https://srjournal.areeo.ac.ir/ Iranian Journal of Soil Research en daily 1 Sun, 23 Aug 2026 00:00:00 +0330 Sun, 23 Aug 2026 00:00:00 +0330 https://srjournal.areeo.ac.ir/article_135742.html Background and Objectives: Soil organic matter is a critical component of productive soils. It influences a wide range of physical, chemical, and biological attributes and processes, including the formation and stabilization of soil aggregates, nutrient cycling, water retention, disease suppression, and cation exchange capacity. The relationships between soil organic matter (SOM) and soil physicochemical properties are profound and multifaceted. One of the main research gaps is the lack of comprehensive, integrated, and quantitative studies that simultaneously investigate the relationships between a wide range of soil physical and chemical properties and SOM under semi-arid climatic conditions. Most previous studies have focused on one or two properties and do not provide a holistic understanding of this complex system. Therefore, the primary objective of this study was to examine the relationships between SOM and selected soil physical and chemical properties, including particle size distribution, structural attributes, salinity, sodium content, and fertility. Materials and Methods: This study was conducted in the semi-arid region of Zanjan. Sixty-eight sampling points were selected from different land uses using available soil maps to examine the relationships between SOM and various physical and chemical soil properties. Standard physical and chemical laboratory methods were used to measure particle size distribution, aggregate stability, bulk density, electrical conductivity, pH, organic matter content, and cation exchange capacity. Descriptive statistics, Pearson correlation, linear regression, analysis of variance (ANOVA), and discriminant analysis were employed to analyze the relationships between SOM and soil properties, and variables showing significant correlations with SOM were further analyzed using ANOVA and discriminant analysis to investigate differences among SOM classes (<1%, 1–2%, 2–3%, and >3%). All statistical analyses, data visualizations, and mean comparison procedures were carried out using Microsoft Excel (2016) and Python (version 3.x).Results: The results showed that cation exchange capacity (R² = 0.31, p < 0.01), bulk density (R² = 0.28, p < 0.01), wet aggregate mean weight diameter (MWDwet) (R² = 0.25, p < 0.01), wet aggregate geometric mean diameter (GMDwet) (R² = 0.24, p < 0.01), and dry aggregate geometric mean diameter (GMDdry) (R² = 0.22, p < 0.01) exhibited stronger relationships with SOM than the other soil properties examined. One-way ANOVA performed for four SOM classes (<1%, 1–2%, 2–3%, and >3%) revealed substantial differences among the groups in terms of soil structural properties and cation exchange capacity. Specifically, significant differences in GMDwet were observed between the <1% SOM group and the 2–3% and >3% SOM groups, as well as between the 1–2% and >3% SOM groups. For MWDwet, significant differences were detected between the <1% SOM group and the 2–3% and >3% SOM groups, and between the 1–2% SOM group and the 2–3% and >3% SOM groups (p < 0.000001, F = 12.23, and p < 0.000001, F = 12.17, respectively). Furthermore, discriminant analysis indicated that soil physical properties were more strongly associated with SOM than soil chemical properties. Conclusion: This study demonstrated that SOM is one of the key factors influencing soil quality, exerting significant effects on both soil physical and chemical properties. The results of correlation, linear regression, and one-way analyses of variance indicated that CEC increased with increasing SOM content. Among all measured soil properties, CEC exhibited the strongest relationship with SOM and showed a significant increase across higher SOM levels. Strong relationships were also observed between SOM and soil physical properties. In particular, increasing SOM was associated with significant improvements in soil aggregate stability, total porosity, and soil moisture content, while bulk density decreased significantly. Furthermore, discriminant analysis revealed that the influence of SOM was more strongly reflected in soil physical properties than in chemical properties. In both stages of the discriminant analysis, structural soil attributes exhibited the highest discriminant coefficients among the SOM groups (<1%, 1–2%, 2–3%, and >3%), highlighting the close association between SOM and soil structural characteristics. Following structural attributes, cation exchange capacity ranked as the second most important discriminating variable.  