Yazar "Sirri, Mesut" seçeneğine göre listele

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    Coleoptera Species Detected on Weeds in Hakkari/Yüksekova Basin
    (Kahramanmaras Sutcu Imam Univ Rektorlugu, 2024) Sirri, Mesut; Ozaslan, Cumali
    The adverse effects of chemical pesticides on human and environmental health have prompted scientists to seek alternative weed control methods. This study aims to identify natural enemies (insects) feeding on weeds that are prevalent in the Hakkari/Y & uuml;ksekova Basin, which boasts rich biological diversity. The survey was conducted in 2020 and 2021. A total of 56 insect species were identified on 19 weed species belonging to 10 different families. A significant portion of these species (26 species) belonged to the Coleoptera order, specifically the Curculionidae (22 species) and Chrysomelidae (4 species) families. Among the Curculionidae family in the region, it was determined that Lixus elegantulus Boheman, 1843, L . bardanae (J.C.Fabricius, 1787), L . scolopax , Larinus onopordi (Fabricius, 1787), L . minutus Gyllenhal, 1835 and Rhabdorrhynchus anchusae Chevrolat, 1854 species could suppress the growth and seed formation of host weeds. Host testing studies are needed for the use of such potential agents in the biological control of weeds.
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    Combining spatial autocorrelation with artificial intelligence models to estimate spatial distribution and risks of heavy metal pollution in agricultural soils
    (Springer, 2023) Gunal, Elif; Budak, Mesut; Kilic, Mirac; Cemek, Bilal; Sirri, Mesut
    Information on spatial distribution and potential sources of heavy metals in agricultural lands is very important for human health and food safety. In this study, pollution degree of lead (Pb), cadmium (Cd), and nickel (Ni) in Yuksekova Plain, located on the border in the southeastern part of Turkey, was evaluated by geoaccumulation index (Igeo), modified contamination factor (mCdeg), and Nemerow pollution index (PINemerow) combined with spatial autocorrelation using deep learning algorithms. A total of 304 soil samples were collected from two different depths (0-20 and 20-40 cm) in the study area, which covered 17.5 thousand ha land. Covariates were determined for spatial distribution models of Pb, Cd, and Ni by factor analysis (FA). Spatial distribution models for surface soils were developed using pedovariables (silt, sand, clay lime, organic matter, electrical conductivity, pH, Ca, and Na) determined by the FA and Igeo and mCdeg values by artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) models. The estimation success of models for different depths was assessed by root mean square error (RMSE), mean absolute percent error (MAPE), and Taylor diagrams. The RMSE and MAPE values showed a strong correlation between heavy metal contents and the covariates. The RMSE values of ANN-Ni0-20, ANN-Ni20-40, ANN-Pb0-20, ANN-Cd0-20, and ANN-Cd20-40 models (0.01240, 0.07257, 0.0039, 0.00045, 0.00044, and 0.04607, respectively) confirmed the success of the models. Likewise, the MAPE values between 0.2 and 8.5% indicated that all models were very good predictors. In addition, the Taylor diagrams showed that the estimation performance of ANFIS and ANN models are compatible. The Igeo(Ni) and Igeo(Pb) values in both models at both depths indicated that strongly to extremely polluted (4-5) areas are quite high in the study area, while the Igeo(Cd) values revealed that unpolluted areas are widespread. The mC(deg) index value showed a moderate to high contamination at the first depth, while very high contamination at the second depth in most of the study area. Spatial distribution of PINemerow revealed that moderate pollution (2-3) is common in both soil depths of the study area. The PINemerow of subsurface layer was between 0.91 and 1 (warning limit class) in a small part of the study area. The results showed that vertical mobility of heavy metals is closely related to pedovariables. In addition, the ANN and ANFIS models are capable of exhibiting the heterogeneity in the spatial distribution pattern of high variation in the data. Thus, the locations with extreme contamination have been accurately determined. The pollution indices calculated considering the commonly used international reference values revealed that heavy metal pollution in some part of the study area reached the detrimental levels for human health and food safety. The results suggested that the pollution indices were more successful than simple heavy metal concentrations in interpreting the pollution risk levels. High-resolution spatial information reported in this study can help policy makers and authorities to reduce heavy metal emissions of pollutants or, if possible, to eliminate the pollution.
