One of the great challenges in reaching zero hunger is to secure the availability of sufficient nourishment in the worst of times such as humanitarian emergencies. Food aid operations during a humanitarian emergency are typically subject to a high level of uncertainty. In this paper, we develop a novel robust optimization model for food aid operations during a humanitarian emergency, where we include uncertainty in the procurement prices, which is one of the primary sources of uncertainty in practice. Due to the multi-period and dynamic nature of food aid operations, we extend this robust optimization model to an adaptive robust optimization model, in which part of the decisions is taken after some of the uncertainty has been revealed. Moreover, we analyse a folding horizon approach for the nominal, robust, and adaptive robust optimization models in which decisions can be altered in later time periods. We compare the different approaches based on a food operations case in Syria. We show that the (adaptive) robust optimization approach outperforms the nominal approach in the non-folding horizon case, while the nominal approach performs best in the folding horizon case. Consequently, in case decisions have to be made early on, we show that applying robust optimization to food aid operations can make a difference. However, in case small adaptations can be made to the decisions taken in later time periods, then food aid operations can use a relatively simple approach in practice and apply a folding horizon approach each month to optimize decisions.