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Predicting the spatial distribution of soil organic carbon stock in Swedish forests using a group of covariates and site-specific data

Hounkpatin, Ozias and Stendahl, Johan and Lundblad, Mattias and Karltun, Erik (2021). Predicting the spatial distribution of soil organic carbon stock in Swedish forests using a group of covariates and site-specific data. Soil. 7 , 377-398
[Research article]

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Abstract

The status of the soil organic carbon (SOC) stock at any position in the landscape is subject to a complex interplay of soil state factors operating at different scales and regulating multiple processes resulting either in soils acting as a net sink or net source of carbon. Forest landscapes are characterized by high spatial variability, and key drivers of SOC stock might be specific for sub-areas compared to those influencing the whole landscape. Consequently, separately calibrating models for sub-areas (local models) that collectively cover a target area can result in different prediction accuracy and SOC stock drivers compared to a single model (global model) that covers the whole area. The goal of this study was therefore to (1) assess how global and local models differ in predicting the humus layer, mineral soil, and total SOC stock in Swedish forests and (2) identify the key factors for SOC stock prediction and their scale of influence.We used the Swedish National Forest Soil Inventory (NFSI) database and a digital soil mapping approach to evaluate the prediction performance using random forest models calibrated locally for the northern, central, and southern Sweden (local models) and for the whole of Sweden (global model). Models were built by considering (1) only site characteristics which are recorded on the plot during the NFSI, (2) the group of covariates (remote sensing, historical land use data, etc.) and (3) both site characteristics and group of covariates consisting mostly of remote sensing data.Local models were generally more effective for predicting SOC stock after testing on independent validation data. Using the group of covariates together with NFSI data indicated that such covariates have limited predictive strength but that site-specific covariates from the NFSI showed better explanatory strength for SOC stocks. The most important covariates that influence the humus layer, mineral soil (0-50 cm), and total SOC stock were related to the site-characteristic covariates and include the soil moisture class, vegetation type, soil type, and soil texture. This study showed that local calibration has the potential to improve prediction accuracy, which will vary depending on the type of available covariates.

Authors/Creators:Hounkpatin, Ozias and Stendahl, Johan and Lundblad, Mattias and Karltun, Erik
Title:Predicting the spatial distribution of soil organic carbon stock in Swedish forests using a group of covariates and site-specific data
Series Name/Journal:Soil
Year of publishing :2021
Volume:7
Page range:377-398
Number of Pages:22
Publisher:COPERNICUS GESELLSCHAFT MBH
ISSN:2199-3971
Language:English
Publication Type:Research article
Article category:Scientific peer reviewed
Version:Published version
Copyright:Creative Commons: Attribution 4.0
Full Text Status:Public
Subjects:(A) Swedish standard research categories 2011 > 4 Agricultural Sciences > 401 Agricultural, Forestry and Fisheries > Soil Science
URN:NBN:urn:nbn:se:slu:epsilon-p-113094
Permanent URL:
http://urn.kb.se/resolve?urn=urn:nbn:se:slu:epsilon-p-113094
Additional ID:
Type of IDID
DOI10.5194/soil-7-377-2021
Web of Science (WoS)000671008100003
ID Code:25053
Faculty:NJ - Fakulteten för naturresurser och jordbruksvetenskap
S - Faculty of Forest Sciences
Department:(NL, NJ) > Dept. of Soil and Environment
(S) > Dept. of Soil and Environment
Deposited By: SLUpub Connector
Deposited On:26 Aug 2021 12:25
Metadata Last Modified:26 Aug 2021 12:31

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