Recalcitrant C Source Mapping Utilizing Solely Terrain-Related Attributes and Data Mining Techniques

Recalcitrant C Source Mapping Utilizing Solely Terrain-Related Attributes and Data Mining Techniques


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نویسندگان: لیلی عاقبتی

کلمات کلیدی: digital soil mapping; environmental covariates; glomalin; modeling; organic carbon; random forest

نشریه: 55597 , 7 , 12 , 2022

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نویسنده ثبت کننده مقاله لیلی عاقبتی
مرحله جاری مقاله تایید نهایی
دانشکده/مرکز مربوطه مرکز تحقیقات ایمونولوژی
کد مقاله 79164
عنوان فارسی مقاله Recalcitrant C Source Mapping Utilizing Solely Terrain-Related Attributes and Data Mining Techniques
عنوان لاتین مقاله Recalcitrant C Source Mapping Utilizing Solely Terrain-Related Attributes and Data Mining Techniques
ناشر 6
آیا مقاله از طرح تحقیقاتی و یا منتورشیپ استخراج شده است؟ خیر
عنوان نشریه (خارج از لیست فوق)
نوع مقاله Original Article
نحوه ایندکس شدن مقاله ایندکس شده سطح یک – ISI - Web of Science
آدرس لینک مقاله/ همایش در شبکه اینترنت

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Agricultural practices affect arbuscular mycorrhizal fungal (AMF) hyphae growth and glomalin production, which is a recalcitrant carbon (C) source in soil. Since the spatial distribution of glomalin is an interesting issue for agronomists in terms of carbon sequestration, digital maps are a cost-free and useful approach. For this study, a set of 120 soil samples was collected from an experimental area of 310 km2 from the Sarab region of Iran. Soil total glomalin (TG) and easily extractable glomalin (EEG) were determined via ELISA using the monoclonal antibody 32B11. Soil organic carbon (OC) was also measured. The ratios of TG/OC and EEG/OC as the glomalin–C quotes of OC were calculated. A total of 17 terrain-related attributes were also derived from the digital elevation model (DEM) and used as static environmental covariates in digital soil mapping (DSM) using three predictive models, including multiple linear regression (MLR), random forests (RF), and Cubist (CU). The major findings were as follows: (a) DSM facilitated the interpretation of recalcitrant C source variation; (b) RF outperformed MLR and CU as models in predicting and mapping the spatial distribution of glomalin using available covariates; (c) the best accuracy in predictions was for EEG, followed by EEG/OC, TG, and TG/OC. View Full-Text

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لیلی عاقبتیسوم

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Agronomy-MDPI_3.pdf1401/04/293741105دانلود