Water and Soil

Abstract Introduction Soil compaction is a significant component that has a detrimental impact on soil structure, inhibits plant development, lowers water penetration rate, diminishes crop production, and raises machine (tractor, cultivator…) usage costs. In general, compaction is the result of applied pressure that results in a rise in bulk density or decreased in soil porosity. One of the most used indices for the investigation and evaluation of soil compaction is based on penetration resistance (PR) of the soil. However, identifying soil compaction by the records of soil penetrometer equipment in the field is time-consuming, expensive, and may produce unreliable results due to instrumental errors; Thus, it is useful to apply different intelligent models to predict PR through easily accessible and low-cost soil parameters. The aim of this study was to compare the performance of three intelligent models -neuro-fuzzy (NF), gene expression programming (GEP), and random forest (RF)—in estimating PR from readily available soil variables.



Materials and methods Disturbed and undisturbed soil samples (n= 105) were collected from 0-10 cm depth of agricultural lands in Ardabil plain, Iran. The values of sand, silt, clay, CaCO3, bulk (BD) and particle (PD) density, organic carbon (OC), field water content (FWC) and saturated water were measured at the soil samples in the laboratory according to the standard methods. Mean geometric diameter (dg) and geometric standard deviation (σg) of soil particles were calculated by sand, silt and clay percentages. Total porosity (n) was computed using BD and PD data. The penetration resistance (PR) of the soil was obtained in the field using cone penetrometer (analog model) at 5 replicates. Data randomly were divided in two series as 78 data for training and 27 data for testing of models. Fifteen different combinations of readily available soil variables were selected as model inputs to estimate PR using neuro-fuzzy (NF), gene expression programming (GEP), and random forest (RF) models. The triangular, sigmoid, trapezoid, Gaussian and bell shape membership functions in the input layer and constant membership function in the output layer by trial and error method were applied in the neuro-fuzzy (NF) modeling using MATLAB program. A set of optimal parameters were chosen before developing a best GEP model in the Gene Xpro Tools 4.0 software. The number of chromosomes and genes, head size and linking function were selected by the trial and error method, and they are 30, 3, 8, and +, respectively. The rates of genetic operators were chosen according to literature studies. Weka software was used planning random forest (RF) models. The accuracy of NF, GEP and RF models in estimating PR were evaluated by coefficient of determination (R2), normalized root mean square error (NRMSE), mean error (ME) and Nash-Sutcliffe coefficient (NS) statistics.



Results and discussion The values of sand (26.26 to 87.43 %), silt (5.99 to 67.18 %), clay (3.99 to 17.34 %), OC (0.30 to 2.41 %), FWC (4.56 to 33.18 mass percent), BD (1.02 to 1.63 g cm-3) and PR (1.10 to 6.60 MPa) indicated good variations in the soils of studied area. There were found significant correlations between PR with FWC (r= - 0.57**), sand (r= - 0.21*), OC (r= - 0.47**) and BD (r= 0.66**). More former researchers also reported that there is a negative and significant correlation between PR with FWC and a positive and significant correlation between PR with BD. The results of NF, GEP and RF models showed that the most suitable variables to predict PR were field water content (FWC), sand, bulk density (BD), total porosity and mean geometric diameter (dg) of soil particles. Values of coefficient of determination (R2), normalized root mean square error (NRMSE), mean error (ME), and Nash-Sutcliffe coefficient (NS) were calculated for the best models based on the test data as follows: 0.50, 0.19, 0.03 MPa, 0.51 for NF; 0.51, 0.20, 0.13 MPa, 0.48 for GEP; and 0.58, 0.20, 0.31 MPa, 0.50 for RF.

Conclusion The results showed that according to the lowest values of normalized root mean square error (NRMSE) and the highest values of Nash-Sutcliffe coefficient (NS), the accuracy of neuro-fuzzy (NF) model to estimate soil PR was more than gene expression programming (GEP) and random forest (RF) models in this study; The input variables of the best NF model in estimating soil penetration resistance (PR) were field water content (FWC), sand and bulk density (BD).

