İsmail Çelik’s research while affiliated with Cukurova University and other places

Bu çalışma, Harran Ovası'nda küçük bir tarla ölçeğinde (12 dekar) yüzey ve yüzey altı toprakların fiziksel özelliklerinin mekânsal dağılımını modellemek, örneklenmeyen noktalardaki değerleri tahmin etmek ve haritalandırmak amacı ile yapılmıştır. Modelleme, tahmin ve haritalamada jeoistatistiksel yöntemler ve haritalama teknikleri kullanılmıştır. Aynı zamanda bir deneme alanı olan çalışma alanında 54 ayrı noktada 0-10 cm ve 10-20 cm derinliklerden bozulmuş ve bozulmamış toprak örnekleri alınmıştır. Toprak örneklerinin tekstürü, agregat stabilitesi, hacim ağırlığı, yarayışlı su içeriği (YSİ), su dolu gözenek hacmi (SDGH) ve toplam gözenekliliği belirlenmiştir. Çalışma alanı topraklarının kil içerikleri % 52 ile % 69 arasında değişmektedir. En yüksek kil içeriği, çalışma alanının batı bölümünde ve yer yer kuzey sınırında görülmektedir. İlk 20 cm derinlikte agregat stabilitesi değerleri % 11 ile % 50 arasında, hacim ağırlığı değerleri 1.08 g cm-3 ile 1.48 g cm-3 arasında ve YSİ değerleri ise % 3.8 ile % 13.6 arasında değişmektedir. Çalışma alanının güney-batı bölümünde SDGH değerleri % 60'ın altında iken geri kalan alanın nerede ise tamamında %60'ın üzerindedir. Hem yüzey hem de yüzey altı topraklarında kil ve silt içeriği, hacim ağırlığı, tarla kapasitesi nem içeriği ve SDGH'ine ait değişkenlik (VK<%15) oldukça düşüktür. Bu durum, bu özelliklerin çalışma alanı genelinde nispeten homojen olduğunu göstermektedir. Hem 0-10 cm hem de 10-20 cm derinliklerde toplam gözeneklilik ve mikro gözeneklilik VK değerleri oldukça düşüktür. Bu durum, çalışma alanı topraklarının su ve besin elementlerini tutma ve depolama yeteneğinin her iki derinlikte de homojen olduğunu gösterir. Elde edilen bilgiler, tarla yönetimi ve toprak koruma uygulamalarının optimize edilmesine ve tarımsal üretimin sürdürülebilirliğine katkıda bulunacaktır. Abstract This study aimed to model the spatial distribution of surface and subsurface soil physical properties at a small field scale (12 hectares) in the Harran Plain, to estimate and map values at unsampled points. Geostatistical methods and mapping techniques were used for modeling, estimation, and mapping. Soil samples were collected at 54 separate points in the study area, representing disturbed and undisturbed samples from depths of 0-10 cm and 10-20 cm. Soil samples were analyzed for texture, aggregate stability, bulk density, available water content (AWC), water filled pore space (WFPS), and total porosity. The clay content of the study area soils ranged from 52 % to 69%, with the highest clay content observed in the western part and sporadically along the northern boundary. Aggregate stability values ranged from 11 % to 50 % in the top 20 cm depth, bulk density values ranged from 1.08 g cm-3 to 1.48 g cm-3 , and AWC values ranged from 3.8 % to 13.6 %. While WFPS values in the southwest part of the study area were below 60%, they were above 60% in almost all other areas. Both surface and subsurface soils exhibited relatively low variability (CV<15 %) in clay and silt content, bulk density, field capacity moisture content, and WFPS, indicating relative homogeneity across the study area for these properties. Total porosity and microporosity CV values were also low at both 0-10 cm and 10-20 cm depths, indicating homogeneity in the water and nutrient retention and storage capacity of the study area soils at both depths. The information obtained will contribute to the optimization of field management and soil conservation practices, enhancing the sustainability of agricultural production.

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 km 2 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.

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<0.01). The wetlands had the highest SOCS (61.46 Mg C ha −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 estima-tors. 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.

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 difered with land use and land cover. Vegetation, soil, and moisture indices were calculated using Sentinel 2 Multispectral Sensor Instrument (MSI) data. Signifcant 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 signifcantly diferent. The land cover has a signifcant efect on SOC in Yuksekova plain. The mean SOCS for continuously ponded felds was 45.58 Mg C ha−1, which was signifcantly diferent from the mean SOCS of arable lands. The mean SOCS in arable lands, with signifcant areas of natural vegetation, was 50.22 Mg C ha−1 and this amount was signifcantly higher from the SOCS of other land covers (p<0.01). The wetlands had the highest SOCS (61.46 Mg C ha−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 signifcant efect 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.

The differences in land use can potentially affect soil quality. In this respect, this study sought to assess the long-term effect of horticulture and arable lands on the soil quality of the clayey Arıklı soils (Entic Chromoxerert). Physical, chemical, biological and fertility indicators of the soil were analyzed to determine the soil quality index (SQI) using the Fuzzy Analytical Hierarchy Process (FAHP) and Geographical Information System (GIS) for a 225 ha area of the Arıklı soil series. The physical quality received the highest weight in the study. The highest weights in physical, chemical, biological and fertility quality were obtained in AWC, pH, OC and P indicators, respectively. SQI in arable lands ranged from 38.60 to 68.91%, with a mean of 51.77%. Compared to arable, SQI in horticulture varied within a narrower range and had a higher mean SQI (59.87%). In this study, 74.2% of the area had a low SQI and 25.8% had medium SQI. Conventional soil tillage and burning of stubble residues in the arable lands had a statistically negative effect on SQI. Compared to arable, in horticulture BD, OC, AWC, MBC, BGEA and K had better scores thus improving SQI. The results concluded that conventional soil tillage practice should be abandoned and reduced or no-till practices should be adopted.

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. Total carbon stock in study area was calculated at 10-cm vertical resolution in 0 to 30 cm depth for 50 sampling locations. Vegetation, soil and moisture indices were calculated using Sentinel 2 Multispectral Sensor Instrument (MSI) data. Significant correlations were obtained between the indices and SOCS, thus, the remote sensing indices 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 lover classes were significantly different. The wetlands had the highest SOCS (61.46 Mg C ha − 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 have significant effect on SOCS which has high spatial variation in the study area. Reliable spatial SOCS information was obtained with the combination of Sentinel-2 guided multi-index remote sensing modeling strategy and the GBDT model. Therefore, the spatial estimation of SOCS can be successfully carried out with up-to-date machine learning algorithms only using remote sensing data. 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 .

Organic farming increases soil sustainability and crop production. Demands for healthy food and a sustainable environment have increased interest in alternative conservative production systems and led to the introduction of the concept of organic farming. Plants are continually dealing with increasingly evolving and potentially harmful external environmental factors. Organic farming is very important to protect the environment, minimize soil degradation and erosion, reduce pollution, optimize biological productivity, and promote a healthy state of health. Soil quality and fertility are a concern today to boost the sustainability of our agricultural system. Soil fertility is more important than the supply of macro and micronutrients to plants in organic farming systems (OFSs). Effective management of fertility takes into account plant, soil organic matter (SOM), and soil biology. This chapter focuses on: (i) impact of organic farming on physical properties of soils; (ii) status of organic farming; (iii) impact of organic farming on plant growth and yield.