https://srjournal.areeo.ac.ir/article_135790.html Background and Objectives: Ecological risk assessment of potentially toxic elements in urban soils and scientific-practical recommendations for reducing environmental risks have attracted widespread attention as a necessary measure to protect and promote sustainable urban development. In this regard, determining the priority of control factors in ecological risk management of potentially toxic elements of urban soil is of great importance in order to develop and accurately prioritize preventive measures and effective strategies for protecting the health of the urban ecosystem based on the importance and necessity of their implementation. Therefore, this study was conducted with the aim of determining the priority of control factors in ecological risk management of potentially toxic elements (As, Pb, Cu, and Mn) in urban soils of Hamedan city in 2023.Materials and Methods: In this study, a total of 135 urban surface soil samples were collected from 15 sampling sites from three different functional regions (i.e., commercial, residential, and industrial). Following sample preparation and acid digestion, the concentrations of the analyzed elements were determined using Inductively Coupled Plasma Optical Emission Spectroscopy (ICP-OES). Statistical analyses were done using SPSS software. Additionally, the potential ecological effects of the studied elements on ecosystem health, the modified ecological risk factor (MEr) and modified ecological risk index (MERI) were calculated. Also, a stochastic ecological risk assessment based on Monte Carlo simulation was performed using Crystal Ball software with 10,000 iterations and a 95% confidence level. Results: The results from determining the content of the elements showed that the highest average content of As and Cu (mg/kg) with 6.91 and 30.9, respectively, belonged to the industrial areas and for Pb and Mn with 31.2 and 293 (mg/kg), respectively, belonged to the commercial areas, revealing the effect of various urban activities and land uses on the spatial variations of the PTE contents. The average MERI index of the elements was equal to 406, indicating the occurrence of very high ecological risk in the study area. The results of the ecological risk assessment based on the stochastic model showed that 100% of the ecological risk values in the study area were in the "very high" risk range. The results of the sensitivity analysis showed that arsenic as a key influencing factor has the greatest impact on ecological risk in the study area. The results showed that the stochastic model compared to the deterministic model significantly improved and increased the accuracy and performance of ecological risk assessment.Conclusion: The results showed that arsenic, as the main and key factor in the occurrence of ecological hazards, was the priority metalloid pollutant for the control and management of the ecological risk of the studied elements in the soil of Hamedan city. Therefore, control of emission sources and regular and periodic monitoring of soil arsenic content with the aim of reducing risk is recommended to ensure the health of the urban ecosystem and protect sustainable urban development.  https://srjournal.areeo.ac.ir/article_135826.html Background and Objectives: Soil salinization is one of the most serious environmental challenges threatening agricultural productivity and soil sustainability worldwide, particularly in arid and semi‑arid regions. Increasing soil salinity negatively affects soil structure, nutrient availability, plant growth, and the activity of soil microbial communities. In recent years, numerous studies have focused on sustainable strategies to mitigate salinity stress and restore salt‑affected soils. Among these strategies, the application of organic amendments, biochar, halophyte plants, improved irrigation management, and beneficial soil microorganisms such as mycorrhizal fungi have received significant attention. Despite the growing body of literature, a comprehensive overview of research trends, influential journals, and major research themes in soil salinization improvement remains necessary. Therefore, the present study aimed to analyze the global research landscape related to soil salinization improvement and to identify major scientific directions, influential publications, and key research topics in this field.Methodology: A bibliometric analysis was conducted using publications related to soil salinization improvement. Relevant scientific articles were retrieved from major academic databases and analyzed using bibliometric indicators. The analysis included annual publication trends, disciplinary distribution, leading journals, and highly cited research articles. Additionally, the top journals publishing studies on soil salinity improvement were identified based on publication counts, citation indicators, and impact factors. The content of highly cited articles was also examined to determine the main research approaches used for saline soil remediation, such as the application of biochar, organic amendments, microbial