Evaluation of soil quality frameworks in rice-wheat systems under integrated nutrient management in Indo-Gangetic plains.

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 (R