Abstract Introduction

Water conveyance tunnels are critical infrastructures for sustainable water supply, particularly in arid and semi-arid regions. However, tunnel excavation can significantly alter groundwater systems, leading to hazards such as sudden groundwater inflow, reduction in aquifer storage, and deterioration of water quantity and quality (Zheng et al., 2021; Li et al., 2024). In fractured and karstic environments, these impacts may propagate over large distances, potentially modifying regional hydrogeological regimes (Chen, 2021; Sadique et al., 2025).

To address these challenges, various predictive approaches have been developed, including numerical modeling, parametric methods, hybrid techniques, and empirical models. Although numerical models provide detailed insights, they require extensive datasets and complex calibration, which limits their applicability in heterogeneous geological settings (Vincenzi et al., 2022). Consequently, parametric and empirical methods such as the Drainage Hazard Index (DHI) and Tunnel Impact Score (TIS) have gained increasing attention due to their flexibility, simplicity, and suitability for preliminary assessments and real-time decision-making (Dematteis et al., 2001; Hassanpour et al., 2021; Abedian & Mojiri, 2023).

The Hezarmasjed water conveyance tunnel, located in northeastern Iran within the Kopet-Dagh tectonic zone, traverses a highly fractured carbonate system hosting numerous springs. Given the dependence of local communities on these springs, assessing the potential impacts of tunnel excavation is essential. This study aims to identify the main controlling factors governing spring discharge variations and to evaluate the vulnerability of springs using both DHI and TIS approaches.



Materials and Methods

The study area is situated in a structurally complex mountainous region characterized by active tectonics, heterogeneous lithology, and significant hydrogeological sensitivity. The tunnel, approximately 8.8 km in length, intersects several geological formations, including carbonate units (Mozduran and Tirgan) acting as primary aquifers, and shale-dominated formations with low permeability. Groundwater flow is predominantly controlled by fracture networks and karstification processes, resulting in rapid hydrological responses to precipitation events.

A total of 45 springs were investigated at varying distances from the tunnel axis. Field data included hydrogeological observations, Lugeon permeability tests, and structural analyses. Permeability values generally ranged from 10⁻⁷ to 2.5×10⁻⁶ m/s, indicating low to moderate hydraulic conductivity, with higher values in fractured and karstified zones.

Two complementary methods were employed for impact assessment. The DHI method (Dematteis et al., 2001) is a semi-quantitative parametric approach based on seven key parameters, including fracture frequency, rock mass permeability, overburden thickness, plastic zone radius, fault–spring interaction, spring type, and distance from the tunnel. These parameters were normalized and combined to calculate a continuous index representing the risk of spring discharge reduction.

The TIS method (Hassanpour et al., 2021) is an empirical approach that evaluates spring vulnerability based on four main factors: tunnel water inflow (40% weight), hydraulic connectivity (40%), distance from the tunnel (15%), and aquifer recharge capacity (5%). The method provides a classification of impact severity, ranging from negligible to significant discharge reduction.



Results and Discussion

The DHI results indicate that spring vulnerability is primarily controlled by structural and hydrogeological parameters, particularly fracture density and permeability. Calculated DHI values range from 0.1 to 1.5, demonstrating a wide spectrum of potential impacts. Approximately 11% of springs are classified as highly to critically affected (DHI ≥ 0.6), while the majority (about 89%) are expected to experience minor to moderate discharge reductions. Springs with high DHI values are typically located in highly fractured carbonate formations with strong hydraulic connectivity, where tunnel excavation acts as a drainage boundary, intercepting groundwater flow paths (Vincenzi et al., 2022; Saberinasr & Dashti Barmaki, 2023).

In contrast, springs located in low-permeability formations or at greater distances from the tunnel exhibit minimal sensitivity, with DHI values below 0.2. More than 70% of springs fall within an intermediate range, indicating moderate but persistent reductions due to partial hydraulic connectivity.

The TIS results highlight the importance of dynamic hydrogeological parameters, particularly tunnel water inflow and hydraulic connectivity. TIS values range from 1.6 to 6.9, reflecting significant variability in system response. Approximately 44% of springs are classified as unaffected, 44% experience moderate discharge reduction, and around 12% are subject to significant decline.

