Miraç Kılıç

Understanding the environmental fate of microplastics (MPs) in agricultural soils remains a major challenge, particularly under field conditions where soil structure and hydraulic processes jointly regulate particle transport and retention. This study investigated whether hydro-physical soil functioning can explain the distribution and accumulation of MPs in pistachio orchard soils from a semi-arid region of southeastern Türkiye. A total of 42 soil samples were analyzed for MP abundance, size distribution, and morphology, together with key hydro-physical properties including texture, porosity, bulk density, aggregate stability, organic matter content, and soil water retention characteristics. To identify the dominant controls on MP occurrence, explainable machine learning approaches combining Random Forest (RF), Gradient Boosting Decision Trees (GBDT), and SHAP (SHapley Additive exPlanations) analysis were employed. Microplastic abundance differed among management systems. Former landfill or construction sites represented the largest proportion of the total recorded microplastic abundance (40.9%), followed by conventionally managed (25.2%), manure-amended (24.5%), and sewage-sludge-amended orchards (9.4%). Median microplastic abundances were 1433, 667, 4633, and 633 particles kg⁻¹ soil, respectively. Fine-sized MPs constituted the dominant particle fraction and exhibited strong associations with pore-system characteristics, indicating that pore-size compatibility governs their retention and mobility within the soil matrix. Morphology-specific analyses further revealed contrasting relationships between soil hydro-physical properties and individual MP forms, suggesting distinct retention pathways for granules, films, fragments, and fibers. Explainable AI analysis identified organic matter, silt content, bulk density, and water retention characteristics as the most influential predictors of MP occurrence. Among the tested models, RF demonstrated superior predictive robustness and generalization capacity. The findings demonstrate that hydro-physical soil functioning plays a central role in determining microplastic fate in agricultural soils and highlight the value of interpretable machine learning frameworks for uncovering the mechanisms underlying contaminant retention and redistribution. Integrating soil structural indicators with explainable artificial intelligence offers a promising pathway for improving microplastic risk assessment in agroecosystems.

Background: Land degradation threatens and the provision of ecosystem services worldwide. Land degradation vulnerability (LDV) assessments still lack the necessary spatial detail and predictive accuracy, and the integration of multiple spectral indices with machine learning remains underexplored. This study addresses the critical importance of spatially mapping vulnerability to land degradation and develops a novel framework that combines advanced machine learning and uncertainty measurement with the STORIE Index Rating (SIR), a semi-quantitative method for assessing potential soil productivity. This framework aims to spatially predict the vulnerability of soils in the study area to land degradation with high accuracy. Methods: This study addresses this gap by introducing HyStoRSM, a novel framework that integrates land-survey-derived data, remote sensing, and machine learning. This study presents a case study of the HyStoRSM framework in the Battalgazi district (940.5 km ² ) of Malatya province, which is representative of continental semi-arid conditions in the upper reaches of the Euphrates Basin in Eastern Anatolia. The framework integrates land survey data (major soil groups, land use capability, slope-depth combination, and erosion severity), spectral indices derived from Landsat 8 OLI/TIRS imagery, and topographic indices calculated from SRTM (Shuttle Radar Topography Mission) data. Landsat 8 and SRTM data from 2023 were processed on the Google Earth Engine platform. Local LDV scores were generated using the geometric mean form of the SIR. An extreme gradient boosting (XGBoost) regression model, optimized using Optuna, estimated continuous LDV scores, while SHapley Additive exPlanations (SHAP) provided insights into feature importance. Results: The optimized XGBoost regression model, with hyperparameters tuned using 5-fold cross-validation with Optuna-based hyperparameter optimization and validated on an independent 30% test dataset, achieved high prediction accuracy ( R ² = 0.74, RMSE = 0.1285, MAE = 0.1002, and Huber Loss = 0.0083). SHAP analysis revealed that the length-slope factor was the most influential variable, followed by the stream power index and the Normalized Difference Vegetation Index (NDVI). These results demonstrated that hydro-topographic variables had a greater impact on LDV than spectral indices. Accordingly, an LDV map at 30 m spatial resolution was produced. Spatial analysis indicated that 21.7% and 20.3% of the study area exhibited high and very high LDV, primarily concentrated in the southern and southeastern regions. Conversely, low and very low vulnerabilities covered 16.9% and 12.4% of the area. Conclusions: The HyStoRSM framework integrates multisource satellite data, land survey data, and advanced machine learning into a single, interpretable framework. This enables proactive, precise land degradation risk management, especially in semiarid regions where terrain and hydrologic controls drive erosion vulnerability.

