Urbanization accelerates soil degradation in peri-urban compared to rural farms

Abstract

Urbanization-induced soil degradation poses a significant threat to the structural resilience and functional capacity of croplands, with profound implications for sustainable agriculture and ecosystem stability. Despite increasing concern, there remains a critical gap in comprehensive, quantitative assessments of how urbanization affects key indicators of soil degradation. This study investigates the influence of urbanization on soil structural integrity, health, and degradation level in corn-cultivated agroecosystems by comparing urban and non-urban farms across four soil types. We developed a comprehensive soil degradation index (CSDI) using total dataset (CSDI-TDS) and a minimum dataset (CSDI-MDS) approaches to evaluate physical and chemical soil degradation indicators. Urban soils exhibited significantly higher soil erodibility (18–25% increase in the K-factor) and heavy metal accumulation (12–102% increase) compared to non-urban counterparts, signaling compromised soil structure and resilience. The average CSDI-TDS and CSDI-MDS scores were elevated by 14–21% and 16–22%, respectively, in urban soils, indicating a marked intensification of degradation linked to urban dynamics. Variables such as modified clay ratio (MCR), soil stability index (SSI), soil organic carbon (SOC), and sodium adsorption ratio (SAR) were identified as primary indexes differentiating soil structural vulnerability in urban and non-urban settings. Corn productivity accounts for a relatively weak 22–25% of the variance in both the CSDI-MDS and CSDI-TDS, indicating a limited influence of CSDIs on crop performance. These findings highlight the utility of the CSDI, especially the CSDI-MDS, as a sensitive and responsive framework for assessing the impacts of urbanization on soil structural health and agroecosystem sustainability. This work contributes to the growing body of research aimed at reinforcing soil biophysical and chemical resilience under the dual pressures of land-use change and climate variability.

Introduction

Soil structure is a central determinant of soil resilience, the capacity of soils to recover from disturbance while maintaining essential ecological and productive functions. While this concept has traditionally been examined in agricultural landscapes, urban and peri-urban soils are increasingly recognized as critical components of broader agroecosystem resilience and environmental sustainability. Urban soils, particularly those in suburban and marginal city zones, play vital roles in ecosystem multifunctionality, including, water quality, soil erosion, carbon storage, hydrologic regulation, climate mitigation, and supporting green infrastructure for human well-being. As the proportion of the global population living in urban areas is projected to increase from 54% in 2016 to 66% by 2050¹, maintaining the structural integrity and functional resilience of urban soils becomes an urgent priority.

However, the physical, chemical, and biological integrity of urban soils is under increasing pressure due to direct and indirect anthropogenic disturbances. Direct impacts such as soil compaction, sealing, and contamination with, sewage, heavy metals and construction debris alter fundamental soil structural functions. Indirect influences, including modified urban microclimates and altered inputs of pollutants and organic materials, further degrade the functional capacity and recovery potential of these soils under environmental stressors such as drought, flooding, and heatwaves^2–5. These structural and functional soil degradation reduce the resilience of urban soils and compromise their ability to deliver critical ecosystem services in the face of global climate change.

Soil degradation refers to the decline in soil quality that impairs its capacity to support plant growth and sustain ecosystem functions, often resulting from improper land use or poor management practices⁶. In urban contexts, degradation processes are accelerated by disrupted water and energy flows, rapid land-use changes, production demands, and industrial and household pollutants. Physically, this degradation is often reflected in higher bulk density, lower structural stability, and diminished water flow and storage capacity, critical indicators of compromised soil structure and health. Chemically, it includes shifts in pH, enhanced salinity, organic carbon depletion, and accumulation of contaminants. Collectively, these changes lead to declines in soil productivity, carbon sequestration potential, and water-holding capacity, with global consequences for climate regulation and human and animal health. For instance, degraded soils contribute significantly to greenhouse gas emissions, with approximately 24% of anthropogenic emissions originating from agricultural soils alone^7–9. The economic impact is also profound, with an estimated US$400 billion in annual losses globally due to soil degradation¹⁰.

Despite the critical role of urban soils in environmental resilience and sustainable development, systematic approaches to assessing soil structural degradation in these landscapes (as such as the overall soil degradation index) remain limited. Previous studies have typically focused on individual soil properties such as bulk density, water infiltration, or organic matter content^11–14, which provide only partial insights into overall soil degradation status. For example, studies by Chen et al. (2025) in China, Paradelo et al. (2025) in Spain, Bayer et al. (2025) in Germany, and Ali et al. (2025) in Africa have documented an increase in salts, heavy metals, soil erodibility (K-factor) and organic pollutants, as well as a reduction in organic matter and nutrients, alongside a decline in biodiversity due to urbanization. This, in turn, accelerate soil degradation and reduce ecosystem productivity. The integration of multiple soil indicators into composite quantitative indices, such as the Comprehensive Soil Degradation Index (CSDI), offers a more holistic and quantitative assessment of soil structural resilience. By statistically selecting and aggregating physical, chemical, and biological indicators into a unified scoring system, the CSDI enhances our ability to detect degradation trends, compare soil health across landscapes, and evaluate the effectiveness of conservation and restoration interventions.

