An approach has been adopted in order to select the most representative and predictive indicators as minimum data set (MDS) for the assessment of rangeland soil quality. Large data sets were employed for the high hill rangeland in the Saral region, Kurdistan province, west of Iran. The correlations between soil properties and plant growth in various landscape units were investigated and interpreted based on statistical analyses. Multivariate statistical techniques were used to determine the minimum set of indicators among chemical and physical variables, as well as soil surface indices of Landscape Function Analyses (LFA) approach that account for at least 70% of the variability in the whole data set of aboveground plant biomass production among land units. The MDS was selected for its ability to predict soil productivity, as site potential of a long-term rangeland exclosure. The efficacy of the chosen MDS was evaluated by performing multivariate regressions of the MDS against each of the plant growth characteristics (P ≤ 0.05). These dependent characteristics included total yield, herbaceous plant production, and utilizable forage. Variations in the plant response variables were best predicted by Nutrient cycling index, Land organization index, and total nitrogen illustrating that plant variables were more sensitive to the chemical rather than the physical properties of the soil.