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Modelling and Prediction of Soil Organic Carbon using Digital Soil Mapping in the Thar Desert Region of India

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Title Modelling and Prediction of Soil Organic Carbon using Digital Soil Mapping in the Thar Desert Region of India
 
Creator P.C. Moharana
S. Dharumarajan
Nirmal Kumar
R.K. Jena
U.K. Pradhan
R.S. Meena
S. Sahoo
M. Nogiya
Sunil Kumar
R.L. Meena
B.L. Tailor
R.S. Singh
S.K. Singh
B.S. Dwivedi
 
Subject Digital soil mapping
quantile regression forest
soil organic carbon
desert regions of India
 
Description Not Available
In the present study, the distribution of soil organic carbon (SOC) was investigated using digital soil
mapping for an area of ~29 lakhs ha in Bikaner district, Rajasthan, India. To achieve this goal, 187 soil
profiles were used for SOC estimation by Quantile regression forest (QRF) model technique. Landsat data,
terrain attributes and bioclimatic variables were used as environmental variables. 10-fold cross-validation
was used to evaluate model. Equal-area quadratic splines were fitted to soil profile datasets to estimate
SOC at six standard soil depths (0-5, 5-15, 15-30, 30-60, 60-100 and 100-200 cm). Results showed that the
mean SOC concentration was very low with values varied from 1.18 to 1.53 g kg-1 in different depths.
While predicting SOC at different depths, the model was able to capture low variability (R2 = 1–7%).
Overall, the Lin’s concordance correlation coefficient (CCC) values ranged from 0.01 to 0.18, indicating
poor agreement between the predicted and observed values. Root mean square error (RMSE) and mean
error (ME) were 0.97 and 0.16, respectively. The values of prediction interval coverage probability (PICP)
recorded 87.2–89.7% for SOC contents at different depths. The most important variables for predicting
SOC concentration variations were the annual range of temperature, latitude, Landsat 8 bands 2, 5 and 6.
Temperature-related variables and remote sensed data products are important for predicting SOC
concentrations in arid regions. We anticipate that this digital information of SOC will be useful for frequent
monitoring and assessment of carbon cycle in arid regions.
Not Available
 
Date 2022-06-03T07:49:41Z
2022-06-03T07:49:41Z
2022-05-28
 
Type Article
 
Identifier • Moharana, P., Dharumarajan, s., Kumar, N., Jena, R., Pradhan, U., Meena, R, Sahoo, S., Nogiya, M., Kumar, S., Meena, Roshan, Tailor, B., Singh, Singhsar, Singh, Surendra, Dwivedi, B., (2022). Modelling and Prediction of Soil Organic Carbon using Digital Soil Mapping in the Thar Desert Region of India. Journal of the Indian Society of Soil Science, 70, 86–96. https://doi.org/10.5958/0974-0228.2022.00009.3
0019638X
http://krishi.icar.gov.in/jspui/handle/123456789/72460
 
Language English
 
Relation Not Available;
 
Publisher Indian Society of Soil Science