Quantitative assessment of soil quality indices for urban croplands in a calcareous semi-arid ecosystem

Abstract

Abstract Urbanization-induced soil quality degradation is a global challenge in both developed and developing nations which can be responsible for the reduced health and productivity of agroecosystems. However, available data are very limited as to the impacts of urbanization on different quantitative scenarios of soil quality indices (SQI). The present study determined and investigated 24 variables including a combination of fertility, salinity, and sodicity attributes and heavy elements of surface soils (0 to 50\xa0cm depth) for 14 soil profiles of urban (10 profiles) and non-urban (4 profiles as control) fields. The quantitative SQI of the urban soils and the control field was compared using two models of the Integrated Quality Index (IQI) and Nemoro Quality Index (NQI), various indicators selecting approaches [total data set (TDS) and minimum data set (MDS)], and scoring methods (linear and non-linear) under different soil types (Calcisols, Cambisols, Fluvisols, and Regosols). The results revealed that urbanization had influenced soil quality both negatively (e.g. an increase in soil salinity (EC), sodium adsorption ratio (SAR), exchangeable sodium percentage (ESP), Soil erodibility factor (K-factor), and heavy metals (Zn, Cu, Cd, Pb, and Ni)) and positively (e.g. an increase in organic matter, total N, available P, and cation exchange capacity), but the negative impacts found to outnumber the positive impacts. Comparing to the control soil, the urban soil exhibited a drop of 12.5–22% and a rise of 16–24% in all SQI scenarios and K-factor, respectively, implying that urbanization has aggravated soil quality degradation. Given both urban and control soils, the highest values of all SQI scenarios were recorded for Fluvisols, followed by Cambisols, Calcisols, and Regosols clearly reflecting the combined influence of soil type (as intrinsic factors) and agricultural management practices (as an extrinsic factor) on soil quality. All SQI scenarios were significantly and positively correlated to one another with correlation coefficients ranging from 0.80 to 0.96, suggesting that any of them could be applied to monitor soil quality. Nevertheless, the SQI determined based on IQI-MDS and the non-linear scoring method had better performance and discrimination in the assessment of soil quality than the other SQI scenarios with respect to direct comparison, Pearson correlation coefficients, sensitive index values, K-factor, time and money-saving, and the regression coefficient between SQI and maize yield.