Multivariate Analysis of Soil Properties in Burdur Province: Principal Component Analysis and Canonical Correlation Approach

This study used multivariate statistical methods such as principal component analysis (PCA) and canonical correlation analysis (CCA) to examine seven measured properties of 580 soil samples collected from agricultural lands in Burdur province.. Our aim is to reveal the complex structure of the soil and the relationships between different properties. PCA analysis identified three principal components that explained 67.9% of the total variance. The first component consists of organic matter, available phosphorus and available potassium; the second component consists of total salt and saturation, and the third component consists of pH and lime properties. These findings indicate that soil properties act together in certain groups. CCA confirmed that there was a statistically significant relationship between these sets of variables, and the initial canonical correlation coefficient was determined to be 0.360. CCA analyses revealed 38.2% of the variance of the variables in Set 1, 57.1% of the variance in Set 2, and 4.9% and 7.4% of the variance shared between the two sets, respectively, indicating a moderate relationship between the variables. These data provide a valuable basis for understanding the interaction between soil fertility and chemical properties, especially under the climatic conditions of Burdur. These analyses are expected to be an important tool for regional agricultural planning and the development of sustainable soil management strategies