With the regulation of pesticides in European agricultural landscapes, it is important to understand how pest populations respond to climate and landscape variables in the absence of pesticides at different spatial–temporal scales. While models have described individual biological processes, few have simulated complete life cycles at such scales. We developed a spatially explicit simulation model of the dynamics of the bird cherry–oat aphid (Rhopalosiphum padi) in a pesticide-free simulated landscape using data from an agricultural landscape located in southwest France. Using GLMMs, we ran two statistical methods, one at the crop level, focusing on aphid densities within each crop individually (wheat and its regrowth, corn, and sorghum), and another at the landscape level where aphid densities were not differentiated by crops. For each season, we analyzed how temperature, immigration, and habitat availability impacted on aphid densities. Predictors of aphid densities varied between crops and between seasons, and models for each individual crop resulted in better predictions of aphid densities than landscape-level models. Aphid immigration and temperature were important predictors of aphid densities across models but varied in the directionality of their effects. Moreover, landscape composition was a significant predictor in only four of the nine seasonal crop models. This highlights the complexity of pest–landscape interactions and the necessity of considering fine spatial–temporal scales to identify key factors that influence aphid densities, essential for developing future regulation methods. We used our model to explore the potential effects of two agronomic scenarios on aphid densities: (1) replacement of corn with sorghum, where increases in available sorghum led to the dilution of aphid populations in sorghum in spring and their concentration in summer, and (2) abandonment of pastures for wheat fields, which had no significant effect on aphid densities at the landscape scale. By simulating potential future agronomic practices, we can identify the risks of such changes and inform policy and decision-makers to better anticipate pest dynamics in the absence of pesticides. This approach can be applied to other systems where agronomic and land cover data are available, and to other pest species for which biological processes are described in the literature.