Zenodo (CERN European Organization for Nuclear Research) · 2012 · 10 citations · 0 references
AgricultureEarth SciencesSoil SciencesSoil ChemistrySoil ClassificationSoil SciencePrincipal Component AnalysisSoil CompositionSoil MappingSoil TextureAgricultural Property ClassificationPrincipal ComponentsSoil Classification MappingSoil Physicochemical PropertiesSoil PropertiesSoil Property MappingSoil Texture AnalysisSoil Texture Classification
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.