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    Effects of long-term conventional and conservational tillage systems on biochemical soil health indicators in the Mediterranean region
    (Taylor & Francis Ltd, 2022) Acir, Nurullah; Gunal, Hikmet; Celik, Ismail; Barut, Zeliha Bereket; Budak, Mesut; Kilic, Seref
    Improved soil health is essential to sustain agricultural production. Therefore, understanding the effects of management on soil health is crucial to implement new agricultural practices. This study aimed to assess the effects of long-term tillage systems on biochemical indicators of a Typic Haploxerert soil under winter wheat-soybean-corn rotation in the Mediterranean region of Turkey. The experiment consisted of two conventional (CT), three reduced (RT), no-tillage (NT), and a strategic tillage practice. The biochemical indicators were total nitrogen (TN), total carbon (TC), soil organic carbon (SOC), microbial biomass carbon (MBC), potential mineralizable nitrogen (PMN), microbial quotient (qM), beta-glucosidase enzyme activity (BGA), and carbon sequestration (Cs) potential. The SOC significantly decreased with the increased tillage intensity, while the tillage had a little effect on PMN, with its highest concentration (78.2 mg kg(-1)) occurring in the NT. The qM was the only indicator found to be higher under CT than RT and similar to the NT. The BGA peaked in NT which was 460.2 and 536.3% higher than that of the CT. The results showed that SOC, MBC, PMN, BGA and Cs were enhanced with the NT and RT systems which favor sustainability of agricultural production.
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    Evaluating the long-term effects of tillage systems on soil structural quality using visual assessment and classical methods
    (Wiley, 2020) Celik, Ismail; Gunal, Hikmet; Acar, Mert; Acir, Nurullah; Barut, Zeliha Bereket; Budak, Mesut
    Current agricultural practices and their impacts on the sustainability of crop production can be evaluated by simple and reliable soil structure assessment tools. The study was conducted to determine the effects of long-term (2006-2017) tillage systems on structural quality of a clayey soil using the visual evaluation of soil structure (VESS) and classical field and laboratory measurements. A field experiment with seven tillage systems, representing both traditional and conservation tillage methods, was conducted on a clayey soil in the Cukurova region, Turkey. Soil samples from 0-10, 10-20 and 20-25 cm depths were analysed for mean weight diameter (MWD), porosity and organic carbon. Penetration resistance (PR) was determined in each treatment plot. The VESS scores (<2) of upper 0-5 cm indicated a good structural quality for all tillage systems. The VESS scores were positively related to PR and MWD and negatively to macroporosity (MaP) and total porosity. In reduced and no-till systems, poorer soil structures were observed in subsurface layers where firm platy and angular blocky structures were defined. Mean VESS score (3.29) in 20-25 cm depth where PR was 3.01 MPa under no-till indicated a deterioration of soil structural quality; thus, immediate physical interventions would be needed. Lower VESS scores and PR values under strategic tillage which was created by ploughing half of no-till plots in November 2015 indicated successful correction of compaction caused by long-term no-till. The results suggest that the VESS approach is sensitive and useful in distinguishing compacted layers within the topsoil.
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    Improvement of spatial estimation for soil organic carbon stocks in Yuksekova plain using Sentinel 2 imagery and gradient descent-boosted regression tree
    (Springer Heidelberg, 2023) Budak, Mesut; Gunal, Elif; Kilic, Mirac; Celik, Ismail; Sirri, Mesut; Acir, Nurullah
    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 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.
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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.