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Long-term intensive tea cultivation is suspected of deteriorating soil quality status and degrading land sustainability. This study aimed to determine the soil quality index of soils in a micro-catchment in Rize Province, Turkey, used for long-term intensive tea cultivation, by means of spatial multi-criteria analysis (SMCA) and standard scoring function (SSF) integrated with geographical information system (GIS) and geostatistics, considering bio-physical-chemical properties of a detailed soil dataset. Soil samples (102) were collected from the surface layer (0–20 cm). In the soil quality index for tea-cultivated soils (TSQI), soil indicators were weighted by an analytical hierarchy. Various indicator units were normalized with the SSF. The TSQI model was divided into five main criteria: (i) physical properties, (ii) chemical properties, (iii) fertility, (iv) biological indicators and (v) soil erosion susceptibility parameters. Principal components analysis (PCA) was applied and minimum dataset (MDS) created to determine the most effective indicators. The spatial distribution pattern of the tea total dataset soil quality index (TSQITDS) and tea minimum dataset soil quality index (TSQIMDS) values were statistically similar. TSQITDS low and very low-class areas accounted for 34.1% of the total area, while TSQIMDS low and very low-class areas constituted 33.6%. These areas, especially those with low soil quality properties, were in the northern and north-western parts of the micro-catchment. TSQITDS very high and high-class areas accounted for 56.2% of the total area, while TSQIMDS very high and high-class areas were found in 55.3% of the total area. These areas are located in the south of the micro-catchment. [ABSTRACT FROM AUTHOR]

Soil formation is highly associated with topographic position and parent material, both of which play roles in the morphological, mineralogical and weathering processes of soils, particularly on the local level. Landscape topography conducts soil formation through affecting biogeochemical process and regulating soil hydrological process in the surface part of the earth, while parent material has a considerable effect on the types and concentrations of elements found in soil. This study aimed to determine how pedogenic and other factors affect soil classification by identifying the geochemical characteristics of soils classified as vertisols, inceptisols and entisols located on different topographies and possessing different parent material. Following macro-morphological identification of six typical soil pedons, samples were taken from different soil layer and their geochemical, mineralogical and other characteristics were analyzed. These characteristics were also evaluated for possible use in identifying soil maturity stages and periods of late-quaternary soil formation. The pedogenic evolution of soils was also examined using genetic ratios, which combine major oxides and selective dissolution analysis (Feo-Fed) values into a single metric. All pedons had Si/Al ratios above 2, indicating arid, sub-arid, sub-humid or high precipitation with cold areas. In addition, Fed (dithionite extractable iron) values were higher than Feo (oxalate extractable iron) and lower Fet (total Fe oxides) values, ranging between 0.007 % and 0.031 %. Therefore, the results clearly showed that local soil geochemical, mineralogical and morphological characteristics in the research area were strongly influenced by topographic conditions and parent material either directly or indirectly. [ABSTRACT FROM AUTHOR]

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In this study, two manufacturing systems, a kanban-controlled system and a multi-stage, multi-server production line in a diamond tool production system, are optimized utilizing neural network metamodels (ts•NNM) trained via tabu search (TS) which was developed previously by the authors. The most widely used training algorithm for neural networks has been back propagation which is based on a gradient technique that requires significant computational effort. To deal with the major shortcomings of back propagation (BP) such as the tendency to converge to a local optimal and a slow convergence rate, the TS metaheuristic method is used for the training of artificial neural networks to improve the performance of the metamodelling approach. The metamodels are analysed based on their ability to predict simulation results versus traditional neural network metamodels that have been trained by BP algorithm (bp•NNM). Computational results show that ts•NNM is superior to bp•NNM for both of the manufacturing systems. [ABSTRACT FROM AUTHOR]