Sustainable land management and accurate land suitability assessment are fundamental pillars for ensuring long-term agricultural productivity and environmental health.. In this study, the FAO's sustainable agricultural land suitability assessment for wheat was carried out using the criteria of slope from topographical factors and pH, calcium carbonate (CaCO₃) and OM from critical soil factors. The aim is to assess the suitability of the land through the integration of geospatial data with fuzzy logic models and to contribute to sustainable agricultural activities. Soil samples were analysed for basic properties. Digital soil maps were produced using ordinary kriging. The Mamdani fuzzy inference technique was used for classification of the land into appropriate classifications in accordance with FAO standards. The study area was classified moderately (59.41%) and slightly (40.58%) suitable for wheat. High calcium carbonate (CaCO₃) concentration, low organic matter were identified as limiting factors. Soil management is recommended to rehabilitate marginally suitable sites and to use them with care. This research highlights the possibility of facilitating sustainable agricultural planning in semi-arid environments through the combination of fuzzy logic with GIS-based techniques for proper land assessment.

Soil organic carbon (SOC) dynamics are central to evaluating land degradation, particularly in semi-arid regions where monitoring SOC-to-clay ratios (an indicator proposed for assessing soil resilience but still debated) remains challenging. This study employs machine learning (ML) models, including Random Forest (RF), Gradient Boosting, Classification and Regression Tree (CART) and Light Gradient-Boosting Machine (LightGBM), to spatially predict SOC-to-clay ratios across part of Şanlıurfa province, Türkiye, a semi-arid region dominated by pistachio cultivation. The study area includes Typic Calcixerepts, Calcic Haploxerepts and Typic Haplotorrerts, reflecting diverse pedological conditions. The efficacy of SOC-to-clay ratio was evaluated relative to a soil quality index (SQI) and identified texture-dependent biases. Results revealed soil texture as the dominant predictor, explaining 34-65% of variance across models, surpassing land use (7–12%). Pasturelands exhibited the highest ratios (0.21–0.47), classified as ‘very good’, due to minimal disturbance and sustained organic inputs, while croplands and pistachio systems showed ‘moderate degradation’ (≤0.26). A moderate correlation between SOC-to-clay ratio and SQI ( r = 0.51) supported its utility, though low explanatory power (R ² = 0.26) suggests complementary indicators are needed to correct for ratio inflation in low-clay soils. Spatial predictions support EU Soil Strategy 2030 priorities, advocating for reduced tillage in croplands and perennial vegetation in pasturelands.