inoculation, and innovative soil and water management strategies. Results: The results revealed a steady increase in the number of scientific publications related to soil salinization improvement over recent years, indicating growing global attention to this issue. The disciplinary distribution showed that most studies are concentrated in soil science, environmental science, agriculture, and ecology. Several leading journals play a major role in disseminating research findings in this field. Analysis of the most cited publications indicated that the application of biochar and organic amendments is among the most widely studied strategies for improving saline soils due to their ability to enhance soil physical, chemical, and biological properties. Biochar has been shown to reduce nutrient leaching, improve nitrogen retention, and mitigate the negative effects of salinity on plant growth. In addition, studies highlighted the important role of soil microbial communities and mycorrhizal fungi in improving plant tolerance to salt stress by enhancing nutrient uptake and increasing the accumulation of osmoprotective compounds such as soluble sugars. Research has also emphasized the use of halophyte plants for the restoration of saline lands and the adoption of improved irrigation techniques, including drip irrigation, as effective management strategies. Overall, the findings demonstrate that integrated biological and soil management approaches are increasingly recognized as sustainable solutions for saline soil remediation.Conclusion: The bibliometric assessment indicates that research on soil salinization improvement has expanded rapidly and continues to evolve toward more sustainable and environmentally friendly remediation strategies. The combined use of biochar, organic amendments, beneficial microorganisms, and improved soil and water management practices shows considerable potential for restoring salt‑affected soils and enhancing agricultural productivity. Future research should focus on long‑term field evaluations, the optimization of amendment application rates, and the integration of biological and management practices to develop efficient and scalable solutions for saline soil rehabilitation.  https://srjournal.areeo.ac.ir/article_135920.html Background and Objectives: This study aimed to evaluate the effects of amino acid–rich hydrolysates produced from fish waste, corn residues, and chicken feathers on growth, mineral nutrition, biomass partitioning, and yield of cucumber (Cucumis sativus L.) under greenhouse conditions. A further objective was to characterize the chemical composition of these organic hydrolysates (total amino acids, organic carbon, and macro- and microelements) and to determine how different sources and concentrations influence source–sink relationships and allocation of assimilates to vegetative versus reproductive organs. The work was also designed to assess the potential of recycling plant and animal residues as sustainable organic fertilizers enriched with amino acids.Materials and Methods: Organic amino acid fertilizers were produced by fermenting finely chopped fish, corn, and chicken feather residues with sugar beet molasses using Lactobacillus (Electrobacillus/Lactobacillus) at room temperature for 30 days, followed by filtration and chemical analysis. Total free amino acids in the liquid fertilizers were quantified by HPLC (C18 reverse-phase column, UV detection at 254 nm), and organic carbon and nutrient elements (N, P, K, Ca, Mg, S, and selected micronutrients) were determined using wet digestion and analysis by ICP-AES. A pot experiment was conducted as a factorial arrangement in a completely randomized design with three amino acid sources (fish, corn, chicken feather) at two application concentrations (2% and 4%), plus an unfertilized control, using cucumber cv. Vihan grown in cocopeat–perlite substrate under controlled greenhouse conditions. Plants were fertigated with a basal nutrient solution and received the organic amino acid fertilizers twice weekly; root, stem, leaf and fruit fresh weight, dry matter, nutrient contents, total plant biomass, and biomass allocation to organs, as well as fruit yield and average fruit weight, were measured and analyzed by ANOVA and Duncan’s test.Results: Chicken feather hydrolysate contained the highest total amino acid percentage (32.46%), while the fish-based amino acid showed the highest total N, P, Ca, and S contents and a distinctive amino acid profile rich in glutamic and aspartic acids. All three amino acids significantly increased root fresh weight and root dry matter compared with the control, with fish hydrolysate producing the greatest root mass (145 g fresh weight and 13.76% dry matter). Although root N, K, and Mg concentrations were sometimes higher in the control, amino acids markedly improved shoot and leaf growth, as reflected in higher leaf