DOI: 10.1016/j.geoderma.2020.114260
论文题名: Assessing countrywide soil organic carbon stock using hybrid machine learning modelling and legacy soil data in Cameroon
作者: Silatsa F.B.T. ; Yemefack M. ; Tabi F.O. ; Heuvelink G.B.M. ; Leenaars J.G.B.
刊名: Geoderma
ISSN: 167061
出版年: 2020
卷: 367 语种: 英语
英文关键词: Generalized boosted regression
; Hybridization
; Legacy soil data
; Random forest
; Soil organic carbon stock
Scopus关键词: Climate change
; Decision trees
; Forecasting
; Interpolation
; Land use
; Machine learning
; Magnesium
; Organic carbon
; Random forests
; Soils
; Spatial distribution
; Climate change mitigation
; Coefficients of variations
; Generalized boosted regression
; Hybridization
; Inverse distance weighting
; Legacy soil datum
; Soil organic Carbon stocks
; Sustainable land managements
; Soil surveys
; carbon sequestration
; climate change
; heterogeneity
; kriging
; machine learning
; regression analysis
; soil carbon
; spatial distribution
; Cameroon
英文摘要: Countrywide estimates of soil organic carbon stock (SOCS) are useful to set up national strategies for sustainable land use management as well as to enhance the accuracy of global SOCS inventories. We appraised the spatial distribution of SOCS at five depth layers (0–15 cm, 15–30 cm, 30–100 cm, 0–30 cm and 0–100 cm) in Cameroon at 100 m spatial resolution, using a national harmonized legacy soil database (Camsodat 0.1) with 1432 georeferenced soil profiles. We assessed the prediction performances of random forest (RF) and generalized boosted regression (GBR), combined with two hybridization approaches of spatial interpolation of the residuals using ordinary kriging (OK) and inverse distance weighting (IDW). The estimates were compared to two global estimates derived from the Harmonized World Soil Database (HWSD) and SoilGrids250m. The SOCS distribution across the country showed a moderate spatial heterogeneity at all depth layers with coefficients of variation between 35% and 47%, and values ranging from 6 to 108 Mg C ha−1 at 0–15 cm, from 4 to 107 Mg C ha−1 at 15–30 cm, from 10 to 276 Mg C ha−1 at 30–100 cm, from 11 to 210 Mg C ha−1 at 0–30 cm and from 21 to 468 Mg C ha−1 at the 0–100 cm layer. Of the selected environmental covariates, terrain and climate attributes were the most relevant to predict the SOCS spatial distribution at country level. The RF model outperformed the GBR model, with about 10% improvement on prediction performance (R2) for most soil depths. The hybridization further slightly improved performance. However, OK was only slightly better than IDW in the overall assessment. Compared to national estimates, SoilGrids overestimated the SOCS by 15% at 0–30 cm depth, while HWSD underestimated SOCS by 26% at the same depth. Overall, about 5.7 Pg C are stored in the top 1 m of soils in Cameroon, with about 50% of that in the top 30 cm. The national distribution of SOCS is consistent with the pattern of agro-ecological zones. Our assessment provides baseline information for sustainable land management and climate change mitigation, as well as for improving the understanding of the spatial distribution of SOCS in Cameroon. © 2020 Elsevier B.V. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/158208
Appears in Collections: 气候变化与战略
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作者单位: Department of Soil Science, Faculty of Agronomy and Agricultural Sciences, University of Dschang (UDs), PO Box 222, Dschang, Cameroon; International Institute of Tropical Agriculture (IITA), ASB-Partnership, PO Box 2008, Messa, Yaoundé, Cameroon; Sustainable Tropical Solution (STS) Sarl, Cité - Verte, Yaoundé, Cameroon; ISRIC – World Soil Information, PO Box 353, AJ Wageningen, 6700, Netherlands
Recommended Citation:
Silatsa F.B.T.,Yemefack M.,Tabi F.O.,et al. Assessing countrywide soil organic carbon stock using hybrid machine learning modelling and legacy soil data in Cameroon[J]. Geoderma,2020-01-01,367