Crop simulation models are sophisticated computational tools designed to represent the growth, development, and yield of crops under varying environmental conditions, management practices, and genetic characteristics. These models integrate a variety of factors such as soil properties, weather patterns, water availability, nutrient dynamics, and crop physiology to predict crop performance over time. Their primary aim is to provide insights into how crops respond to both natural and human-induced changes, making them invaluable for climate change assessment and adaptation strategies in agriculture. One of the key features of crop simulation models is their ability to simulate crop growth on a daily or even hourly basis. This temporal resolution allows researchers and farmers to capture short-term environmental effects, such as temperature fluctuations, rainfall events, or heat stress periods, which can significantly influence plant development. By simulating processes like photosynthesis, transpiration, biomass accumulation, and phenological stages, these models offer a dynamic representation of crop growth rather than relying on static yield estimates. This feature is particularly useful for assessing climate change impacts, as it allows the identification of vulnerable stages in crop development and the potential shifts in growing seasons due to temperature and precipitation changes. Another important feature is the incorporation of genotype-specific parameters. Modern crop models are designed to account for the genetic variability among crop varieties, which determines traits such as drought tolerance, heat sensitivity, nutrient uptake efficiency, and growth rate. By including these parameters, models can predict how different varieties might perform under future climate scenarios, aiding in the selection of resilient cultivars. This feature is crucial for climate adaptation planning,