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Influence of sample size and machine learning algorithms on digital soil nutrient mapping accuracy.

Environmental monitoring and assessment·2025
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Soil quality under different land uses in eastern India: Evaluation by using soil indicators and quality index.

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Related Experiment Video

Updated: Dec 9, 2025

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Spatial structure, parameter nonlinearity, and intelligent algorithms in constructing pedotransfer functions from

Poulamee Chakraborty1, Bhabani S Das2, Hitesh B Vasava2

  • 1Agricultural and Food Engineering Department, Indian Institute of Technology Kharagpur, Kharagpur, WB, 721302, India. poulameec@gmail.com.

Scientific Reports
|September 15, 2020
PubMed
Summary

This study demonstrates using the Indian soil legacy database to create accurate pedotransfer functions (PTFs) for estimating soil cation exchange capacity (CEC). This method efficiently utilizes existing data, reducing the need for new soil sample collection.

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Area of Science:

  • Soil Science
  • Data Science
  • Environmental Science

Background:

  • Pedotransfer functions (PTFs) estimate soil properties from basic data.
  • National soil legacy databases offer extensive but sparsely distributed data.
  • Developing robust PTFs typically requires large, regionally focused datasets.

Purpose of the Study:

  • To develop a methodology for extracting efficient training datasets from sparse soil legacy data.
  • To estimate soil cation exchange capacity (CEC) using the pedotransfer function (PTF) approach with legacy data.
  • To demonstrate the feasibility of using the Indian soil legacy (ISL) database for PTF development.

Main Methods:

  • Geostatistical and correlation analyses of the ISL database.
  • Application of non-linear correlation measures and predictive algorithms.
  • Selection of a comprehensive training dataset for CEC estimation.

Main Results:

  • Legacy data possess diverse spatial and correlation structures suitable for PTF development.
  • A methodology was developed to extract high-prediction-accuracy training data from the ISL database.
  • The selected training data exhibited comparable spatial variation and nonlinearity to test datasets.

Conclusions:

  • Existing soil legacy data can be effectively utilized for developing robust, region-specific PTFs.
  • This approach bypasses the need for extensive new soil data collection.
  • Opens new avenues for leveraging large volumes of historical soil data.