In today's era artificial intelligence is quite popular, one of the most effective algorithms used is Artificial Neural Networks (ANN). In this study, the determination of soil quality using the Soil Management Assessment Framework (SMAF) model in areas where tea cultivation is carried out at the micro-watershed scale and the predictability of soil quality using ANN were evaluated. According to the results, the soil quality indices of tea-growing areas were generally classified as "medium" between 55 and 70 %. Among the evaluated features for determining soil quality, the highest relative importance value was for soil organic carbon content (13 %) and potential mineralizable nitrogen (13 %), whereas the lowest values were for exchangeable potassium (4 %) and sodium adsorption ratio (SAR) (4 %). In addition, when comparing the actual and predicted values for soil quality prediction using ANN, the Lin's concordance correlation coefficient (LCCC), ratio of performance to deviation (RPD), and R 2 values were found to be 0.93, 2.95, and 0.89, respectively. Significant properties for the determined values within a 90 % predicted interval were found to be organic matter, microbial biomass carbon, bulk density, and aggregate stability of the soils. Moreover, the uncertainty values (standard deviation) in the model predictions were determined to be within the range of 1.01-4.56 %. Consequently, the Soil Quality Index (SQI) obtained from the SMAF model using 12 soil properties in tea-growing areas could be accurately predicted using ANN. As a result of this study, digital maps showing the spatial distribution of SQI and the predicted uncertainties can help monitor SQI levels in this area., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Today, data mining has become a relevant topic in digital soil mapping. In this current study, prediction of some soil properties and their spatial distribution were examined by machine learning algorithms (Support Vector Machine, Artificial Neural Network) using reflectance values of Triplesat satellite image bands in Vezirköprü district of Samsun province. The band data obtained from different wavelengths revealed positive correlations between the electrical conductivity and calcium carbonate equivalent contents of the soils. The support vector machine algorithm was the most successful to estimate the textural fractions, organic matter, electrical conductivity, and calcium carbonate equivalent contents of the soils using the bands obtained from satellite images. The mean absolute error for estimating sand, silt, and clay contents by support vector machine was 4.05%, 3.05%, and 3.66%, respectively. Texture classes were determined with an accuracy of 82% with support vector machine and 60% with artificial neural network. In all estimations, the highest percentage of error was for calcium carbonate equivalent content with very low estimation reliability. The mean absolute percentage of error values for this property are 101.13% and 51.61% for artificial neural network and support vector machine, respectively. Also, in both algorithms, the most successfully estimated soil property was clay fraction of soils. It was also investigated the spatial distribution of actual and estimated values using various interpolation methods (Kriging, inverse distance weighting-, radial basis function). Considering the spatial distributions, it was determined that the most successful method was kriging for sand, silt, and clay contents and inverse distance weighting for electrical conductivity, calcium carbonate equivalent, and organic matter contents. According to our findings, it is concluded that successful estimations and spatial distributions can be made by the support vector machine algorithm using band data from different wavelengths., (© 2023. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