Despite its promising results in farmlands, the application of CSDI in urban environments remains scarce, limiting its utility for guiding soil management and policy decisions in these increasingly important landscapes. This study addresses that gap by applying and evaluating the CSDI framework within urban and adjacent non-urban soils. Specifically, our objectives were to: (a) quantify individual soil degradation indicators relevant to soil structural health; (b) identify the most influential soil properties contributing to urban soil degradation; and (c) assess differences in overall soil degradation status, as measured by CSDI, between urban and non-urban soils. The findings aim to contribute to the growing body of knowledge on soil physical health and resilience, supporting more informed strategies for urban land management and agroecosystem sustainability in the context of global environmental change.

Materials and methods

Study site

This study was conducted in the peri-urban agricultural zone of Urmia, located in West Azerbaijan Province, northwestern Iran (45° 07′ 20ʺ E to 45° 07′ 38ʺ E; 37° 31′ 44ʺ N to 37° 34′ 58ʺ N) (Fig. 1). The area experiences a Mediterranean semi-arid climate with warm summers and cold winters, alongside a xeric soil moisture regime and a mesic temperature regime. Agricultural activity dominates land use, including cultivation of cereals, oilseeds, vegetables, grapes, apples, and medicinal crops. Summer corn (Zea mays L.) is a principal crop, typically cultivated in rotation systems under flood irrigation, with a growing period of approximately 120 days (June–September). The dominant variety is single-cross 704, yielding between 5 and 8.5 t ha^− 1. Fertility management includes annual tillage, application of 40–50 t ha^− 1 of decomposed manure, 200–250 kg ha^− 1 of urea, and 100–150 kg ha^− 1 of triple superphosphate. Water for irrigation is sourced from both diverted surface flows from Urmia City and groundwater wells. These practices are representative of conventional smallholder systems situated at the interface of urban influence and agricultural use, an area of increasing importance for studying soil resilience.

Fig. 1.

The location map of the study region in northwestern Iran (https://www.qgis.org/ and https://www.google.com/earth/).

Field sampling and soil classification

Soil samples were collected from 10 smallholder farms (1.5-2 ha each) exposed to direct urban pressures. Five composite samples were obtained randomly per farm, totaling 50 samples. Each composite sample integrated five subsamples taken from 0 to 50 cm depth. Profile pits were excavated at each site for classification and description, and soils were classified using the World Reference Base (WRB) system¹⁵. The major soil types identified were Calcisols, Cambisols, Fluvisols, and Regosols. At each non-urban site, one profile per soil type and five composite samples were collected to serve as controls, subsequently sampled and analyzed. The sampling protocol was identical at both urban and non-urban sites The study design involved the selection of paired urban and non-urban sites, matched for key characteristics such as slope, soil use, parent rock, physiographic condition, and soil order. This allowed us to directly attribute changes in soil indicators to the effects of urbanization. Texture classification revealed clay and sand content ranged from 20 to 50% and 10–50%, respectively, representing a diverse range of loam to clay textures. All samples were air-dried and sieved to < 2 mm prior to analysis.

Laboratory analysis procedures

To assess the structural integrity and degradation status of soils, a comprehensive suite of physical and chemical indicators was measured. The hydrometer method was utilized to ascertain the distribution of soil particle sizes¹⁶. The soil pH and its electrical conductivity (EC) were assessed through the saturated paste and the saturated extract methods, respectively¹⁷. Soil organic carbon (SOC) was estimated by wet oxidation technique, using K₂Cr₂O₇ in conjunction with H₂SO₄ (Nelson and Sommers, 1982). The percentage of soil organic matter (SOM) was estimated by multiplying the percentage of SOC by a factor of 1.75. Soil cation exchange capacity (CEC) was determined utilizing a 1 N NH₄OAc, which was buffered to a pH of 8.2 ¹⁷. The extraction of soil heavy metals was performed by digesting 2 g of dry soil with 10 ml of 4 M HNO₃, followed by the addition of 2 ml of deionized water and 3 ml of 30% H₂O₂, and subsequent heating at 95 °C for 15 min on a heating block¹⁸. Their concentrations, specifically Zn, Cu, Cd, Pb, and Ni, were measured using flame atomic absorption spectrometry (Shimadzu AA-6300), which was calibrated using certified standard reference solutions. The soil ionic compound consisting of Ca²⁺, Mg²⁺, and Na⁺, along with the sodium absorption ratio (SAR), exchangeable sodium, and exchangeable sodium percentage (ESP), was determined utilizing the methodology established by the US Salinity Laboratory in 1954¹⁹.

The soil stability index (SSI), the soil crusting index (SCI), the modified clay ratio (MCR), and the K factor (t ha h ha^− 1 MJ^− 1 mm^− 1) related to soil erodibility (K-factor) were respectively computed using Eqs. (1), (2), (3), and (4)^20–23.

1

2

3

4

where SOC represents the percentage of organic carbon in Eq. (1). In Eq. (2), the variables Si_f, Si_C, Cl, and OM denote the percentages of fine silt, coarse silt, clay, and organic matter, respectively. In Eq. (4), The variables Sa, Si, Cl, and OC denote the percentages of sand, silt, clay, and organic carbon, respectively, and SN1 is equals to 1 - S/100.

