Geogenic perspectives on potassium dynamics and plant uptake: insights from natural and submerged conditions across different soil types with machine learning predictions
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 analyzed for potassium dynamics in the soil plant continuum. Soil potassium (K) dynamics were studied under submerged and non-submerged con...
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2025-01-01
|
| Series: | Frontiers in Soil Science |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fsoil.2025.1539477/full |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832589815677190144 |
|---|---|
| author | Saibal Ghosh Gourav Mondal Shreya Chakraborty Sonali Banerjee Sumit Kumar Riddhi Basu Pradip Bhattacharyya |
| author_facet | Saibal Ghosh Gourav Mondal Shreya Chakraborty Sonali Banerjee Sumit Kumar Riddhi Basu Pradip Bhattacharyya |
| author_sort | Saibal Ghosh |
| collection | DOAJ |
| description | 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 analyzed for potassium dynamics in the soil plant continuum. Soil potassium (K) dynamics were studied under submerged and non-submerged conditions, and potassium content was analyzed 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), particularly Step-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 favorable 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. |
| format | Article |
| id | doaj-art-0b2d559411d5463a885d13c88f9ebd21 |
| institution | Kabale University |
| issn | 2673-8619 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Soil Science |
| spelling | doaj-art-0b2d559411d5463a885d13c88f9ebd212025-01-24T07:13:30ZengFrontiers Media S.A.Frontiers in Soil Science2673-86192025-01-01510.3389/fsoil.2025.15394771539477Geogenic perspectives on potassium dynamics and plant uptake: insights from natural and submerged conditions across different soil types with machine learning predictionsSaibal GhoshGourav MondalShreya ChakrabortySonali BanerjeeSumit KumarRiddhi BasuPradip BhattacharyyaFour different soil types including red, alluvial, calcareous, and black soils along with rice cultivated on them were collected from various parts of India and analyzed for potassium dynamics in the soil plant continuum. Soil potassium (K) dynamics were studied under submerged and non-submerged conditions, and potassium content was analyzed 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), particularly Step-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 favorable 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.https://www.frontiersin.org/articles/10.3389/fsoil.2025.1539477/fullpotassium dynamicspotassium uptakerice cultivationsubmerged conditionsmachine learning predictions |
| spellingShingle | Saibal Ghosh Gourav Mondal Shreya Chakraborty Sonali Banerjee Sumit Kumar Riddhi Basu Pradip Bhattacharyya Geogenic perspectives on potassium dynamics and plant uptake: insights from natural and submerged conditions across different soil types with machine learning predictions Frontiers in Soil Science potassium dynamics potassium uptake rice cultivation submerged conditions machine learning predictions |
| title | Geogenic perspectives on potassium dynamics and plant uptake: insights from natural and submerged conditions across different soil types with machine learning predictions |
| title_full | Geogenic perspectives on potassium dynamics and plant uptake: insights from natural and submerged conditions across different soil types with machine learning predictions |
| title_fullStr | Geogenic perspectives on potassium dynamics and plant uptake: insights from natural and submerged conditions across different soil types with machine learning predictions |
| title_full_unstemmed | Geogenic perspectives on potassium dynamics and plant uptake: insights from natural and submerged conditions across different soil types with machine learning predictions |
| title_short | Geogenic perspectives on potassium dynamics and plant uptake: insights from natural and submerged conditions across different soil types with machine learning predictions |
| title_sort | geogenic perspectives on potassium dynamics and plant uptake insights from natural and submerged conditions across different soil types with machine learning predictions |
| topic | potassium dynamics potassium uptake rice cultivation submerged conditions machine learning predictions |
| url | https://www.frontiersin.org/articles/10.3389/fsoil.2025.1539477/full |
| work_keys_str_mv | AT saibalghosh geogenicperspectivesonpotassiumdynamicsandplantuptakeinsightsfromnaturalandsubmergedconditionsacrossdifferentsoiltypeswithmachinelearningpredictions AT gouravmondal geogenicperspectivesonpotassiumdynamicsandplantuptakeinsightsfromnaturalandsubmergedconditionsacrossdifferentsoiltypeswithmachinelearningpredictions AT shreyachakraborty geogenicperspectivesonpotassiumdynamicsandplantuptakeinsightsfromnaturalandsubmergedconditionsacrossdifferentsoiltypeswithmachinelearningpredictions AT sonalibanerjee geogenicperspectivesonpotassiumdynamicsandplantuptakeinsightsfromnaturalandsubmergedconditionsacrossdifferentsoiltypeswithmachinelearningpredictions AT sumitkumar geogenicperspectivesonpotassiumdynamicsandplantuptakeinsightsfromnaturalandsubmergedconditionsacrossdifferentsoiltypeswithmachinelearningpredictions AT riddhibasu geogenicperspectivesonpotassiumdynamicsandplantuptakeinsightsfromnaturalandsubmergedconditionsacrossdifferentsoiltypeswithmachinelearningpredictions AT pradipbhattacharyya geogenicperspectivesonpotassiumdynamicsandplantuptakeinsightsfromnaturalandsubmergedconditionsacrossdifferentsoiltypeswithmachinelearningpredictions |