fresh weight, particularly under the fish fertilizer. In the stem, fish hydrolysate significantly enhanced P and K concentrations relative to the control, while chicken feather at 2% gave the highest Ca and Mg contents, indicating source-specific effects on mineral partitioning. For reproductive traits, fish and chicken feather fertilizers produced the highest individual fruit fresh weight (about 75.7 g, 20% higher than control), whereas fish at 2% concentration maximized total fruit yield, increasing it by roughly 40% compared with the unfertilized control. Biomass allocation analysis showed that fish hydrolysate at 2% led to the highest proportion of total biomass directed to fruits (74%), while corn fertilizer favored allocation to leaves.Conclusion: Amino acid–enriched hydrolysates derived from recycled plant and animal residues can effectively improve cucumber growth, nutrient uptake, and yield, with fish-based hydrolysate showing the strongest overall biostimulant effect. The results indicate that amino acids in these fertilizers enhance nutrient acquisition and translocation and modulate source–sink relations so that a greater share of assimilated carbon and nitrogen is directed to reproductive sinks, thereby increasing fruit weight and total yield. The observed reduction of some root nutrient concentrations under amino acid treatments, together with higher allocation of biomass and nutrients to fruits, supports the role of amino acids in regulating phloem loading and partitioning rather than simply enriching the root nutrient pool. Overall, amino acid hydrolysates from fish, corn, and chicken feather residues represent promising sustainable organic fertilizers that can recycle agro-industrial wastes while enhancing cucumber productivity and resource-use efficiency in protected cultivation systems.  https://srjournal.areeo.ac.ir/article_135921.html Background and Objectives: Canola (Brassica napus L.) is one of the most important oilseed crops, and its production in arid and semi-arid regions is increasingly constrained by water scarcity and drought stress. Improving water productivity and yield stability under limited irrigation conditions is therefore essential for sustainable canola production. Biostimulants as organic growth stimulants have recently gained attention as an eco-friendly strategy to mitigate drought-induced yield losses. In addition, increasing evidence suggests that the effectiveness of biostimulants is closely related to environmental conditions and plant physiological status, particularly under water-limited scenarios. Under drought stress, these compounds can enhance root development, improve nutrient uptake efficiency, and regulate osmotic balance, leading to better maintenance of photosynthetic activity and yield formation. However, their responses are often variable depending on the type of biostimulant, intensity of water stress, and crop genotype, which makes it necessary to evaluate their performance under field conditions using integrated approaches.Materials and Methods: This study aimed to evaluate the effects of organic growth stimulants on seed yield, seed oil content, and water productivity of canola under normal irrigation and water stress conditions, and to rank treatments using the VIKOR multi-criteria decision-making method. A split-plot experiment based on a randomized complete block design with three replications was conducted over two growing seasons at the Moghan Agricultural Research Station. Irrigation treatments (main plots) included normal irrigation and water stress, while sub-plots consisted of foliar application of amino acids, humic acid, fulvic acid, seaweed extract, and an untreated control. In addition, a Monte Carlo simulation approach with 2000 iterations was applied to assess the stability of the VIKOR ranking under uncertainty in criterion weights.Results: Mean comparison results showed that water stress reduced seed yield by 5.4%, seed oil content by 9.2%, and water productivity by 4.5% compared with normal irrigation. In contrast, application of growth stimulants increased seed yield by 10.47–15.03%, seed oil content by 5.76–11.26%, and water productivity by 9.84–17.21% compared with the control. Among treatments, amino acid under water stress produced the highest seed yield (3703 kg ha⁻¹) and water productivity (1.79 kg m⁻³), whereas humic acid under normal irrigation resulted in the highest seed yield (3844 kg ha⁻¹) and seed oil content (43.9%). VIKOR analysis indicated that amino acid under water stress had the lowest Q value (Q = 0.022), ranking first as the most desirable compromise solution. Monte Carlo sensitivity analysis further confirmed the robustness of this treatment, which retained the first rank in 82.65% of simulation scenarios.Conclusion: Overall, the results demonstrate that drought stress significantly reduces canola yield and quality parameters, while