Comprehensive soil degradation index (CSDI)

To holistically assess soil degradation status and structural resilience, a Comprehensive Soil Degradation Index (CSDI) was computed using both a Total Dataset (TDS) and Minimum Dataset (MDS) approach²⁴.

5

where S₁ = score of each degradation indicator (range: 0–1), Wi = weight derived from factor analysis, n = number of selected indicators. The membership function used to standardize indicators was defined as follows: For positive correlation with degradation (e.g., SAR, SCI):

6

For negative correlation with degradation (e.g., SOM, SSI):

7

This index approach enhances our capacity to quantify soil functional loss and guide land management practices aimed at improving soil physical resilience and agroecosystem functionality.

Statistical analysis

Pairwise mean comparisons between peri-Urban and Rural farms conducted using t-test. Data were checked for normality with the Kolmogorov-Smirnov test and for homogeneity of variance with Levene’s test. The PCA was then employed to determine the weighting coefficients (Wi) of these indicators for the CSDI calculation. This method was chosen as it is a robust, unsupervised technique for dimensionality reduction that identifies the underlying structure of the dataset without requiring a priori grouping, which was appropriate for deriving integrated index weights from our multivariate soil data.The suitability of the data for PCA was confirmed by a Kaiser-Meyer-Olkin (KMO) statistic and a significant Bartlett’s test of sphericity (p < 0.001) The PCs with an eigenvalue greater than 1 were retained for interpretation. The Wi for each soil indicator was calculated as the proportion of its communality contributed to the sum of all communalities of the retained components. The contribution of individual indicators to the CSDIs, expressed as a percentage, was computed from their normalized weights (Wi × 100). Comparative analysis between urban and non-urban sites for individual indicators and CSDI values was performed using Duncan’s multiple range test at a 5% significance level. All statistical analyses were conducted using SPSS software (version 16.0; SPSS Inc., Chicago, IL, USA).

Results and discussion

Soil degradation physicochemical indicators

A notable difference was observed in the distribution of soil particle sizes, ranging from 15 to 56.5% for clay, 11 to 47.5% for silt, and 17 to 46.5% for sand. This analysis led to the identification of five distinct soil textural classes: loam, clay loam, silty clay loam, silty clay, and clay (Fig. 2). These variations can be attributed to the differing geo-pedological characteristics present in the study area, including factors such as physiographic position, slope, and soil type^25,26.

Fig. 2.

The soil textural classes categorized according to the USDA for the study area.

The estimated values for the Soil Structural Instability Index (SSI) and the Modified Clay Ratio (MCR), which serve as quantitative measures of soil erodibility and susceptibility, ranged from 0.77% to 5.16% and from 0.76 to 4.13, respectively, across various soil types (Table 1). These results indicate that most of the soils have low structural stability (SSI < 5%) and are also susceptible to erosion (MCR > 3)^27,28. Higher SSI values suggest enhanced soil structural stability, whereas higher MCR values signify increased soil compaction, thereby increasing susceptibility to degradation and erosion²⁹. The Soil Crusting Index (SCI) values suggest a moderate level of crusting in the majority of the study soils, falling within the range of 0.2 to 2³⁰.

Table 1.

A statistical summary of selected degradation indicators in the urban soils.

Soil degradation indicator	Unit	Min	Max	Mean	SD	CV (%)
Clay	%	15.00	56.50	35.38	11.58	32.75
Silt	%	17.00	46.50	34.99	5.65	16.16
Sand	%	11.00	47.50	29.51	10.21	34.60
SSI	%	0.77	5.16	2.37	1.08	45.50
SCI		0.29	1.53	0.86	0.29	34.08
MCR		0.76	5.13	2.10	1.09	51.64
pH		7.36	8.06	7.76	0.18	2.31
EC	dS m− 1	0.39	4.39	1.27	1.00	78.84
SOC	%	0.40	1.98	0.95	0.43	45.45
SOM	%	0.69	3.42	1.64	0.75	45.45
SAR		2.36	6.33	3.98	0.85	21.39
ESP	%	2.97	7.60	4.91	0.99	20.22
K-factor	t ha h ha− 1 MJ− 1 mm− 1	0.029	0.038	0.034	0.002	6.77

SSI soil stability index, SCI soil crust index, MCR modified compaction ratio, EC electrical conductivity, SOC soil organic matter, SOM soil organic matter, SAR sodium absorption ratio, ESP exchangeable sodium percentage, K-factor K factor of soil erodibility.

The soil pH, Sodium Adsorption Ratio (SAR), and Exchangeable Sodium Percentage (ESP) ranged from 7.36 to 8.06, 2.36 to 6.33, and 2.97 to 7.6%, respectively, indicating an alkaline and non-sodic condition in the majority of soil samples. The salinity levels of most soil samples were below 4 dS m⁻¹, with only 6% of the soils exhibiting higher salinity, suggesting that severe salinity issues are largely absent across the study area. Soil Organic Carbon (SOC) values ranged from 0.4% to 1.98%, classifying most samples within the low (0.6% < SOC < 1%) to moderate (1% < SOC < 1.8%) range^31–33.

