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Prediction of Soil Inorganic Carbon at Multiple Depths Using Quantile Regression Forest and Digital Soil Mapping Technique in the Thar Desert Regions of India

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Title Prediction of Soil Inorganic Carbon at Multiple Depths Using Quantile Regression Forest and Digital Soil Mapping Technique in the Thar Desert Regions of India
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Creator Pravash Chandra Moharana
S. Dharumarajan
Brijesh Yadav
Roomesh Kumar Jena
Upendra Kumar Pradhan
Sonalika Sahoo
Ram Swaroop Meena
Mahaveer Nogiya
Roshan Lal Meena
Ram Sakal Singh
Surendra Kumar Singh
Brahma Swarup Dwivedi
 
Subject Desert regions of India
digital soil mapping
quantile regression forest
soil inorganic carbon
 
Description Not Available
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 ± 0.01, 81.6 ± 0.07 and 186.7 ± 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.
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Date 2023-12-21T11:12:42Z
2023-12-21T11:12:42Z
2023-09-05
 
Type Article
 
Identifier Moharana, PC, Dharumarajan, S, Yadav, B, Jena, RK, Pradhan, UK, Sahoo, S, Meena, RS, Nogiya, M, Meena, RL, Singh, RS, Singh, SK, Dwivedi, BS (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, DOI: 10.1080/00103624.2023.2253840
0010-3624
http://krishi.icar.gov.in/jspui/handle/123456789/81084
 
Language English
 
Relation Not Available;
 
Publisher Taylor & Francis Online