Spatial Prediction of Soil Particle-Size Fractions Using Digital Soil Mapping in the North Eastern Region of India

Roomesh Kumar Jena, Pravash Chandra Moharana (), Subramanian Dharumarajan, Gulshan Kumar Sharma, Prasenjit Ray, Partha Deb Roy, Dibakar Ghosh, Bachaspati Das, Amnah Mohammed Alsuhaibani, Ahmed Gaber and Akbar Hossain ()
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Roomesh Kumar Jena: ICAR-Indian Institute of Water Management, Bhubaneswar 751023, India
Pravash Chandra Moharana: ICAR-National Bureau of Soil Survey and Land Use Planning, Nagpur 440033, India
Subramanian Dharumarajan: ICAR-National Bureau of Soil Survey and Land Use Planning, Regional Centre, Bengaluru 560024, India
Gulshan Kumar Sharma: ICAR-Indian Institute of Soil and Water Conservation, Research Centre, Kota 324002, India
Prasenjit Ray: ICAR-Indian Agricultural Research Institute, New Delhi 110012, India
Partha Deb Roy: ICAR-Indian Institute of Water Management, Bhubaneswar 751023, India
Dibakar Ghosh: ICAR-Indian Institute of Water Management, Bhubaneswar 751023, India
Bachaspati Das: ICAR-Indian Institute of Water Management, Bhubaneswar 751023, India
Amnah Mohammed Alsuhaibani: Department of Physical Sport Science, College of Education, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
Ahmed Gaber: Department of Biology, College of Science, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia
Akbar Hossain: Division of Soil Science, Bangladesh Wheat and Maize Research Institute, Dinajpur 5200, Bangladesh

Land, 2023, vol. 12, issue 7, 1-20

Abstract: Numerous applications in agriculture, climate, ecology, hydrology, and the environment are severely constrained by the lack of detailed information on soil texture. The purpose of this study was to predict soil particle-size fractions (PSF) in the Ri-Bhoi district of Meghalaya state, India, using a random forest model (RF). For the modeling of soil particle-size fractions, we employed 95 soil profiles (456 depth-wise layers) gathered from a recent national land resource inventory as well as currently accessible environmental variables. Sand, silt, and clay content were predicted using the Random Forest model at varied depths of 0–5, 5–15, 30–60, 60–100, and 100–200 cm. Our results showed the R 2 for sand was found to be 0.30 (0–5 cm), 0.28 (5–15 cm), and 0.21 (15–30 cm). For the sand, silt, and clay fractions, respectively, the concordance correlation coefficient (CCC) was found to be greater in the 0–30 cm, 0–60 cm, and 0–15 cm depths. When there is a reasonably close monitoring of the coverage probability with a confidence level along the 1:1 line, prediction interval coverage probability (PICP) gives a decent indicator of what to anticipate. The most crucial variables for the prediction of sand and silt were channel network base level (CNBL) and LS-Factor, whereas Min Temperature of Coldest Month (°C) (BIO6) was discovered for clay prediction. For all three soil texture fractions, the range between the 5% lower and 95% higher prediction bounds was large, indicating that the existing spatial predictions may be improved. The maps of soil texture were significantly more precise, and they accurately depicted the spatial variations of particle-size fractions. Additionally, there is still a need to investigate novel methodologies for extensive digital soil mapping, which will be very advantageous for many international initiatives.

Keywords: digital soil mapping; environmental variables; random forest; uncertainty analysis; particle-size fractions (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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