A reliable evaluation of crop nutritional status is crucial for supporting fertilization aiming at maximizing qualitative and quantitative aspects of production and reducing the environmental impact of cropping systems. Most of the available simulation models evaluate crop nutritional status according to the nitrogen (N) dilution law, which derives critical N concentration as a function of above-ground biomass. An alternative approach, developed during a project carried out with students of the Cropping Systems Masters course at the University of Milan, was tested and compared with existing models (N dilution law and approaches implemented in EPIC and DAISY models). The new model (MAZINGA) reproduces the effect of leaf self-shading in lowering plant N concentration (PNC) through an inverse of the fraction of radiation intercepted by the canopy. The models were tested using data collected in four rice (Oryza sativa L.) experiments carried out in Northern Italy under potential and N-limited conditions. MAZINGA was the most accurate in identifying the critical N concentration, and therefore in discriminating PNC of plants growing under N-limited and non-limited conditions, respectively. In addition, the present work proved the effectiveness of crop models when used as tools for supporting education.