Crop models simulate plant growth and nutrient uptake under varying environmental and management conditions. By integrating weather data, soil characteristics, and crop physiology, these models provide valuable insights into crop development and nutrient dynamics throughout the growing season. One of the key strengths of crop models, such as CHN, developed by Arvalis, is their ability to perform scenario analysis. Users can evaluate the impact of different fertilization strategies on yield, quality and resource use efficiency. Integrating crop models into Decision Support Systems (DSS) involves incorporating decision rules that reflect the farmer’s primary objectives—such as maximizing yield, improving protein content, complying with environmental regulations, or optimizing nutrient use efficiency. This integration allows for dynamic, site-specific recommendations that adapt to changing conditions and goals, particularly under conditions of climate variability and regulatory constraints. Ultimately, they help predict optimal fertilization timing and dosage, supporting more precise and efficient nutrient management.