Over 30 years ago the International Benchmark Sites Network for Agrotechnology Transfer (IBSNAT) project was initiated using a novel approach for evaluating alternative crop management options. The IBSNAT Project was funded by the US Agency for International Development (US AID) to help improve crop production and food security in developing countries using a systems approach and it has laid the foundation for the Decision Support System for Agrotechnology Transfer (DSSAT). The IBSNAT Project brought together a group of crop modelers from various universities and international institutes to develop a suite of crop models that could provide reasonable yield prediction across a wide range of environments. These crop models were embedded in DSSAT to provide the users with a friendly interface, common tools for data processing, and utilities for application of the crop models. In addition, DSSAT included a set of standard input files for weather and soil conditions and crop management, sometimes referred to as the minimum data set. Professor Jones has played an instrumental role in the original design of DSSAT and its further development and advancement. Although the IBSNAT project ended in 1993, the development of DSSAT has continued without financial support, but through collaboration of a dedicated group of scientists. DSSAT has expanded from the original models for soybean, maize, wheat, and peanut to crop models for over 25 different food, feed, fuel and fiber crops. The models have been combined into one Cropping System Model (CSM) that has the same soil water, nitrogen, phosphorus and organic carbon balance simulations across all crops, but uses unique modules for the simulation of crop growth and development for each crop. Applications of the models can be conducted for single season evaluation with observed data, multiple seasons for yield and economic risk analysis, and crop rotations for long-term economic and environmental sustainability. The models have been used for a diverse range of studies, including plant breeding and genomics, irrigation and fertilizer management, climate variability and decision making, in-season yield forecasting, irrigation water use projections, nitrogen leaching and soil degradation, crop rotation and long-term soil fertility, climate change impact and adaptation, and food security. DSSAT’s success is due to the networks of crop model users and crop model developers and the associated crop modeling training workshops that have been held across the globe on a regular basis. DSSAT’s success is also due to the emphasis on model evaluation with observed experimental data prior to any application. With the advances in computer and information technologies and the questions that are being posed by stakeholders such as growers, producers, and policy makers, the need for DSSAT and similar tools and decision aids will continue to increase. As such, it is extremely important that we continue to improve the underlying science of the models in order to warrant that the models provide the best possible solutions for a wide range of new applications to help solve real-world problems.