Introduction

Soil quality represents the capacity of soil to function within managed ecosystems to sustain plant productivity, maintain environmental quality, and support human health. As defined by foundational researchers, continuous monitoring is an absolute prerequisite for implementing sustainable land management and achieving optimal economic productivity without degrading natural resources. Various mathematical frameworks quantify soil quality by converting complex physico-chemical properties into integrated composite indices, notably the Nemoro Quality Index (NQI), Simple Additive Quality Index (IQIa), and Weighted Additive Quality Index (IQIw). Assessing soil quality traditionally requires a comprehensive Total Data Set (TDS), which demands extensive laboratory labor, significant financial resources, and often yields redundant data. To address this, the Minimum Data Set (MDS) approach utilizes Principal Component Analysis (PCA) to extract a highly representative, cost-effective subset of soil properties.Despite extensive literature on the spatial distribution of soil quality globally, comprehensive studies investigating temporal dynamics over long-term agricultural cycles remain critically scarce in the arid and semi-arid regions of Iran. Spatiotemporal monitoring is indispensable for identifying emerging hotspots of soil degradation such as salinization, sodification, and structural decline before they induce irreversible agronomic losses. Consequently, this study aimed to meticulously evaluate the spatial and temporal evolution of soil quality across agricultural lands in Alborz Province over a 13-year period (2011 to 2024), employing both TDS and MDS methodologies to map changes and identify primary drivers of degradation.



Materials and Methods

The research was conducted within the agricultural landscapes of Alborz Province, located in the central agroecological zone of Iran. The study area features an arid and semi-arid climate with a mean annual precipitation of 238 mm and an average temperature of 14.2 °C. To capture spatial heterogeneity and temporal evolution, topsoil samples (0–30 cm depth) were collected from 23 long-standing farms during two sampling epochs: 2011 and 2024. A robust array of 15 physical and chemical properties was analyzed. Physical analyses encompassed particle size distribution (sand, silt, and clay percentages) and bulk density (Bd). Chemical characterizations included electrical conductivity (EC), soil reaction (pH), soil organic carbon (OC), calcium carbonate equivalent (TNV), available phosphorus (P), available potassium (K), Sodium Adsorption Ratio (SAR), and available micronutrients (Fe, Cu, Mn, Zn, B).To construct the MDS, PCA was executed using statistical software. The Kaiser-Meyer-Olkin (KMO) measure and Bartlett’s test confirmed dataset validity for factor analysis. Principal components (PCs) exhibiting eigenvalues greater than 1.0 were retained. Within these PCs, the indicator with the highest absolute loading factor, alongside indicators within 10% of this maximum, were selected to constitute the final MDS.

Selected properties were standardized into dimensionless utility scores (ranging from 0 to 1) employing non-linear fuzzy membership logic. Three functions were utilized: "More is better" for properties beneficial to growth (OC, P, K, Fe, Mn, Cu, Zn); "Less is better" for detrimental properties (Bd, EC, TNV, SAR, B); and "Optimum range" for balanced parameters (pH, sand, silt, clay). Communality values generated by PCA were transformed into proportional weighting factors. Ultimately, integrated soil quality was quantified using IQIw, IQIa, and NQI models computed for both MDS and TDS. Geospatial distribution and temporal flux of these indices were mapped utilizing Ordinary Kriging interpolation within a Geographic Information System (GIS) environment.



Results

Descriptive statistics revealed that the agricultural soils predominantly featured loam and clay-loam textures, averaging 25% clay, 39% silt, and 36% sand. The chemical environment was slightly alkaline, with a mean pH around 8.36. Regional soils did not exhibit critical restrictions concerning extreme salinity, severe sodicity, or micronutrient deficiencies that would hinder dominant crops like wheat and corn. Nevertheless, the region is constrained by inferior structural stability stemming from a severe organic carbon deficiency, averaging merely 0.67% in 2011 and 0.77% in 2024. Temporal variance analysis demonstrated that most soil properties remained statistically stable over the 13-year interval, with the exclusive exceptions of pH, available zinc, and copper, which showed significant alterations.PCA dimensionality reduction was highly effective (KMO = 0.763, Bartlett’s test p < 0.001). Three principal components possessed eigenvalues greater than unity (2.868, 1.288, and 1.02), explaining 73.96% of the cumulative variance. In the primary component, OC exhibited the highest loading factor (0.842), followed by available K (0.820) and P (0.774). Bulk density dominated the second component (loading 0.831), and EC dictated the third component (loading 0.901). Thus, OC, K, P, Bd, and EC were isolated as definitive MDS variables. Weighting scores highlighted that physical soil properties universally received lower relative weights compared to chemical properties, underscoring primary physical limitations within the region.Calculations of the comprehensive indices demonstrated wide spatial heterogeneity. Median scores of the IQIw, IQIa, and NQI algorithms placed over 50% of the sampled territory into the "Very High" (Grade I) classification. Statistical comparisons of the grand mean soil quality index for Alborz Province revealed no significant overarching change from 2011 to 2024. Furthermore, exceptional positive correlations (p < 0.01) between TDS and MDS indices proved that the simplified dataset is a reliable, robust surrogate for regional soil quality estimations.



Discussion and Conclusion

Although the aggregate mean soil quality for Alborz Province appeared statistically static across the 13-year timeline, spatially explicit geostatistical mapping uncovered profound, localized temporal transformations. Chronological comparison of kriged index maps from 2011 and 2024 demonstrated that spatial evolution was highly non-uniform. A generally positive trajectory, indicating relative enhancement, was detected across the western, northwestern, and select central sectors of the province.Conversely, spatial interpolation models exposed a continuous corridor of soil degradation extending toward the western and southwestern peripheries. The southwestern agricultural belt—specifically the regions south of Nazarabad and Eshtehard—was conclusively identified as a highly vulnerable focal point of deterioration. This localized drop in soil quality metrics is inextricably tied to intrinsic clay and clay-loam textures, exacerbated by a progressive intensification of electrical conductivity and sodium accumulation observed between 2011 and 2024. This spatial degradation aligns with previous studies indicating severe structural risks along the Qazvin and southwestern Alborz borders. Furthermore, agricultural zones in the eastern and northeastern quadrants demonstrated rigid stagnation, persistently lingering in moderate quality classes with no discernible improvement.These outcomes firmly underscore that prevailing barriers to maximum agricultural yield in Alborz Province remain fundamentally physical, delineated by the paramount influence of bulk density and severe organic carbon deficiency within the PCA framework. Addressing these ingrained physical constraints is paramount. To effectively halt creeping degradation in endangered southwestern corridors and elevate baseline quality thresholds in stagnant eastern regions, strategic shifts in soil management are urgently necessitated. The extensive incorporation of organic soil amendments, rigorous application of conservation tillage regimens, and systematic retention of crop residues represent critical interventions required to rehabilitate structural integrity, augment organic carbon reservoirs, and combat compounding threats of salinization.



Keywords: Soil Quality Index, Nemero and Temporal Monitoring