Application of Principal Component Analysis (PCA) and Interpolated Spatial Distribution Maps in Soil Quality Control of Dragon Fruit Farms in Tay Ninh Province, Vietnam

Accurate and timely assessment of soil quality is essential for sustainable farming and efficient agricultural management. The Soil Quality Index (SQI) is heavily influenced by fluctuations in physical and chemical soil indicators. Twelve composite soil samples were collected from the dragon fruit cultivation area in Tay Ninh, Vietnam. The dataset included eleven soil parameters: pH, electrical conductivity (EC), total organic carbon (TOC), cation exchange capacity (CEC), available phosphorus (P_av), ammonium (NH4+), bulk density, particle density, clay, silt, and sand. Principal Component Analysis (PCA) was employed to identify the Minimum Dataset (MDS) and determine parameter weights. These were combined with land index scores to estimate the SQI. Additionally, a spatial distribution map of SQI was created using the Inverse Distance Weighting (IDW) method in ArcGIS 10.8. Results showed that the MDS is influenced by four principal components, which explain 89.02% of the total data variance. Key parameters include CEC, P_av, EC, and pH, with weights of 0.37, 0.26, 0.22, and 0.15, respectively. The average soil quality score was 41.6%, with 58.3% classified as degraded. The study demonstrated that combining PCA and GIS provides a comprehensive and intuitive approach to SQI evaluation. Furthermore, developing agronomic maps based on large sample sizes supports sustainable agricultural management, as evidenced by these research findings.