Author Information

R. David Hammer
John W. Philpot

Abstract

Intensive land use is requiring more detailed information about patterns and magnitudes of soil variability than can be acquired through traditional soil survey techniques. Discriminant analysis is a mathematical method of numerical classification which could be used to identify discrete populations of soils in their natural settings. The hypothesis of this study was that discriminant analysis could be used to group soils on landtypes on the Mid-Cumberland Plateau. A large data set (132 observations of 29 soil variables) was collected from three landtypes at two Cumberland Plateau locations. Discriminant analysis was used to classify the soil observations into landtypes. Canonical correlation was used to identify soil properties most responsible for separating soils into groups related to landtypes. Not all of the collected soil properties were important discriminators, so variables with low canonical loading scores were eliminated. A total of 13 soil variables representing three genetic soil horizons was required to correctly classify all 132 observations into correct landtypes. Canonical correlations were 0.979 and 0.970 with 29 variables and 0.968 and 0.941 with 13 variables on canonical variates one and two, respectively. Soil variables from Bt horizons alone did not classify all observations into correct landtypes. Discriminant analysis, in conjunction with canonical correlation, shows promise for identifying key variables for numerically classifying soils into related populations.

Keywords

canonical canonical correlation, multivariate statistical procedures, canonical loading scores

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Apr 26th, 5:10 PM

SOIL PROPERTIES AND LANDTYPES--CLASSIFICATION AND IDENTIFICATION WITH DISCRIMINANT ANALYSIS

Intensive land use is requiring more detailed information about patterns and magnitudes of soil variability than can be acquired through traditional soil survey techniques. Discriminant analysis is a mathematical method of numerical classification which could be used to identify discrete populations of soils in their natural settings. The hypothesis of this study was that discriminant analysis could be used to group soils on landtypes on the Mid-Cumberland Plateau. A large data set (132 observations of 29 soil variables) was collected from three landtypes at two Cumberland Plateau locations. Discriminant analysis was used to classify the soil observations into landtypes. Canonical correlation was used to identify soil properties most responsible for separating soils into groups related to landtypes. Not all of the collected soil properties were important discriminators, so variables with low canonical loading scores were eliminated. A total of 13 soil variables representing three genetic soil horizons was required to correctly classify all 132 observations into correct landtypes. Canonical correlations were 0.979 and 0.970 with 29 variables and 0.968 and 0.941 with 13 variables on canonical variates one and two, respectively. Soil variables from Bt horizons alone did not classify all observations into correct landtypes. Discriminant analysis, in conjunction with canonical correlation, shows promise for identifying key variables for numerically classifying soils into related populations.