| Abstract: | Soil physical, chemical, and biological properties in a rice field located at the Surin Rice Research Center, Thailand, were evaluated as indicators for predicting organic rice (Kao Dok Mali 105 variety) production and yield. Four treatments under different management practices were studied. They included (1) conventional farming (CF) receiving chemical fertilizer application; (2) organic plot receiving green manure (GM) addition; (3) organic plot receiving rice straw (RS) addition; and (4) control plot (CT) without any external plant nutrient source. Soil quality in the four treatments was assessed based upon selected physical, chemical, and biological parameters. Key findings are as follows: cation exchange capacity (CEC), electrical conductivity (EC), pH, soil organic matter (SOM), and essential macronutrients nitrogen (N), phosphorus (P), and potassium (K)] were low in all plots. Soil biological properties including potential N mineralization (PMN), soil basal respiration (BR), microbial biomass carbon (MBC) and microbial biomass N (MBN) in all treatments were also low. Principal component analysis (PCA), using 15 soil properties, showed significant differences among farm management practices. Soil chemical and biological properties best related to soil quality included P, N, and SOM (for chemical properties) and MBC, MBN, and BR (for biological properties). Based on significant relationships between yield (r > 0.75) and the soil properties (r > 0.55), selected soil biological (MBC, MBN, and BR) and chemical (TOP total organic phosphorus], TK total potassium], TN total nitrogen], SOC soil organic carbon], and SOM) properties were determined to be suitable soil-quality indicators, respectively. A soil-quality indicator for predicting rice yield was computed using multiple regression analyses. The regression model (Y = ?1.685 + 0.333 (MBN) + 0.640 (TK) ? 0.282 (SOC), r2 adjusted = 0.962) was used for predicting yield. Grain yield of rice (RMSE = 0.046 t ha?1, D index = 0.45) was obtained using this regression model. |
