Yazar "Kilic, Mirac" seçeneğine göre listele

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    Comparison of Biological Indicators of Soil Quality of Horticultural Crops Based on No-tillage and Non-synthetic Systems
    (Springer, 2023) Celik, Ahmet; Kilic, Mirac; Ramazanoglu, Emrah; Belliturk, Korkmaz; Sakin, Erdal
    The aim of this study was to evaluate the microbial biomass carbon content, microbial biomass nitrogen content and enzyme activities of soils sampled in February, May and September 2018 as well as in February 2019. In soils where different horticulture crops grow, soil microbial carbon (Cmic) and nitrogen content (Nmic), macro- and micronutrients and soil enzyme activities were determined through statistical evaluations. Mean Cmic and Nmic values showed statistically significant seasonal fluctuations. In addition, the Nmic levels of the soils in the winter sampling were lower than those collected in the spring and summer months. The highest soil dehydrogenase enzyme activity was found in apple and date palm orchards, and the lowest in olive orchards. Soil urease enzyme activity varied between cultivars. Cherry and plum orchard soils have the highest and lowest urease enzyme activity. The results of this study revealed that the biological properties of the soil, which is the most sensitive indicator of soil quality, are positively affected in horticultural agriculture without soil tillage and synthetic application with an environmentally friendly approach, and that the biological properties of different fruit trees vary under the same conditions.
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    Efficiency of Land Degradation Neutrality Assessment Indicators of United Nation to Identify Land Degradation in a Semi-Arid Environment
    (Wiley, 2025) Kilic, Mirac; Gunal, Hikmet
    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 T & uuml;rkiye. 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.
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    Land suitability assessment for rapeseed potential cultivation in upper Tigris basin of Turkiye comparing fuzzy and boolean logic
    (Elsevier, 2024) Budak, Mesut; Kilic, Mirac; Gunal, Hikmet; Celik, Ismail; Sirri, Mesut
    Assessment of land suitability is a prerequisite for the conservation and maintenance of land productivity and the improvement of land use and management systems. This study assessed land suitability for rapeseed (Brassica napus L.) production using topography, climate, and soil data by analytical hierarchy process (AHP) and the Mamdani Fuzzy Inference System (MFIS). The study area covers 3737 km2 of land in the Diyarbakir province of southeastern Turkiye. The weights of topography, soil and climate factors in AHP were determined by expert opinions and the information in related literature. They were included in the whole process, mainly membership functions and rule base stages in the MFIS. The highest weighted factor was slope (0.264), followed by altitude (0.121), annual average temperature (0.114) and soil texture (0.112). The MFIS-based land suitability assessment indicated that the proportions of moderately (S2), marginally (S3) and currently not suitable (N1) land classes in the study area were 71.35%, 18.75% and 9.9%, respectively. The AHP results showed that 98.94% of the land was S3, and 1.06% was N1. The compatibility of AHP and MFIS methods in N1 land units was 96.05%, while the agreement for S2 and S3 land classes was not sufficiently high. The suitability of rapeseed cultivation has been more sensitively assessed by the fuzzy continuous classification obtained by the MFIS method.
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    Machine Learning-Based Smart Prediction of Future Suitability of Maize in Northern Regions of Turkey Under CMIP6 Climate Scenarios
    (Springer Int Publ Ag, 2026) Kilic, Mirac; Birol, Murat; Gunal, Hikmet; Bayrakli, Betul; Kilic, Orhan Mete; Tariq, Aqil
    This study examines the effects of climate change on maize (Zea mays L.) cultivation in Turkey's Bart & imath;n and Zonguldak provinces. The objective was to assess current and future land suitability for maize under changing climatic conditions to support adaptive agricultural planning. We developed an integrated approach combining Coupled Model Intercomparison Project Phase 6 (CMIP6) climate model data with a fuzzy logic system to incorporate expert knowledge and manage environmental uncertainties. Environmental variables included climate parameters, soil properties, and topographic factors. Two machine learning (ML) algorithms, Random Forest and LightGBM, were employed to analyze land suitability for three periods: current (1981-2023), mid-century (2040-2070), and late-century (2070-2100). Current climate analysis reveals that approximately 50% of the study region is moderately suitable for maize cultivation, with less than 1% classified as highly suitable. Under the CMIP6 SSP5-8.5 scenario, projections indicate marginally suitable areas will expand by mid-century due to rising temperatures and altered rainfall patterns. By late-century, a dramatic shift is expected, with approximately 71% of the area becoming highly suitable, demonstrating significant changes in cultivation potential. Random Forest outperformed LightGBM, achieving 97.4% accuracy compared to 82.9%, with superior precision, recall, and F1-scores across all suitability classes. This research provides critical insights for developing adaptive agricultural strategies in response to climate change. The integration of ML techniques with high-resolution climate models offers robust predictions essential for policymakers and land managers to optimize agricultural productivity and ensure food security.