Developing land suitability models for strategically critical agricultural products to expand sustainable agricultural policies and sensitive agriculture management has become a significant trend. This study aims to improve a unique land suitability model for hazelnut cultivation by applying the criteria set (7 main criteria, 35 sub-criteria) including qualitative and quantitative reasons, integrated fuzzy analytic hierarchy process, inverse distance weighting, multi-criteria decision analysis, geographic information system, and weighted linear combination approaches. The model developed in the present study was applied and tested in Ünye District of Ordu Province, where hazelnut production in the Eastern Black Sea region of Turkey is an important economic activity. While 71.17% of the study area is classified as very highly suitable, highly suitable, and moderately suitable, 28.83% of the study area has marginally suitable and unsuitable properties for hazelnut cultivation. Generally, it was determined that the coastal parts of the study area were the most suitable areas for hazelnut growing. The hazelnut land suitability model's two main criteria impacting the final score values are climatic and topographic conditions, respectively. Heavy metal pollution and physical, chemical, and fertility conditions related to soil properties followed these, respectively. The first ten sub-criteria with the highest weight value were determined as elevation, annual average temperature, annual average precipitation, aspect, annual average relative humidity, nickel (pollution), slope, annual average maximum temperature, lead (pollution), and soil depth, respectively. Existing hazelnut cultivation areas were used to test the model. Of the existing cultivation areas, 75.59% coincided with the very highly suitable, highly suitable, and moderately suitable classes presented in this study, while 17.15% were in marginally suitable and 7.26% in unsuitable classes. The study results reveal that the hazelnut land suitability model developed is suitable in mild climate conditions. Using this model as a general transition model will be beneficial to test it in areas containing similar climatic conditions and various soil properties. This study will create a rational background in ensuring the sustainable food production system and security, agricultural land use planning, strategic planning and management of the hazelnut plant, increasing agricultural productivity and income, and the ecosystem., (© 2021. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Anthropogenic activities, in addition to climate change caused the drying of Urmia Lake in Iran, since 2005. Dust storms blown from the dried lakebed have created serious environmental hazards in adjacent areas. These crises would jeopardise achieving United Nations Sustainable Development Goals (UN SDGs) and emphasise the need for evaluating the spatial distribution of soil enrichment of potentially toxic elements (PTEs) (As, Cr, Cu, Ni, Pb and Zn). Conventional assessment would require a costly sampling method to map potentially polluted areas. Digital soil mapping (DSM) has proved to be a cost-efficient method for soil mapping, however its application in mapping enrichment of PTEs in soil is still lacking. This study aims to map and project the potential pollution of PTEs in the Urmia Lake area using digital mapping techniques and Landsat-8 OLI satellite images. A total of 129 surficial soil samples were collected as ground control. Enrichment factors (EFs) of PTEs and the Modified Pollution Index (MPI) were spatially predicted using two machine learning models. Covariates were derived from a suite of Landsat-8 spectral indices. The bootstrapping method was used to analyse the uncertainties. The results showed that Random Forests performed well in estimating EFs of several PTEs. Spectral indices using NIR and SWIR bands were key to predict these PTEs and MPI. The digital maps demonstrated that the study area was enriched with As, Cu and Pb at moderate to significant levels. Regions under the lower ecological level (elevation <-1274 m) had significantly larger enrichment than those of higher elevation. Based on MPI, 43% of the area was categorised as moderately polluted, and 31% of the area was moderately-heavily polluted. Possible sources of PTEs were discharges from farmlands, landfills, and industries. Our results revealed that the Urmia Lake desiccating has caused severe environmental challenges and needs immediate restoration., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2021 Elsevier B.V. All rights reserved.)

The main purpose of this study is to identify suitable potential areas for agricultural activities in the semi-arid terrestrial ecosystem in the Central Anatolia Region. MCDA was performed in fuzzy environment integrated with GIS techniques and different geostatistical interpolation models, which was chosen as the basis for the present study. A total of nine criteria were used, as four terrain properties and five soil features to identify potential sites suitable for agriculture lands in Central Anatolia which covers approximately 195,012.7 km 2 . In order to assign weighting value for each criterion, FAHP approach was used to make sufficiently sensitive levels of importance of the criteria. DEM with 10 m pixel resolution used to determine the height and slope characteristics, digital geology and soil maps, CORINE land use/land cover, long-term meteorological data, and 4517 soil samples taken from the study area were used. It was identified that approximately 30.7% of the total area (59,921.8 ha) is very suitable and suitable for potential agriculture activities on S1 and S2 levels, 42.7% of the area is not suitable for agricultural uses, and only 27% of the area is marginally suitable for agricultural activities. Besides, it was identified that 34.8% of the area is slightly suitable.

The aim of this study was to produce a land productivity dynamic map of a degraded catchment located in dryland ecosystem via a land degradation assessment using three indicators, namely land use, land productivity, and soil organic carbon density. The study was conducted in the Mogan Catchment, Turkey, between 2000 and 2010. The study embraced the current trend for assessing ecosystem services over wide areas. For this purpose, satellite images were used to determine changes in land use and vegetation density. In addition, a total of 834 soil samples were collected from the surface soil in 2000 and 2010 to assess the soil organic carbon density. In more than 37% of the catchment area of approx. 37,100 ha, land productivity had declined, while about 43% of the catchment showed early signs of decline. Analysis of long-term changes and the conversion of levels of vegetative or standing biomass into land productivity dynamics (LPD) is only the first step. Current land management practices are contributing to serious, widespread land degradation, with only a very small area of the catchment showing a stable or increasing LPD for the period from 2000 to 2010. The implementation of land management policies and practices in order to achieve sustainable land management are urgently required.