The soil erodibility factor (K-factor), a quantitative measure of the intrinsic erodibility of specific soil types³⁴, ranged from 0.029 to 0.038 t ha h ha⁻¹ MJ⁻¹ mm⁻¹. However, the role of anthropogenic processes cannot be ruled out in influencing the magnitude of this factor in a region where soils are subject to long-term intensive and continuous cultivation practices, such as tillage and traditional flood irrigation. The average K-factor value fell within the moderate erosion category (0.02 < K-factor < 0.04 t ha h ha⁻¹ MJ⁻¹ mm⁻¹)³¹, with the highest values observed in the Regosols soil type, followed by Calcisols, Cambisols, and Fluvisols, respectively (Fig. 3).

Fig. 3.

The means comparison of K - factor values between urban and un-urban soils across different soil types. Different letters show significant differences at p < 0.05 based on the t-test.

Urban soils exhibited an increase in K-factor values ranging from 18% to 25% compared to non-urban soils across various soil types, indicating that urbanization has exacerbated soil erodibility (Fig. 3). These findings align with conclusions drawn from prior research conducted in various regions, such as studies by Hossain et al.³⁵ in Bangladesh, Lu et al.³⁶ and Zhang et al.³⁷ in China, Moges and Tegbar³⁸ in Africa, and Sarwar et al.³⁹ in Pakistan.

Soil heavy metals

The levels of total heavy metals in the soil, which serve as indicators of soil health deterioration, exhibited significant variability. Specifically, the concentrations ranged from 55 to 184 mg kg⁻¹ for Zn, 23 to 122 mg kg⁻¹ for Cu, 0.5 to 1.5 mg kg⁻¹ for Cd, 30 to 122 mg kg⁻¹ for Pb, and 31 to 100 mg kg⁻¹ for Ni (Table 2). The coefficient of variation (CV) was found to be greater than 15% for all heavy metals, reflecting moderate (CV = 16–36%) to high (CV > 36%) variability⁴⁰. This CV variation implies large spatial differences in Cd, Pb, and Ni concentrations, whereas Zn and Cu exhibit more moderate variations. We conducted a comparative analysis of heavy metal concentrations in urban versus non-urban soils, revealing significant increases in metal levels across all soil types (Fig. 4). Urban soils exhibited elevated concentrations of Zn, Cu, Cd, Pb, and Ni, with increases ranging from 19% to 40%, 23% to 56%, 39% to 74%, 57% to 102%, and 12% to 88%, respectively, when compared to their non-urban counterparts (Fig. 4). The high concentration of these metals is likely due to discharges and waste from nearby food processing and storage facilities, dyeing plants, metal plating operations, and plastic manufacturing activities. Additionally, vehicular traffic and the use of agrochemicals may also contribute to the accumulation of heavy metals in the study area^29,41,42. These findings are consistent with research conducted in various global regions, including China⁴³, Europe⁴⁴, India⁴⁵, and Russia⁴⁶, where similar industrial activities have been shown to contribute substantial amounts of heavy metals to agricultural soils.

Table 2.

A statistical summary of heavy metals in the urban soils.

Soil heavy metal	Unit	Min	Max	Mean	SD	CV (%)
Zn	mg kg− 1	59.10	184.87	82.24	22.77	27.69
Cu	mg kg− 1	22.91	121.50	38.80	18.90	48.72
Cd	mg kg− 1	0.49	1.54	0.92	0.35	37.73
Pb	mg kg− 1	29.06	99.55	57.08	19.06	33.39
Ni	mg kg− 1	22.12	122.13	59.81	33.99	56.82

Fig. 4.

The means comparison for the heavy metals values between urban and un-urban soils across different soil types. Different letters show significant differences at p < 0.05 based on the t-test.

Soil degradation index

A comprehensive soil degradation index (CSDI) was developed in the study area by employing both the Total Dataset (TDS) and Minimum Dataset (MDS) approaches to thoroughly assess the degradability of the soil. The TDS encompasses all quantifiable and obtainable soil data, offering a comprehensive evaluation of soil degradation. Nevertheless, determining the TDS can be costly, labor-intensive, and time-consuming. To address these challenges, the MDS approach was implemented with the aim of reducing the number of indicators used in evaluating the CSDI. This method focuses on essential indicators that provide sufficient information for a reliable assessment of the index^3,47–49.

The CSDI-T and CSDI-M models were each classified into five distinct grades, ranging from I to V^3,50 (Table 3). A higher grade or value assigned to the CSDI signifies a greater degree of soil degradation, whereas a lower grade or value indicates minimal soil degradation.

Table 3.

Categorization of comprehensive soil degradation index.

CSDI model	V (very high)	IV (high)	III (moderate)	II (low)	I (very low)
CSDI - TDS	> 0.5	0.41–0.5	0.32–0.41	0.23–0.32	< 0.23
CSDI - MDS	> 0.52	0.42–0.52	0.32–0.42	0.22–0.32	< 0.22

The TDS indicator approach

The calculation of the Comprehensive Soil Degradation Index associated with the Total Degradation Score (CSDI-TDS) was performed using 14 soil degradation indicators: SCI, SSI, MCR, K-factor, pH, EC, SOC, SAR, ESP, and heavy metals (Zn, Cu, Cd, Pb, and Ni). Among these, SSI, MCR, SOC, and SAR exhibited the highest contributions to the CSDI-TDS, whereas Ni demonstrated the least impact, based on the weighting and commonality of the soil degradation indicators (Table 4). The inverse correlations observed between SSI (r = -0.56, p < 0.01) and SOC (r = -0.62, p < 0.01) with the CSDI-TDS, alongside the direct correlations identified for MCR (r = 0.59, p < 0.01) and SAR (r = 0.57, p < 0.01), confirm the significant role of these indicators in either mitigating or facilitating soil degradation, respectively. These findings align with the current understanding that well-structured soils (reflected in higher SSI and SOC values) promote resilience and ecosystem functionality, while physical constraints and salinity-related stresses (e.g., MCR and SAR) contribute to soil degradation.

