Evaluation Of Soil Quality Frameworks In Rice-Wheat Systems Under Integrated Nutrient Management In Indo-Gangetic Plains

Authors

• Tridiv Ghosh, ICAR - Indian Agricultural Research Institute, New Delhi
• Bappa Das, ICAR - Central Coastal Agricultural Research Institute, Goa
• Debashis Chakraborty, ICAR - Indian Agricultural Research Institute, New Delhi
• Vinod Kumar Singh, ICAR-Central Research Institute for Dryland Agriculture
• Debarup Das, ICAR - Indian Agricultural Research Institute, New Delhi
• Pramila Aggarwal, ICAR - Indian Agricultural Research Institute, New Delhi
• Abhijit Sarkar, ICAR - Indian Institute of Soil Science, Bhopal
• Prakash Kumar Jha, Mississippi State University
• P. V. Vara Prasad, Kansas State University

ORCID

Jha: https://orcid.org/0000-0001-5973-711X

MSU Affiliation

College of Agriculture and Life Sciences; Department of Plant and Soil Sciences

Creation Date

2026-04-29

Abstract

Sustaining soil quality under intensive land use systems to feed the growing population is a major challenge in the developing world. A prerequisite for effective soil quality analysis is the selection of a minimum data set (MDS) which effectively represents a given production system. As soil is a complex and dynamic system, there is no universal consensus on the ideal set of soil indicators for assessing soil quality. To address this challenge, this study evaluated multiple machine learning-based feature selection techniques in conjunction with conventional principal component analysis (PCA) for selection of the MDS, with the hypothesis that non-linear machine learning-based feature selection algorithms could better capture the complexity of soil quality dynamics. Weights for the selected indicators were computed using both PCA and entropy methods, aiming to develop a novel framework for soil quality analysis in relation to crop yield. Integrated nutrient management practices had a significant influence on soil physical, chemical and biological properties, all of which are essential for maintaining soil health and sustaining crop productivity. MDS selection and weight assignment through PCA provided strong correlations with crop yield (R2 = 0.91, p < 0.01 for rice; R2 = 0.93 p < 0.001 for wheat), comparable to the performance of MDS selection using PCA with entropy-based weighting (R2 = 0.88, p < 0.01 for rice; R2 = 0.90, p < 0.01, for wheat). When the goal is to maximize crop yields, the use of MDS selected through PCA and PCA-based weights for soil quality index (SQI) calculation appears to be more effective for a better representation of the production system. The proposed framework holds promise for application across diverse cropping systems and management practices, offering valuable insights for enhancing sustainable crop production.

Publication Date

10-27-2025

Publication Title

Scientific Reports

Publisher

Nature Research

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.

Recommended Citation

Ghosh, T., Das, B., Chakraborty, D. et al. Evaluation of soil quality frameworks in rice-wheat systems under integrated nutrient management in Indo-Gangetic plains. Sci Rep 15, 37511 (2025). https://doi.org/10.1038/s41598-025-21630-1