningful sub-areas (mountains and basins). Soil great group was discovered the most parsimonious dependent variable based on model results (misclassification error rate of 30.0% based on a test data set). Geomorphology (as measured by several landform variables) best explains the distribution of soil types. The terrain analysis variables did not explain a large amount of variance within the models. Dividing the study area in two separate modeling units increased overall model accuracy. Our results suggest that soil taxonomic class can be predicted with reasonable accuracy from environmental variables. In addition, the technique can provide limited insight into the variables that are most responsible for driving soil development in a given area. This technique could be used in soil survey to extrapolate obvious soil landscape relationships from one site to another, allowing soil experts to concentrate their field mapping effort in unique areas. © 2004 Elsevier B.V. All rights reserved. Keywords: Soil survey; Classification tree modeling; Predictive soil mapping 1. Introduction Geographic information science (GIS) and technol- ogy has great potential to improve the efficiency and ∗ Corresponding author. Tel.: +1 805 893 8525; fax: +1 805 893 7782. E-mail address: pscull@mail.colgate.edu (P. Scull). quality of the methods used to gather spatial soil infor- mation (Hewitt, 1993; Gessler et al., 1995; McBratney and Odeh, 1997). GIS based predictive soil mapping is necessary because soil data are being used by scientists in increasingly sophisticated ways. For example, eco- logical and hydrological process models used to eval- uate global change require chemical and physical soil data (Burrough and McDonnell, 1998). Technological 0304-3800/$ – see front matter © 2004 Elsevier B.V. All rights reserved. doi:10.1016/j.ecolmodel.2004.06.036