Worldwide topsoil loss through erosional processes creates substantial annual losses that threaten global food security and essential ecosystem functions. The Shared Socioeconomic Pathway 5 - Fossil-fueled Development (SSP5-8.5) forecasts reduced overall precipitation volumes, enhanced extreme rainfall events, and substantially elevated erosion susceptibility across the Mediterranean Basin. Within the Ceyhan Basin context, existing research lacks kilometer-resolution precipitation datasets spanning the 1995-2014 and 2041-2060 timeframes under SSP5-8.5 conditions for concurrent R factor calibration procedures. Considering these research gaps, this investigation seeks to assess the SSP5-8.5 pathway following AR6 temporal frameworks for mean annual soil loss rates (t ha⁻¹ yr⁻¹) throughout the Ceyhan Basin employing RUSLE methodology. Soil loss quantification utilized the Revised Universal Soil Loss Equation (RUSLE) framework. Computational analyses were performed using Google Earth Engine (GEE) cloud-based infrastructure at 250 m spatial resolution, incorporating CHIRPS datasets for the 1995-2014 baseline timeframe and NEX-GDDP-CMIP6 collections for the 2041-2060 projection period. Soil erodibility parameter (K) derivation employed SoilGrids 2.0, while topographic parameters originated from SRTM datasets. Conservation management parameter (P) was extracted from Copernicus Global Land Cover Layers collections. Reference period (1995-2014) mean soil loss measured 4.424 t ha⁻¹ yr⁻¹, with projections indicating an increase to 5.182 t ha⁻¹ yr⁻¹ during 2041-2060 under SSP5-8.5 conditions. Rainfall erosivity values demonstrated 7.6% enhancement, with peak values ranging from 239,689 MJ·mm·ha⁻¹·h⁻¹·yr⁻¹ to 258,017 MJ·mm·ha⁻¹·h⁻¹·yr⁻¹. Analysis revealed 93.8% of the study region maintaining existing erosion classifications, while 69,402 hectares will experience transitions from very low to low erosion categories. High-resolution climate dataset integration from CMIP6 combined with transition matrix methodologies indicate emerging erosion hotspots throughout southern and central basin areas with intensified erosion processes in environmentally critical zones.

The management of agricultural lands plays a crucial role in the global carbon (C) cycle. Intensive soil tillage is one of the primary factors contributing to the mineralization of soil organic carbon (SOC), leading to its release into the atmosphere as CO₂. This study determined the soil organic carbon stock (SOCS) in pistachio-growing areas, which have expanded significantly in recent years in Kurtalan district, Siirt province. Spatial variation of SOCS was also mapped within the study area to ensure its long-term monitoring over time. For this purpose, soil samples were collected from a depth of 0–20 cm in 72 pistachio orchards. The soil organic C (SOC) content and bulk density (Bd) of the collected soil samples were determined, and SOCS values were calculated to generate spatial distribution maps. The SOC content ranged from 2.85 g kg⁻¹ to 26 g kg⁻¹, while SOCS varied between 8.97 t ha⁻¹ and 73.74 t ha⁻¹. Significant variability in SOC and SOCS was observed among soil samples belonging to different texture groups. Additionally, SOC and SOCS values significantly decreased as orchard age increased. The SOC and SOCS values decreased by 20.3% and 22.7%, respectively in pistachio orchards older than 20 years compared 5 years old or younger orchards. This decline in SOC content was also reflected in the SOC:Clay ratio, a key indicator of soil structure stability, which dropped from an average of 0.32 in younger orchards to 0.20 in older orchards. This suggests that agricultural practices in pistachio orchards disrupt soil aggregation, accelerating organic C mineralization and reducing the soil’s ability to retain organic matter over time. These results show the urgent need for sustainable soil management strategies to preserve soil integrity, enhance C retention, and support long-term agricultural sustainability in semiarid pistachio cultivation areas.

The primary objective of this study was to contribute to a deeper understanding of land degradation neutrality (LDN), with a specific focus on Malatya province, located in the semi‐arid region of eastern Türkiye. This case study served as a foundation for establishing a framework to monitor land degradation over time. The research employed three key sub‐indicators—land cover (LC), land productivity (LP), and carbon stocks—following the guidelines of SDG 15.3.1. To assess changes in these sub‐indicators, the SDG 15.3.1 indicator was calculated using 2015 as the baseline year and the preceding period (2000–2015) for comparison. The results indicated that approximately 3.24% of the study area experienced land degradation, primarily attributed to a decline in LP. Of this degradation, about 89.15% was linked to LP loss, while LC (5.91%) and soil organic carbon (SOC) (4.94%) contributed smaller proportions. The study also identified regional variations in land degradation, with intensified degradation occurring in areas undergoing rapid urbanization.

