Assessing soil quality, the role of indigenous knowledge and biological indicators in novelle soil quality research

This bibliographic study examines soil quality assessment indicators in scientific publications using bibliometrics and meta-analysis with a context-specific methodological focus, offering scalable insights for future global valuation. A systematic literature review of 118 scientific publications on soil quality assessment from the pandemic period in Argentina identified regionally sensitive soil quality indicators. MCA revealed relationships among indicators categories in a two-dimensional space. Crop factors beyond yields, integrated physicochemical-biological statistical analysis, ecoregions, and Indigenous knowledge indicators explained more than 30 % of the total dataset variance, shaping it with contextual tendencies. These time- and site-specific soil quality indicators, identified through categorical variable associations via MCA lower-dimensional clustering, and corroborated by k-means robustness, highlight period- and region-specific trends. Notably, they emphasize the significant contributions of female researchers from diverse ecoregions in scientific publications. Upon transferable, context-sensitive, and defined frameworks, this interdisciplinary work addresses multiple United Nations Sustainable Development Goals. Forthcoming machine learning applications could swiftly expand soil quality assessments across broader spatial and temporal scales, enabling worldwide scalability. • Key soil quality indicators during the pandemic were extracted from 118 peer-reviewed articles focused on Argentina’s soils. • Physicochemical, biological, ecoregional, and indigenous indicators explained 30% of variance via multivariate analysis. • Female scientists significantly advanced soil quality research during the pandemic across Argentina’s diverse ecoregions. • Context-sensitive, spatiotemporal indicator databases support global soil quality assessment in variable environments.