Table 4.

The commonality and weight of soil degradation indicators for TDS and MDS approaches.

Soil degradation indicator	TDS approach		MDS approach
	Commonality	Weight	Commonality	Weight
SSI	0.965	0.093
SCI	0.769	0.074
MCR	0.955	0.090	0.955	0.174
pH	0.591	0.057
EC	0.578	0.055	0.578	0.105
SOC	0.912	0.088	0.912	0.166
SAR	0. 954	0.092	0. 954	0.164
ESP	0.910	0.087
K-factor	0.721	0.069	0.721	0.131
Zn	0.660	0.063
Cu	0.642	0.062
Cd	0.707	0.068	0.707	0.129
Pb	0.715	0.069	0.715	0.130
Ni	0.338	0.032

SSI soil stability index, SCI soil crust index, MCR modified compaction ratio, EC electrical conductivity, SOC soil organic matter, SOM soil organic matter, SAR sodium absorption ratio, ESP exchangeable sodium percentage, K-factor K factor of soil erodibility.

CSDI-TDS values ranged from 0.23 to 0.66, with a mean value of 0.42 across different soil types, indicating a considerable variability in degradation status, spanning from grade II to grade V (Table 3). Most soils (42%) were categorized as grade III based on the CSDI-TDS model, followed by grade IV (34%), grade II (6%), and grade V (6%). Grade III represents a moderate level of limitation for soil health and plant growth, whereas grade IV signifies more severe constraints negatively affecting both soil functioning and crop productivity. Among soil types, Fluvisols were predominantly classified as grade II, while Calcisols, Cambisols, and Regosols fell under grades III and IV. However, based on average CSDI-TDS values, Fluvisols were classified as grade III, whereas Calcisols, Cambisols, and Regosols received grade IV designations (Table 5). The comparatively lower degradation status in Fluvisols may be attributed to their higher SSI and SOC values and lower levels of other degradation-related physicochemical indicators, such as SCI, MCR, K-factor, and SAR. While SSI and SOC positively influence the final CSDI-TDS values promoting soil structural resilience and health—the latter group of indicators exerts a negative impact by reflecting degradation processes. These observations are consistent with findings in recent literature emphasizing the influence of soil type on degradation dynamics and soil quality^29,51,52. Furthermore, urban soils showed significantly elevated CSDI-TDS values rising by 14 to 21% across various soil types compared to their non-urban counterparts, indicating that urbanization intensifies soil degradation (Fig. 5). The most pronounced increase occurred in Regosols, followed by Cambisols, Calcisols, and Fluvisols, suggesting that Regosols are particularly vulnerable to urban-related degradation pressures. Regosols, as young and poorly developed soils with minimal horizonation, low fertility, and limited organic carbon, are highly susceptible to erosion and degradation. Their vulnerability is largely determined by their parent material and local environmental conditions, making them difficult to manage for agriculture. Nonetheless, Regosols play important roles in supporting natural ecosystems and maintaining landscape functions¹⁵. These intrinsic characteristics likely contribute to their greater degradation potential compared to other soil types in the study area.

Table 5.

Comparison of CSDIs values and CSDIs grades among the different types of urban soils.

CSDI model	Fluvisols	Calcisols	Cambisols	Regosols
	MeanSD	MeanSD	MeanSD	MeanSD
CSDI-TDS	0.406a0.113 (III)	0.416a0.081(IV)	0.419a0.011 (IV)	0.441ab0.074 (IV)
CSD-MDS	0.411a0.095 (III)	0.416a(III)	0.425a(IV)	0.449ab0.103 (IV)

*Values with the same lowercase letters within each row are not significantly different at P < 0.05.

Fig. 5.

The means comparison of comprehensive soil degradation index (CSDI) values for CSDI-TDS (a) and CSDI-MDS (b) between urban and un-urban soils across different soil types. Different letters show significant differences at p < 0.05 based on the t-test.

The MDS indicator approach

Principal Component Analysis (PCA) was used to identify key soil degradation indicators influencing the CSDI based on the Minimum Data Set approach (CSDI-MDS). The PCA revealed five principal components (PCs) with eigenvalues greater than 1 and individual contributions to total variance exceeding 5%, collectively accounting for 74.4% of the total variance (Table 6). The variance explained by PC1 through PC5 was 20.5%, 16.7%, 15.6%, 12.6%, and 9.0%, respectively.

Table 6.

The outcomes of the principal component analysis (PCA) of the chosen soil degradation indicators.

