Systematic review and bibliometric analysis of innovative approaches to soil fertility assessment and mapping: trends and techniques

The twenty-first century marks a significant shift in soil fertility evaluation, driven by advancements in pedometrics and Digital Soil Mapping (DSM). Pedometrics introduces quantitative methods to assess soil variability using statistical and geostatistical techniques, enhancing understanding of soil properties. DSM builds on this by creating high-resolution predictive maps, offering valuable data for researchers and practitioners. An in-depth bibliometric analysis on the Scopus platform (2000–2023) revealed 133 articles on pedometrics and an impressive 1,172 on DSM, underscoring growing interest in these technologies.The integration of Geographic Information Systems (GIS) and Remote Sensing (RS) has further advanced these fields, enabling extensive geospatial data collection and real-time monitoring. Machine Learning (ML) has also been transformative, facilitating complex pattern recognition and predictive analysis to improve soil fertility mapping and management. A review of 364 studies from 2000 to 2023 highlights the development and impact of these technologies, detailing their advantages and limitations. The surge in related publications and citations since 2000 reflects a rising interest in sustainable agriculture and environmental management. Significant milestones occurred in 2019 and 2022 with the introduction of new soil management technologies, while RS and GIS technologies surged in popularity in 2016 and 2020, driven by satellite advancements like Sentinel and Landsat. The capabilities of ML techniques were notably effective in 2019 and 2022. Countries like India, China, and Iran have been key adopters, transforming soil fertility mapping into a non-invasive, large-scale process that enhances agricultural decision-making.This transition emphasizes the value of specialized publications that advocate for GIS, RS, pedometrics, and DSM, which are crucial for addressing environmental challenges. In conclusion, integrating traditional and advanced methodologies provides a holistic, adaptable approach to sustainable land management, supporting data-driven decisions to enhance agricultural and environmental sustainability.