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ORIGINAL RESEARCH article

Front. Soil Sci.
Sec. Pedometrics
Volume 5 - 2025 | doi: 10.3389/fsoil.2025.1539477
This article is part of the Research Topic Advanced Geochemical Mapping and Geochemical Background/Baseline: An Environmental Perspective View all 4 articles

Geogenic Perspectives on Potassium Dynamics and Plant Uptake: Insights from Natural and Submerged Conditions Across Different Soil Types with Machine Learning Predictions

Provisionally accepted
Saibal Ghosh Saibal Ghosh Gourav Mondal Gourav Mondal Shreya Chakraborty Shreya Chakraborty Sonali Banerjee Sonali Banerjee Sumit Kumar Sumit Kumar Riddhi Basu Riddhi Basu Pradip Bhattacharyya Pradip Bhattacharyya *
  • Indian Statistical Institute, Giridih, India

The final, formatted version of the article will be published soon.

    Four different soil types including red, alluvial, calcareous, and black soils along with rice cultivated on them were collected from various parts of India and analysed for potassium dynamics in the soil plant continuum. Soil potassium (K) dynamics were studied under submerged and non-submerged conditions, and potassium content was analysed in rice roots, shoots, and grains, along with other soil properties. Red (S1: 5.9) and alluvial (S5: 5.16) soils were moderately acidic, while black (S8: 8.01) and calcareous (S7: 8.1) soils were alkaline. Black soil (S8) had the highest cation exchange capacity (CEC: 31.25 cmol (p+)/kg) and clay content (41.2%), while alluvial soil had the most organic carbon (S5: 1.74%). Submerged conditions enhanced potassium availability, with red soil showing the highest levels of water-soluble K (WsK), exchangeable K (ExK), and non-exchangeable K (NEK), particularlyStep-K and constant rate K (CR-K) forms. Rice potassium content was highest in grains, followed by shoots and roots, with red soil containing the most available potassium. A strong correlation was found between soil potassium forms and rice plant potassium uptake. Sensitivity analysis indicated that WsK and ExK from non-submerged soil to be the most favourable forms for potassium uptake, especially in the rice roots and grains. Machine learning models, particularly Random Forest, accurately predicted potassium availability and uptake, highlighting their potential in optimizing soil fertility and advancing precision agriculture for better crop yields and soil health.

    Keywords: Soil Potassium Dynamics, Step -K and constant rate K, potassium uptake, Rice cultivation, Submerged conditions, Machine Learning Predictions Soil Potassium Dynamics, Step -K and constant rate K CR -K, Machine learning predictions

    Received: 04 Dec 2024; Accepted: 02 Jan 2025.

    Copyright: © 2025 Ghosh, Mondal, Chakraborty, Banerjee, Kumar, Basu and Bhattacharyya. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Pradip Bhattacharyya, Indian Statistical Institute, Giridih, India

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