Component	PC1	PC2	PC3	PC4	PC5
Eigenvalue	2.86	2.34	2.18	1.76	1.27
Variability (%)	20.45	16.73	15.60	12.58	9.04
Cumulative (%)	20.451	20.451	20.451	20.451	20.451
Indicators	Eigen vector or factor loading
SSI	0.183	0.961	− 0.063	− 0.011	0.058
SCI	0.853	− 0.139	0.142	0.038	− 0.006
MCR	0.939	0.130	− 0.076	0.016	− 0.070
pH	− 0.431	0.274	0.316	0.055	0.477
EC	0.709	0.219	0.038	− 0.073	0.140
SOC	− 0.089	0.944	0.019	− 0.041	0.107
SAR	0.060	− 0.050	0.974	0.003	− 0.003
ESP	0.058	− 0.050	0.954	0.011	0.002
K-factor	− 0.289	− 0.621	0.449	− 0.061	0.287
Zn	0.188	− 0.359	− 0.240	0.469	0.468
Cu	− 0.139	0.103	− 0.050	0.711	0.284
Cd	− 0.067	0.051	− 0.089	0.727	− 0.453
Pb	− 0.056	− 0.052	0.005	− 0.094	− 0.837
Ni	− 0.067	0.079	− 0.105	− 0.560	− 0.058

Bold-underlined soil gradation indicators are the selected indicators for inclusion in the MDS dataset.

SSI soil stability index, SCI soil crust index, MCR modified compaction ratio, EC electrical conductivity, SOC soil organic matter, SOM soil organic matter, SAR sodium absorption ratio, ESP exchangeable sodium percentage, K-factor K factor of soil erodibility.

In PC1, the indicators MCR, SCI, and EC exhibited high loadings. MCR and EC were selected for inclusion in the MDS due to the significant correlation between MCR and SCI, and the non-significant correlation between EC and the other two variables (Table 7). In PC2, SOC and K-factor were included in the MDS selection. SOC demonstrated a significant correlation with SSI and a non-significant correlation with the K-factor (Table 7). Due to its higher loading and stronger correlations, SAR was selected over ESP in PC3. For PC4 and PC5, the heavy metals Cd and Pb were selected based on their high factor loading values (both exceeding 0.6). As a result, from the original set of 14 soil degradation indicators, seven were selected for inclusion in the MDS: MCR, EC, SOC, K-factor, SAR, Cd, and Pb. These indicators are frequently cited in soil degradation assessments^53–56, highlighting their relevance in evaluating soil function.

Table 7.

Pearson correlation coefficients between selected soil degradation indicators in the urban soils.

	pH	EC	SAR	ESP	Zn	Cu	Cd	Pb	Ni	SCI	SOC	SSI	MCR	K-factor
pH	1.00	− 0.29*	0.18	0.19	− 0.07	0.18	− 0.06	− 0.31*	0.01	− 0.16	0.29*	0.16	− 0.30*	0.27
EC	− 0.29*	1.00	0.08	0.08	0.12	− 0.02	− 0.19	− 0.05	− 0.03	0.37*	0.17	0.32*	0.53*	− 0.22
SAR	0.18	0.08	1.00	0.92**	− 0.14	− 0.02	− 0.12	0.00	− 0.05	0.13	− 0.05	− 0.10	− 0.04	0.35*
ESP	0.19	0.08	0.92**	1.00	− 0.13	− 0.01	− 0.12	0.00	− 0.06	0.13	− 0.05	− 0.10	− 0.05	0.37*
Zn	− 0.07	0.12	− 0.14	− 0.13	1.00	0.57*	0.08	− 0.26	0.43*	0.10	− 0.25	− 0.22	0.07	0.14
Cu	0.18	− 0.02	− 0.02	− 0.01	0.57*	1.00	0.25	− 0.21	0.39*	− 0.15	0.05	0.04	− 0.13	− 0.09
Cd	− 0.06	− 0.19	− 0.12	− 0.12	0.08	0.25	1.00	0.52*	− 0.16	0.06	0.00	0.01	0.03	− 0.10
Pb	− 0.31*	− 0.05	0.00	0.00	− 0.26	− 0.21	0.52*	1.00	0.12	− 0.06	− 0.10	− 0.09	− 0.03	− 0.15
Ni	0.01	− 0.03	− 0.05	− 0.06	0.43*	0.39*	− 0.16	0.12	1.00	− 0.04	0.08	0.06	− 0.01	− 0.06
SCI	− 0.16	0.37*	0.13	0.13	0.10	− 0.15	0.06	− 0.06	− 0.04	1.00	− 0.19	0.02	0.88**	0.00
SOC	0.29*	0.17	− 0.05	− 0.05	− 0.25	0.05	0.00	− 0.10	0.08	− 0.19	1.00	0.94**	− 0.01	− 0.38*
SSI	0.16	0.32*	− 0.10	− 0.10	− 0.22	0.04	0.01	− 0.09	0.06	0.02	0.94**	1.00	0.29*	− 0.58*
MCR	− 0.30*	0.53*	− 0.04	− 0.05	0.07	− 0.13	0.03	− 0.03	− 0.01	0.88**	− 0.01	0.29*	1.00	0.43*
K-factor	0.27	− 0.22	0.35*	0.37*	0.14	− 0.09	− 0.10	− 0.15	− 0.06	0.00	− 0.38*	− 0.58*	0.43*	1.00

SSI soil stability index, SCI soil crust index, MCR modified compaction ratio, EC electrical conductivity, SOC soil organic matter, SOM soil organic matter, SAR sodium absorption ratio, ESP exchangeable sodium percentage, K-factor K factor of soil erodibility.

