干旱区生态环境知识资源中心(Arid): Prediction of Soil Inorganic Carbon at Multiple Depths Using Quantile Regression Forest and Digital Soil Mapping Technique in the Thar Desert Regions of India

Arid

DOI	10.1080/00103624.2023.2253840
	Prediction of Soil Inorganic Carbon at Multiple Depths Using Quantile Regression Forest and Digital Soil Mapping Technique in the Thar Desert Regions of India
	Moharana, Pravash Chandra; Dharumarajan, S.; Yadav, Brijesh; Jena, Roomesh Kumar; Pradhan, Upendra Kumar; Sahoo, Sonalika; Meena, Ram Swaroop; Nogiya, Mahaveer; Meena, Roshan Lal; Singh, Ram Sakal; Singh, Surendra Kumar; Dwivedi, Brahma Swarup
通讯作者	Moharana, PC
来源期刊	COMMUNICATIONS IN SOIL SCIENCE AND PLANT ANALYSIS
ISSN	0010-3624
EISSN	1532-2416
出版年	2023
卷号	54 期号:21 页码:2977-2994
英文摘要	Soil inorganic carbon (SIC) is important carbon reservoirs in desert ecosystems. However, little attention was paid to estimate carbon stock in these regions. In the present study, the distribution of SIC stock was investigated using digital soil mapping in Bikaner district, Rajasthan, India. A total of 187 soil profiles were used for SIC estimation by Quantile regression forest model. Landsat data, terrain attributes and bioclimatic variables were used as environmental variables. Ten-fold cross-validation was used to evaluate model. Equal-area quadratic splines were fitted to soil profile datasets to estimate SIC at six standard soil depths (0-5, 5-15, 15-30, 30-60, 60-100 and 100-200 cm). The SIC in the study area ranged from 0.27 to 27.85 g kg-1 in 0-5 cm and 0.31 to 27.84 g kg-1 in 5-15 cm, respectively. The model could capture reasonable variability (R2 = 11-21%) while predicting SIC for different depths. The Lin's concordance correlation coefficient values ranged from 0.20 to 0.32, indicating poor relationship between the predicted and observed values. The values of prediction interval coverage probability (PICP) recorded 86.4-91.1% for SIC at different depths. Annual precipitation and precipitation seasonality were the most important covariates in soil below the 30 cm depth. The predicted SIC stocks were 10.3 & PLUSMN; 0.01, 81.6 & PLUSMN; 0.07 and 186.7 & PLUSMN; 0.13 Mg ha-1 at 0-15, 0-100 and 0-200 cm, depth, respectively. The uncertainty analysis suggests that there is room to improve the current spatial predictions of SIC. It is anticipated that this digital mapping of SIC will be useful for assessment of carbon cycle in arid regions.
英文关键词	Desert regions of India digital soil mapping quantile regression forest soil inorganic carbon
类型	Article
语种	英语
收录类别	SCI-E
WOS记录号	WOS:001058827800001
WOS关键词	ORGANIC-CARBON ; SEQUESTRATION ; STORAGE ; UNCERTAINTY ; SALINITY
WOS类目	Agronomy ; Plant Sciences ; Chemistry, Analytical ; Soil Science
WOS研究方向	Agriculture ; Plant Sciences ; Chemistry
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/395804
推荐引用方式 GB/T 7714	Moharana, Pravash Chandra,Dharumarajan, S.,Yadav, Brijesh,et al. Prediction of Soil Inorganic Carbon at Multiple Depths Using Quantile Regression Forest and Digital Soil Mapping Technique in the Thar Desert Regions of India[J],2023,54(21):2977-2994.
APA	Moharana, Pravash Chandra.,Dharumarajan, S..,Yadav, Brijesh.,Jena, Roomesh Kumar.,Pradhan, Upendra Kumar.,...&Dwivedi, Brahma Swarup.(2023).Prediction of Soil Inorganic Carbon at Multiple Depths Using Quantile Regression Forest and Digital Soil Mapping Technique in the Thar Desert Regions of India.COMMUNICATIONS IN SOIL SCIENCE AND PLANT ANALYSIS,54(21),2977-2994.
MLA	Moharana, Pravash Chandra,et al."Prediction of Soil Inorganic Carbon at Multiple Depths Using Quantile Regression Forest and Digital Soil Mapping Technique in the Thar Desert Regions of India".COMMUNICATIONS IN SOIL SCIENCE AND PLANT ANALYSIS 54.21(2023):2977-2994.