Mapping the Evolution of DSSAT Model Research: Trends, Transitions, and Future Frontiers

This study presents a comprehensive bibliometric analysis of the DSSAT crop modeling field from 1990 to 2024, identifying its evolutionary trajectory and emerging frontiers. A comprehensive bibliometric analysis and network visualization were conducted using VOSviewer, CiteSpace, and Bibliometrix. Analyzing 6165 Scopus-indexed publications, we found the research focus has shifted from foundational yield simulation and calibration toward addressing complex climate-water-food challenges. Three distinct developmental phases were identified: an initial establishment phase, a methodological refinement phase, and a current technology integration phase dominated by machine learning and remote sensing applications. The results reveal that machine learning, model-data fusion, and sustainability assessment represent the most active research frontiers. This analysis provides a systematic map of the field’s intellectual structure and offers evidence-based predictions for its future development, highlighting the transition of DSSAT from a specialized crop model to an interdisciplinary decision-support platform for sustainable agriculture.