Crop scientists seek to identify the trait or combinations of traits that can enhance the crops’ ability to withstand water deficits. The progress, however, has been slow, in part, because the agricultural system is a complex network of multiple interacting biotic and abiotic factors. Moreover scientists are often limited to trial-and-error approaches. Mathematical models could potentially help to mitigate this problem. They can represent the complexities of these agricultural challenges in ample scope that integrate current knowledge of biotic and abiotic factors via inputs from plant physiology, plant pathology and genetics, current agronomic practices, climate, and soil characteristics, among others. Taking into account the above factors, the crop growth simulation models can calculate/predict crop growth and yield. In addition, models can be used to perform sensitivity analysis to adjust for the plant characteristics that will enhance crop performance under different growing conditions.  The purpose of this study is to utilize crop growth simulation models to systematically test the consequences of modification of key water-related traits in a 108-day elite corn hybrid over a sequence of 20 years of weather information from Central Illinois under non water-limited environment; and Western Nebraska under different irrigation regimes.