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    Analysis of spatial and temporal changes of RUSLE-K soil erodibility factor in semi-arid areas in two different periods by conditional simulation
    (Taylor & Francis Ltd, 2022) Baskan, Oguz
    Soil erosion is the most important soil degradation process threatening semi-arid and arid areas. In this study, the change in the RUSLE-K soil erodibility values due to changed climatic conditions over a 10-year period was used as a tool to investigate soil erosion potential with Sequential Gaussian Simulation (SGS) method in the Mogan catchment in Turkey. For this purpose, soil erodibility values were determined for soil samples taken from the same coordinates in 2000 and 2010, and erosion susceptibility distribution maps were produced. The results showed that even though land use practices remained unchanged, soil erodibility values changed spatially and temporally, with the relationship dependent on climatic factors. More specifically, the effects of decreased precipitation, exacerbated by increased evaporation and more prolonged dry periods, made some areas in the catchment more susceptible to erosion. The vulnerability soils in the catchment increased markedly, with the area classified as subject to 'very severe erodibility' increasing more than threefold, from 762 ha to 2477 ha. This study has reinforced the need for 'minimal disturbance' land use, supplemented by monitoring that incorporates meteorological and soil test data, to minimize soil erosion and maximize the sustainability of agricultural activities in semi-arid and arid areas.
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    Application of regression kriging and machine learning methods to estimate soil moisture constants in a semi-arid terrestrial area
    (Elsevier Sci Ltd, 2023) Tuncay, Tulay; Alaboz, Pelin; Dengiz, Orhan; Baskan, Oguz
    In the current study, the use of regression-kriging (RK), artificial neural networks (ANN), support vector machines (SVM), and random forest (RF) methods from machine learning algorithms, were used to estimate field capacity (FC), permanent wilting point (PWP), available water content (AWC) and their performance was compared. A data set obtained from 354 surface soil samples taken randomly, mostly from agricultural areas is used. The soil data set includes pH, EC, calcium carbonate equivalent (CaCO3 equivalent), particle size distribution, and bulk density (BD) values. The results showed that while FC showed a negative strong correlation (p < 0.001) with sand (r:-0.69), BD (r:-0.85), and silt (r:-0.47), it showed a positive strong correlation (p < 0.001) with C (r: 0.90). Similarly, PWP showed a negative strong correlation with (p < 0.001) sand (r:-0.73), BD (r:0.88), and silt (r:-0.42) but a positive strong correlation (p < 0.001) with C (r: 0.90). While AWC showed a negative strong correlation (p < 0.001) with sand (r:-0.61), BD (r:-0.76), it found a positive strong correlation (p < 0.001) with FC (r: 0.97), clay (r: 0.83), and PWP (r: 0.74). In the stepwise regression results showed that particle size were prominent as the most important factor in the regression equation created for FC, PWP and AWC. Moreover, FC is the most important factor to predict AWC. For the soil FC, ANN was best with excellent accuracy (RPD = 2.71), followed by SVM (2.42), RF (2.21) while RK was poor accuracy (1.10 and 1.04). Similarly, among the machine learning algorithms (RF and SVM), ANN obtained superiority by producing lower RRMSE (7.84%), RMSE (2.83%), MAE (2.37%), MAPE (7.45%), with the largest Lin's concordance correlation coefficient (LCCC) (0.961) compared to other methods. For PWP and AWC, ANN was the best algorithm with excellent and good accuracy RPD 3.17 and 1.95 respecively. In addition, other machine learning algorithms have been the same value range in terms of LCCC. Therefore, we recommend the ANN machine-learning algorithm is more favorable to predict FC, PWP and AWC than both RK and other machine learning methods.