organic growth stimulants effectively mitigate these negative effects. In particular, amino acid application under water stress conditions provided the best trade-off among yield, quality, and water productivity, showing both superior performance and high ranking stability. Therefore, integrating organic biostimulants with deficit irrigation management can be considered an effective strategy for improving canola productivity and sustainability in semi-arid environments. Moreover, the multi-criteria decision-making results highlighted that relying solely on individual performance indicators may lead to suboptimal selection of treatments, as different stimulants showed contrasting advantages across yield, oil content, and water productivity. The VIKOR analysis provided a more balanced and integrated ranking by simultaneously considering all evaluated criteria, thereby identifying compromise solutions that best satisfy the conflicting objectives of productivity and resource efficiency.   https://srjournal.areeo.ac.ir/article_136003.html Background and Objectives: Soil erosion is currently recognized as a critical global challenge with severe social, economic, and environmental consequences. Accurate estimation of soil erodibility is a pivotal factor in the precise simulation of erosion processes and sustainable land management. In recent years, Digital Soil Mapping (DSM) coupled with Machine Learning (ML) algorithms has emerged as a powerful approach for handling complex environmental datasets. The primary objective of this study was to evaluate the influence of three groups of environmental covariates—soil properties, geomorphometric indices derived from a Digital Elevation Model (DEM), and multi-sensor remote sensing data (Landsat 8 and Sentinel-2)—on the spatial modeling of water erodibility (Factor K) and wind erodibility (Erodible Fraction - EF) in the Sistan Plain. Specifically, four widely used ML algorithms, including Regression Tree (rpart), Support Vector Machine (SVM), Cubist, and Random Forest (RF), were employed. Water erodibility (K) was calculated using three distinct equations: USLE, EPIC, and Sharpley and Smith, while wind erodibility was determined using the Fryrear method.Materials and Methods: To achieve the research objectives, 1200 surface soil samples were collected from agricultural lands in the study area, and relevant physicochemical properties for the K factor and EF index were measured. Remote sensing variables were extracted from Landsat 8 (OLI sensor) and Sentinel-2 (MSI sensor) imagery. Landsat 8 data with a spatial resolution of 30 m were acquired from the United States Geological Survey (USGS) archive, while Sentinel-2 data (10, 20, and 60 m resolution) were obtained from the Copernicus Open Access Hub to enhance analysis accuracy. The dataset comprised six main spectral bands, four soil-related spectral indices (Clay, Carbonate, Salinity, and Gypsum), five vegetation indices (NDVI, EVI, PVI, RVI, SAVI), and one innovative index (KfactorRS Index). To mitigate the risk of overfitting and reduce dimensionality, the Boruta algorithm was implemented for precise feature selection for each target variable. Subsequently, the dataset was randomly split into training (80%) and testing (20%) subsets. The predictive performance and accuracy of the ML models were evaluated using seven statistical metrics: Mean Error (ME), Root Mean Square Error (RMSE), Normalized RMSE (NRMSE), Pearson correlation coefficient (r), Coefficient of Determination (R²), Ratio of Performance to Deviation (RPD), and Lin’s Concordance Correlation Coefficient (CCC).Results: The results indicated a significant negative correlation (p < 0.01) between water erodibility coefficients (K) and the wind erodibility index (EF). A justifiable behavioral pattern observed in this study was the emergence of a negative correlation between spectral vegetation indices and clay content; a phenomenon that, contrary to initial assumptions, stems from vegetation degradation driven by salinity accumulation in fine-grained soils. Furthermore, the unexpected direct relationships of the KEPIC and KShSm equations with organic carbon, and clay, alongside an inverse relationship with very fine sand, indicate a high sensitivity to textural variables that introduces statistical bias under the region's specific conditions. Consequently, applying these indices is not recommended without recalibration tailored to the local geomorphic setting. Regarding model performance, the Cubist algorithm demonstrated superior predictive capability for most variables (EF, KEPIC, KShSm), while the Random Forest (RF) model outperformed others in predicting KUSLE. Both Cubist and RF, as ensemble-based decision tree methods, showed high competence in modeling soil erosion complexities. Although the SVM model yielded reasonable performance across all variables, it never ranked as