“…The values of the whole dataset considering the selected soil quality indicators (SQI) were analyzed using the software SPSS 23.0 (IBM, USA). In order to obtain the minimum feasible dataset to calculate a soil quality index, specific parameters based on the main parent materials (limestones and marls), land uses (grazing, rainfed agriculture, fruits and vegetables, forestry, riparian vegetation) (Fernández et al, 2020; Keshavarzi et al, 2020; Pulido, Barrena‐González, Badgery, Rodrigo‐Comino, & Cerdà, 2018; Rodrigo‐Comino, Keshavarzi, Bagherzadeh, & Brevik, 2019) and from the literature (Armenise, Redmile‐Gordon, Stellacci, Ciccarese, & Rubino, 2013; Guo, Sun, Ouyang, Han, & Li, 2017; Sun, Zhou, & Zhao, 2003; Triantafyllidis, Kosma, & Patakas, 2018; Yu, Liu, Zhang, Li, & Zhou, 2018) such as soil texture (sand and clay), OM, CaCO 3 , total N, pH, available water at 33 (field capacity) and 1,500 KPa (wilting point) were selected. After this, a universal soil quality index was designed to establish representative indicators by assigning weights for the selected SQI to allow estimating and interpreting a unique index.…”