High-risk springs, such as Sirzar and Bamchenar, exhibit strong hydraulic connectivity and are located within sensitive recharge zones. Notably, the results indicate that hydraulic connectivity can dominate system behavior even at distances exceeding 1000 m, confirming previous findings that connectivity is more influential than geometric proximity (Hassanpour et al., 2021).

Conversely, springs located in areas with higher recharge capacity and weaker hydraulic connectivity show minimal impact, even when tunnel inflow is relatively high. This behavior reflects the buffering capacity of aquifer systems, which can partially compensate for tunnel-induced drainage.

A comparative analysis of the two methods demonstrates that the DHI approach emphasizes intrinsic geological and structural conditions, providing a continuous representation of vulnerability, whereas the TIS method focuses on functional aquifer responses and offers a more practical classification of impact severity. The consistency between both methods in identifying high-risk springs supports the reliability of the integrated approach.



Conclusion

This study demonstrates that tunnel excavation can significantly affect groundwater systems, particularly in fractured and karstic aquifers. The integrated application of DHI and TIS methods provides a comprehensive framework for assessing these impacts.

The DHI method is effective for identifying intrinsic vulnerability and worst-case scenarios, highlighting the role of geological structures, permeability, and fracture density. Approximately 11% of springs are at high to critical risk, while most are expected to experience moderate impacts.

In contrast, the TIS method offers a more operational perspective by incorporating dynamic hydrogeological parameters, classifying springs into significant, moderate, and negligible impact categories. The results emphasize the dominant role of hydraulic connectivity and aquifer recharge capacity in controlling system response.

Overall, the combined use of parametric and empirical approaches enhances the reliability of impact assessments and provides a robust basis for groundwater management in tunnel projects. This integrated methodology is particularly suitable for complex hydrogeological environments where data limitations restrict the application of numerical models.

Abstract Introduction

Tillage management is one of the main factors affecting soil quality and the sustainability of agricultural systems. Intensive conventional tillage can weaken soil structure, accelerate organic matter decomposition, and alter microbial activity due to continuous mechanical disturbance of the soil. In contrast, conservation tillage systems such as reduced tillage and no‑tillage minimize soil disturbance and often retain crop residues on the soil surface, which can improve soil aggregation, enhance soil organic carbon storage, and increase nutrient retention. These systems also help reduce soil erosion, improve water infiltration, and create more favorable conditions for soil microorganisms. In semi‑arid regions, where soil moisture is limited and soil degradation can occur rapidly, maintaining soil structure and fertility is particularly important for crop production. Durum wheat is an important cereal crop in many semi‑arid areas, and its productivity is closely related to soil health and nutrient availability. Therefore, understanding how different tillage systems influence soil properties is essential for developing sustainable management strategies. This study was conducted to evaluate the effects of different tillage systems and crop presence on selected physical, chemical, and biological properties of soil.

Materials and Methods

The experiment was conducted as a split-split-plot arrangement within a randomized complete block design (RCBD) with three replications. The treatments consisted of three tillage systems, including conventional tillage (CT), reduced tillage (RT), and no-tillage (NT), assigned to the main plots. The subsoiling treatments, including subsoiling and no-subsoiling, were allocated to the subplots, and two crop treatments, including control (uncultivated soil) and durum wheat (Triticum durum), were assigned to the sub-subplots. Soil samples were collected after the crop growing period from the experimental plots and prepared for laboratory analysis. A set of soil quality indicators representing physical, chemical, and biological aspects of soil functioning was measured. Physical and chemical assessments included pH, electrical conductivity (EC), water dispersible clay (WDC), and mean weight diameter (MWD) of aggregates, which were used to evaluate salinity condition and aggregate stability. Chemical analyses included soil organic matter (SOM), soil organic carbon (SOC), total nitrogen (TN), available phosphorus (P), and available potassium (K) as the main indicators of soil fertility and nutrient status. Biological properties were determined through basal soil respiration (BR) and substrate induced respiration (SIR), in order to estimate microbial activity and the potential metabolic response of soil microorganisms to an added substrate. In addition, microbial biomass carbon was considered in the interpretation of biological responses where relevant. The collected data were statistically analyzed using analysis of variance (ANOVA) to test the significance of the main effects of tillage and crop treatment as well as their interaction effects on soil properties. Mean comparisons were performed using appropriate post hoc tests at the selected probability level.