the superior model. Conversely, the rpart model exhibited the weakest performance, particularly for KEPIC and KShSm, indicating the inability of simple single-tree models to capture the non-linear complexities of soil erosion in this region.Conclusion: Although Random Forest is widely recognized as one of the most proven and practical algorithms in DSM, this study demonstrated that rule-based models such as Cubist—which offer greater flexibility in modeling local non-linear relationships—can outperform RF in specific scenarios. Nevertheless, the consistency and stability of RF across all variables (consistently ranking first or second) and its absence of poor performance establish it as a safe and robust choice for predicting soil properties and erosion parameters in digital soil mapping studies.  https://srjournal.areeo.ac.ir/article_136018.html Background and Objectives: Fugitive dust emissions and dust storms represent a critical environmental and land-management problem in arid and semi-arid regions, where sparse vegetation, surface disturbance, drought, and salinity accelerate wind erosion and soil degradation. Conventional dust suppression and stabilization practices often face limitations such as short service life, high water consumption, recurring maintenance, or ecological incompatibility. Enzyme-Induced Carbonate Precipitation (EICP) has emerged as a bio-mediated alternative that can improve soil structure through calcium carbonate bonding without the operational complexity of handling living microorganisms. This study aims to (i) explain the fundamental mechanism of EICP and its relevance to dust control, (ii) identify the key operational and soil-related parameters governing treatment efficiency, (iii) assess the suitability of different urease sources—particularly plant-derived urease—for large-scale applications, and (iv) summarize the principal technical, environmental, and research challenges that must be addressed to enable reliable field deployment in critical dust hotspots.Methodology: An analytical narrative review was conducted by synthesizing research on urease-driven carbonate precipitation, EICP-based soil improvement, and surface stabilization for wind erosion mitigation. The literature was examined with emphasis on: reaction chemistry (urea hydrolysis and carbonate generation), precipitation behavior (CaCO₃ content, crystal morphology, and spatial distribution), treatment implementation (spraying, percolation, shallow injection, and multi-cycle application), and field-relevant performance indicators (crust formation, strength improvement, permeability variation, and erosion resistance). The review also compared urease sources (commercial/purified, microbial, and plant-derived) in terms of availability, cost, activity stability, and environmental compatibility, and compiled reported limitations including non-uniform cementation, pore clogging risk, by-product management, and durability under weathering.Results: The reviewed evidence indicates that EICP can significantly enhance near-surface cohesion by precipitating calcium carbonate within pore spaces and at grain contacts, thereby promoting interparticle bonding and forming a protective crust. Such crusting can increase surface resistance to aerodynamic forces and reduce particle detachment, supporting the potential use of EICP as a dust suppression measure in loose sandy and silty soils commonly associated with active dust sources. Treatment performance is strongly controlled by temperature and pH (which affect urease kinetics and carbonate availability), enzyme activity and dosage, urea and calcium concentrations and their molar balance, and the choice of calcium salt. Soil texture and pore structure influence reagent transport and precipitation uniformity; coarse materials may allow deeper penetration but require repeated cycles to achieve adequate crust integrity, whereas finer materials can produce stronger surface sealing but may exhibit localized clogging and heterogeneity. Application strategy is likewise decisive: surface spraying is practical for large areas but tends to create depth gradients in CaCO₃, while percolation or shallow injection can improve penetration at the cost of higher operational complexity. Plant-derived urease is highlighted as a promising route for scaling EICP, since it may reduce costs and improve accessibility for extensive treatment in remote drylands; however, variability in extraction protocols, enzyme stability, and activity quantification can limit reproducibility unless standardized procedures are adopted. Across studies, persistent constraints include uneven precipitation distribution, uncertain long-term performance under wetting–drying and salt cycles, and environmental concerns related to ammonium generation and potential salinity impacts.Conclusion: EICP is a developing yet highly promising technology for