* and ** indicate significant level at 0.05 and 0.01, respectively.

These selected indicators significantly affect soil functions and ecosystem sustainability by influencing physical stability, erosion processes, microbial diversity, and overall plant productivity. Among them, MCR, SOC, and SAR were identified as the most influential indicators in assessing CSDI-MDS, receiving the highest weight values, followed by the K-factor, Pb, Cd, and EC (Table 4). SOC and SAR, in particular, are widely used across studies assessing both soil health and degradation^52,57–59, due to their crucial influence on soil physicochemical properties, productivity, crop yield, and microbial communities⁶⁰.

The final CSDI-MDS model was formulated using the assigned weights for each selected indicator as shown in Eq. 8:

8

The calculated CSDI-MDS values for different soil types ranged from 0.26 to 0.54 for Fluvisols, 0.23 to 0.66 for Calcisols, 0.41 to 0.43 for Cambisols, and 0.33 to 0.56 for Regosols. The mean CSDI-MDS values followed the same ranking as the CDSI-TDS model: Regosols > Cambisols > Calcisols > Fluvisols, illustrating the substantial influence of soil type on degradation processes (Table 5).

These variations in CDSI values across soil types are largely due to different land management strategies. Sustainable agricultural practices—such as crop rotation, conservation tillage, and reduced agrochemical use—can reduce soil degradation, enhance productivity, and improve ecosystem resilience. In contrast, poor management practices—including inefficient irrigation and excessive tillage exacerbate degradation and pose long-term risks to agriculture, ecosystems, and human well-being^54,60,61.

Based on the CSDI-MDS model, Fluvisols exhibited a moderate degradation level (Grade III), while other soil types were classified as highly degraded (Grade IV). The most pronounced differences in CSDI-MDS values were observed between Fluvisols and Regosols (Table 4). These differences are primarily attributed to MCR and SOC, two of the most impactful indicators. The average MCR was 1.7 for Fluvisols and 3.4 for Regosols, while mean SOC was 0.9% for Fluvisols and 0.35% for Regosols (Tables 1 and 4). MCR was the highest-weighted factor in the MDS model, followed closely by SOC. MCR tends to increase CSDI values due to its detrimental effects on soil compaction and infiltration, whereas SOC reduces degradation through its beneficial role in soil aggregation and structural stability. Previous studies have highlighted MCR and SOC as key indicators of soil degradation and productivity, given their sensitivity to agricultural management practices^11,62–64.

The negative impact of MCR on soil degradation is linked to its effects on soil structure, porosity, and hydraulic properties, rendering soils less resilient to stressors such as drought, heavy rainfall, and mechanical pressures. This in turn leads to reduced plant productivity and vegetation cover^60,65. On the other hand, SOC enhances soil porosity, water infiltration, and root penetration, helping to protect the soil surface and maintain resilience under adverse conditions^60,63. The observed positive correlation between MCR and CSDI-MDS (r = 0.61, p < 0.01) and the negative correlation between SOC and CSDI-MDS (r = -0.57, p < 0.01) further emphasize the pivotal roles of these indicators in driving or mitigating degradation.

CSDI-MDS values in urban soils showed a notable increase, ranging from 16% to 22% higher than their non-urban counterparts across different soil types (Fig. 5). This increase led to a shift in average degradation grades, particularly for Calcisols, Cambisols, and Regosols, which were downgraded from Grade III in non-urban areas to Grade IV in urban settings. These results corroborate previous findings that urbanization intensifies soil degradation through compaction, organic carbon loss, decreased permeability, and contamination by metals and organics^1,37,43,46. Regosols exhibited the highest urban-induced increase in CSDI-MDS (20–27%), followed by Cambisols (18–22%), Calcisols (17–20%), and Fluvisols (14–18%). This pattern closely mirrors the findings from the SQI-TDS model, reinforcing the role of urban pressures in exacerbating soil structural degradation and weakening resilience.

Validation of CSDI with corn yield

Crop performance metrics may serve as valuable indicators of soil degradation, offering opportunities to refine the selection of evaluation criteria and enabling a more effective assessment of soil health. Both the CSDI-TDS and CSDI-MDS models exhibited a slight negative relationship with corn yield, suggesting that these models are only marginally effective in explaining the observed variations in corn productivity (Fig. 6). This limited relationship between CSDIs and maize yield is likely attributable to the selection of specific soil characteristics such as physical indicators and heavy metal concentrations that may not directly influence crop performance. Previous research has shown that soil indices can be strongly correlated with yield, particularly when based on indicators directly tied to soil fertility and nutritional status^29,66,67.

Fig. 6.

The linear regression analysis of the relationship of CSDIs models versus corn yield (Zea mays).