Results and Discussion

The results showed that tillage management significantly influenced the measured soil properties. Conventional tillage generally resulted in higher EC and water dispersible clay, indicating weaker structural stability, whereas no tillage improved aggregation and produced the highest mean weight diameter of soil aggregates. These results confirm that reduced soil disturbance promotes aggregate formation and protects soil structure. Soil organic matter and soil organic carbon increased as tillage intensity decreased, with the highest values observed under no tillage. This trend indicates that conservation systems reduce oxidation of organic materials and favor carbon accumulation within stable aggregates. Nutrient availability also improved under conservation practices, particularly in the no tillage treatment, where nitrogen, phosphorus, and potassium values were generally higher than in conventional tillage. Crop treatment had a positive effect on several soil properties. Soils under durum wheat showed higher soil organic matter, soil organic carbon, and nutrient concentrations than uncultivated control soils, probably because of root residues, rhizosphere activity, and enhanced microbial interactions. Biological indicators also responded significantly to management practices. Basal respiration was greater under conventional tillage, suggesting more rapid decomposition of soil organic matter under intensive disturbance. In contrast, lower respiration values under no tillage reflected greater stabilization of organic carbon. Substrate induced respiration was significantly affected by tillage, crop treatment, and their interaction, indicating that microbial activity was highly sensitive to both soil disturbance and plant presence.

Conclusions

The findings demonstrate that tillage management has an important role in controlling soil quality in durum wheat systems. Conservation practices, especially no tillage, improved soil structure, increased soil organic carbon and organic matter, and enhanced nutrient availability compared with conventional tillage. Durum wheat cultivation also contributed positively to soil improvement through rhizosphere effects and organic inputs. Overall, the combination of no tillage and durum wheat cultivation appears to be a suitable management strategy for improving soil quality and supporting sustainable production in semi arid conditions.

Abstract Introduction: Degradation of water quality has emerged as one of the most critical challenges facing national water resources. Surging water demand has led to the over-exploitation of groundwater and a continuous decline in aquifer levels, subsequently triggering environmental degradation and a deteriorating trend in groundwater quality. Consequently, analyzing temporal trends and spatial variations of groundwater quality is vital for sustainable water resource management. This study aims to investigate the temporal trends of groundwater quality parameters using the Mann-Kendall test and Sen’s Slope Estimator, while analyzing spatial variations through the Groundwater Quality Index (GQI) in the Bahabad aquifer.

Materials and Methods: In this study, a total of 16 observation wells were initially considered for groundwater quality assessment. Among them, 9 wells with complete and reliable datasets were selected for detailed analysis. The required hydrochemical data were obtained from the Yazd Regional Water Authority and included major groundwater quality parameters: calcium (Ca²⁺), magnesium (Mg²⁺), sodium (Na⁺), chloride (Cl⁻), sulfate (SO₄²⁻), and total dissolved solids (TDS). A uniform temporal period from 2002 to 2023 was considered for all selected wells to ensure consistency in trend analysis.

To evaluate temporal variations in groundwater quality, non-parametric statistical methods, including the Mann–Kendall test and Sen’s slope estimator, were employed. These methods are widely used in hydrochemical studies due to their robustness against non-normal data distributions and their ability to detect both the direction and magnitude of trends over time.

Spatial variations in groundwater quality parameters were analyzed using a range of interpolation techniques, including both deterministic and geostatistical approaches. The applied methods consisted of Inverse Distance Weighting (IDW), Radial Basis Functions (RBF), Local Polynomial Interpolation (LPI), Global Polynomial Interpolation (GPI), and Kriging. All spatial analyses were performed in a GIS environment. The optimal interpolation method for each parameter was selected based on statistical performance indicators, primarily the lowest Root Mean Square Error (RMSE) and the highest coefficient of determination (R²).

