Quantification of soil quality under semi-arid agriculture in the northwest of Iran

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 agricultural 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.