Korean Journal of Agricultural Science (농업과학연구)

Institute of Agricultural Science, CNU (충남대학교 농업과학연구소)
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Digital mapping of soil carbon stock in Jeolla province using cubist model

  • Park, Seong-Jin (Division of soil and fertilizer, National Institute of Agricultural Science, RDA) ;
  • Lee, Chul-Woo (Division of soil and fertilizer, National Institute of Agricultural Science, RDA) ;
  • Kim, Seong-Heon (Division of soil and fertilizer, National Institute of Agricultural Science, RDA) ;
  • Oh, Taek-Keun (Department of Bio-Environmental Chemistry, College of Agriculture and Life Science, Chungnam National University)
  • Received : 2020.11.05
  • Accepted : 2020.11.27
  • Published : 2020.12.01
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Abstract

Assessment of soil carbon stock is essential for climate change mitigation and soil fertility. The digital soil mapping (DSM) is well known as a general technique to estimate the soil carbon stocks and upgrade previous soil maps. The aim of this study is to calculate the soil carbon stock in the top soil layer (0 to 30 cm) in Jeolla Province of South Korea using the DSM technique. To predict spatial carbon stock, we used Cubist, which a data-mining algorithm model base on tree regression. Soil samples (130 in total) were collected from three depths (0 to 10 cm, 10 to 20 cm, 20 to 30 cm) considering spatial distribution in Jeolla Province. These data were randomly divided into two sets for model calibration (70%) and validation (30%). The results showed that clay content, topographic wetness index (TWI), and digital elevation model (DEM) were the most important environmental covariate predictors of soil carbon stock. The predicted average soil carbon density was 3.88 kg·m-2. The R2 value representing the model's performance was 0.6, which was relatively high compared to a previous study. The total soil carbon stocks at a depth of 0 to 30 cm in Jeolla Province were estimated to be about 81 megatons.

Keywords

Acknowledgement

본 연구는 농촌진흥청 국립농업과학원 농업과학기술 연구개발사업(과제번호: PJ01510201)의 지원에 의해 수행되었습니다.

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