Prior to spatial modeling, the normality of the dataset was evaluated using the Kolmogorov–Smirnov and Shapiro–Wilk tests, which are commonly applied to assess data distribution characteristics. The results of these tests were used to determine whether data transformation was required before applying interpolation and statistical analyses.

Finally, the Groundwater Quality Index (GQI) was calculated to provide an integrated assessment of groundwater quality conditions. By aggregating multiple hydrochemical parameters into a single index, GQI enables a comprehensive evaluation of groundwater quality and facilitates spatial comparison across the aquifer. This index was used as a key tool for interpreting overall water quality status and supporting groundwater management decisions.

Results and Discussion: The results indicate that while the groundwater quality of the Bahabad aquifer remained within the “Acceptable” category during the study period, a slight but statistically significant downward tendency in quality was observed. Spatial distribution analysis revealed that salinity patterns and ionic concentrations are influenced more by anthropogenic pressures such as over-pumping, water table drawdown, and inter-basin water transfer than by natural geological factors.

Temporal analysis highlighted significant spatial heterogeneity; for instance, Mg²⁺ exhibited a significant increasing trend across all stations (Sen’s slope ranging from 0.05 to 0.23), whereas Na⁺ and SO₄²⁻ showed decreasing or insignificant trends in certain wells. TDS displayed variable behavior, showing generally stable or slightly decreasing trends, while a sharp increase was observed at one specific station.

Furthermore, a comparison of interpolation techniques showed that under conditions of limited sampling density and weak spatial correlation, deterministic methods often yielded higher predictive accuracy than geostatistical models.

Conclusion: Despite only minor overall fluctuations in the GQI (ranging approximately between −0.5% and +0.1%), the index maps did not fully capture the intensity of localized quality degradation in critical parameters such as TDS, Na⁺, and SO₄²⁻. This indicates that composite indices like GQI may partially neutralize opposing trends among individual parameters, thereby providing a more generalized representation of groundwater quality conditions.

Trend analysis of individual parameters revealed considerable spatial heterogeneity across the aquifer. Magnesium and chloride exhibited consistent increasing trends in most monitoring wells, indicating progressive salinization, whereas sodium and sulfate showed mixed or insignificant trends in several locations. Total dissolved solids demonstrated spatially variable behavior, with localized sharp increases despite generally stable or declining trends elsewhere. This variability reflects the complex interaction between local hydrogeological conditions and human-induced pressures and confirms that groundwater quality evolution cannot be adequately described using a single parameter or uniform trend assumption.

The comparison of interpolation techniques further highlighted an important methodological implication: under conditions of limited sampling density and weak spatial autocorrelation, deterministic methods can provide more reliable estimates than geostatistical approaches. This emphasizes the need to adapt modeling strategies to data availability and hydrogeological settings, particularly in arid and semi-arid regions where monitoring networks are often sparse.

Although the Groundwater Quality Index proved effective in providing an overall assessment of groundwater status, it was less sensitive to localized deterioration in key parameters such as TDS, sodium, and sulfate. This confirms that composite indices, while useful for communication and management purposes, may mask emerging local-scale risks if used in isolation. Therefore, they should be applied alongside parameter-specific analyses rather than as standalone decision-making tools.

Overall, the findings highlight the urgent need for improved groundwater management in the Bahabad aquifer. Controlling excessive abstraction, regulating industrial withdrawals, and strengthening long-term monitoring programs are essential to prevent further degradation. Integrating spatial analysis with trend-based assessments can provide a more reliable framework for sustainable groundwater management, particularly in arid regions experiencing increasing water stress.

