Establishing a Minimum Data Set for Soil Quality Assessment in Paddy Fields: Incorporating Soil Properties and Terrain Attributes

This study evaluates soil quality and its influence on rice yield in paddy fields by integrating soil properties and terrain attributes within a multivariate framework. The objective of this study was to provide a methodological template for coupling soil quality indicators with topographic context to support site-specific nutrient management in coastal and upland rice systems. In this study, 80 topsoil samples (0–30 cm) were collected from areas ranging from lower coastal to higher elevations. A total of 21 soil properties relevant to rice cultivation were determined. We established three groups of minimum data sets (MDS): (1) using all soil indicators (MDS1), (2) considering physical, chemical, and biological indicators separately (MDS2), and (3) calculating the norm values for soil indicators alongside five terrain attributes derived from a digital elevation model (DEM), including elevation, slope, topographic wetness index (TWI), stream power index (SPI), flow direction (FD), the nearest straight-line distance from the sampling point to the river (Dr), and the nearest straight-line distance from the sampling point to the coastline (Ds) (MDS3). Soil quality was evaluated using the integrated quality index (IQI) and the Nemerov quality index (NQI). The analysis indicated that IQI-MDS1 and IQI-MDS3 had the highest priority based on the sensitivity index (SI(, and efficiency ratio (ER), while NQI-MDS2 was assigned the lowest priority. Notably, IQI-MDS3 and NQI-MDS2 explained 71 and 48% of the variation in rice yield, respectively. Consequently, IQI-MDS3 emerged as the most suitable method for comprehensive soil quality assessments in the paddy fields. Spatial distributions of IQI and NQI revealed higher soil quality in upland relative to lowland. Upland soils exhibited enhanced fertility, as demonstrated by higher soil organic carbon (OC), total nitrogen (TN), available phosphorus (AP), and available potassium (AK), which correspond to increased enzymatic activities. The mapping of rice yield demonstrated that lower-yield paddy fields were predominantly located in coastal areas, likely due to a decline in soil quality, terrain attributes, and proximity to the Sea. The results indicate that integrating terrain attributes with soil quality indicators improves the explanation of yield spatial variability in paddy fields. The MDS3 methodology supports site-specific nutrient management, facilitating sustainable rice production and minimizing environmental degradation in similar agroecosystems.