title: Spatial 3D distribution of soil organic carbon under different land use types.
abstract: Soil organic carbon (SOC) has been assessed in three dimension (3D) in several studies, but little is known about the combined effects of land use and soil depth on SOC stocks in semi-arid areas. This paper investigates the 3D distribution of SOC to a depth of 1 m in a 4600-ha area in southeastern Iran with different land uses under the irrigated farming (IF), dry farming (DF), orchards (Or), range plants on the Gachsaran formation (RaG), and range plants on a quaternary formation (RaQ). Predictions were made using the artificial neural networks (ANNs), regression trees (RTs), and spline functions with auxiliary covariates derived from a digital elevation model (DEM), the Landsat 8 imagery, and land use types. Correlation analysis showed that the main predictors for SOC in the topsoil were covariates derived from the imagery; however, for the lower depths, covariates derived from both the DEM and imagery were important. ANNs showed more efficiency than did RTs in predicting SOC. The results showed that 3D distribution of SOC was significantly affected by land use types. SOC stocks of soils under Or and IF were significantly higher than those under DF, RaG, and RaQ. The SOC below 30 cm accounted for about 59% of the total soil stock. Results showed that depth functions combined with digital soil mapping techniques provide a promising approach to evaluate 3D SOC distribution under different land uses in semi-arid regions and could be used to assess changes in time to determine appropriate management strategies.
authors.affiliation: Faculty of Natural Resources, Behbahan Khatamal Anbia University of Technology, Behbahan, Iran. aramirian@yahoo.com.
authors.forename: A
authors.identifier: http://orcid.org/0000-0001-5482-5487
authors.initials: A
authors.lastname: Amirian Chakan
authors.affiliation: Faculty of Agriculture and Natural Resources, Ardakan University, Ardakan, Iran.
authors.forename: R
authors.initials: R
authors.lastname: Taghizadeh-Mehrjardi
authors.affiliation: Department of Geography, Brigham Young University, Provo, UT, USA.
authors.forename: R
authors.initials: R
authors.lastname: Kerry
authors.affiliation: Department of Plant Science, South Dakota State University, Brookings, SD, 57007, USA.
authors.forename: S
authors.initials: S
authors.lastname: Kumar
authors.affiliation: Faculty of Natural Resources, Behbahan Khatamal Anbia University of Technology, Behbahan, Iran.
authors.forename: S
authors.initials: S
authors.lastname: Khordehbin
authors.affiliation: Faculty of Natural Resources, Behbahan Khatamal Anbia University of Technology, Behbahan, Iran.
authors.forename: S
authors.initials: S
authors.lastname: Yusefi Khanghah
mesh_terms: D000383:Agriculture / Q000379:methods; D002244:Carbon / Q000032:analysis*; D002681:China; D004784:Environmental Monitoring / Q000379:methods*; D007492:Iran; D008962:Models, Theoretical; D016571:Neural Networks, Computer; D012044:Regression Analysis; D012987:Soil / Q000737:chemistry*
chemical_list: D012987:Soil; D002244:Carbon
keywords: Digital soil mapping; Soil carbon storage; Spatial distribution; Spline depth functions