Among the two models, the predictive accuracy of the CSDI-TDS surpassed that of the CSDI-MDS in forecasting corn yield, with the CSDI-TDS explaining up to 25% of the variation in corn yield, compared to 21% explained by the CSDI-MDS. Despite this, a significant positive correlation was found between the CSDI-TDS and CSDI-MDS models (CSDI-MDS = 0.7511 × CSDI-TDS + 0.1027; R² = 0.829, p < 0.01), indicating that the CSDI-MDS model can effectively mirror the performance of the CSDI-TDS in assessing soil degradation (Fig. 7). The use of the MDS model offers practical advantages, including a reduced number of indicators, decreased laboratory analysis time, and lower costs associated with variable identification²⁹. Therefore, the CSDI developed via the MDS approach is not only robust in integrating soil degradation indicators into composite scoring models but is also advisable for application in similar agroecological contexts. These findings underscore the potential of simplified indicator sets for the reliable assessment of soil structural degradation, contributing to the broader goals of sustaining cropland productivity and enhancing agroecosystem resilience under increasing environmental stress.

Fig. 7.

The linear regression analysis of the relationship between CSDI - TDS and CSDI - MDS models.

Scientific recommendations and managements

Our findings demonstrate that the CSDI constitutes a major methodological advance, offering a more robust framework for conceptualizing and managing soil degradation. To translate this potential into widespread impact, we recommend the following management and policy strategies. (1) The CSDI-MDS score will form the basis for Soil Resilience Zoning, which categorizes land based on its degradation status and inherent vulnerability. Parcels identified with high CSDI-MDS values, indicating either active degradation or high susceptibility due to factors like elevated MCR or diminished SSI, will be designated for conservation. This designation triggers regulatory requirements, including development restrictions or the implementation of certified soil mitigation strategies, (2) Implement the CSDI framework to systematically identify non-urban agricultural parcels with low soil degradation scores. These areas should be prioritized for legal protection status, acknowledging their critical contribution to regional food security and the maintenance of key ecosystem services, (3) to develop user-friendly CSDI-MDS guides, calculators, and support services tailored to local farmers and soils, (4) to establish compulsory buffer zones equipped with vegetative filters between roadways or industrial areas and urban agricultural sites to intercept runoff and mitigate the 12–102% rise in heavy metals identified in the research, (5) To develop incentive programs for urban farmers, focusing on incentives for adopting eco-friendly techniques that increase SOC (e.g., composting and cover crops) and improve SSI (through methods like no-till farming or reduced tillage), (6) Shift research and advisory services towards establishing “soil structural resilience” as a key objective, separate from short-term yield considerations. This necessitates conducting extensive studies on the economic advantages of techniques that improve CSDI scores, such as the avoided costs of erosion, compaction, and pollution remediation. The principal policy implication of this research is the necessity of transitioning from a paradigm of simple farmland preservation to one of active quality and resilience enhancement. The CSDI-MDS framework serves as an operational, science-based instrument to facilitate this transition for key stakeholders, including planners, regulators, and farmers.

Conclusion

The impact of urbanization on soil degradation indicators was assessed in a semi-arid ecosystem across different soil types using a comprehensive soil degradation assessment framework. Urbanization has led to significant alterations in multiple indicators of soil degradation (e.g., K-factors and soil heavy metals), as well as in the CSDIs values, suggesting that soil quality degradation is exacerbated by urban expansion. Urban soils exhibiting an 18–25% increase in erodibility (K-factor) and a 12–102% accumulation of heavy metals compared to non-urban soils across various soil types. An increase of 14–21% and 16–22% was recorded in the CSDI-TDS and CSDI-MDS models, respectively, in urban soils compared to their non-urban counterparts. This increase resulted in a shift in the average CSDI-TDS and CSDI-MDS grades for most soils, which fallen by one grade level in urban areas (from Grade III to Grade IV) compared to non-urban regions. The variation in CSDI values across soil types following the order Regosols > Cambisols > Calcisols > Fluvisols reflects the influence of both intrinsic (soil type) and extrinsic (agricultural-urbanization processes) factors on soil degradation. This finding highlights the complex interplay between soil genesis and anthropogenic disturbance in shaping soil structural health. Soil degradation rates exhibited a weak correlated with corn yield, with CSDI models accounting for 22–25% of the yield variance. Among the degradation indicators, the MCR, SSI, SOC, and SAR were identified as the most influential contributors to the CSDI models, underscoring their diagnostic value in assessing soil physical and chemical resilience. The outcomes of this study lay the groundwork for advancing soil management strategies in urban and peri-urban areas, ultimately supporting the development of more sustainable agricultural systems in these landscapes. However, achieving more representative CSDIs depends on fostering stronger collaboration between researchers, land management practitioners, and policy stakeholders. Therefore, we recommend further validation of the CSDI frameworks across diverse regions and environmental settings to enhance their general applicability and improve their robustness as tools for monitoring soil structural degradation and guiding land use planning.

Author contributions

Salar Rezapour, Amin Nouri, and Parisa Alamdari wrote the main manuscript text and Maedeh Shokohi prepared Figs. 1, 2, 3, 4, 5, 6 and 7. All authors reviewed the manuscript.

Funding

The authors gratefully acknowledge Urmia University for the financial support of this research project.

Data availability

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

Declarations

Competing interests

The authors declare no competing interests.