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    Mapping and modelling land degradation vulnerability in a semi-arid region: a case study from Battalgazi District, Turkiye
    (Peerj Inc, 2026) Kilic, Mirac
    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.
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    Predicting soil hydraulic conductivity using stacked deep neural networks: Long-term tillage impacts on a Vertisol in the Eastern Mediterranean
    (Elsevier, 2026) Celik, Ismail; Kahraman, Omer Faruk; Kilic, Mirac; Gunal, Hikmet
    Accurate prediction of soil hydraulic conductivity (Ks) is crucial for understanding water movement and improving soil management, particularly under diverse tillage systems. The objective of this study was to develop and validate a Stacked Deep Neural Network (Stacked DNN) model for predicting Ks using easily measurable soil physical and hydro physical properties under long term tillage practices. Soil samples were collected from 0-15 cm and 15-30 cm depths across conventional tillage (CT), no-tillage (NT), reduced tillage (RT), and strategic tillage (ST). The Ks values at 15 cm depth were measured using a Guelph permeameter at 5 cm and 10 cm water heads, ranged from 0.024 to 1.101 cm/h for the 0-10 cm soil depth. The average values from these measurements were used for model training and validation. Predicted Ks values for 0-15 cm and 15-30 cm depths varied significantly among tillage systems and depths, reflecting the effects of soil management on hydraulic behavior. Conventional tillage with residue burning (CT2) recorded the highest predicted Ks at 0.339 cm h-1 (0-15 cm) and 0.091 cm h-1 (15-30 cm). In contrast, no-tillage (NT) exhibited the lowest values, averaging 0.081 cm h-1 and 0.128 cm h-1 at respective depths, likely due to surface compaction. Reduced tillage (RT2) demonstrated balanced performance, with predicted values of 0.484 cm h-1 (0-15 cm) and 0.186 cm h-1 (15-30 cm), suggesting enhanced water infiltration compared to other reduced tillage methods. The Stacked DNN model achieved superior predictive accuracy, with an R2 of 0.986 and an RMSE of 0.029 cm h-1, outperforming individual machine learning models such as Random Forest (R2 = 0.902) and XGBoost (R2 = 0.917). Bulk density, macroporosity, and mean weight diameter were identified as critical variables influencing Ks predictions, highlighting the model's ability to capture complex nonlinear relationships. These findings emphasize the effectiveness of ensemble machine learning approaches in modeling Ks and the significant influence of tillage practices on soil hydraulic properties. Moreover, the ability to accurately predict Ks has substantial practical implications for sustainable agriculture, as it enables the optimization of irrigation practices, informed soil management, and improved water resource conservation to support long-term crop productivity and environmental stewardship.
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    Soil organic carbon-to-clay ratio as a proxy for land degradation: machine learning-based spatial prediction in semi-arid agricultural lands
    (Cambridge Univ Press, 2025) Kilic, Mirac; Ozturk, Mustafa; Gunal, Hikmet; Zhang, Yakun; Budak, Mesut
    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 & Scedil;anl & imath;urfa province, T & uuml;rkiye, 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 (R2 = 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.
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    Tree-based algorithms for spatial modeling of soil particle distribution in arid and semi-arid region
    (Springer, 2024) Abakay, Osman; Kilic, Mirac; Gunal, Hikmet; Kilic, Orhan Mete
    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.