SSSAJ Journal of Natural Resources and Life Sciences Education
HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS
 QUICK SEARCH:   [advanced]


     


This Article
Right arrow Figures Only
Right arrow Full Text Free
Right arrow Full Text (PDF) Free
Right arrow Alert me when this article is cited
Right arrow Alert me if a correction is posted
Services
Right arrow Similar articles in this journal
Right arrow Similar articles in Web of Science
Right arrow Alert me to new issues of the journal
Right arrow Download to citation manager
Right arrow reprints & permissions
Citing Articles
Right arrow Citing Articles via HighWire
Right arrow Citing Articles via Web of Science (14)
Right arrow Citing Articles via Google Scholar
Google Scholar
Right arrow Articles by Triantafilis, J.
Right arrow Articles by McBratney, A.B.
Right arrow Search for Related Content
PubMed
Right arrow Articles by Triantafilis, J.
Right arrow Articles by McBratney, A.B.
GeoRef
Right arrow GeoRef Citation
Agricola
Right arrow Articles by Triantafilis, J.
Right arrow Articles by McBratney, A.B.
Related Collections
Right arrow Soil Methods/Instrumentation
Right arrow Soil Classification and Mapping
Soil Science Society of America Journal 65:403-413 (2001)
© 2001 Soil Science Society of America

DIVISION S-5-PEDOLOGY

Creation and Interpolation of Continuous Soil Layer Classes in the Lower Namoi Valley

J. Triantafilisa, W.T. Wardb, I.O.A. Odeha and A.B. McBratneya

a Australian Cotton Cooperative Research Centre, Dep. of Agricultural Chemistry and Soil Science, Ross Street Building A03, The Univ. of Sydney, NSW Australia 2006
b Faculty of Environmental Sciences, Griffith Univ., Nathan, Qld 4111, Australia

Corresponding author (johnt{at}acss.usyd.edu.au)

The major errors associated with soil classification and mapping are due to subjective allocation of individuals to classes and incongruities between the classification system and the natural continuous variability of the soil mantle. Fuzzy clustering algorithms can be applied to resolve both errors. In this study we numerically classified 1419 soil horizon samples using fuzzy k-means (FKM) and fuzzy k-means with extragrade (FKME) analysis. Each sample was characterized by 12 chemical and textural attributes that were used for the numerical classification. The fuzzy classes produced were mapped at various depths using a method that considered the unity of class membership and local kriging. The use of a confusion index enabled the representation of the continuous nature of membership between the classes mapped and highlighted areas where the collection of additional information may be appropriate. The resulting classes reflect sensible and practical groupings that are easily related to the natural structure of the landscape. Silt and clay contents were the most distinguishing attributes in identifying the various geological and geomorphic components. Differences in soil-forming process were well highlighted by organic C (Org. C), P, electrical conductivity (EC1:5), pH, and Cl- content. We concluded that the fuzzy clustering algorithms and geostatistical techniques provide a worthwhile approach to soil classification and representation of the soil continuum.

Abbreviations: EC1:5, electrical conductivity • FKM, fuzzy k-means • FKME, fuzzy k-means with extragrade • FPI, fuzziness performance index • NCE, normalized classification entropy • Org. C, organic C




This article has been cited by other articles:


Home page
Agron. J.Home page
J. Triantafilis, B. Kerridge, and S. M. Buchanan
Digital Soil-Class Mapping from Proximal and Remotely Sensed Data at the Field Level
Agron. J., July 7, 2009; 101(4): 841 - 853.
[Abstract] [Full Text] [PDF]


Home page
Soil Sci.Home page
D. G. Sullivan, J. N. Shaw, and D. Rickman
IKONOS Imagery to Estimate Surface Soil Property Variability in Two Alabama Physiographies
Soil Sci. Soc. Am. J., September 29, 2005; 69(6): 1789 - 1798.
[Abstract] [Full Text] [PDF]


Home page
Soil Sci.Home page
J. Triantafilis, I.O.A. Odeh, and A.B. McBratney
Five Geostatistical Models to Predict Soil Salinity from Electromagnetic Induction Data Across Irrigated Cotton
Soil Sci. Soc. Am. J., May 1, 2001; 65(3): 869 - 878.
[Abstract] [Full Text] [PDF]




HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS
The SCI Journals Agronomy Journal Crop Science
Journal of Natural Resources
and Life Sciences Education
Vadose Zone Journal
Journal of Plant Registrations Journal of
Environmental Quality
The Plant Genome
Copyright © 2001 by the Soil Science Society of America.