The 2015 Global Hunger Index suggests that despite progress in reducing hunger worldwide, hunger levels in 52 of 117 countries in the 2015 Global Hunger Index remain “serious” or “alarming.” Since achieving and maintaining food and nutrition security (FNS) remains a goal for all countries, it is important to understand the individual, national, and global factors that affect FNS. This paper proposes an analytical framework to identify and analyze the respective roles of key long-term drivers of FNS. We start by identifying what the key variables affecting FNS are at the household and country level, and then we continue by defining what the main exogenous or endogenous drivers affecting these variables are. We discuss the key drivers of both aggregated food supply and demand and therefore their impact on prices. Specifically, for aggregated food demand, we discuss demographic factors, income growth, changes in dietary preferences, aggregated domestic distortions, and overall quality of the food system. With respect to the drivers of aggregated food supply, we discuss land available for food products and the drivers behind land availability, the share of waste/losses generated by the food system, and the normalized average yield. We define yield as the amount of nutrients produced by unit of land. It depends both on the physical yield of the crop or the livestock and on the quality of the food produced. It also can be affected by the changes in production patterns linked to the different dietary patterns of the consumers and by climate change. We emphasize the fact that in many cases, key drivers may have ambiguous effects on the FNS situation of different agents. For instance, more liberal trade policies will affect real income, terms of trade, demand and supply, returns of factors, foreign direct investments, and food prices and thus may lead to the improvement of the global-level FNS, that is, the FNS of the majority of the population. At the same time, more liberal trade policies may bring food insecurity to some households. Therefore, careful quantitative assessment is needed for each policy option. Finally, we propose a typology of variables that will help modelers adapt their models to study the different drivers through both direct and indirect effects.