Contents lists available at ScienceDirect Ecological Indicators journal homepage: www.elsevier.com/locate/ecolind Original Articles Quantification of soil quality under semi-arid agriculture in the northwest of Iran Somayeh Hamidi Nehrani a , Mohammad Sadegh Askari a, ⁎ , Saeed Saadat b , Mohammad Amir Delavar a , Mehdi Taheri c , Nicholas M. Holden d a Department of Soil Science, Faculty of Agriculture, University of Zanjan, Zanjan, Iran b Soil and Water Research Institute (SWRI), Agricultural Research, Education and Extension Organization (AREEO), Karaj, Iran c Zanjan Agricultural and Natural Resources Research and Education Center, Zanjan, Iran d UCD School of Biosystems and Food Engineering, University College Dublin, Dublin 4, Ireland ARTICLE INFO Keywords: Conventional tillage Irrigated farming Rain-fed farming Discriminant analysis Principal component analysis Zanjan province ABSTRACT Current management practices are thought to be having adverse impacts on soil quality for semi-arid agriculture. A multidimensional quantification of soil quality was developed and tested under irrigated and rain-fed agri- cultural systems in the northwest of Iran. Thirty-four chemical, biological and physical soil quality indicators were quantified at two depths with mono-cropping and crop rotation (n = 154). Discriminant analysis (DA) and principal component analysis (PCA) were applied to identify a minimum data set (MDS) for developing soil quality indices (SQI). Soil organic carbon (SOC), soluble sodium (Na), geometric mean diameter of soil aggregate (GMD) and available zinc (Zn) were identified using PCA, and GMD, Zn and soil microbial respiration (SMR) were identified using DA. Six SQIs were produced using non-linear and linear scoring equations and integration approaches based on two independent MDS. SQIs were significantly different between irrigated and dry farming at both depths (P-value < 0.05), although there was no impact of crop rotation under conventional tillage. The best index was produced using linear scoring and additive integration based the MDS selected using discriminant analysis. While the PCA is the conventional technique for reducing data redundancy, DA identified a more useful MDS than PCA. The importance of soil aggregate stability, heavy metal pollution, biological activity, organic carbon content and soil sodicity was noted for monitoring and assessing the main threats to SQ, these did not have to be directly quantified for a useful SQI, but need to be understood for interpretation. The SQI provided a rapid, reproducible and reliable method for multifaceted assessment of soil quality. The study indicated adverse impact on soil quality of management systems operating in the semi-arid regions of Iran under rain-fed farming. 1. Introduction The ability of soil used for agriculture to support the productive function is essential for the prevention of soil degradation and to secure food supply. Soil erosion is an important type of degradation, particu- larly in semi-arid climate (Jones et al., 2014). Intensive and continuous cultivation, removing residues, low application of organic fertilizer leads to organic matter decline and cultivation on steep slopes are the main causes for the reduction of soil productivity under dry farming systems in semi-arid regions, typified by Iran. Heavy metal pollution is also regularly reported as a threat affecting soil in agro-ecosystems (Chen et al., 2018). Soil erosion, salinization, degradation and pollution have been reported as threats to soils under agricultural management systems in Iran (e.g. Nabiollahi et al., 2018; Raiesi, 2017; Golchin and Asgari 2008; Vaezi and Bahrami, 2014) and countries with similar conditions that represent a significant proportions of global population (Vasu et al., 2016). Soil Quality (SQ) is “the capacity of a soil to function within ecosystem boundaries to sustain biological https://doi.org/10.1016/j.ecolind.2019.105770 Received 12 June 2019; Received in revised form 16 August 2019; Accepted 24 September 2019 Abbreviations: ANOVA, analysis of variance; B, available boron; BD, bulk density; Cd, available cadmium; CF, coarse fraction (> 2 mm); Cl – , chloride; Cs, carbon stock; Cu, available copper; DA, discriminant analysis; EC, electrical conductivity; Fe, available iron; GMD, geometric mean diameter; HCO 3 – , bicarbonate; K, available potassium; Ks, saturated hydraulic conductivity; MBC, microbial biomass carbon; MDS, minimum data set; Mn, available manganese; MWD, mean weight diameter; Na, soluble sodium; Ns, nitrogen stock; P, available phosphorus; Pb, available lead; PC, principal component; PCA, principal component analysis; qCO 2 , metabolic quotient; qmic, microbial quotient; SAR, sodium adsorption ratio; SD, standard deviation; SMR, soil microbial respiration; SO 4 2– , sulfate; SOC, soil organic carbon; SQ, soil quality; SQI, soil quality index; TDS, total data set; TN, total nitrogen; TNV, calcium carbonate equivalent; Zn, available zinc ⁎ Corresponding author at: Department of Soil Science, University of Zanjan, University Blvd., Zanjan, IR 45371-38791, Iran. E-mail address: askari@znu.ac.ir (M.S. Askari). Ecological Indicators 108 (2020) 105770 1470-160X/ © 2019 Elsevier Ltd. All rights reserved. T