2024, Vol. 7, Special Issue 3

Soil organic carbon which is transient in nature, undergoes decomposition process but its rate of loss or retention in soil, depends on the management practices, weather conditions and it is not responsive to changes of soil quality in short-term with various soil or crop management practices due background levels and natural variability. Hence, a study was carried in Long Term Fertilizer Experiment (LTFE), GKVK, Bengaluru, to study various soil carbon pools and productivity relationship with the help of Principal component analysis. Large datasets are increasingly common and are often difficult to interpret. Principal Component Analysis (PCA) is used for reducing the dimensionality of datasets, increasing interpretability and also time reducing information loss. It can be done by creating new uncorrelated variables that maximize variance. Finding such variables, the principal components, reduces to solving an eigenvalue, and the new variables can be defined by the dataset, hence making PCA an adaptive data analysis model.