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    Assessing soil fertility index based on remote sensing and gis techniques with field validation in a semiarid agricultural ecosystem
    (Academic Press Ltd- Elsevier Science Ltd, 2021) Tuncay, Tulay; Kilic, Seref; Dedeoglu, Mert; Dengiz, Orhan; Baskan, Oguz; Bayramin, Ilhami
    Among the greatest challenges of the arid and semiarid regions is the need for more crop production to meet the increasing demand of the growing population. This study aimed to compare SFI classes with both yield values and vegetation index values derived from satellite images. A total of 281 soil samples were taken at a 1-km resolution in order to quantify the spatial dynamics of soil physical, chemical and fertility indicators. Of the study area, 40.0% had very high fertile and high fertile soils, while 26.7% of the area had moderately fertile soils. Only about one-third of the total area had low and very low fertility. These results were validated using a 3-year yield values belong to parcels, and vegetation index derived from Sentinel 2A images. A strong relationship of SFI with yield (r2 = 0.88) and RE-OSAVI (r2 = 0.83) was found. Therefore, we suggested that SFI can be used to determine the sufficiency potential of soils for plant growing and management according to sustainable principles in similar ecologies provided that similar sample size should used.
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    Computational intelligence applied to the least limiting water range to estimate soil water content using GIS and geostatistical approaches in alluvial lands*
    (Wiley, 2021) Alaboz, Pelin; Baskan, Oguz; Dengiz, Orhan
    The use of machine learning methods in pedotransfer functions has attracted considerable attention in recent years. These methods are fast and effective in solving complex events. The least limiting water range (LLWR) feature is very important in terms of water uptake by the plant and root development in agricultural production. In this study, the predictability of the LLWR feature was investigated with artificial neural networks, deep learning (DL) and the k-nearest neighbour (k-NN) algorithm from machine learning methods. Estimated values obtained from the model with the best estimation accuracy and observed values were evaluated through a geostatistical method from which their spatial distribution maps were created. In the present study, which was carried out on alluvial lands with different soil properties, the LLWR values of soils vary between 5.5% and 25.9%. Field capacity, bulk density, clay, organic matter, and lime content properties, which have a high correlation with the LLWR, were taken into consideration in the estimation methods. DL was determined as the best estimation method (mean absolute error [MAE]: 0.94%; root mean square error [RMSE]: 1.45%; coefficient of determination [R-2]: 0.93), and the worst was k-NN (MAE: 2.00%; RMSE: 2.55%; R-2: 0.77) for the LLWR. In addition, the LLWR can be estimated with high accuracy by using ReLU and softmax functions in the DL method. The study shows that distribution maps created with LLWR values obtained by observed data and the DL method have a very similar pattern.
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    Physico-chemical and mineralogical changes of lithic xerorthent soils on volcanic rocks under semi-arid ecological conditions
    (Univ Nacional De Colombia, 2022) Demir, Sinan; Alaboz, Pelin; Dengiz, Orhan; Senol, Huseyin; Yilmaz, Kamil; Baskan, Oguz
    This study investigates the mineralogical changes and soil development processes of young soils formed on va-rious bedrocks of volcanic origin under the same land use/land cover and climate conditions. The current study was conducted in Lithic Xerorthent soils formed on tuff, trachybasalt, and trachyandesite bedrock between San-dikli-Suhut districts of Afyonkarahisar. The three soil profiles excavated in the study area were classified in Enti-sols order based on Soil taxonomy. The primary minerals, sanidine and muscovite, and the clay minerals, smec-tite, kaolinite, and illite, were widely determined in three soil profiles which were named Profile I (PI), Profile II (PII), and Profile III (PIII). According to the chemical alteration index (CIA) values, which indicate weathering, the soils formed on the tuff bedrock were slightly weathered (77.04%). The chemical weathering index (CIW) in the soils' surface horizons formed on the trachybasalt and trachyandesite bedrock are classified as non -weathe-ring rocks with 24.43% and 33.88%. Basic cations are found at high levels in the tuff bedrock. The determination of phillipsite, gismondin and calcite minerals is an indication that the mineral content of the bedrock and the bedrocks have a significant effect on soil formation. The relationship between the bedrock and the soil has been revealed. As a result of the study, it was concluded that there were significant differences in their physico-chemical characteristics, weathering rates, and mineralogical properties. However, they were characterized as young soils since they do not contain any subsurface diagnostic horizons on the volcanic bedrock under the same climatic and land use/land cover conditions.