Crop simulation models are powerful scientific tools that have emerged as a response to the increasing challenges posed by climate variability, population growth, and the need for sustainable agricultural practices. They are mathematical representations of crop growth, development, and yield as influenced by environmental factors, management practices, and genetic characteristics. By integrating diverse information such as soil conditions, climate data, crop physiology, and management inputs, these models allow researchers and policymakers to predict crop performance under various scenarios. Their use has become central to climate change assessment tools, since they enable the study of how different climatic conditions, such as changes in temperature, rainfall, and carbon dioxide concentration, affect agricultural productivity and food security. The features of crop simulation models lie in their ability to capture the complex interactions among soil, plant, climate, and management. One fundamental feature is process-based modeling, in which the physiological and biophysical processes of crops, such as photosynthesis, respiration, transpiration, and nutrient uptake, are mathematically represented. This allows the model to simulate plant growth in response to varying environmental conditions rather than simply extrapolating from historical data. Another important feature is the modular design of many crop models, meaning that they consist of different sub-models representing specific processes, such as soil water balance, nutrient cycling, crop phenology, and biomass accumulation. This modular structure provides flexibility, allowing scientists to adapt models to different crops, soils, and climates. Another feature is the ability to incorporate genetic and cultivar-specific parameters. Different crop varieties have varying growth cycles, tolerance to drought, or responsiveness to fertilizers, and crop simulation models can capture these differences by adjusting parameters. This enables the evaluation of crop breeding strategies for climate-resilient varieties. Additionally, many models have the capacity to simulate interactions between crops and management practices such as irrigation, fertilization, tillage, and planting dates. This management-focused feature makes them highly valuable for decision support in agriculture. Crop simulation models also rely on input data, which is both a strength and a limitation. They require detailed datasets on weather conditions (temperature, precipitation, solar radiation), soil properties (texture, depth, organic matter), crop characteristics, and management interventions. With accurate inputs, the models can provide reliable predictions, but the quality of outputs is highly dependent on the quality and resolution of the input data. To address variability and uncertainty, many models allow sensitivity analysis, scenario testing, and probabilistic simulations. This ability to test “what if” scenarios makes them powerful in exploring future climate and management outcomes. One of the most widely recognized applications of crop simulation models is climate change impact assessment. Since agriculture is highly sensitive to climatic variations, these models are used to predict the potential effects of rising temperatures, changing rainfall patterns, and elevated carbon dioxide levels on crop productivity. For example, models can simulate how a wheat crop might perform in a region under scenarios of 2°C temperature rise or