The aim of this study is to analyze the similarity of soils formed on limestone/marl alluvial parent material and different topography using some physical and chemical properties via cluster analysis (CA) and multidimensional scaling analysis (MDSA). Physical and chemical soil properties included in this study are texture, CaCO3, organic matter, pH, electrical conductivity, cation exchange capacity, and available water content. The study was carried out in Çetinkaya region located on Bafra Deltaic Plain. The study area has two main physiographic units. The first one is the flat or gently slope alluvial lands (0-2 %), and the other one involves hills with slopes ranging from middle to steep (3-20 %). The soil in the study area is mainly classified as entisol, inceptisol and vertisol. According to the CA results, while C horizons of the soils formed on alluvial deposits (typic ustifluvent and typic ustipsamment) bear similarity, Ap horizons of the soils formed on lime/marl parent material (vertic ustorhent, vertic calciustept, and calci haplustert) appear in the same group. Additionally, in order to support CA, MDSA was performed. Significant correlations were observed between the results of both analyses.

This study, conducted in 2011, researches the effects of different tillage practices on the physical soil quality of clayey soil. This soil quality index (SQI) assessment was made by studying the changes in physical soil functions such as suitability for root development, facilitation for water entry, movement and storage, and resistance against surface degradation based on tillage management. When compared with the control parcel, statistically significant decreases were seen in the SQI with different tillage practices (p < 0.05). Among the tillage practices, the highest SQI was seen with the plow + rotary tiller + direct seeding machine, while the lowest SQI was seen with the direct drilling practice. On the other hand, the statistically insignificant effects of tillage practices on the soil quality of the study area were considered to be a result of either the study period or the joint effect of soil texture and climatic features. Thus, long-term tillage practices were recommended in order to get healthier information about soil quality by considering soil and climatic conditions. In addition, for heavy clayey soils, reduced tillage practices, which included plowing, were thought to develop physical soil qualities of root development and water movement.

The Salt Lake Specially Protected Area is a unique ecosystem for both agricultural activities and natural life in Turkey. In the present study, an attempt was made to develop a conceptual land use strategy and methodology, taking into account ecological factors for regional development in the Salt Lake Specially Protected Area. A detailed Geographic Information System (GIS) analysis was done to create a comprehensive database including land use, land suitability, and environmental factors (soil, climate, water quality, fertilizing status, and heavy metal and pesticide pollution). The results of the land suitability survey for agricultural use showed that, while 62.6% of the study area soils were classified as best and relatively good, about 15% were classified as problematic and restricted lands, only 22.2% of the study area soils were not suitable for agricultural uses. However, this is not enough to derive maximum benefit with minimum degradation. Therefore, environmental factors and ecological conditions were combined to support this aim and to protect the ecosystem. Excessive irrigation practices, fertilizer and pesticide application, and incorrect management practices all accelerate salinization and degradation. In addition to this, it was found that a multi-layer GIS analysis made it easy to develop a framework for optimum land use and could increase the production yield preserving the environmental conditions. Finally, alternative management and crop patterns were undertaken to sustain this unique ecosystem, considering water, soil, climate, land use characteristics, and to provide guidance for planners or decision makers.