Abstract Introduction

The heavy reliance on chemical N-P-K fertilizers in intensive vegetable farming has raised significant environmental and health concerns. Nitrate accumulation in leafy greens, especially spinach, is a major food safety challenge. Nitrate is directly linked to nitrogen metabolism and accumulates in plant tissue when its uptake exceeds the plant's capacity to use it for growth and protein synthesis. Plant growth-promoting bacteria (PGPB) offer a sustainable alternative by enhancing nutrient bioavailability through mechanisms such as phosphate solubilization, nitrogen fixation, and siderophore production. Growth-promoting bacteria prevent nitrate accumulation in leaves by improving nitrogen uptake and transport. While the efficacy of PGPB is well-documented in cereal crops, their potential to mitigate the negative effects of reduced fertilization in high-demand vegetables like spinach remains under-explored.

Materials and Methods

This study aimed to evaluate the efficiency of PGPB in improving spinach characteristics under greenhouse conditions using a factorial experiment based on a completely randomized design. The treatments consisted of two levels of N-P-K fertilizer (100% of recommended rate and 70% of the recommended rate) and four levels of microbial inoculation (no bacteria, Pseudomonas putida, Phytobacter diazotrophicus, and a mix of both). The experiment was conducted in 3 kg pots. Six seeds were sown 1 cm below the soil surface and one milliliter of each strain was inoculated under each seed. The soil moisture of the pots was maintained at approximately 75% of field capacity moisture by weight during the experiment. After two weeks, the plants were thinned to three plants. The plants were harvested 50 days after planting. Parameters including leaf nitrogen (N), phosphorus (P), potassium (K), iron (Fe), chlorophyll content, nitrate concentration, and nitrate reductase enzyme activity were measured.



Results

The results showed that the interaction effect of fertilizer level and bacterial inoculation was significant for all measured traits. Microbial inoculation, particularly the mix of bacteria, effectively compensated for the yield loss caused by the 30% reduction in chemical fertilizer. The highest leaf fresh weight was obtained in both the 100% and 70% recommended NPK fertilizer treatments when plants were inoculated with the combined bacterial consortium, representing 1.7- and 2.2-fold increases, respectively, compared with the corresponding non-inoculated treatments. Application of 100% of the recommended NPK fertilizer in combination with the mixed bacterial inoculum increased leaf nitrogen (62%), phosphorus (23%), potassium (51%), and iron (32%) contents compared with the corresponding non-inoculated treatment. The second-highest contents of these nutrients were observed in the treatment receiving 70% of the recommended fertilizer rate together with the mixed bacterial inoculum. Total chlorophyll content declined with reduced fertilizer application; however, bacterial inoculation compensated for this reduction, such that no significant difference was observed between the inoculated treatments and the treatment receiving 100% of the recommended fertilizer rate. The highest total chlorophyll content was measured in the treatment receiving 100% of the recommended fertilizer rate together with the mixed bacterial inoculum,

which was 34% higher than the corresponding non-inoculated treatment. The highest leaf nitrate content (6123 mg kg⁻¹) was recorded in the treatment receiving 100% of the recommended fertilizer rate without bacterial inoculation. The lowest leaf nitrate content was measured in the treatment receiving 70% of the recommended fertilizer rate combined with the mixed bacterial inoculum, representing a 25% reduction compared with the corresponding non-inoculated treatment. The highest nitrate reductase activity was measured in the treatment receiving 70% of the recommended fertilizer rate together with the mixed bacterial inoculum, showing a 47% increase compared with the corresponding non-inoculated treatment. There was a negative correlation between nitrate content in the leaf and nitrate reductase activity. The results indicated that chlorophyll content was positively correlated with the contents of nitrogen, phosphorus, potassium, and iron, as well as with nitrate reductase activity. Plant fresh weight was also positively correlated with chlorophyll content, the contents of nitrogen, phosphorus, potassium, and iron, and the activity of nitrate reductase.





Conclusion

Overall, these findings suggest that the combined application of Pseudomonas putida and Phytobacter diazotrophicus with 70% of the recommended NPK fertilizer rate may represent a promising strategy to reduce chemical fertilizer use by 30% without substantial yield loss, while enhancing the nutritional quality of spinach. Nevertheless, further validation under field conditions and diverse environmental settings is required before this approach can be recommended for widespread agricultural use.