인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
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지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2021.5
- 수록면
- 247 - 256 (10page)
이용수
초록· 키워드
Estimation of soil organic carbon (SOC) stocks is highly relevant considering that SOCs is the central driver in soil fertility and climate change mitigation. This study aims to (i) evaluate the SOC stock in the first 0 - 30 ㎝ and 0 - 100 ㎝ soil layer on a national scale from spatially explicit explanatory environmental variables and a legacy soil database and (ii) the spatial distribution of SOCs at national scale through digital mapping technique. A spatial model was established using Cubist, a decision tree algorithm and based on soil data (s factor), climatic (c factor), topographic (r factor). Results showed that soil texture, soil parent, mean annual precipitatio and elevation were the most important predictors of SOCs. The Cubist prediction model had a Root Mean Squared Error (RMSE) equal to 19.5 at 0 - 30 ㎝, 68.7 at 0 - 100 ㎝. The predicted mean SOC stock from fitted models was 35 ton C ㏊<SUP>-1</SUP> for 0 - 30 ㎝ depth, 87 ton C ㏊<SUP>-1</SUP> for 0 - 100 ㎝ soil depth. In total, soil stored approximately 330 Mt C for 0 - 30 ㎝ depth and 842 Mt C for 0 - 100 ㎝ depth.
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목차
- ABSTRACT
- Introduction
- Materials and Methods
- Results
- Conclusions
- References
참고문헌
참고문헌 신청최근 본 자료
UCI(KEPA) : I410-ECN-0101-2021-523-001748776