environmentally compatible soil stabilization and dust control. By enabling carbonate-based cementation without reliance on viable microbial cultures, it offers practical advantages for deployment under harsh field conditions. The literature supports its capacity to form erosion-resistant surface crusts and improve mechanical properties relevant to wind erosion mitigation, but translation to routine field practice requires targeted optimization. Future progress depends on establishing performance-based design criteria linked to dust-control outcomes, improving treatment uniformity through refined delivery protocols, standardizing plant-urease extraction and activity reporting, and implementing effective management strategies for ammonium and salinity risks. Well-designed pilot and long-term field trials in representative dust hotspots are essential to validate durability, quantify environmental trade-offs, and define robust guidelines for large-scale application.   https://srjournal.areeo.ac.ir/article_136098.html Background and Objectives: Soil fertility is the capacity of soil to supply essential nutrients for plant growth and is a key factor in food security and sustainable agriculture. Low organic carbon is one of the most important constraints on soil fertility in Iran, with about 62% of soils containing less than 1% organic carbon. In addition, phosphorus and potassium deficiencies have been reported in 71.8% and 21% of agricultural soils, respectively. Preparing accurate nutrient maps using geostatistical methods, particularly kriging, can help identify fertility constraints and improve agricultural management. However, the risk component has received less attention in many studies. In risk zoning, the probability that a soil property falls above or below a specified threshold is determined. Therefore, the objective of this study was to produce risk maps of organic carbon, available phosphorus, and exchangeable potassium across approximately 336,000 hectares of the plains of Kermanshah Province using both parametric and nonparametric methods, including indicator kriging. Materials and Methods: The study area is located in Kermanshah Province, between 46°22′ and 47°41′ E longitude and 33°48′ and 34°47′ N latitude. In this study, 530 surface soil samples were randomly collected from a depth of 0–30 cm, and soil organic carbon, available phosphorus, and exchangeable potassium were measured in the laboratory. Ordinary kriging was used to predict these properties, while universal kriging was applied when a spatial trend was detected, and different variogram models were fitted. Risk zoning was performed using both parametric and nonparametric approaches, including indicator kriging. In the nonparametric approach, continuous data were transformed into binary values based on the defined threshold. Finally, the performance of the two methods was evaluated using cross-validation and ROC curves. Results: After removing outliers from the initial 530 samples, geostatistical analysis was performed on 525 soil organic carbon samples, 475 phosphorus samples, and 488 potassium samples. The mean soil organic carbon content was 1.35% (range: 0.1–3.61), the mean available phosphorus content was 8.6 mg kg⁻¹ (below the critical threshold of 15 mg kg⁻¹), and exchangeable potassium ranged from 158 to 878 mg kg⁻¹. The coefficient of variation was moderate for soil organic carbon and potassium, but high for phosphorus. Analysis of the spatial trend showed that about 17% of the variation in potassium could be explained by spatial trend; therefore, ordinary kriging was used to predict soil organic carbon and phosphorus, whereas universal kriging was applied for potassium. In risk zoning, the data were transformed into binary values based on the critical thresholds of 1% for soil organic carbon, 15 mg kg⁻¹ for phosphorus, and 330 mg kg⁻¹ for potassium. The spherical model provided the best fit for phosphorus, while the Matérn model performed best for soil organic carbon and potassium. ROC curve analysis showed that the parametric method outperformed the nonparametric method for soil organic carbon and phosphorus (AUC ≈ 0.64 and 0.59, respectively), whereas for potassium the two methods performed similarly, with an AUC of about 0.73. Conclusion: The results of this study showed that the parametric method was more efficient than the non-parametric method for risk zoning of soil organic carbon and available phosphorus. However, for exchangeable potassium, both methods showed similar performance. The observed discrepancy in model performance may be attributed to limited sample size, the spatial distribution of samples across the study area, and local agricultural management practices. Overall, the generated risk maps provide a reliable framework for implementing integrated soil fertility management strategies and site-specific fertilizer recommendations.