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Rainfed agriculture represents the central pillar of the world’s future challenges to ensure enough food and to generate adequate income to better feed the poor and hungry people. Under the rainfed conditions of Morocco, wheat cropping systems – the population’s basic staple food – are subject to a set of limitations that seasonally impact crop production and farmers’ incomes, thus national food security. In the last decades, the major constraints were often related to the country’s Mediterranean-type climate, through the intense recurrence of drought events and high inter- and intra-annual rainfall fluctuations. The frequency of drought events has increased five-fold in Morocco, going from one extreme event out of fifteen normal years during the 30s, 40s, 50s, 60s, and 70s, to one drought year out of three during the last two decades. Likewise, the various forms of soil degradation have also major impacts that impede wheat crop intensification and affect population livelihoods. An example of soil degradation is nutrient depletion resulting from inappropriate and unsustainable fertilization practices that fail to replace the nutrients extracted from agricultural products, along with nutrient losses due to soil erosion and leaching of chemical or natural fertilizers. As a result, the limitations on production often extend beyond environmental factors and also imply the inadequate crop management strategies adopted by farmers. In Moroccan rainfed areas, the crop production limitations linked to management practices are frequently attributable to small farmers' limited access to knowledge or financial constraints, what limits their capacity to implement effective strategies. Advanced technologies such as remote sensing and crop modeling are crucial in assessing wheat cropping systems in Moroccan rainfed areas. Traditional experiments-based agronomic research struggles to comprehend the complex interactions between genotype, environment, and management (G×E×M). For this reason, crop modeling approaches offer significant advantages over conventional methods, including the ability to provide more accessible, rapid, cost-effective, and comprehensive insights into cropping systems. Furthermore, crop modeling approaches may have the potential to produce more accurate predictive knowledge and enhance our understanding of the status of cropping systems. Our findings during this thesis project show the effectiveness of crop models to evaluate and improve wheat cropping systems under rainfed conditions of Morocco, through highlighting our contributions to the three primary themes (or missions) of crop modeling applications: i) Preserving food security: The accurate predictive capabilities of empirical or mechanistic models play a critical role in monitoring crop growth and yield at the field level. Consequently, the application of these models provides a significant opportunity for improving seasonal crop yield forecasting and drought early warning systems in Moroccan rainfed areas. Furthermore, interpreting the core structure of crop models is instrumental in assessing the impact of external factors such as environmental conditions or farmers' practices on yield variability (i.e., yield gap assessment) at the field level. ii) Supporting general adaptation strategies to face climate change effects and extreme events: Crop models can help to understand the climate change and extreme events effects on wheat system productivity under rainfed conditions. In this context, crop modeling works were 6 conducted to evaluate the added values of new crop management strategies (e.g., no-till farming, rotation practice, genotypes selection programs etc.), and to propose general adaptations across a wide range of spatial and temporal scales (e.g., specific pedo-agro-climatic adaptations). iii) Recommending within-season and field level crop management advice: The use of mechanistic models, such as APSIM-wheat in this study, allows capturing the impacts of climate variability and specific crop management practices at within-season and field level. Our study highlights the effectiveness of these models as decision support tools for recommending optimal crop management practices, particularly with regards to N and P fertilizer application in Moroccan rainfed agriculture.