Accurate estimation of particle size distribution across a large area is crucial for proper soil management and conservation, ensuring compatibility with capabilities and enabling better selection and adaptation of precision agricultural techniques. The study investigated the performance of tree-based models, ranging from simpler options like CART to sophisticated ones like XGBoost, in predicting soil texture over a wide geographic region. Models were constructed using remotely sensed plant and soil indexes as covariates. Variable selection employed the Boruta approach. Training and testing data for machine learning models consisted of particle size distribution results from 622 surface soil samples collected in southeastern Turkey. The XGBoostClay model emerged as the most accurate predictor, with an R2 value of 0.74. Its superiority was further underlined by a 21.36% relative improvement in XGBoostClay RMSE compared to RFClay and 44.5% compared to CARTClay. Similarly, the R2 values for XGBoostSilt and XGBoostSand models reached 0.71 and 0.75 in predicting sand and silt content, respectively. Among the considered covariates, the normalized ratio vegetation index and slope angle had the highest impact on clay content (21%), followed by topographic position index and simple ratio clay index (20%), while terrain ruggedness index had the least impact (18%). These results highlight the effectiveness of Boruta approach in selecting an adequate number of variables for digital mapping, suggesting its potential as a viable option in this field. Furthermore, the findings of this study suggest that remote sensing data can effectively contribute to digital soil mapping, with tree-based model development leading to improved prediction performance.

Carbon sequestration in earth surface is higher than the atmosphere, and the amount of carbon stored in wetlands is much greater than all other land surfaces. The purpose of this study was to estimate soil organic carbon stocks (SOCS) and investigate spatial distribution pattern of Yuksekova wetlands and surrounding lands in Hakkari province of Turkey using machine learning and remote sensing data. Disturbed and undisturbed soil samples were collected from 10-cm depth in 50 locations differed with land use and land cover. Vegetation, soil, and moisture indices were calculated using Sentinel 2 Multispectral Sensor Instrument (MSI) data. Significant correlations (p≤0.01) were obtained between the indices and SOCS; thus, the remote sensing indices (ARVI 0.43, BI −0.43, GSI −0.39, GNDI 0.44, NDVI 0.44, NDWI 0.38, and SRCI 0.51) were used as covariates in multi-layer perceptron neural network (MLP) and gradient descent–boosted regression tree (GBDT) machine learning models. Mean absolute error, root mean square error, and mean absolute percentage error were 3.94 (Mg C ha ⁻¹), 6.64 (Mg C ha⁻¹), and 9.97%, respectively. The simple ratio clay index (SRCI), which represents the soil texture, was the most important factor in the SOCS estimation variance. In addition, the relationship between SRCI and Topsoil Grain Size Index revealed that topsoil clay content is a highly important parameter in spatial variation of SOCS. The spatial SOCS values obtained using the GBDT model and the mean SOCS values of the CORINE land cover classes were significantly different. The land cover has a significant effect on SOC in Yuksekova plain. The mean SOCS for continuously ponded fields was 45.58 Mg C ha⁻¹, which was significantly different from the mean SOCS of arable lands. The mean SOCS in arable lands, with significant areas of natural vegetation, was 50.22 Mg C ha⁻¹ and this amount was significantly higher from the SOCS of other land covers (p−1), followed by the lands principally occupied by natural vegetation and used as rangelands around the wetland (50.22 Mg C ha⁻¹). Environmental conditions had significant effect on SOCS in the study area. The use of remote sensing indices instead of using single bands as estimators in the GBDT algorithm minimized radiometric errors, and reliable spatial SOCS information was obtained by using the estimators. Therefore, the spatial estimation of SOCS can be successfully determined with up-to-date machine learning algorithms only using remote sensing predictor variables. Reliable estimation of SOCS in wetlands and surrounding lands can help understand policy and decision makers the importance of wetlands in mitigating the negative impacts of global warming.