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

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-fre...

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Published inAgronomy (Basel) Vol. 12; no. 7; p. 1653
Main Authors Siami, Arezou, Aliasgharzad, Nasser, Maleki, Leili Aghebati, Najafi, Nosratollah, Shahbazi, Farzin, Biswas, Asim
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.07.2022
Subjects
Agricultural practices
Antibodies
Arbuscular mycorrhizas
Carbon
Carbon sequestration
Data mining
Digital Elevation Models
Digital mapping
digital soil mapping
environmental covariates
Fungi
glomalin
Hyphae
Mapping
modeling
Monoclonal antibodies
Organic carbon
Organic soils
Prediction models
random forest
Regression analysis
Soil mapping
Spatial distribution
Terrain
Iran
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Summary: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.
ISSN:2073-4395
2073-4395
DOI:10.3390/agronomy12071653