Physical Sciences and Mathematics Commons^™

Applying Principal Component Analysis To Soil-Landscape Research-Quantifying The Subjective, R. David Hammer, John W. Philpot, Jon M. Maatta

Conference on Applied Statistics in Agriculture

Principal component analysis is a multivariate statistical procedure that can be used to identify factors (correlated subsets of variables) in large data sets. This statistical method appears useful for scientists investigating soil processes, but it has received little attention. Reported applications of principal component analysis share a common fault--subjective, user-specified analytical options apparently are not recognized, for they are not discussed. Reported data sets are often small, have low observations-per-variable ratios, and lack tests of robustness. A large soil data set is used to demonstrate systematic procedures for an optimum rotated principal component solution. This solution retained 21 variables aligned …