Assessing soil quality is a critical strategy for diagnosing soil status and anticipating concerns in land use systems for agricultural sustainability. In this study, two soil quality assessment indices, the Integrated Quality Index (IQI)
and Nemoro Quality Index (NQI), were employed using two indicator selection methods: Total Data Set (TDS) and Minimum Data Set (MDS), focusing on agricultural fields in Golestan province, Iran. A total of 89 soil samples
were collected and analyzed for particle size distribution, organic carbon, calcium carbonate equivalent (CCE), electrical conductivity (EC), pH, and plant-essential nutrients, including nitrogen, phosphorus, potassium, zinc,
copper, manganese, and iron. Principal component analysis (PCA) was used to extract MDS from TDS, and geostatistical adaptation and correlation analyses were performed to determine the optimal soil quality evaluation index. Our results show that the exponential model better suits the spatial structure of soil quality indicators (IQIMDS: 0.955). Conformity and correlation analyses indicate that the IQI index outperformed the NQI index in estimating soil quality. The superiority of the TDS technique over the MDS technique in terms of accuracy (IQITDS’s kappa: 0.155). Linear relationships between different methods showed a higher correlation coefficient (R2 = 0.43) through the application of IQI. This study suggests the use of IQIMDS to provide a reliable measurement that is particularly useful in assessing the quality of agricultural soil.