Soil Environmental Management Zoning for Tianmuhu White Tea Plantations

Tea, as one of the world's most important economic crops, has its quality and yield profoundly influenced by the soil environment. To achieve precise soil management in tea plantations, this study systematically investigated the spatial variability of soil properties and established management zones (MZs) in the core production area of Tianmuhu White Tea. Representative soil samples were collected and analyzed for fertility indicators including soil organic matter (SOM), nitrogen (N), phosphorus (P), and potassium (K), heavy metal contents including arsenic (As), cadmium (Cd), chromium (Cr), copper (Cu), mercury (Hg), and lead (Pb), and the beneficial element selenium (Se). The spatial distribution patterns of these elements were first elucidated using the Inverse Distance Weighting (IDW) interpolation method. The results revealed significant variability among soil elements within the study area, with Hg exhibiting the highest coefficient of variation (CV = 71.04%) and As the lowest (CV = 22.89%). Correlation analysis indicated significant positive correlations between SOM and N and K (R = 0.49-0.69), and significant negative correlations between SOM and Cd and Se (R = 0.37-0.59). Significant positive correlations were also observed between Cr and As, and among Pb, Cu, and Hg (R = 0.35-0.38), suggesting potential common sources. Subsequently, this study constructed three sets of evaluation indices: soil fertility based on SOM, N, P, K, heavy metal pollution based on As, Cd, Cr, Cu, Hg, Pb, and Se content. The effectiveness of three spatial clustering algorithms-K-means (KM), Fuzzy C-means (FCM), and ISODATA (ISO)－in delineating MZs for these criteria was comprehensively compared. The findings indicate that the ISO algorithm performed optimally for soil fertility zoning, effectively reconciling the spatial heterogeneity among various fertility indicators. The FCM algorithm was more suitable for characterizing the gradual spatial patterns of composite heavy metal pollution, yielding superior zoning results. For Se, due to its relatively uniform spatial distribution, all three algorithms produced consistent and reliable zoning results. The MZ schemes developed in this study clarify the spatial differences in soil environmental quality within the Tianmuhu White Tea production area, providing direct decision-making basis and technical support for differentiated fertilization, precise pollution risk prevention and control, and efficient utilization of Se resources in local tea plantations, thereby promoting the sustainable development of the local tea industry.