PAID ACCESS | Published on : 27-Sep-2025 | Pages: 173-186 | Doi : 10.37446/volbook092024/173-186
The Soil Quality Index (SQI) has become a valuable integrative tool for evaluating soil health by combining multiple soil indicators spanning physical, chemical, and biological attributes. This chapter delves into the statistical approaches necessary for SQI development and highlights their practical applications in both field-level experiments and large-scale regional assessments. It provides an overview of the conceptual evolution of soil quality from earlier productivity-focused definitions to broader, multifunctional ecosystem frameworks now recognized by international organizations. The importance of SQI is underscored in the context of sustainable land management, climate adaptation, and food security. The chapter systematically outlines the major components of SQI development: selection of relevant indicators, appropriate data collection strategies, preprocessing and normalization techniques, and dimensionality reduction methods such as Principal Component Analysis (PCA) and regression-based models. Various indicator transformation approaches such as linear, non-linear, threshold-based, and expert-defined scoring functions are discussed in relation to their applicability across different soil parameters. Methods for index aggregation, including additive models, geometric means, and advanced decision-support tools like fuzzy logic and Analytic Hierarchy Process (AHP), are evaluated. The utility of spatial analytics and predictive modeling using geostatistics, cluster analysis, and machine learning techniques is also addressed. Challenges such as scale sensitivity, seasonal variability, inconsistent data quality, and the incorporation of indigenous knowledge are critically examined. The chapter concludes with a forward-looking perspective on integrating remote sensing, digital soil platforms, and artificial intelligence to enhance the scalability and practical utility of SQI in guiding soil health management and policy decision-making.
Abiotic stress, Biostimulants, Micro-organisms, Nutrient use efficiency
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