Spatial prediction of soil aggregate stability and soil organic carbon in aggregate fractions using machine learning algorithms and environmental variables

• Published in: Geoderma Regional Vol. 27; p. e00440
• Main Authors: Zeraatpisheh, Mojtaba, Ayoubi, Shamsollah, Mirbagheri, Zahra, Mosaddeghi, Mohammad Reza, Xu, Ming
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.12.2021
• Subjects: Aggregate stability; Cambisols; cost effectiveness; Digital soil mapping; forests; Hyrcanian forest; Land use change; landscapes; Luvisols; neural networks; paddies; prediction; rough rice; soil aggregates; Soil erosion; soil organic carbon; Soil organic matter; support vector machines; sustainable land management; tea; Soil erosion; Hyrcanian forest; Cambisols; Digital soil mapping; Luvisols; Land use change; Soil organic matter; Aggregate stability

Knowledge about the spatial variability of soil aggregate stability indices, soil organic carbon (SOC) in various aggregate sizes, and aggregation across the landscape is crucial for sustainable land use planning and management practices. Direct traditional measurements for the target variables, as...