Irrigated corn is the single largest irrigated crop in the USA western Corn Belt and Great Plains, but decreasing water availability threatens its economic viability. Litigation between ‘downstream' and ‘upstream' users has resulted in restrictions on the amount of water available to growers in several major watersheds. In some areas, growers will be limited to pumping only 50-80% of the full water requirement for a corn crop. In contrast, existing best-management practices rely on soil water depletion thresholds for a fully-irrigated crop without restrictions on water supply. Currently there is no reliable framework for real-time irrigation scheduling decisions during the growing season when irrigation supply is limited. We proposed a framework that uses real-time crop growth simulation to aid irrigation scheduling with limited water supply to achieve a maximum yield. The core of the framework is an ability to assess, in a real-time mode, crop growth and yield responses from water input at times when water input is needed. Using fuzzy logic, the framework will combine the real-time model-based crop growth assessment with growers' real-time but subjective crop growth assessment, short-term rain forecast, as well as growers' tolerance for risk. Through this process, the system will be able to identify the times when water input is critical for maximizing yield as well as the times when irrigation could be skipped in order to save water for use at critical moments. It will also quantify the optimal amount of irrigation water at each time of irrigation. Our goal is to develop a robust and user friendly irrigation decision support based on this framework. Such a tool has the capability to substantially increase grain output per unit of applied irrigation water, which is crucial for water conservation and the economic viability of irrigated crop production in these water-limited regions.