The assessment of soil quality is crucial for the sustainable development of agriculture in semi-arid regions. This study highlights the importance of considering a varied selection of indicators when assessing soil quality by examined the influence of soil type factor on the modelling Soil Quality Index (SQI) using Minimum Data Sets (MDS) constructed as part of the Total Data Set (TDS) through two methods, namely, additive (SQIA) and weighted (SQIW). A total of 716 soil samples (0-30 cm) collected from Doukkala irrigated perimeter of Morocco, were analyzed for physicochemical properties (Texture, pH, EC, SOM, CaCO3, CEC, macronutrients and micronutrients). These samples represented six soil type, including Vertisols, Aridisols, Histosols, Entisols, Mollisols, and Oxisols. Moreover, by employing principal component analysis (ACP), we established an MDS that encapsulated the essential indicators for the soil quality assessment. After determined the MDS contribution in the modelling of the SQIs for each soil type separately, a soil quality maps were generated by grouping together all the SQIs models generated for all soil type. The performance of each model is validated by the Sensitivity Index and the correlation with crop yields. Using both Linear and Non-Linear models for scoring function, the MDS includes Sand, EC, P2O5, CaO, CaCO3, NO3-N, NH4-N, Cu, Fe, and Zn from twenty indicators of the TDS. The results showed that these MDS significantly varied depending on soil type and the soil quality maps generated based on SQI estimated by the Non-Linear additive method (SQIA-NL) showed moderate a high quality in the studied area than the SQI by weighted method. This finding found that the individual contribution of selected the MDS is strongly affected by soil types and the models used to indicators transformed and the SQI computation.