Abstract: Categorical data based on description of the
agricultural landscape imposed some mathematical and analytical
limitations. This problem however can be overcome by data
transformation through coding scheme and the use of non-parametric
multivariate approach. The present study describes data
transformation from qualitative to numerical descriptors. In a
collection of 103 random soil samples over a 60 hectare field,
categorical data were obtained from the following variables: levels of
nitrogen, phosphorus, potassium, pH, hue, chroma, value and data on
topography, vegetation type, and the presence of rocks. Categorical
data were coded, and Spearman-s rho correlation was then calculated
using PAST software ver. 1.78 in which Principal Component
Analysis was based. Results revealed successful data transformation,
generating 1030 quantitative descriptors. Visualization based on the
new set of descriptors showed clear differences among sites, and
amount of variation was successfully measured. Possible applications
of data transformation are discussed.