The main objective of the study was to find out the effects of various organic matter sources such as bio-solid (BS) and tea waste (TW) on macro nutrient content of eroded soils. In order to determine different soil erodibility levels (slightly, moderately and severely), erosion ratio (ER) and soil erodibility factor (K) parameters were used. Soil samples used in this research were taken from bulk surface (0-20 cm depth) located on agricultural lands of Asagi Aksu village that is 20 km far from Samsun province in the north part of Turkey. These lands have been used as agricultural activity. Some properties of the soil classified as Vertic calciudoll were determined as follows; fine in texture, organic matter content varies between 0.83% and 0.90%. In addition, pH and EC values of these soils are 8.0-8.1 and 0.64-0.79 dSm( - 1) respectively. This study was conducted by applying four different doses of BS and TW (0%, 2%, 4% and 6%) in eroded soils under greenhouse condition. Each treatment was replicated three times in a split block design. After 18 weeks incubation period associated parameters were determined in all pots. According to analysis results, it was found that while BS treatments increased total N, available P and exchangeable K content of all eroded soils comparing to control treatment, effectiveness of TW on P is very low in slightly and moderately erosion levels. BS and TW applications increased K content in soils. However, it was determined not significant statistically between effectiveness of these various organic residues whereas, N and P values significantly correlated with BS and TW (P < 0.05). In addition, change of N, P and K values depending on the application doses and soil erosion levels were found statistically significant (P < 0.001).

The objective of this research was to assess vulnerable soil erosion risk with qualitative approach using GIS in Ankara-Guvenc Basin. The study area is located about 44 km north of Ankara and covers 17.5 km2. The selected theme layers of this model include topographic factor, soil factors (depth, texture, impermeable horizon) and land use. Slope layer and land use-land cover data were prepared by using DEM and Landsat-TM satellite image. According to land use classification, the most common land use type and land cover are rangeland (50.5%) then, rainfed (36.4%), week forest land (3.2%), irrigated land (0.7%) and other various lands (rock out crop and lake) (9.2%). Each land characteristic is also considered as a thematic layer in geographical information systems (GIS) process. After combination of the layers, soil erosion risk map was produced. The results showed that 44.4% of the study area is at high soil erosion risk, whereas 42% of the study area is insignificantly and slightly susceptible to erosion risk. In addition, it was found that only 12.6% of the total area is moderately susceptible to erosion risk. Furthermore, conservation land management measures were also suggested for moderate, high and very high erosion risk areas in Ankara-Guvenc Basin.

Land quality assessment is of great important for decisions on sustainable land uses and the conservation of ecosystems of high biodiversity value. The main objective of this study was to determine properties of land quality of specially protected area of the Salt Lake using CORINE land quality assessment methodology and to propose a revision for this model. In addition to field study and laboratory analysis, digital soil map, digital elevation model, satellite image and climatic data were used and analyzed with remote sensing and geographic information systems techniques to generate actual land quality map and to form database for study area. Results show that 42.1% of the study area has high quality, 28.7% was classified as moderate quality and 4.0% was determined as low quality. On the other hand, some places classified as moderate quality, were not accurately classified. These places should be low land quality due to content of high salt concentration. Therefore, to resolve this insufficient situation, some soil parameters such as chemical properties should be collaborated with this model. In this case, moderate land quality level of the study area decreased from 28.7 to 12.3%, whereas 4.0% of low quality land of the study area increased to 20.8%. In addition, this research was also carried out to provide guidance for planner or decision makers about specially protected area of the Salt Lake.

Natural forestland soils in the high land mountain ecosystems on the eastern Black sea region of Turkey are being seriously degraded and destructed due to intensive agricultural practices. In this study we examined four soil profiles selected from four sites in each of three adjacent land use types which are native forest, pasture and cultivated fields with corn and hazelnut to compare the soil physical, chemical and morphological properties modified after natural forestland transformation into cultivated land. Disturbed and undisturbed soil samples were collected from four sites. The effects of agricultural practices on soil properties taken from each three adjacent land use types were most clearly detected in the past 50 years with the land use change. Land use change and subsequent tillage practices resulted in significant decreases in organic matter, total porosity, total nitrogen and reduced soil aggregates stability. However, contents of available P were improved by application of phosphorous fertilizers in cultivated system. There was also a significant change in bulk density among cultivated, pasture and natural forest soils. Depending upon the increase in bulk density and disruption of pores by cultivation, total porosity decreased accordingly. The data show that long term continuous cultivation of the natural forest soils resulted in changes in physical and chemical characteristics of soils.