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

Author

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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)

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.

Suggested Citation

  • Roomesh Kumar Jena & Pravash Chandra Moharana & Subramanian Dharumarajan & Gulshan Kumar Sharma & Prasenjit Ray & Partha Deb Roy & Dibakar Ghosh & Bachaspati Das & Amnah Mohammed Alsuhaibani & Ahmed G, 2023. "Spatial Prediction of Soil Particle-Size Fractions Using Digital Soil Mapping in the North Eastern Region of India," Land, MDPI, vol. 12(7), pages 1-20, June.
    • Handle: RePEc:gam:jlands:v:12:y:2023:i:7:p:1295-:d:1180414
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    References listed on IDEAS

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    1. Tomislav Hengl & Gerard B M Heuvelink & Bas Kempen & Johan G B Leenaars & Markus G Walsh & Keith D Shepherd & Andrew Sila & Robert A MacMillan & Jorge Mendes de Jesus & Lulseged Tamene & Jérôme E Tond, 2015. "Mapping Soil Properties of Africa at 250 m Resolution: Random Forests Significantly Improve Current Predictions," PLOS ONE, Public Library of Science, vol. 10(6), pages 1-26, June.
    2. De-Cai Wang & Gan-Lin Zhang & Ming-Song Zhao & Xian-Zhang Pan & Yu-Guo Zhao & De-Cheng Li & Bob Macmillan, 2015. "Retrieval and Mapping of Soil Texture Based on Land Surface Diurnal Temperature Range Data from MODIS," PLOS ONE, Public Library of Science, vol. 10(6), pages 1-14, June.
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    Cited by:

    1. Amit Kumar & Pravash Chandra Moharana & Roomesh Kumar Jena & Sandeep Kumar Malyan & Gulshan Kumar Sharma & Ram Kishor Fagodiya & Aftab Ahmad Shabnam & Dharmendra Kumar Jigyasu & Kasthala Mary Vijaya K, 2023. "Digital Mapping of Soil Organic Carbon Using Machine Learning Algorithms in the Upper Brahmaputra Valley of Northeastern India," Land, MDPI, vol. 12(10), pages 1-17, September.
    2. Ning Wang & Mamattursun Eziz & Donglei Mao & Nazupar Sidekjan, 2023. "Fractal Characteristics of the Particle Size Distribution of Soil along an Urban–Suburban–Rural–Desert Gradient," Land, MDPI, vol. 12(12), pages 1-14, November.

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