Application of PCA and interpolated spatial distribution maps in soil quality control of dragon fruit farms in Tay Ninh Province, Vietnam

Abstract

Accurate and timely assessment of soil quality is one of the core activities in sustainable farming and agricultural management. Soil quality index (SQI) is greatly affected by fluctuations in physical chemistry indicators of soil. 12 composite soil samples were collected in the dragon fruit growing area, Tay Ninh, Vietnam. The total data set (TDS) has 11 soil parameters, including pH, EC, TOC, CEC, P_av, NH₄⁺, bulk density, particle density, clay, silt, and sand, were determined. Principal Component Analysis (PCA) is used to determine the Minimum Dataset (MDS) and weights. These are then combined with scores from land index scoring functions to estimate the Soil Quality Index (SQI). Furthermore, a spatial distribution map of SQI based on the IDW method in ArcGIS 10.8 was also constructed. The results indicate that the MDS depends of four Principal Components (PC), explaining 89.02 of the total data variance, with representative parameters CEC, P_av, EC, and pH corresponding to weights of 0.37, 0.26, 0.22, and 0.15, respectively. The average soil quality assessment accounted for 41.6% and the degraded type accounted for 58.3%. The study demonstrated that the combination of PCA and GIS provided a comprehensive and intuitive SQI evaluation. This helps managers monitor soil degradation over a large area more effectively.