Principal Component Analysis for the Characterization in the Application of Some Soil Properties
The objective of this research is to study principal
component analysis for classification of 67 soil samples collected from
different agricultural areas in the western part of Thailand. Six soil
properties were measured on the soil samples and are used as original
variables. Principal component analysis is applied to reduce the
number of original variables. A model based on the first two
principal components accounts for 72.24% of total variance. Score
plots of first two principal components were used to map with
agricultural areas divided into horticulture, field crops and wetland.
The results showed some relationships between soil properties and
agricultural areas. PCA was shown to be a useful tool for agricultural
areas classification based on soil properties.
[1] Boruvka L., Vacek O. and Jehlicka J., 2005. Principal component
analysis as a tool to indicative the origin of potentially toxic elements in
soils. Geoderma 2005;128, pp.289-300.
[2] Dragovic S. and Onjia A. Classification of soil samples according to
their geographic origin using gamma-ray spectrometry and principle
component analysis. Journal of Environmental Radioactivity 2006; 89,
pp.150-158.
[3] Sousa S.I.V., Fernando G. Matins, Maria C. Alvim-Ferra, and Maria C.
Pereira. "Multiple linear regression and artificial neural networks based
on principal component to predict ozone concentrations."
Environmental Modelling & Software 22, 1 (January 2007), pp.97-103.
[4] Mico C., Recatala L., Peris M. and Sanchez J. Assessing heavy metal
sources in agricultural soil of an European Mediteranean area by
multivariate analysis. Chemoshere 2006; 65, pp.863-872.
[5] Jolliffe I.T. Principal component analysis. Springer-verlag,
Newyork.1986.
[1] Boruvka L., Vacek O. and Jehlicka J., 2005. Principal component
analysis as a tool to indicative the origin of potentially toxic elements in
soils. Geoderma 2005;128, pp.289-300.
[2] Dragovic S. and Onjia A. Classification of soil samples according to
their geographic origin using gamma-ray spectrometry and principle
component analysis. Journal of Environmental Radioactivity 2006; 89,
pp.150-158.
[3] Sousa S.I.V., Fernando G. Matins, Maria C. Alvim-Ferra, and Maria C.
Pereira. "Multiple linear regression and artificial neural networks based
on principal component to predict ozone concentrations."
Environmental Modelling & Software 22, 1 (January 2007), pp.97-103.
[4] Mico C., Recatala L., Peris M. and Sanchez J. Assessing heavy metal
sources in agricultural soil of an European Mediteranean area by
multivariate analysis. Chemoshere 2006; 65, pp.863-872.
[5] Jolliffe I.T. Principal component analysis. Springer-verlag,
Newyork.1986.
@article{"International Journal of Earth, Energy and Environmental Sciences:52084", author = "Kamolchanok Panishkan and Kanokporn Swangjang and Natdhera Sanmanee and Daoroong Sungthong", title = "Principal Component Analysis for the Characterization in the Application of Some Soil Properties", abstract = "The objective of this research is to study principal
component analysis for classification of 67 soil samples collected from
different agricultural areas in the western part of Thailand. Six soil
properties were measured on the soil samples and are used as original
variables. Principal component analysis is applied to reduce the
number of original variables. A model based on the first two
principal components accounts for 72.24% of total variance. Score
plots of first two principal components were used to map with
agricultural areas divided into horticulture, field crops and wetland.
The results showed some relationships between soil properties and
agricultural areas. PCA was shown to be a useful tool for agricultural
areas classification based on soil properties.", keywords = "soil organic matter, soil properties, classification,
principal components", volume = "6", number = "5", pages = "244-3", }