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    Dicle Havzası Toprak Özelliklerinin Yersel Değişimlerinin Jeoistatistik ve Coğrafi Bilgi Sistemleri ile Belirlenmesi ve Haritalanması
    (Türkiye Tarımsal Araştırmalar Dergisi, 2018-03-12) Budak, Mesut; Günal, Hikmet; Çelik, İsmail; Acar, Nurullah; Sirri, Mesut
    Toprak özelliklerinin mesafeye bağlı değişkenliklerinin belirlenmesi, incelenmesi ve haritalanması yoğun tarımsal üretim yapılan arazilerde uygun amenajmanların geliştirilmesi ve üretimin sürdürülebilirliğinin sağlanması açısından son derece önemlidir. Bu çalışmada ülkemizin su rezervlerinin büyük bir kısmının yer aldığı, önemli sulama projelerinin gerçekleşmekte olduğu Dicle Havzası’nda bir kısım fiziksel ve kimyasal toprak özellikleri belirlenmiş, mesafeye bağlı değişkenlikleri modellenmiş ve haritalanmıştır. Toprak örneklemeleri, Diyarbakır ile Siirt illeri arasında 5 x 5 km gridlere ayrılmış 4.341 km2’lik alanda her gridin yaklaşık köşesinden toplam 175 noktada 0-20 cm derinlikten alınmıştır. Toprak özelliklerinin 5 km’den kısa mesafelerdeki değişimlerinin daha doğru tahmin edilebilmesi amacıyla ardışık iki gridin köşe noktaları arasında 250 m, 750 m ve 1750 m mesafelerden de 33 toprak örneği alınmıştır. Alınan bozulmuş örneklerin tekstür (kum, kil ve silt), organik madde, kireç, toprak reaksiyonu, elektriksel iletkenlik, alınabilir fosfor ve potasyum analizleri yapılmıştır. Mesafeye bağlı değişkenliğin modellenmesi ile örneklenme yapılmayan noktaların ilgili özellikleri tahmin edilmiş ve yersel değişim haritaları oluşturulmuştur. Çalışma alanında en düşük değişkenliğin pH (% VK= 3.9) ve en yüksek değişkenliğin ise alınabilir fosfor (% VK= 137.77) konsantrasyonunda olduğu görülmüştür. En yüksek range değerine sahip toprak özelliği elektriksel iletkenlik (135.4 km) iken en küçük range değeri pH (4.74 km) için elde edilmiştir. Her bir özelliğin en düşük ve en yüksek olduğu yerlerin rahatlıkla tespit edilebildiği toprak haritaları, uygun amenajman yöntemlerinin belirlenmesi, sorunların giderilmesi ve girdilerin en uygun kullanımı açısından son derece yararlı araçlardır.
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    Impact of climate change on habitat suitability of an endemic herbivore [Hydrothassa anatolica (Chrysomelidae: Chrysomelinae)] in Türkiye
    (Akademiai Kiado Zrt, 2024) Sirri, Mesut; Bal, Neslihan; Farooq, Shahid; Ozaslan, Cumali
    The rich floristic and faunistic diversity of T & uuml;rkiye hosts numerous endemic species, particularly in the southeastern region. Climate change could exert negative impacts on the distribution of endemic species and cause their extinction. Hydrothassa anatolica S,ahin & & Ouml;zdikmen, 2019 (Chrysomelidae: Chrysomelinae) is an endemic species distributed in the Hakkari province of T & uuml;rkiye. This study assessed the impacts of climate change on the habitat suitability of H. anatolica using maximum entropy (MaxEnt) model. The occurrence records of the species were collected through surveys in Y & uuml;ksekova district of Hakkari province during 2022 and 2023 and used in the modeling exercise. Habitat suitability of H. anatolica was predicted for 2021-2040, 2041-2060, 2061-2080 and 2081-2100 under two shared socioeconomic pathways (SSPs), i.e., SSP1-2.6 (low greenhouse gas emissions scenario) and SSP5-8.5 (very high greenhouse gas emissions scenario). A total 12 occurrence records and 9 bioclimatic variables were used to predict the habitat suitability under current and future climatic conditions. The results indicated that bio 6 (minimum temperature of the coldest month) bio18 (precipitation of warmest quarter) will mediate the distribution of H . anatolica under current and future climatic conditions. The areas with wet summers and cold winters were predicted highly suitable for H . anatolica. . The model predicted that the species will expand its distribution range in the future under both climate change scenarios.
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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.
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    Natural enemies feeding on some Centaurea species in the Yuksekova basin
    (Elsevier, 2022) Sirri, Mesut; Ozaslan, Cumali; Sert, Osman; Alfarraj, Saleh
    Background: Excessive and unconscious use of pesticides in agricultural areas negatively affects ecosystem services and biodiversity and threatens human and environmental health. Therefore, natural enemies (biological control agents) that could be utilized to suppress the infestation of diseases, pests and weeds have attracted the attention of scientists globally. There are limited studies on the occurrence of natural enemies on Centaurea species in the Yuksekova basin, Turkey. The Yuksekova basin has a rich floristic diversity; however, remained unexplored and underutilized. Limited use of pesticides, and the presence of natural enemies feeding on weeds in the region have recently attracted the attention of researchers for searching biological control agents. Asteraceae is the dominant family in the region with the highest diversity, causing significant yield losses in agricultural area of the basin. Methods: Therefore, preliminary studies were conducted to determine the natural enemies feeding on the genus Centaurea. The region was divided into 10 x 10 cm systematic grids and occurrence of Centaurea species, and their natural enemies were recorded. Results: The survey identified 10 species belonging to Centaurea genus in the study area. Different insect species, i.e., Lixus pulverulentus Scopoli, Larinus grisescens Gyllenhal and Bangasternus orientalis Capiomont belonging to Curculionidae (Coleoptera) family were observed to feed and spend biological periods on Centaurea behen L., Centaurea pterocaula Trautv. and Centaurea iberica Trev. ex Spreng species. Conclusions: It is estimated that the natural enemies recorded on Centaurea species could be potentially used in biological control of the species on which they were recorded in the current study. However, detailed studies on host specificity and efficacy of the identified insect species are needed. (c) 2022 The Author(s). Published by Elsevier B.V. on behalf of King Saud University. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).