Spatial prediction of soil organic carbon using machine learning techniques in western Iran

• Published in: Geoderma Regional Vol. 21; p. e00260
• Main Authors: Mahmoudzadeh, Hamid, Matinfar, Hamid Reza, Taghizadeh-Mehrjardi, Ruhollah, Kerry, Ruth
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.06.2020
• Subjects: air temperature; Aridisols; carbon sequestration; dryland farming; forest soils; forests; grasslands; Iran; irrigated farming; Land use; landscapes; Machine learning algorithms; orchards; prediction; rain; Random forests; roughness; soil organic carbon; Soil organic carbon stocks; soil sampling; Spatial prediction; support vector machines; texture; Iran; Spatial prediction; Machine learning algorithms; Aridisols; Soil organic carbon stocks; Random forests; Land use

Estimation of soil organic carbon (SOC) is very useful for accurate monitoring of carbon sequestration. However, there are still significant gaps in the knowledge of SOC reserves in many parts of the world, including western Iran. To partially fill the gap, 865 soil samples were used with 101 auxili...